4 Halloween Ideas For Girl Geeks

Looking for a last minute Halloween costume? Here are some of our favorite women worth talking about – and dressing up as for inspiring STEAM Halloween costumes!

Katherine Johnson – Mathematician

Katherine Johnson.

NASA Research Mathematician Katherine Johnson calculated trajectory for spacecraft missions. She verified results made by electronic computers to calculate the orbit for spacecraft.

Her work was made famous in the book and movieHidden Figures” about African-American women mathematicians who fought against segregation, discrimination and sexism to work and excel at NASA. Go watch it if you haven’t already!

Her alma mater erected a statue of Katherine Johnson, and a children’s book “Counting on Katherine” has been published. Check out these adorable girls who rocked the Hidden Figures look!

Grace Hopper – Computer Scientist

Admiral Grace Hopper.

Grace Hopper joined the U.S. Navy during World War II and was assigned to program the Mark I computer.

She was at Harvard as a research fellow when a moth was found to have shorted out the Mark II, and is sometimes given credit for the invention of the term “computer bug” — though she didn’t actually author the term, she did help popularize it.

She also popularized the idea of machine-independent programming languages, which led to the development of COBOL. Check out this professor’s great Grace Hopper costume!

Maggie Gee – Pilot

Maggie Gee in her pilot’s uniform.

Did you know that not a single major airport in the United States is named for a woman?

There’s a campaign to rename Oakland Airport for Maggie Gee. A physicist and researcher, she was one of the first American women trained to fly military aircraft, and was one of only two Chinese-American women to serve as a pilot in Women Airforce Service Pilots in WWII. As a WASP pilot, she helped male pilots train for combat, as female pilots were not allowed to serve in combat at that time.

A children’s book based on her life “Sky High” has been published. You can easily buy or make an “Amelia Earheart” costume and share the story of Maggie Gee!

Frida Kahlo – Painter

Frida Kahlo, circa 1937.

Known as one of Mexico‘s greatest artists, Frida Kahlo is remembered for self-portraits, pain and passion, and vibrant colors. Having suffered from polio as a child, she then nearly died in a bus accident as a teenager and endured 30 operations. She has created approximately 200 paintings, sketches and drawings. In 2006, her self-portrait went for over $5 million at Sotheby’s auction.

You can visit her museum in Mexico City, where her belongings are on display throughout the Blue House, as if she still lived there. Many Frida Kahlo books and toys have been produced. Beyoncé dressed as Frida Kahlo a few Halloweens ago.

More Resources:

Podcast Highlights: 6 Quick Lessons on Branding

Branding to Stand Out - Personal Branding

Whew! We just wrapped our 20th podcast episode, and now we’re taking a look back over the past few months at all of the amazing conversations we’ve had, the laughs we’ve shared, and the tough topics we’ve tackled… and we figured it’s the perfect time for the Girl Geek X team to share our top takeaways that women in tech and allies everywhere can benefit from!

We’ll be doing this via a mini-series of blog posts in the coming weeks, where we’ll break down our key learnings, salient moments, and hard-hitting realizations and share them with the community as bite-sized nuggets that you can quickly devour while waiting for everyone to join your morning conference call. (Can you hear me now? Everyone please mute!)

If you haven’t already subscribed to the Girl Geek X podcast, head on over to iTunesSpotifyStitcher, or Google Play and get ready to start binge listening! 

First up, Girl Geek X CEO & Co-Founder Angie Chang is delving into her favorite (and our most recent!) release on the Girl Geek X Podcast — Episode 20: Branding to Stand Out.

Why this topic matters, and why it’s her favorite episode:

Angie Chang, CEO & Co-Founder of Girl Geek X
Angie Chang, CEO & Co-Founder of Girl Geek X

“I enjoy how we as the Girl Geek X team can talk thru the uncomfortable reality of branding for women in their work lives and work places. Each place and situation is different, so it was fun to hear the diverse perspectives we all have, and share some common themes in how we feel we show up at work and how to be most effective while being true to our selves.

6 Quick Takeaways

6. “Part of what a brand is, is an emotional connection. It’s how you’re perceived. It’s how we’re perceived in the workplace. And I would say, as a woman in business, and as women often are told in tech companies, you’re either too nice or too aggressive. Or, you’re too mean. Or, you’re too sloppy. Or you’re too proper, or whatever. The list can go on and on. Everybody in this room has some anecdote of a time when they felt they got conflicting messages or they weren’t quite sure ‘how do I show up in this meeting?’

I think for a lot of us, throughout our career we’ve found a way to find that balance of, how can we show up at work in a way to be super effective and so that people listen and we can do really good work? And how do we stay true to who we are?” —Khobi Brooklyn, VP of Communications at Aurora

Sukrutha Bhadouria, CTO & Co-Founder of Girl Geek X
Sukrutha Bhadouria, CTO & Co-Founder of Girl Geek X

5. “When I was very deliberate about what brand I wanted for myself or what I wanted to be known for, I then was very clear about what opportunities I wanted to seek out for myself, in addition to what I was already doing. That helped me.” —Sukrutha Bhadouria, Co-Founder & CTO at Girl Geek X and Sr. Manager, Engineering at Salesforce

4. When I was earlier in my career as a Girl Geek, I would run from the idea and the topic of branding. Because I’m like, ‘That’s just marketing.’ I didn’t want to deal with that.

As you get more experienced in your career, you start to see the bigger picture and how your manager or other people need to be able to pick you out from a crowd. And then the branding issue becomes something that you actually pay attention to — what you want to be known for, and then tying it to your authentic self and making sure it’s aligned.” — Angie Chang, Co-Founder & CEO at Girl Geek X

Leah Mcgowan-Hare, VP of Trailhead Evangelism at Salesforce
Leah McGowan-Hare, VP of Trailhead Evangelism at Salesforce

3.Focus on the value you add and everything else will begin to fall in place. It’s really easy to get caught up in that branding piece, particularly with social media and all this good stuff. And I’m always like, well, let’s take step back. What is your story? What are you trying to build? What is the story you’re trying to create?” —Leah McGowen-Hare, VP of Trailhead Evangelism at Salesforce

2.Your brand goes so much farther beyond the one specific company that you’re working in. It really exists in your whole network. It’s how you represent yourself to your whole network. Within your job, outside of your job.

I think what’s tied all the things that I do together is definitely storytelling and social impact. With everything that I’ve done in my career and all the outside of work things that I’ve been doing, those are the threads that tie them together. That’s how people view me. Regardless of what aspect of my career I’m showing up in.” —Rachel Jones, Podcaster at Girl Geek X

1. “Your brand in college is not the brand you had in your 20s, and is not the brand you had in your 30s, in your 40s, in your 50s and 60s. Your personal brand is going to continually be a work in progress.” — Gretchen DeKnikker, COO at Girl Geek X

Check out the full episode or podcast transcript for more great insights on organizational and personal branding for women in business, or subscribe to our YouTube channel for even more insightful content on topics that matter to women and allies.

About the Author

Amy Weicker - Head of Marketing at Girl Geek X

Amy Weicker is the Head of Marketing at Girl Geek X, and she has been helping launch & grow tech companies as a marketing leader and demand generation consultant for nearly 20 years. Amy previously ran marketing at SaaStr, where she helped scale the world’s largest community & conference for B2B SaaS Founders, Execs and VCs from $0 to $10M and over 200,000 global community members. She was also the first head of marketing at Sales Hacker, Inc. (acquired by Outreach) which helps connect B2B sales professionals with the tools, technology and education they need to excel in their careers.

Episode 20: Branding

Branding to Stand Out - Personal Branding

Resources mentioned in this podcast:


Angie Chang: Welcome to Girl Geek X podcast, connecting you with insights for women in tech. This is Angie, founder of Girl Geek X and Women 2.0.

Sukrutha Bhadouria: And this is Sukrutha. By day I’m an engineering manager.

Gretchen DeKnikker: This is Gretchen. I’ve been working in tech for over 20 years.

Rachel Jones: This is Rachel, the producer of this podcast, and we’re the team behind Girl Geek X. This podcast brings you the best of Girl Geek X events, dinners, and conferences. Where we’ve been elevating women in tech for over 10 years.

Angie Chang: Today we’ll be discussing branding.

Rachel Jones: Why is it important for women in the tech world to think about their brand?

Sukrutha Bhadouria: It’s really hard to set yourself apart and stand out in the sea of other people working in tech and, especially, with whichever company you’re at. There are so many other people doing similar jobs as you. As the company that you might be working at gets larger and larger or so, it’s really, really important to be cognizant of what it is that you want to be known for.

Gretchen DeKnikker: Part of it is just understanding yourself and your own identity and what you care about. It’s the self reflection that’s actually the important part of the branding, not necessarily the, I’m so on brand in all my Insta posts, or something. Of understanding who you are at the core. What you believe in and what you want to associate your name with.

Angie Chang: I feel like as, when I was earlier in my career as a Girl Geek, I would run from the idea the topic of branding. Because I’m like, “That’s just marketing.” I didn’t want to deal with that. As you get more experienced in your career, you start to see what Sukrutha talked about which is the bigger picture and how your manager or other people need to be able to pick you out from a crowd. And then the branding issue becomes something that you actually pay attention to. What you want to be known for, and then tying it to, as Gretchen said, your authentic self and making sure it’s aligned.

Gretchen DeKnikker: Yeah, I think you see companies that have inauthentic brands. They’re trying to be something that they aren’t and it just comes across and it works in such a negative way. There was so much talk this year around Pride of all these companies that were changing their logos to rainbows, who had literally never done anything else and how inauthentic that was.

Gretchen DeKnikker: I see a lot of it now with a lot of fashion designers who are trying to get on this size inclusive bandwagon and talk about it. But they don’t change the way that they present their clothing and they don’t change anything else, they just add a few more sizes and they’re like, “We’re inclusive.” It really smacks of inauthenticity.

Gretchen DeKnikker: Is that a word?

Rachel Jones: Yes.

Gretchen DeKnikker: Okay, good.

Rachel Jones: I think about that idea of inauthenticity a lot with branding. Just for me personally, I’ve struggled with this. Just doing podcasts and just being not great at self promotion in general. It’s so rare for me to post on Instagram or on Twitter, here’s a thing that I made. Just wanting to avoid… I just want to do the thing. I don’t need to be out in the world as the person who does the thing. But I think branding and having a personal brand doesn’t just have to mean, oh, I’m using this to get Insta famous. It’s also how you announce who you are just to the people who are around you.

Rachel Jones: When I first moved to the Bay, about a year ago, I was really putting myself out there as a podcaster. Just believing that and claiming it, even if I wasn’t putting it out publicly online, that really helped me just find a lot of opportunities. Because everyone that I was interacting with, anytime that anyone they knew just mentioned the word podcast, then they’re like, “Oh, Rachel knows about this. I should connect you.”

Rachel Jones: So I think knowing your brand and putting that out there, it really helps to unlock your career transition and your career trajectory.

Gretchen DeKnikker: Right. And I think– Sorry.

Sukrutha Bhadouria: Go ahead.

Gretchen DeKnikker: I think, also, not to conflate self promotion with brand. That they’re two distinct things.

Sukrutha Bhadouria: Yeah, I was thinking the same thing. I found that, when I was very deliberate about what brand I wanted for myself or what I wanted to be known for, I then was very clear about what opportunities I wanted to seek out for myself, in addition to what I was already doing. That helped me.

Gretchen DeKnikker: I was thinking about this last night as I was thinking about we’re going to record this episode today. This mentor I have told us about this three word exercise that you do for your own company brand. The company that I founded, we did it. I was looking at it last night to be like, “What were our three words?” It was irreverent, soulful, and effortless. And I was like, “Oh, two of those three are actually my personal brand of being irreverent and soulful.” But we can talk some other time about how you run that exercise.

Gretchen DeKnikker: But it’s really cool. What I really loved about my mentor is she even has her three word exercise for her marriage. So they have three words that are their marriage. Thinking about it in the context of the company, there would be marketing copy and I could just send it back and be like, “It’s not irreverent enough. It’s too corporate, it’s too whatever.” Or, when we’re debating a product feature and it’s like, “Is that effortless?” Or, something about it being soulful. That humans were behind this thing. That humans are part of every interaction and how that really guided the company and I could totally see how it could guide your marriage.

Gretchen DeKnikker: I was thinking maybe I should take at least two of those words that I feel like apply to me and I’ll find my third word and then I have my brand. But I’ve never thought about it so explicitly before.

Sukrutha Bhadouria: For me, I wanted to be known for… You know, when someone asks for something, or there’s work to be done and I sign up for it that it’ll get done. That people can trust for sure if I’ve signed up to say I would do anything or execute on something, it will happen. I don’t know how to phrase that in one word.

Rachel Jones: Dependable?

Sukrutha Bhadouria: Dependable?

Angie Chang: What’s a better word?

Gretchen DeKnikker: Yeah, we can punch that up a little bit. We can thesaurus this later. We need a way more exciting–

Rachel Jones: Set aside more time for wordsmithing.

Gretchen DeKnikker: Yeah. You know I’ll play this exercise with you for hours, Sukrutha. I love this stuff.

Sukrutha Bhadouria: Yeah, I definitely want to do it. Angie, what about you?

Angie Chang: I haven’t had the problem of working in a bigger workplace for a decade and needing to distinguish myself. I think, as someone who is in very small companies most of the time, my brand is more about the women in tech aspect of creating communities of women. First, in entrepreneurship and then in the Girl Geekdom. And just amplifying voices and creating this place where women are doing great, interesting things and just making sure that people know about it, as well. Since day to day, a lot of workplaces are very male dominated. I think that became my brand.

Gretchen DeKnikker: Your hobby became your brand.

Angie Chang: If you had to ask me, when I graduated college would I wanted to have done this? No, I didn’t even know about this. It’s just a really interesting pathway to get here. I found feminism in my first job after college because that was the moment I realized the world is the way it is. I was like, “Oh, this is different than UC Berkeley” and then realizing that we needed to have places, in the evenings at big tech companies here in the Silicon Valley in San Francisco Bay Area, where we can feel empowered and see others like ourselves who are also really excited to build new technologies and fast forward our careers together.

Sukrutha Bhadouria: Do you think that it’s mostly at a big company that you need a personal brand, or do you feel like one would need it regardless?

Angie Chang: I think you’ll need it regardless. It just happens differently.

Sukrutha Bhadouria: Yeah, I agree.

Angie Chang: I didn’t get to build a brand around a job per se because I’ve done so many one year, two year stints. I feel like more now that it’s been 15 years in this Silicon Valley life, then you’re like, “Okay, I guess my brand is women in tech.”

Angie Chang: And then, is that really something you want to be known for and as your core competency? I’m like, “I don’t know.” It feels like a really fun side hobby so I’m still negotiating my brand.

Rachel Jones: I think, following that, your brand goes so much farther beyond the one specific company that you’re working in. It really exists in your whole network. It’s how you represent yourself to your whole network. Within your job, outside of your job.

Rachel Jones: I think it’s similar with you having your entrepreneurial projects and me having podcasting. I think what’s tied all the things that I do together is definitely storytelling and social impact. With everything that I’ve done in my career and all the outside of work things that I’ve been doing, those are the threads that tie them together. That’s how people view me. Regardless of what aspect of my career I’m showing up in.

Sukrutha Bhadouria: Khobi Brooklyn moderated a conversation on personal branding during our dinner with Aurora. Here’s the story of how she found her own brand.

Khobi Brooklyn: We’re going to talk a little bit about brand and building a personal brand. And what that means and how that can have an impact on your career. I think what’s interesting is, a lot of us have a brand but maybe we don’t think about it. Because what is a brand? We often think about companies and what a brand is in a company but the reality is that we all show up in some way. Really, when it comes down to it, it’s how you show up.

Khobi Brooklyn: Part of what a brand is, is an emotional connection. It’s how you’re perceived. It’s how we’re perceived in the workplace. And I would say, as a woman in business, and as a woman in often at tech companies, you’re either too nice or too aggressive. Or, you’re too mean. Or, you’re too sloppy. Or you’re too proper, or whatever. The list can go on and on.

Khobi Brooklyn: I think, for me at least, and I think for a lot of us up here, throughout our career we’ve found a way to find that balance of, how can we show up at work in a way to be super effective and so that people listen and we can do really good work? And how do we stay true to who we are?

Khobi Brooklyn: I’ll give you one personal example. I spent the first part of my life being an athlete. Every coach I ever had said, “You need to be really serious. You’re here to win. Put your head down and win.” And I literally was told not to smile because it would waste too much energy and I needed to be putting that energy into winning the race. So that’s how I shaped my brand in the beginning. I was very serious. I never smiled. I was heads down. I was there to win.

Khobi Brooklyn: Then, I got into communications and I ended up in meetings with other people. I got feedback that I was way too serious and that I needed to smile. In fact, I was literally told I needed to be a ray of sunshine in every meeting. I thought to myself, “I’m not a ray of sunshine. That’s not who I am.” Of course, I don’t want to be bitchy, but I’m also not the sunshine at the table. It was conflicting. It was super challenging for me to find out how can I be true to who I am? But clearly, I need to smile more if I’m going to be effective in the workplace. I think that’s just one example.

Khobi Brooklyn: I’m sure everybody in this room has some anecdote of a time when they felt they got conflicting messages or they weren’t quite sure how do I show up in this meeting? Everybody else in this meeting is in sweatshirts but I love to wear floral prints. Or, you know, like seriously. Or, everybody else in this meeting is super serious and I like to crack a joke every so often. Is that okay? I think that’s something that we all think about.

Gretchen DeKnikker: I just felt like when I was listening to it, like, girl, same.

Sukrutha Bhadouria: Yeah, me, too.

Gretchen DeKnikker: I just really think being told… I’ve definitely had to soften up a lot to be heard. I was just really relating to the like, “Oh, these are the amount of things I have to do to get along.” But I think, at some point, in your career, you come in as who you are and then your environment gives you feedback and shapes you to a certain extent.

Gretchen DeKnikker: But at some point you also have enough authority, or enough experience, or whatever it is, that you get to be more of yourself. And you get to bring that back in. I’m just not a person who’s going to take anything too seriously. I’m going to make inappropriate jokes. I’m going to curse all the time. That’s just sort of me and if it’s not a cool thing for you, we’re probably not going to get along so let’s not work together anyway, kind of a thing? But, for a long time, you don’t get to define that.

Sukrutha Bhadouria: Yeah, I felt like she was talking about me. I’ve got the, you’re too nice, and you’re too serious. I’ve got the, you don’t seem… You come across like you don’t know what you’re talking about. And then I’ve also got the opposite, where I come across like I think I know too much. Finding that balance has been so hard.

Sukrutha Bhadouria: And then I’ve realized, I can’t overthink it too much because then I start to become this really totally different person. Sometimes, you know, people just get more comfortable when they don’t know you they jump to conclusions and they brand you a certain way. But once they become more familiar with your working style, they then realize what your true working style is.

Gretchen DeKnikker: It’s what we were saying earlier, right? If you’re trying to be someone that you’re not, it comes across as so inauthentic. If I was going around and I was being very prim and proper and professional all the time, you guys would all I think that I had a fever or something. Right?

Rachel Jones: I know we’ve had similar conversations in our episode about personality and how that can be shaped just by the people around you and their expectations. I think it’s really interesting to think about your brand as negotiation between being true to yourself and being effective and showing up effectively in a workplace. Because we talked about branding being a way to announce your career intentions and have people support you in that. But sometimes, things about your brand that rub people the wrong way can keep you from effectiveness.

Gretchen DeKnikker: I can’t even list all of the ways that I’ve got in my own way.

Sukrutha Bhadouria: Oh yeah, it’s still happening to me.

Gretchen DeKnikker: I haven’t stopped. I’ve just slowed down a little bit.

Rachel Jones: I was going to ask how to navigate that and not do that? But, yeah, it sounds like we’re all kind of struggling.

Sukrutha Bhadouria: I mean, I’m struggling less. But I’m still struggling. You get better at it but you’re not… You may or may not meet that ideal, perfect state because you’re constantly trying to reinvent and improve yourself.

Gretchen DeKnikker: And you’re a human. Who has a life. It’s just not all perfect all the time.

Sukrutha Bhadouria: You don’t ever want to get to where it’s all good feedback. You want something that’s an area of improvement so you can focus on something else.

Gretchen DeKnikker: A little bit, but I don’t know. I think maybe that’s the advice of the feedback that really feels like it’s asking you to be someone other than who you are. Why I never worked for a really big company was that I just didn’t think I could fit into that mold. That I need to be better behaved all the time, and that that would feel very stifling for me.

Sukrutha Bhadouria: Feedback about your brand, in my opinion, or how you’re perceived, specifically, is just a wish list. I don’t think you should take everything super seriously. Especially, if, like you said Gretchen, it’s like disingenuous. I just look at it and I’m like, “Hey, who is this feedback coming from and do I really need to be too smiley today? Or too serious today? Do I really, really need to follow that feedback?”

Angie Chang: I think the most memorable feedback I got was to smile more when I was working a front desk at an event. But I don’t know… When you’re working and you’re very stressed, and you’re trying to be effective, oftentimes sometimes your face may not be the happy, shining person people want you to be. That’s just a point for improvement.

Gretchen DeKnikker: Also, do you guys just bristle whenever smile and woman are in the same sentence? I just have such an issue with that at this point.

Rachel Jones: There are women who have been super successful and branded themselves as colder, harder, not as a warm. So, it works for some people. Not having to feed into these expectations and that can even build a stronger brand for yourself. It just depends on how that helps or hurts your career.

Sukrutha Bhadouria: I looked at it, also, who do I want to be working for or working with people who always look angry or look serious? No, I don’t want that, either. I want it to be a comfortable environment. But that does not mean that the perception that you’ve created is the true one.

Sukrutha Bhadouria: For example, when I was pregnant, I was really going through a difficult pregnancy and I was sick all the time. I was told that there was a lot of feedback that I was looking angry. Constantly. So one day, I just turned around and I said, “Well, I am angry because I’m really tired. So, people just need to be more patient with me.” I think that made it easier for people to be more sensitive. Sometimes somebody is going through something difficult. You don’t know what they’re going through.

Angie Chang: That’s interesting that you got that feedback.

Sukrutha Bhadouria: Yeah, I don’t think I’ll ever forget it.

Angie Chang: So, on top of what Khobi said, in response to feedback that we get, I think there’s also so much that we can do in the modern day of Twitter and LinkedIn, being able to control our own authentic brand with social networks.

Rachel Jones: It’s interesting that you bring up the point of social media giving people more control over how they express themselves. I think it’s definitely a really powerful tool for that. But at the same time, sometimes maintaining a brand on social media just necessitates so much performance that it can lead to more inauthenticity at the time.

Angie Chang: That is true. I feel like I see on LinkedIn, a lot of LinkedIn employees going, “Here is my picture of the week.” And it’s very consistent and almost inauthentic, but it is… I’m sure Google employees would be using Google Plus or Wave if it still existed. And Twitter employees are the ones who use Twitter a lot. That performance is kind of normal for the job.

Angie Chang: For example, Sukrutha’s at Salesforce. A lot of Salesforce employees are really excellent at Twitter. I think it’s part of working in this day and age that we are always on the social networks asserting our “loving our job” hashtag “love your job” or something, that people do almost feels like performance art sometimes.

Gretchen DeKnikker: I think, on the topic of social media, if it’s not authentic to you to use it, then it doesn’t really make… It’s not very on brand to post things because you feel like you should.

Angie Chang: Leah McGowen-Hare, VP of Trailhead Evangelism at Salesforce, shared her own thoughts on this during our 2018 Elevate conference.

Leah McGowen-Hare: I often tell people, “You see my glory.” People be like, ‘Oh, you know, you just sashay up there. You just get up there and you do this.” And I go, “But, what you don’t know is my story.” And everybody has a story. I think, while it’s wonderful and it’s amazing to be on these stages and sharing and inspiring, really knowing sort of a piece of the story behind the scenes, has a lot more power. From my perspective. So, I’m going to share with you very little bit about my story.

Leah McGowen-Hare: I share this because people often go, “Well, Leah, I have questions about branding and my branding.” And I’m often like, “Don’t focus on your branding. Focus on the value you add and everything else will begin to fall in place.” It’s really easy to get caught up in that branding piece, particularly with social media and all this good stuff. And I’m always like, “Well, let’s take step back. What is your story? What are you trying to build? What is the story you’re trying to create?”

Leah McGowen-Hare: With my story, I moved from Anderson. I moved out from New York offices to San Fran. I started working for a company called PeopleSoft as a developer. I did a lot of development there. After doing development for a while, I realized, “You know, I’m good at this. I’m okay. I’m good.” But there was a piece missing for me. And that was the interaction with other people. I really liked interacting with people. Even talking about technology. So, my manager, who was really nice, at the time said, “You know, Leah, when you’re in the office, morale goes up but productivity goes down.” And I was like, “What!” She goes, “You get this but I think there’s something more you could do. I think there’s something different, a different path, that you should look at.”

Leah McGowen-Hare: While she wasn’t saying I didn’t want you in my group, she was just saying, “I don’t think this is serving your innate talents well.” So she said, “What about, there’s this position to be a trainer. Training developers how to code using the PeopleSoft tools.” And I was like, “Trainer? Mmmm, no way, that’s too close to my parents. My father’s a professor. My mother’s a teacher. I’m not trying to become my parents.” She was like, “Just give it a go and see what it’s like. Just go ahead and try it.”

Leah McGowen-Hare: So, I went in and tried out. Well, tried out, because you actually had to do a test teach for this position. A little begrudgingly. I did it and I then soon quickly realized I actually loved it. It mixed the two things that I loved, which was technology and talking to people. I really stepped out on faith and was like, “Okay, I’m going to try something that I didn’t think was for me.” And it turned out it was.

Leah McGowen-Hare: My story is lots of curves and turns and downward turns, upward turns. It’s just been amazing and it’s been lots of learning that I’ve truly embraced. And I’ve just learned to be open to opportunities that I may not initially seek for myself but allowing myself to at least try and go out and take a risk.

Rachel Jones: I think people think about brands, like what you said with the exercise to choose three words, it’s just like, oh here’s this little thing that I’m going to present. But that misses how, even behind the three words that you would choose in that exercise, it’s a whole lifetime of experiences that help you get to that point. When we get so focused on just putting up one little Instagram story, or one LinkedIn update, people can miss that whole narrative over just one moment that exists just to get a little attention.

Angie Chang: Yeah, I really like Leah’s talk, which was titled, Focus on the Story and Not the Glory. That definitely is a reminder to ourselves as we live our lives, what do we want our story to be at the end of our lives? Not necessarily what’s going to be on our tombstones, but what do we want to be known for and focus on that – not necessarily the times we get to give a TED talk or have nice Instagram story, but in the long run – what are our goals?

Gretchen DeKnikker: I think also not being so focused over like, this is my brand and so these other things don’t make sense. I follow Ijeoma Oluo on Instagram, and she’s an author that I love who wrote a book that everyone should be reading called, So You Want to Talk about Race? But half of her posts, she does amazing makeup every day. She posts something and then she posts a picture of which palette she used and whatever. And those are both her. It’s very authentic.

Gretchen DeKnikker: So, I followed her because I love what she has to say on topics of racial discussions. But I keep following her because I’m like, “Oh, wow! You’re making me care about makeup all of a sudden.” I think bringing whatever pieces of yourself, even if they don’t make sense, or you think that they don’t tell this cohesive story, they do make sense in the story of you.

Angie Chang: That makes sense. Giving people more data points and that’s actually really interesting.

Sukrutha Bhadouria: No one is uni dimensional. There are so many ways to represent yourself.

Rachel Jones: Following that, there was a point in Leah’s story where she first got presented with the opportunity to do the training role, and even just by the sound of the role, the name of the role, she automatically was like, “No, that’s not for me.” But, yeah, just being able to actually try it out and do the work, it was tapping into skills she already had and interests that she already had. Just being able to widen her idea of her brand a little bit really unlocked a huge part of what she seemed to have been meant to do.

Angie Chang: And now she’s a VP at Salesforce, which is really impressive.

Rachel Jones: So, our conversation so far has been focused a lot on personal branding. But company branding is also a really big part of this topic. Does anyone have thoughts on why it’s important to think about how companies brand themselves or advice?

Gretchen DeKnikker: When we wrote the copy, and particularly job descriptions, at my company, trying to keep that irreverent voice. But it also helped people who wanted a more regimented type company from… They would look at that and just be like, “These people don’t take themselves seriously enough. I don’t want to work there.” Which would be great because you want the people who are going to fit your brand and your culture.

Sukrutha Bhadouria: Yeah, the brand that the company has is directly related to the people that they attract to come work there. That’s really important to have a brand that matches the people that you want to come work at your company.

Gretchen DeKnikker: I don’t know if you guys remember, but we did the three word exercise for Girl Geek in the beginning. I had to go back and look at what our words were. It will be interesting, two years later, to decide if they still made sense.

Gretchen DeKnikker: One was transcend, meaning creating a future where being a female isn’t notable, and going above and beyond. One was belonging, and one was empowerment. We were never totally happy with transcend as the word. It didn’t quite match. The concept is right but maybe the word isn’t. The other two, I feel like, are very much–

Sukrutha Bhadouria: Yeah, it’s still relevant.

Gretchen DeKnikker: Woven in. But I thought that was really interesting because I was like, “Wait, we did do this for Girl Geek early on.”

Sukrutha Bhadouria: Yeah, and it was so difficult for me to do it. I remember now. I must have been difficult to work with at that time because it was so new for me. I never had to do it for anyone but myself. When you are coming up with your company’s brand or your company’s vision, be patient and definitely work through it.

Gretchen DeKnikker: Don’t be afraid. It’s just a bunch of words. It just has to be a word that means all of the other words to you. It doesn’t really have to make sense to other people as much.

Sukrutha Bhadouria: You’re not setting it in stone, too.

Gretchen DeKnikker: Right. Well, I don’t know. It’s in a PowerPoint.

Angie Chang: When I think about companies and brands, these days you always hear about which companies have taken on defense contracts that are being protested by their employees.

Gretchen DeKnikker: We see you, Chef.

Angie Chang: We see companies that are supplying software to ICE, as well. As I think the next generation of young people very much care. As we’ve seen in this climate strike that has affected millions of young people. That they strongly believe in doing the right thing. And I think Google was one of the few companies, maybe the only company, that explicitly had a motto of Don’t Be Evil. Which they changed.

Angie Chang: And now every company is really trying to keep their employees because they’re starting to do things for a profit and not really listening to their employees. I’m sure there’s a lot to be done there, and sometimes it just feels like you have… There’s always compromises to be made to work at a big company.

Angie Chang: I remember I was in an inter- Yeah, this is also the point- I was thinking about this. I know this isn’t really relevant. But I was at an interview with a company that had a really good reputation as an employer. And someone, a white man, literally asked me, across the table, and says something about, “Open the kimono.” I was like, “This is really off brand for this company.” And I gave this feedback to the recruiter. But I felt really surprised that for an employer that has such a good reputation, they still manage to have that happen in an interview.

Rachel Jones: I think that’s where you get into the authenticity of brands. Obviously, right now, a lot of companies are trying to brand themselves as super eco friendly, or like they have a really positive social impact. At the same that they’re doing a lot of contracts or making decisions that don’t look so great. Then it’s on us, as consumers, to see the extent to which people are authentic to their brands because anyone can perform this level of social caring. But, at the same time, the decisions that they’re making behind closed doors don’t support it at all.

Gretchen DeKnikker: So, we actually have an extra segment from our interview with Aline Lerner, founder and CEO of interviewing.io. She gives us some advice for how companies can brand themselves better to attract employees.

Aline Lerner: When we were earlier in our growth, we spent some time trying to identify who the right customers are for interviewing.io. The companies that tend to have the hardest time hiring are also ones that don’t really have a brand. We were trying to figure out, can we serve smaller companies or are we just going to be like we’ll just help Uber hire? Both have their merits.

Aline Lerner: One thing that we discovered is that there are some traits among companies that do well on our platform. And a lot of that has to do with branding. That doesn’t mean you have to be a household name. But, on our platform, when you’re a candidate… Everything is candidate driven. We don’t have recruiters that call you and try to match you with companies. We just say, “If you’re an engineer, here are all the companies we work with. Book interviews with any of them. We’re not going to pressure you. It’s self serve. You do what you need to do and we will just get out of the way and empower you to run your own life.”

Aline Lerner: But that means that you’re looking at this long list of employers. Some names, like an Uber, you might recognize, but then there are smaller companies that you may never have heard of. Those companies just have a few seconds to capture your attention. Some of them do very well and some don’t. We’ve tried to see what engagement looks like on smaller companies and what makes people click stuff and not click stuff.

Aline Lerner: The reality is that it’s really important to be authentic and to own the things that make you special. So many companies that are smaller are like, “Ooh, we are a startup which means you can have impact.” And maybe that was cool before there were a lot of startups, but that’s not a differentiator anymore. A lot of the time, companies are scared to say something polarizing about themselves because they don’t want to miss out on talent.

Gretchen DeKnikker: There is such a lack of–

Aline Lerner: Conviction?

Gretchen DeKnikker: Yeah.

Aline Lerner: Yeah. That’s actually in your interest. The companies that just own whatever their culture is, own their flaws, own… Yeah, we use a shitty stack. And what? You know? Or, yeah we do advertising but it’s awesome for this reason. We’re not going to change the world through hyper targeted, Silicon Valley… No, but we have shit ton of data and it’s awesome and maybe you don’t care about mission and that, maybe, you’re one of those people. It doesn’t matter, right? Not everybody has to have a social impact into their company but everyone’s kind of trying.

Aline Lerner: Just own who you are. Figure out what that is. When you describe it, describe it the way you’d describe it to a friend that doesn’t know what your company does. Instead of trying to write a bunch of weird bullet points. All this is obvious, but for some reason, it’s so hard to do it at work. To just get out of that corporate mindset and just be like, “Yeah, we do this!” But that’s the kind of tone and writing that has helped our customers craft the brand that gets attention.

Sukrutha Bhadouria: I feel like Aline, when she talked about smaller companies needing to have their message go through in just a few seconds, that was really something that I’d been thinking about. How do you make sure that your brand is clear in just a few seconds without it sounding fake? Because that’s all the amount of time you have to attract a potential employee.

Rachel Jones: I thought that one thing that she said that was interesting was, try to describe your company as you would describe it to a friend. I think that’s an interesting challenge when you’re thinking about how to communicate it so quickly. A lot of people, when you have to communicate about yourself in a short amount of time, you default to zippy, fun kind of words. But just coming back to a simple this is what we do as you describe it to a friend can really stand out.

Sukrutha Bhadouria: Yeah, and you can practice it to a… Oftentimes when you meet people, they’re going to be like, “What do you do? Where do you work?” And you just try it there and you practice it. You see the reactions and modify it along the way based on that.

Rachel Jones: So one thing that I really like about this quote is Aline talking about this fear of alienating. Where people don’t create a strong brand because they just don’t want to exclude people. That’s not what a brand is. When you’re making a product, your product is for someone. You can’t just say, “We don’t want our brand to be polarizing because we might miss out on customers.” But I think, yeah, if you do that you end up with such general messaging that you’re missing the customer that you actually are going after. Because they don’t see themselves in what you’re putting out there.

Gretchen DeKnikker: When you’re trying to be all things to all people and you just sort of end up with this word salad and you can go to, especially newer start ups, and you just go one after the other after the other and you read a paragraph and you’re like, “I have no idea what this company does.” I read a paragraph, over and over again, and I’m like, “I still don’t know what they do.”

Gretchen DeKnikker: But being like, “We do this for these people.” But I think at an early stage start up, they’re so afraid of like, “But we want to sell the salespeople but we also want to sell the marketing people and we also want to sell HR people, and so, we don’t want to hone in on any one message for fear we’ll miss out on something.” Instead, you become nothing to no one.

Gretchen DeKnikker: I think there’s the companies that go in hard, right? Like, Expensify. It’s just like expense reports that don’t suck. There were probably a lot of discussions of like, “But we do reporting! But we do blah blah!” There’s all these other features but just honing in on that one thing has really worked well for them. And then, they were really early in this more… Less corporate marketing style, too? Now there’s a lot that are trying to be clever or controversial or something and it comes across a little bit disingenuous. But also trying to go down that route.

Angie Chang: So, having a very good, edgy tagline.

Gretchen DeKnikker: Well, if you are edgy, then it makes sense. If you are not edgy… Angie, you should not have an edgy tagline. I should not have a nice tagline. These are not things that go.

Angie Chang: Yeah, right now in the BART, there is nothing but advertising for Facebook. I think at Power Montgomery there’s a ton of advertising for the Facebook Groups product. Their tagline, apparently, is More Together. As I’ve noticed on this advertising. And I was like, interesting. Marketing’s definitely thought hard about this.

Angie Chang: In Aline’s talk, when she talks about writing a very colloquial company brand, I’m sure that marketing fights with product and everyone else about what that tagline, or that brand, should be. Hopefully, the best one. And it can always switch every season or another campaign. Or not.

Rachel Jones: Does anyone have final thoughts on branding?

Sukrutha Bhadouria: To summarize, I think I’m learning that it’s super important to continually, not just create your brand but, keep it up to date. Constantly reevaluate what it is that it says about you because you are going to continue to build your skills as you grow in your career so that will automatically evolve your brand, as well. Don’t just stick to the one that you created when you were straight out of college. Then, yeah, it’s not just the personal brand that’s important. It’s the company’s brand that’s important, as well, and staying true to your brand without being fake about it is super important.

Gretchen DeKnikker: I think it’s a work in progress and you should always think of it that way and don’t be afraid to… Have an idea and try to stay on brand with your idea. But if something starts feeling off, then you need to go back through and think about, is it the brand that’s off or have I changed or what’s going on?

Gretchen DeKnikker: I think it’s definitely something worth reexamining every now and then. I think Sukuthra mentioned that. Your brand in college is not the brand you had in your 20s, and is not the brand you had in your 30s, in your 40s, in your 50s and 60s. It’s going to continually be a work in progress.

Rachel Jones: I think this conversation is really challenging me to think about branding outside of the social media space. It’s really about just how you move through the world and how that allows people to come alongside you and support you in what you’re trying to do.

Angie Chang: That’s a really good way of putting that.

Gretchen DeKnikker: Yes. Well said.

Rachel Jones: Thank you.

Angie Chang: Thanks for listening to this episode of the Girl Geek X podcast. Please rate and review us on your favorite podcasting app and we’ll be back soon with more advice from women in tech.

Rachel Jones: This podcast is produced by me, Rachel Jones, with event recording by Eric Brown, and music by Diana Chow. To learn more about Girl Geek X, or buy tickets to our next dinner, visit girlgeek.io. Where you can also find videos and transcripts from all our events.

Angie Chang: This podcast was sponsored by Aurora. Aurora works at the intersection of rigorous engineering and applied machine learning to address one of the most challenging, important, and interesting opportunities of our generation. Transforming the way people and goods move. This podcast is also sponsored by interviewing.io. Interviewing.io lets software engineers practice technical interviewing anonymously and land great jobs in the process. Become awesome at technical interviews, get fast tracked at amazing companies, and find your next job all in one place.

Scale Your Career with Open Source: Girl Geek X Confluent Talks & Panel (Video + Transcript)

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Angie Chang and Sukrutha Bhaduoria speak

Girl Geek X team: Angie Chang and Sukrutha Bhadouria welcome the sold-out crowd to Confluent Girl Geek Dinner in San Francisco, California.  Erica Kawamoto Hsu / Girl Geek X

Transcript from Confluent Girl Geek Dinner – Lightning Talks:

Angie Chang: Hi, everybody, thank you for coming out tonight on a Sunday night. This is our first Girl Geek dinner on a Sunday night after over 10 years of hosting almost weekly Girl Geek dinners. My name is Angie Chang, founder of Girl Geek X. I wanted to say thank you for coming out on a weekend. It’s really great to see everyone’s faces here at Confluent in San Francisco, to meet everyone, and also really excited to introduce Sukrutha, my co-organizer at Girl Geek X, who is six weeks into her maternity leave. So she has the littlest Girl Geek now.

Sukrutha Bhadouria: Hi, everyone. Welcome. Like Angie said, so nice to see such a huge crowd on a Sunday. I honestly can’t tell the difference anymore between a weekend and a weekday. But thanks for reminding me it’s a Sunday. But hey, I really wanted to explain, we always do this, we ask how many of you, is it your first time at a Girl Geek dinner? So do raise your hands. Wow. What’s been amazing in the last, I don’t know, a little over 12 months is that that number’s been increasing and increasing. And that’s been great because we want more and more people to join our community.

Sukrutha Bhadouria: Why we do this is we want to elevate more women in tech, in various roles in tech, and each dinner and each event is sponsored by a different company. And these companies are kind enough to host all of you in their space and they provide you with great content through their talks. We also use this content in our podcasts because Angie and I used to do these long drives to Girl Geek dinners all across the Bay. And we started to talk about what else we should do besides dinners. And now in the last 11 years, we’ve evolved beyond dinners to podcasts and virtual conferences as well. So we’ve had two virtual conferences so far, and we want to make it annual. Do check out our podcasts. And we want to know if you have any other ideas for what you’d like the content to be, please share it with us. Do share on social media tonight. Know we have a lot of great speakers tonight. So do share what you’re learning tonight with the #girlgeekxconfluent. I can’t speak full sentences anymore. But that’s all I had to say. I don’t want to take any more time. Thank you again to Confluent for making this happen. Thanks.

Dani Traphagen speaking

Senior Systems Engineer Dani Traphagen emcees Confluent Girl Geek Dinner.  Erica Kawamoto Hsu / Girl Geek X

Dani Traphagen: All right, what a beautiful crowd. There’s so many of you here and we absolutely love to see that. So welcome, everyone. We are really happy to have you here for dinner in our special San Francisco office. This is actually a satellite office to our home down in Palo Alto, and we’ll actually be moving soon next Wednesday to a new home in Mountain View. So we’re absolutely thrilled to have this stellar company of Girl Geek Dinner, dinners here at Confluent tonight, and I’ll bet you’re wondering what we do here at Confluent. So I’ll have a couple words about that. I actually luckily consult people about that in the subject of my day to day life.

Dani Traphagen: So my name is Dani Traphagen, and I am a senior systems engineer here at Confluent. What I do on my day to day is I work with account executives, specifically in the sales organization. So I technically consult large organizations anywhere, basically above $1 billion in revenue on how to leverage our technology. So that’s what I do. I really like my job. I love working with large companies on how to leverage our infrastructure and working specifically in the software realm on how to use real time software specifically.

Dani Traphagen: So this is my third company doing this kind of work. I have a background in database technology. And this is my third open source project and working in an enterprise on that. I actually ended up transitioning from bioengineering, though, specifically, into a career in tech after college, and this was many years ago. I will not tell you how many years ago. And I heavily leveraged events exactly like this to end up making that transition. So I really believe in them and the power of them, networking with people, making key mentoring relationships, and learning from role models, like some of the ones that you’ll hear from tonight, and kind of how full circle things are here, which is super bizarre. One of the men here tonight, Peter Feria, was at one of the events that I went to. He’s one of our videographers. Tim Berglund, who if you know anything about the Confluent’s ecosystem itself, and who’s kind of who in that world, you’ll see a lot of his videos online. He is one of our developer relationship folks. And he was my first boss at a company called DataStax.

Dani Traphagen: So it’s kind of crazy how full circle things go. So I really encourage you to meet, to network, to speak with people. And to just kind of learn more about all the things that you could possibly do. So now, a word about kind of what we do in this very building that you’re in right now, to just kind of bring things to a real visceral meaning. So Confluent provides enterprises exceptional expertise and tooling around the open source project Apache Kafka, and Apache Kafka as a fundamental way of moving event driven data from different sources within an organization to other interested parties within that same organization.

Dani Traphagen: So the way that I like to think about it is pretty much like the true heartbeat of your data pipeline. And it has become the central nervous system of many organizations, those specific organizations that I consult. With the Confluent stack, businesses can support streaming data use cases and optimize their insights and user experiences for many of their mission critical applications. So these are applications that are essential to their day to day operations. It has become an industry standard for the modern enterprise.

Dani Traphagen: Apache Kafka is an extremely robust technology, and it was co-created by tonight’s speaker, and pardon me, I should probably use my mic here. It was co-created by tonight’s speaker, Neha Narkhede, who is also Confluent’s co-founder and Chief Product Officer. It was inspired back during her time at LinkedIn in an effort to help manage the massive scaling efforts, along with her fellow Confluent co-founders. Neha has been an exceptional role model for so many women, including myself, and she has shown me in a sea of Bills and Elons and Steves, that something more is so possible in this world. And that has left a truly indelible mark in my path. So please join me in giving her a sincere and warm welcome.

Neha Narkhede: Thank you, Dani, for a very warm welcome and welcome Girl Geeks to Confluent’s very first Girl Geek dinner. It’s been such a long time since I first spoke at a Girl Geek event. This was about seven or eight years ago when I was an engineer at LinkedIn. Today, I’m so humbled to be hosting one and be here in front of all of you. I hope that you learn something new from this event. I hope that you make new introductions, and thank you all for taking the time to be here.

Neha Narkhede speaking

Confluent co-founder Neha Narkhede talks about starting and scaling the billion-dollar infrastructure startup at Confluent Girl Geek Dinner.  Erica Kawamoto Hsu / Girl Geek X

Neha Narkhede: So let me start off by telling you a little bit about myself. I was born and brought up in India. I learned computers at the age of eight, mostly to play video games and draw on MS Paint. So while I didn’t learn programming while learning to write, like all the whiz kid stories that you might have heard in the Valley, it did interest me enough to take up computer science. So I moved to the U.S. to get my masters in Georgia Tech. After that, I took a job in a big company, Oracle, mostly to find a safe path into an H1B visa. This was during the 2008 crash. Pretty soon, I realized the power of the open source community to accelerate my growth and learn new things. So I specifically applied to a company that had a real investment in open source communities, LinkedIn.

Neha Narkhede: I taught myself distributed systems on the job. I was lucky enough to be on a team that got a chance to create a very popular distributed system called Apache Kafka. We open sourced it, it went viral. I sourced a business opportunity around Confluent, pitched it to my teammates. Fortunately enough for me, they agreed to start this company with me. This was five years ago. Today, we’re more than 900 people worldwide and growing very quickly. Over time, I’ve worn many hats. I started off as an engineer, and then I ran engineering teams, and I transitioned to product, so quite a few changes. That was a little bit about my technical journey. For fun, I travel, I go scuba diving, and I engage in a fair bit amount of retail therapy.

Neha Narkhede: So most of my career has been about introducing this new category of software called Kafka and event streaming into the world. So to tell you a little bit about why we started this, we were facing a pretty unique challenge at LinkedIn. And the challenge was that our users could use our product and they were using it 24 hours a day, in a very real time fashion. But all the software that LinkedIn had could only get access to all that data and studied enough to produce more patterns and produce better products, maybe a couple times a week. So that was pretty slow. We wanted to take that all the way down to real time experiences. And so this meant processing billions of events a day in real time. There was nothing out there that did that. So we started Apache Kafka to solve this very problem to process lots of events in real time. And to basically give all of LinkedIn software access to all of its data at a millisecond level.

Neha Narkhede: We thought that this couldn’t have just been LinkedIn’s problems, so we open sourced it and we were right. Pretty soon, in the early days, Silicon Valley companies, all the top tech brands that you can think of, adopted Apache Kafka. After that, it entered the enterprise. And today, we know that about 60% of Fortune 100 companies depend on Kafka as a foundational technology platform. And any company that starts off as a digital one, they ingest all their data is in Kafka from day one.

Neha Narkhede: So anytime this sort of an adoption happens for infrastructure software, there’s a lot more to it than a good product. There’s usually a paradigm shift that drives such a change. And 10 years ago, that paradigm shift was that every company was not only becoming a software company, but it was literally getting turned into software. So what do I mean by that? You don’t call a cab company anymore, you go to your app, and the entire ride is managed entirely in software. You don’t go to an ATM Teller, the whole transaction happens online. So entire parts of businesses and businesses themselves are being replaced by software. So that’s the entire sort of business paradigm shift. But that’s leading to a lot of technology paradigm shifts.

Neha Narkhede: So the rise of the public cloud for developer velocity, rise of machine learning to use data and software to make better business decisions, mobile first customer experiences, and last but not the least, event streaming, because all of these trends, if you look at them, they all need access to data in real time. And event streaming is sort of the underlying paradigm that ties all of these things together. So not only was Kafka, of course, a great product, all of these changes were happening at the same time over the last 10 years that led to that massive adoption curve that I showed you.

Neha Narkhede: Event streaming is disrupting entire industries. To give you an idea, this is what Kafka users and Confluent customers are doing with Apache Kafka. Your ride sharing apps are powering your ETA feature and surge pricing using Kafka. Your bank is doing your credit card fraud detection using Kafka. Practically every retail company is doing real time inventory management using Kafka behind the scenes and your Netflix movie recommendations are also powered by Kafka. This sort of a widespread adoption of Kafka was possible, largely because of a large and thriving open source community. That was sort of the impetus behind Kafka’s adoption. I just want to say that the same open source community can act as a real catalyst for your own career growth. This is what it did for me, and it can get broad reach. You can learn from a pretty large community of people. You can diversify learning. You can be part of actually multiple communities at the same time versus one particular company.

Neha Narkhede: Large foundational technologies today start off as open source. So if you’re in the community, you’re part of a paradigm shift in and of itself. And I think that kind of impact is pretty large, because whatever you work on gets adopted across many businesses versus one particular one. You get to learn quite a lot. So this is the theme for today, I thought I’d mention. So that was a little bit about the what, in my journey, I did want to spend a few minutes about what it felt like, my experience, my career has felt a little bit like this, an obstacle race of sorts, and not all of those obstacles were technical in nature. And in fact, many times I’ve had to work 2X harder than my male counterparts to get the same thing.

Neha Narkhede: And while that might sound a little stressful and unfair, I want to share some perspective that my brother shared with me. He’s a many time Ironman finisher. He says that if you have to swim a mile in the ocean, and you train to swim a mile in the pool and expect it to feel similar, you’re going to be disappointed. It’s the currents that you need to prepare for. So that keeps me going quite a bit. In the moment, it feels like an obstacle race. But when I zoom out, and I look back at the last 10 years, I’ve started realizing that it feels like crossing a chasm. And I like to call this the credibility chasm. This is a phenomenon that I’ve observed where underrepresented minorities early on in their careers, they get marginalized, doubted, have to work much harder than everybody else to prove themselves over and over again, until you finally cross and make it somewhere on the other side, where sort of the opposite happens, you get noticed pretty easily, you get celebrated pretty widely for your achievements. While I have not crossed this chasm, what keeps me going are two things, long term thinking and a lot of grit, judgment to make decisions sort of not optimized towards the short term objective, but towards some long term goal and the stubborn persistence to just keep going.

Neha Narkhede: I believe this grit is rooted at an early childhood value, that many of you who grew up in middle class urban India will identify with. This is what we now know as the growth mindset. My parents sort of instilled this value in me that if you were open to learning and worked very, very hard, that you can actually learn anything you want to and you can be whoever you wanted to. And that sort of has stuck with me the value of education and hard work. How many of you in the audience know what this picture is about? Blurt it out.

Neha Narkhede: Yeah, this is the ISRO project managers or scientists. ISRO is India Space Research Organization. This picture was taken when they were celebrating a successful Mars mission. They put a satellite in Mars’ orbit, and they made an attempt at probably one tenth, the cost of any other mission in the world that has done that. This picture went viral when it was published. When a young girl in India looks at this picture, I think she believes with conviction that she can be one of these scientists when she grows up. And I had the privilege to be inspired by a lot of role models, even though not these particular ones. And I can say that role models are a primary driver, I think of a lasting change. And I get very excited when I look at that picture. But not just role models, but I think, the one last thing I want to leave you with is developing a real sisterhood will take us very far in seeing the change we want to see in the industry.

Neha Narkhede: What do I mean by that? Little gestures go a very long way. Pull a fellow woman aside who you think is screwing up, give her direct feedback, all the guys I know do that very often and it helps a long way, stop a conversation in a meeting to hear her out, vouch for each other very loudly in calibration discussions, and give credit if possible and very frequently publicly, these are all the little things we can do to sort of see the change we want to see in the industry. So that is sort of something I want to leave you with. With that, I’m going to conclude this very short talk and now we can move toward the next part of the segment. All right.

Dani Traphagen: All right, and for this next part of the process tonight, we’re actually going to have a panel session with Angie and Neha. So I’ll leave them to it.

Angie Chang: Awesome. So I have some prepared questions to ask you. Thank you for that presentation. So people might know Kafka from its creation at LinkedIn. And for those who don’t know what it is, can you briefly summarize what it is and how it’s evolved as a technology?

Neha Narkhede: Yeah, so Kafka is a highly scalable pub-sub messaging system. That’s how it started. What it does is it sits at the heart of your company’s data center. It connects up all the applications and all the data systems so that they can share data in real time and process data in real time to power all the things that you saw, real time customer experiences. Over time, we added functionality to Kafka that made a lot of sense. So the a-ha moment in Kafka is that it was not only scalable, which no other messaging system was, but it could remember, it could store data, so you can rewind and reprocess data. And that’s what caused its success in the world. Over time, we added related functionality, we added connectors so you can move data from all the other systems in a plug and play manner. We added stream processing so you can do sort of SQL on top of Kafka like maps and joins and aggregates.

Neha Narkhede: So this sort of combined functionality of pub-sub, connectors, and stream processing is what is now called an event streaming platform. So Kafka has evolved from a pub-sub system into an event streaming platform.

Angie Chang: Awesome. Yeah, I’m getting really familiar with event streaming platform as a category now. Let’s talk about your career. You started as an engineer, and then became an engineering manager, startup founder, and now you’re running product. Can you share something from your playbook with everyone here?

Neha Narkhede: Playbook? So yeah, that’s a lot of changes into it. So my playbook is I do a couple of things when I have to deal with a lot of change, like the first thing is I do believe, I firmly do believe in the growth mindset. So when I encounter something new, I’m fairly sure that if I spend enough time on it, that I can learn the ropes of it. The second thing I do is sort of this crazy knowledge gathering. So I read every book on the new subject, I reach out to experts, and I set up time and ask them questions. I just sort of like to learn a new area before I jump into it. And the third thing I do is reflection. I sort of sit down and try to calibrate myself on how I’m doing in that new area. And I talk to a couple of my close champions to sort of get their view on the subject, and then just keep iterating from there on. So that’s sort of my, I don’t know if it’s playbook, but I do that very often.

Angie Chang: It sounds good. There aren’t too many infrastructure unicorn companies that were started by women. I think we could only think of one, Diane Greene of VMware, and now adding to that list Confluent and Neha as a co-founder. And we hear women starting consumer companies, and they’re on the cover of magazines, but they’re often consumer. And before the infrastructure startups, we don’t see any women starting B2B infrastructure companies. So what is it about you, Neha, that’s different in that you can do this? And how can we get more women in infrastructure to start companies?

Neha Narkhede: Yeah, it’s one of my pet peeves is that there aren’t a lot of us starting, not only just infrastructure, but B2B companies. I think there’s there’s some luck and a lot of hard work. But if I were to hypothesize on why that is, I think there are a few things maybe. The first is that it’s a very male dominated field to begin with. And so when you don’t see people that look like you as founders of B2B companies, and when you know that starting a company is probably like five or 10 years of very hard work, then you may not be encouraged to take that very first step.

Neha Narkhede: So that’s probably one reason. The second one is, and I think I can only hypothesize is, I think there’s some perception that women may be uniquely qualified to start consumer companies or marketplace companies, because you have a better understanding of the end consumer. And while I’m really happy about the rise of consumer unicorns, I think that’s the same reason that women are successful with consumer companies, this is the same reason they will be successful with B2B companies is we’re smart, capable people. But I think that will change over a period of time. 2019 is probably the first year when we saw so many unicorn companies that were started by women. That’s like the first step in the change. I think we need a couple more women starting B2B companies. I will say that starting B2B companies is much more of a playbook than starting consumer companies. Predicting company behavior is a lot easier than predicting consumer behavior, so if you’re thinking of starting a company, I can tell you that a B2B company will be easier to start and grow. And I think we just need to see a couple more successful examples to tip that.

Angie Chang: Definitely, I think, so I would definitely like to say, people that we know on a first name basis, Mark, Larry, whatnot, now we add Neha to that. So tell your friends. We need more role models out there. So thank you for hosting us tonight. We talk a lot about women in tech in general. So let’s focus on the leadership aspect. Based on your own experience, what’s the greatest barrier for getting women into leadership positions?

Neha Narkhede: Wow, I wish there was just one barrier. That way, it would be much easier to cross. I’ll say a few things. I think we hear a lot about the imposter syndrome, and I can tell you that not just women, but men face it too. I think the reason it’s so much more magnified for minorities is because this sort of external feedback loop is much more skeptical than the usual, “Go man, you can just kill it, and you can do this.” So it’s a lot harder, but I can tell you what it does for leadership is it can sort of not encourage you to take risks, because if you think about what leadership is, you’re there to take a few calculated risks, and then lead successful execution of that.

Neha Narkhede: And so, if you can think about it, just sort of take the first leap. The second thing is, there’s a ton of unconscious bias, and just sort of you experience it, as you grow in your career. And the impact it has is women are evaluated on experience and men are evaluated on potential. So the same thing that you think you deserve and which you do, you get it later down the line. And for that, I would say that ask for that thing until you hear a very loud and clear no. Hearing a no never killed anybody, and it will only help you in that journey, and that’s what I’ve done. But I think those are a couple of really big barriers, I would say. There are a lot of upsides too. So when you’re growing in your career as a minority, it’s much easier for you to get noticed and so it’s much easier for you to recruit good mentors. There are people in the Valley who want to help women in tech and want to help minorities in tech, and they will gladly allow you to sort of reach out and give you the time.

Angie Chang: Great. The theme for tonight’s Confluent Girl Geek dinner is open source for career paths. And what are some things that Girl Geeks can do to leverage the open source community for their technical growth and learning?

Neha Narkhede: All right? Well, let me start off by saying that I don’t want to recommend open source as sort of a silver bullet for your career growth. But I will say that any outcome is driven by a series of choices you make. So in so far that open source is one of the choices that are presented to you, I would say think very seriously and probably even take the leap. The reason is that you get a lot of broad reach, you can learn a lot of things, but you also don’t need to invest all your time. So there are many ways to get involved, you can get involved in community discussions and critique designs or you can submit newbie patches or you can take up a full time job and get paid to work in open source.

Neha Narkhede: I think the best thing about the community is evangelism. So if you try out a new software, write a blog post about your user experience or if you want to critique the design, write a blog post and explain what you thought was good and bad about the design. I can tell you that Confluent has done a lot of successful recruiting by reaching out to people who wrote blog posts, and not just good ones, the critiques also. And so you will get noticed and obviously learn a lot along the way when you write about something.

Angie Chang: That’s really good advice, to write a blog post. All right, let’s get down to the nitty gritty for the technical in the audience. Kafka is known for its scalability. So where there is a continuous flow of streaming events, what’s the operational challenge in navigating new software versions, especially if something is backward incompatible? And how do you ensure the high quality of service?

Neha Narkhede: Lots and lots of things to say here. I think this would be a a segment of its own. But I’ll say two important things. I think really paying attention to the public APIs and contracts of your particular system is really, really critical. And especially so for infrastructure software, because a lot of applications depend on it and you have to be clearly careful about compatibility.

Neha Narkhede: Something the Kafka community did to be careful about this is a discipline we call Kafka improvement proposals. So we took a leaf out of the Python community playbook, and we introduced this discipline, because over time, you don’t get time to review code patches. Everyone gets busy. So this is a discipline where we ask people to write a wiki on the public API and contract chain so that the community can pay closer attention to version compatibility, and also the user experience, so that goes a long way.

Neha Narkhede: The second thing, because you asked for quality of service, I think being able to operate that software as a service in a company or as a fully managed service goes a really long way. I think there’s one investment I can say about high quality of service, it’s operating it as a fully managed service. So something people may not know about Kafka is Kafka, its first claim to fame was it just worked right out of the box. And that happened because we always deployed the version of Kafka into internal LinkedIn systems and it’s sort of baked in production for some time, before we even released the version into the open source community. So the community always got a well tested baked in version. That wouldn’t have been possible if we couldn’t deploy it internally at LinkedIn. So it goes a pretty long way.

Angie Chang: Great. And final question, what is one thing that surprised you, something that you believed in earlier in your career, and isn’t true today?

Neha Narkhede: Fun. So I moved here to the Valley from a different country. So the impression that I had about the Valley, which is pretty well known is that that’s where the American Dream gets made, that it’s a very meritocratic environment, as long as you’re smart, and you can work hard that you can win. That is mostly true except for underrepresented minorities. It’s a little bit harder than that. So I was surprised about that. I came in with big, optimistic eyes, and I was a little taken aback by all the challenges.

Neha Narkhede: The other thing that surprised me, I think, is the tech industry’s appetite for failure. And that one is really important. There is a ton of opportunity, no matter whether you succeed or fail, and that was from my upbringing, that was sort of a surprise for me as well, whether you succeed or fail, there’s always going to be a good opportunity waiting for you. So I would say, definitely go ahead and take that leap. There’s probably something else that is good waiting for you, no matter whether you succeed or fail. And so take those risks.

Angie Chang: That’s great advice. Thank you so much for this fireside chat. And I think we will now hand the stage over to the Confluent Girl Geeks.

Neha Narkhede: Thank you for having me, Angie.

Angie Chang: Thank you.

Dani Traphagen: All right. Well, that was fantastic. I don’t know about all of you but my favorite part was about getting more of us into infrastructure, and kind of developing the B2B experience and more diverse voices there. I think that Neha outlined an excellent roadmap of how to start a business, so I hope you were taking notes. I think all of those questions were fantastic on how to do that and the play by play of it. So next up, we’re going to have Bret Scofield. And she’s going to give a lightning talk on her experience here at Confluent.

Bret Scofield speaking

UX Researcher Bret Scofield speaking at Confluent Girl Geek Dinner.   Erica Kawamoto Hsu / Girl Geek X

Bret Scofield: Thanks, Dani. All right, so I’m Bret Scofield. I do UX research at Confluent. And I wanted to start off with a little bit of background about me. So the theme throughout my career has been building things from scratch. So in undergrad, I built metal sculptures, did a lot of welding, all that sort of stuff. When I graduated, I transitioned into digital products and I worked as a product designer for quite a while. And then in the past few startups that I’ve been at, the big theme has been building a discipline and a team from scratch.

Bret Scofield: And so that’s what I want to talk to you all about tonight. So I wanted to talk through, this is a brief listicle, there’s a lot of stuff from Twitter, I love Twitter so tweet me. But the big thing here is five things that you need to learn or that you need before you build something like UX research or any sort of discipline from scratch.

Bret Scofield: So the first thing, sorry, the first thing is defining UX research. And because I think a lot of people in here have familiar with engineering, et cetera, and maybe you haven’t worked with a UX researcher before, so essentially, UX research is narrowing the gap between these two groups of people. So I think there’s always one set of people who are building a product, and then another set of people who are using that product. And the people who are using that product are doing all these amazing things with it. They’re talking about it, they’re doing unexpected things, et cetera, and they’re all these amazing insights.

Bret Scofield: And so the goal of UX research is really to narrow that gap between the people who are building the product and the people who are using it. It’s just sharing those insights with the people who are building things. And they will make better products if they know and can empathize with the people who are using it. So there you go.

Bret Scofield: So the first thing that I think you need to build something like UX research is fertile ground to build. And so when I came to Confluent, the concept of UX research existed. It wasn’t formalized, or anything, but a lot of product managers, designers, et cetera, were speaking with customers about designs, about ideas that we had, et cetera. And they knew that this was super important to do and to get feedback from our customers, from our users, all that sort of stuff.

Bret Scofield: And so when I came in, the work that I did to establish UX research is really just taking what had already existed, formalizing it, adding a bit more rigor, putting it on a regular schedule, that sort of thing. And with this tweet, I don’t necessarily think that Jay, our CEO, knows what I do, but I hope that Neha does. And so I hope that UX research can continue to grow.

Bret Scofield: The second thing that you need is really three people. And so the first person that you need is an unconditional believer. And so I think a lot of us, as underrepresented minorities, you’re going to have tough days when you’re trying to establish things. And so like with UX research, a lot of times there’s days when people are like, “What are you doing? What’s the value of this? I’ve never done UX research before. I’ve never heard of this thing.” And so you need someone who’s always in your corner, who’s always believing in you, and is willing to talk through those tough days.

Bret Scofield: The other two people that I think you need are a sponsor and a mentor. And so a sponsor is someone generally in your organization who can connect you to the right projects that have good visibility, high impact, all that sort of stuff. And then the last person is a mentor. So the best mentors that I have have been outside of my organization, and I think that’s really necessary. They don’t have to be outside of your organization, but they should be outside of your management chain. And I think that’s necessary because you really want that unbiased feedback. You don’t want people who are incentivized to have you act a certain way or do certain things.

Bret Scofield: So yeah, I think that mentor should be outside. And ideally, they’ve been in tech or your industry for longer than you have. And I think that’s super important, because essentially, they’ve seen the same situation happen 15, 20 times, and you get to leverage that knowledge. You don’t have to go through 15 or 20 like I want to smack my head against the wall, you get to leapfrog.

Bret Scofield: Then the third thing is just enough knowledge. So a lot of times, I think that as a UX researcher, we think that we have to inhabit and totally become the people that we’re studying. And so with enterprise software, these people are sys admins. There’s just no way I’m ever going to become a sys admin, a lot of them were born in the command line, all that sort of stuff. However, it’s super important that I do a certain amount of research and learn this is what the command line is. I need to be able to tell what people are doing in there, what their intentions are, et cetera. But I don’t need to know every single thing. And so it’s very important in the past experience I’ve had to learn but to draw the line and not totally become a sys admin.

Bret Scofield: The fourth thing is balancing strategic and tactical work. So I think the impulse when you’re starting something new is to right away be like, “How can I provide value? Let me do this tactical stuff that’s going to provide value and insights to the team. They can take action on it right away, that sort of thing.” And I want to encourage you to do that, of course, but to also balance it out with strategic work. And by strategic work, in the context of UX research, one of the things that we do that’s useful for the next six months to two years, et cetera, is personas and journey mapping. So deeply understanding our people, deeply understanding where are they touching the product, how are they feeling at each of those points, et cetera.

Bret Scofield: Whereas the tactical work is with an individual team. We’re focused on what can they take away from this research and immediately put into practice. So yes, so I think the fourth thing is really that mix of short term. This is valuable right away, and the long term, this can be valuable for a longer horizon.

Bret Scofield: And then the last message, I think, is to get popular first and then get selective. So in the beginning, people aren’t going to know what UX researcher is, they’re going to come to you with all this kind of science fair sort of ideas. And that’s awesome. You should say yes, to all of them, do all of them. And then as you’re doing good work, as you’re finding insights, all that sort of stuff, your reputation and your legend is going to grow. And then you can start getting selective about those projects and take the really high impact ones, that sort of thing.

Bret Scofield: So those are the five tips if you’re building UX research or any new discipline within your organization. The other thing here is that, the next thing that I’m looking at is people to join the team. We anticipate hiring in the next quarter or so. So please, if you’re interested in being the second UX researcher at Confluent, come chat with me. And I also want to hand over to Liz Bennett from our software engineering side.

Liz Bennett speaking

Software Engineer Liz Bennett talks about her epiphany about being a hedgehog in a workplace at Confluent Girl Geek Dinner.  Erica Kawamoto Hsu / Girl Geek X

Liz Bennett: Thanks, Bret. Okay, right. I’m Liz Bennett. I’m a software engineer at Confluent. So I’ll start with a little bit about myself. I went to school at Oberlin College, and I studied music and computer science. And after graduating, I went to LinkedIn. And I was on the newsfeed team there. And I really enjoyed being on the newsfeed team. But it didn’t take long for the itch to join a startup to get to me. So I went to Loggly, which is a cloud based logs as a service company. And that was really great. I learned a lot about streaming infrastructure. I got to work with Kafka a lot and Elasticsearch.

Liz Bennett: But after a few years, I wanted to expand my skill set. And so I joined the data platform team at Stitch Fix. And the data platform team is the team that builds all of the infrastructure and tools for the data scientists at Stitch Fix. And for those of you who might not know, Stitch Fix has an absolutely gargantuan data department. There were I think, 100 data scientists there when I joined. So I really got a chance to level up my big data skills. And I also built all of their logging infrastructure and their data integration infrastructure from scratch. But after about three years, I went looking for another change. And as of six days ago, I’m now at Confluent.

Liz Bennett: Neha asked me if I would speak at this. And my first thought was, “Yes, I would love to.” My second thought was, “What the heck am I going to talk about?” And at this time, I was between jobs. I had just left Stitch Fix, I was waiting to join Confluent. And the only thing I could think about was this job change I had just done. It was really actually quite a difficult experience. It was really painful, much more so than any of my other job changes.

Liz Bennett: So I wanted to just tell my story, and I hope that it might be useful for some of you out there, now or in the future. Okay, so why did I join Confluent? I could also frame this as why did I leave Stitch Fix, because that was kind of the real crux of what was going on. Every other job change I’d had before, I knew what I wanted, I knew what kind of opportunity I was looking for. It was much easier. This time, the one thing I knew was that something didn’t feel right at Stitch Fix. It didn’t feel like the right fit. It took so long to really put my finger on it. I was completely blindsided by it when I joined.

Liz Bennett: And I waited three years and it never felt right. It never got better. So I did a lot of soul searching and I came to a few realizations and I realized that the team I was on and the role I had was fundamentally mismatched with who I am as a person. So everyone, I’m a hedgehog. Has anybody heard the parable of the fox and the hedgehog? Anybody? So I heard this recently on the Hidden Brain podcast. And as soon as I heard it, it seemed to explain a lot of things for me. So the parable comes from this quote by the ancient Greek poet Archilochus. And the quote is, “The fox knows many things but the hedgehog knows one big thing.”

Liz Bennett: And over the years, this has been interpreted to be like there are two kinds of people in this world, foxes and hedgehogs kind of thing. And some psychologists have even used this as a way to describe two kinds of cognitive styles and people. There’re foxes who draw on a wide variety of experiences, and they use many different strategies to solve problems. They’re comfortable with nuance and even contradictions. Hedgehogs, on the other hand, they see the world through the lens of one unifying idea. They love to think in terms of big pictures.

Liz Bennett: And as soon as I heard this, I was like, “I’m a hedgehog.” I told my best friend about it, too. And she’s like, “Yep, you’re a hedgehog.” And I also knew, all of my teammates at Stitch Fix were all foxes. My manager was a fox, my manager’s manager was a fox, not in a good way. No, just kidding. And so I thought, “Okay, is that what’s going on here? Should I just go find another data platform team somewhere else, hoping that there’s going to be more hedgehogs there?” And in the end, I decided no, that’s not what’s going on. What’s happening is the team I’m on is just a better fit for foxes. As a hedgehog, I need to find an entirely different kind of team. So I kept thinking this through, and I came up with something that ultimately really illuminated this problem for me. And it was a really useful device when I was trying to explain to my friends and my colleagues, and especially my manager, why I was leaving Stitch Fix and joining Confluent.

Liz Bennett: So I like to call it the product platform spectrum. What is the product platform spectrum? It is the spectrum of teams that exists within a technology company, that span product, customer facing teams on one end, all the way down to internal, low level infrastructure teams on the other end. And depending on where you are on the spectrum, your role is going to feel really different. So at the top of the spectrum, you have your product teams, these teams are very close to the customer, they’re generally the source of revenue for the company. You’re really close to the company mission, there tends to be a lot of separation of roles and separation of expertise, like they’ll be UX researchers on product teams.

Liz Bennett: Supporting the product teams, there’s usually platform teams, and companies invest in platform teams because hiring somebody on a platform team is like hiring somebody on all of your product teams. That makes them all more effective and more productive. Platform teams, though, are further away from the customer. They tend to wear more hats, I think. There’s less specialized roles. I think they tend to own more surface area. They have to own more technologies and services. Underneath the platform team, in some companies, this will vary, but very often, there’ll be infrastructure teams. And these teams own the very bottom layer of data systems and services. They’re like the bedrock that the whole rest of the company sits on top of.

Liz Bennett: And these teams are great because their work is leveraged across the whole company. They’re also the furthest away from customers. And there’s no platform team supporting them. So they kind of have to write their own tools. They do a lot of dog fooding. They can be very autonomous, though, they, they set their own strategies, they do their own research, and they’re masters of their own destiny. So at Stitch Fix, I was on the very bottom layer of infrastructure. I built all of the Kafka infrastructure, I was doing that. And where I really wanted to be, though, was at the very top of the product spectrum. That was where I had been my last couple of roles.

Liz Bennett: Since I’m a hedgehog, given my hedgehog nature, I thought it would be much more comfortable in that role again, where I could really focus on deepening my skills as a software engineer, and also be really close to the company mission. So how can I get from the bottom of the spectrum to the very top? Could I get there while I was at Stitch Fix? My answer basically was no. The product teams at Stitch Fix were mostly full stack Ruby on Rails engineers, and I didn’t want to completely retool myself just to get to the top of the product spectrum.

Liz Bennett: So at that point, I realized, okay, I need to make a change. I need to leave Stitch Fix. But where? What do I do? Well, smeared across this whole spectrum is our B2B vendors. So there’ll be platform as a service companies selling products to platform–or product teams. There’s infrastructure as a service companies selling products to platform teams. So all I really actually needed to do was go from here, at the very bottom of the spectrum, took one little step into the B2B space. And suddenly, I was going to be at the very top of the product spectrum again.

Liz Bennett: So I mean, the one thing is I left a consumer business and went to a B2B business, but I was in the B2B business at Loggly, so I felt pretty confident that that was going to be fine. So at this point, I realized I needed to, I knew where I needed to go, I knew what sector I needed to go to. So then the last question is why Confluent? Why did I pick Confluent?

Liz Bennett: Well, I had been working with event infrastructure for the last three years at Stitch Fix, and I had become absolutely obsessed with this mission, Confluent’s mission. And being a hedgehog, I just wanted to go deeper. I wanted to keep doing it. And I wanted to focus on that. And I realized, what could be more satisfying than going from building event infrastructure for one company at Stitch Fix to going to Confluent where I could build it for the whole entire world? So that’s my story. I hope that it’s useful for some of you out there. Transitioning jobs is really tough. I don’t think people talk about it nearly as much and give it as much credit for how hard it is. So if anybody wants to talk more afterwards, I’m super happy. Connect with me on LinkedIn. And thanks, everybody, for coming. All right, so I’ll hand it off to Priya.

Priya Shivakumar speaking

Senior Director of Product Priya Shivakumar talks about her career jungle gym at Confluent Girl Geek Dinner.  Erica Kawamoto Hsu / Girl Geek X

Priya Shivakumar: Hello, everyone. I hope everyone’s having a great time tonight. I certainly am. It’s a pleasure to be in the company of all of you. So my talk is going to be a little bit about my career path, some learnings that I’ve had along the way, and how that’s come to apply to what I do at Confluent. All of us are looking to grow in different ways. And so the paths we take sort of reflect that. But for me, the common theme throughout has been to continuously broaden my perspective and keep learning along the way. That’s kind of the key decision driver for me.

Priya Shivakumar: And so my career path has looked something like this. I’ve used this format, instead of the format that my colleagues and friends have used before me, for two reasons. One, because it would actually age me and the second because it just wouldn’t fit on one slide. So this is kind of the path that I took. Growing up, early on, I developed a passion for engineering. My dad’s an electrical engineer, and he encouraged my brother and I to sort of take things apart to learn how they work. And I remember him and I taking apart quite a few VCRs and a few transformers actually in his station to get to the magnets inside. And those magnets were coveted positions. So I naturally gravitated to engineering for my undergrad. And from there, my career has spanned three key disciplines: engineering, product, and consulting. And I’ll talk to you a little bit about each one of those.

Priya Shivakumar: So in engineering, it was about building the product, how do you build a product. I enjoyed all aspects of problem solving, logic, it was a natural fit. Success was mostly individual in nature. I could have been successful without having interacted with another soul, potentially, or at least having a little bit of interaction maybe.

Priya Shivakumar: But I did not get to see how my code was being used. What was the impact it was having, who are my customers. And so that’s the reason why I moved out of engineering. As I stepped into IT consulting as an engagement manager at BearingPoint first, and then later into product management, the focus shifted over to customers, stakeholders, clients. There were a lot of competing priorities and a dearth of resources, and that’s the name of the game. And that required–success now meant being able to influence people, align teams, kind of create common goals and create common objectives. And that’s a very difficult and hard skill to acquire.

Priya Shivakumar: And what little I know of it, I will attribute to my consulting days. An example comes to mind, there was a post merger integration project. One large company had acquired another large company. And as a result of that, the system and the processes we were putting in place would result in the elimination of 30 to 40 jobs. And the data that we needed to build the system had to come from these very same people. So you can imagine how painful and difficult it was. And this particular example actually falls on the extreme end, but most consulting projects have some element of tension or friction in them. Think about it. You’re an outsider, you’re trying to advise somebody how they should do their job, nobody likes to be told that, one. Two, they may have some kind of perception that they may lose some control. They don’t like that. And they may also kind of think that their domains are going to shrink, or there may be a job loss in the future.

Priya Shivakumar: And so all of these things create for some very delicate waters that you need to navigate and kind of balance. So I think that was a core skill but it’s still a learning. It’s never, I mean, I wouldn’t say I’ve completely mastered that. But that’s something that I picked up a little bit in consulting. I would like to share another key thing that kind of happened during this time. So when I was at BearingPoint, after a couple of years in IT consulting, the work got repetitive. And like most of you here, I have a healthy paranoia about stagnancy. I wasn’t learning. And I went to my MD, my managing director. And I told her that I really wanted to move into strategy, from IT consulting to strategy consulting, and BearingPoint had both of those practices. She was supportive, she was actually well intentioned, and she sort of grew me within my role, but I had tech expertise, and I was a billable resource. So it did not make business sense for her to move me to the strategy consulting practice within BearingPoint.

Priya Shivakumar: So I realized early on that that was not going to happen. But I had to stay put for two more years. And that’s because I was trying to get my green card. I was pregnant with my first child. And the key here is that that’s okay. Right? That’s okay. There will be times in your life when other priorities take over. There’ll be times when you have obstacles that cannot be overcome, things that are outside of your control, like immigration things. So in those instances, it’s okay to set your own pace. Take your time, wait, rather, bide your time. And when the time’s right, get up and get going again. Just know what it is you want and what makes you happy. Go after that, though. So in my case, I wrote my GMAT in my ninth month of pregnancy, finished out my B-School essays in my maternity leave, got my green card, and I was out of there.

Priya Shivakumar: So the next thing I did was I went into, post B-School, I joined LEK Consulting. It’s a niche strategy consulting firm. And the reason I joined that was because I primarily wanted to work only in strategy, in the broad discipline that is management consulting. So it was a bit of an insane choice to make. I call it insane because it required me to work 60 to 80 hours a week. And the work itself was intense. We were advising veterans in an industry, typically the C suite, about what they should do to grow their business. It required you to get up to speed on their industry within a short period of time, do the research that was needed to draw insights from data, model out the market size trends, things like that, and then advise them about what they need to do to kind of grow the company by X percent.

Priya Shivakumar: So that was intense. And my husband was traveling on a weekly basis. And I had a two year old to take care of, two and a half, three year old to take care of. So there was a fundamental thing that I did, which not only helped me survive, but succeed in that role. I still wanted that role. I still went and got that role. But the fundamental thing that I did was hiring the right child care, and people say this all the time, but I cannot enunciate that enough. I applied the same rigor that I would to my job to finding child care.

Priya Shivakumar: So to me, the criteria that I defined for that was that I needed an au pair who would be with us 24/7. She had to be educated so that my child would learn from her and it would be easy for the whole interaction for the family. Also, I preferred someone who would have taken on responsibility early on in their life, so that she could independently run the place. And so the au pair was Aleja. She was 24 years old from Colombia. She had a law degree. She had taken care of two siblings while growing up and while her parents were working hard on their small business.

Priya Shivakumar: She came home. She just seamlessly became part of our family and completely ran my household, enabling me to focus on my work. So there will be inflection points in your career. And during those times, you have to get the support that you need. Do not skimp on that. I’ve seen too many people make that mistake. And it just results in burnout and a lot of not very good things. So I would highly encourage you to do that.

Priya Shivakumar: One other–then the time came to become a partner at LEK, it gave me pause. It was a very lucrative path and one that was a sure path, actually. But the reason I paused and decided to leave consulting, first was because I realized becoming a partner meant greater focus on sales, and lesser focus on problem solving and casework, which is what I truly enjoyed. The second part was that as part of an advisory or a think tank, that’s what you do. You advise, and you walk away, right? There is an innate satisfaction in seeing things come to fruition, things that you build, whether it’s the product that you build that launches, or a strategy that you can come up with that is activated, and you see that work in the market. And I really missed that aspect of it. And lastly, the industries we were working on weren’t super exciting to me and I was always passionate about high tech. And that’s why I moved back into VMware, and now to Confluent.

Priya Shivakumar: So putting it all together, the engineer in me loves the innovation we’re driving at Confluent. We are fundamentally changing Kafka in ways to make it ready for the cloud. The market is nascent, there are no clear answers, this data is limited. And this is where I really lean on my strategy consulting frameworks to answer questions like how should we price cloud, what segments to go after, what features are important by which segment?

Priya Shivakumar: I think there’s significant competition in the market but I do believe that we are uniquely positioned to really make Confluent’s mission successful. So putting it all together, I would say my execution from my engineering days and strategy from consulting and product thinking from VMware, enable me to drive this key initiative at Confluent, which is to grow the cloud business. Thanks so much. Dani, over to you.

Dani Traphagen: Thank you so much, Priya. All right, so everyone, I hope that that was really informative and gave you some food for thought. And now let’s actually have some food. But before we do that, just a couple of quick announcements. The other thing that we’re doing, as well, is providing a Women in Tech events at Confluent’s headquarters down in Mountain View in November. So if you’re interested in that, there’s sign up sheets also out at the front. And we’re going to do a quick Q&A before we start to network and have some food as well. So I’m going to invite everybody up for that right now.

Liz Bennett and Neha Narkhede speaking

Confluent girl geeks: Software Engineer Liz Bennett speaking on a panel with Neha Narkhede at Confluent Girl Geek Dinner.  Erica Kawamoto Hsu / Girl Geek X

Liz Bennett: Okay, who’s our first victim?

Jiang: Hi, I’m Jiang, and I am particularly interested in the theme of this talk, because it’s talking about how open resource open opportunities for your career. I’m just wondering, how do you all kind of assess opportunities in your career? For myself, I kind of felt like sometimes it’s really hard to find the good opportunities. And I’m particularly interested how people looking for opportunities and how they consider those are the good opportunities.

Liz Bennett: Well, such a tough question. I think for me, I’ve usually, the best opportunities I’ve found are the ones where there’s the biggest vacuum, I guess, like there’s the biggest need for people who have your skills or experience or something that you want to learn. And you just go and find those vacuums, and you fill them as fast and as well as you can. I think that would be my short answer. Yeah.

Priya Shivakumar: I think that’s a great question. I want to add to that a little bit. I think, you go through the interview process, right? It’s really important to understand the culture of the place during that process as you meet people. One of the things at Confluent that I absolutely fell in love with was this smart but humble…requirement, almost. And that was very apparent throughout my interviews. I had eight separate interviews, and each person sort of embodied that requirement, I would say, and then I also look for how many women are at that company. And how many women are at the top. It indicates a certain thing, and it should not be… it is an important thing. Those are some of the things I look for, among other things.

Dani Traphagen: Next question?

Audience Member: Hi, I have a question regarding, I’m somebody who came from large companies and worked in the large company environment. And in that, you’re reporting to a manager, who reports into another manager, who reports into director or whatever. So you get a lot of hierarchy. So I recently switched from that large company environment. And now I’m at a startup where I reported to the CEO. So I’m curious, how do you manage that dynamic of this isn’t just my manager, but it is my manager, but they’re also here and not here. So any insights you have on how to define that relationship, how to set the tone for that relationship?

Neha Narkhede: I could probably add a little bit. So because you talked a little bit about the big company to start up transition, depending on the stage of your startup, early days or early years are all about survival, and that’s what the CEO is responsible for. So likely, they do not have a lot of time to tackle the day to day issues that a manager’s supposed to tackle as much as they would like to. Just the practicalities of running a start up don’t allow for that time. And so I would suggest sort of look for mentorship elsewhere, if you are running into those kind of problems, but really ask the person like, “What is your biggest problem?”.

Neha Narkhede: And that was sort of my way of working at LinkedIn is nobody really wanted to work on this Kafka problem. It was sort of just something that my co-founder, Jay was dealing with, and I sort of asked him, “What’s the biggest problem on your plate?”, and he was like, “Well, there’s this Kafka thing, but no one really wants to work on it.”, because it was sort of a mess of a situation at LinkedIn that we had to clean up using Kafka.

Neha Narkhede: So I think the what I learned from that is, if you work on the biggest problem their business is facing, and the CEO is likely to know that biggest problem, you’re quickly going to become a go to resource and you’re quickly going to learn quite a lot that would then position you for other opportunities in the company. So that’s sort of the way to look at it is expectation management is not going to have a lot of time for all the day to day problems as well as asking for what’s the biggest problem on their plate that you can take off.

Jenia: Hi. Thank you for your talks. Jenia, a founder of a B2B startup with its first paying customers. So I wanted to ask you all with the limited resources that companies have, especially in the beginning, how to make customers happy? What are the secrets like hacks?

Neha Narkhede: Well, I can add a little bit. So early days, and I imagine you’re probably talking about pre product market fit. And so I think pre product market fit is a lot more of an art than a science. I think Priya talked a lot about managing data to draw insights that happens later in the life of a startup. Early days is all about landing your first 10 customers. So it’s incredibly important to not worry too much about over fitting the problem, because first time customers are going to ask for the world, but it’s really, really important that you land them successfully, because then you know which are the next 100 customers that you want. So that’s probably a really important thing. Life is going to be very hard when you satisfy all the problems of your first 10 customers. You should just go in expecting that to be the case. That’s very expected. But landing your first 10 customers is probably your your biggest and most important problem in that phase.

Mike: Hi. My name is Mike. I have a question for you, Neha. I want to know what is your best practice or solutions that worked to receive feedback at the company and from your employees. I mean, there is so much to read in different books about recommended ways to receive feedback from employees. But having worked for a couple of companies, I see it being quite difficult for people at C level positions, specifically, to receive feedback from engineers. I want to know what are the things that work for you, like when was the last time that a really junior engineer could openly and honestly share with you feedback, how you receive that. I would appreciate your thoughts on that.

Neha Narkhede: That’s a great question. So something someone said to me reminded me of this when you asked this question, and he said that, Neha, you got to be careful in this stage of your company, because fat fingers cannot make small changes. And what he was really trying to say is, the more your company grows, and now we’re 900 people and more, is it’s going to be harder and harder for you to get that feedback.

Neha Narkhede: I think a couple things have helped us at Confluent to get that feedback, and I couldn’t deny that it’s getting a little bit harder, the first thing is setting the tone of the culture from the very early days. So when we started the company, all the founders, we encouraged a lot of open dialogue, a lot of open sort of pushback. You can actually get up and challenge the founders on their ideas, or even the CEO on many occasions, all the engineers could do that and they felt comfortable doing that. So when new people join the company, they could see that debate happening on an open Slack channel. So everybody could see how the people are dealing with it and we encouraged that sort of debate quite a bit.

Neha Narkhede: I think that sort of has helped us quite a bit. The second thing is anonymous sort of feedback channels, doing surveys in the company that sort of scales when you get to a certain size. And then what has helped me in particular is I have these friends who are sort of in different parts of the organization, and they’re at the beginner sort of medium levels. So they collect feedback, and they sort of bring it back to me, and they’re sort of my champions in their processes. There are some engineers who are going to bring sort of gossip or chat that’s happening at the lunch table. And I have other sort of champions sitting here that have gotten me feedback on what I should be careful about. All of that sort of really helps. You got to make sure that you have some of those champions sprinkled around in your organization, who could actually give you the second degree feedback, because people are not going to come and give you that feedback directly as you grow your organization.

Denise Hummel: Hi. I’m Denise Hummel, and I’m the founder and CEO of a technology enabled diversity and inclusion firm. And I’m way older than like 90% of you and it’s blowing my mind because I’m, generally speaking, the mentor of trying to move women through middle management to senior leadership. And I look at you guys, and you are an inspiration to me. So my first firm was a consulting firm that I scaled to 65 countries and sold to Ernst and Young, and became a senior partner there leading culture, inclusion, and innovation. And I thought, wow, this is just the story of the century. I was a single mom who raised two kids on my own while I was building this company. And then when I got there, I felt unable to navigate the nuance of standing out and fitting in, and everything that I had known to be the core of who I was and why I was successful as an entrepreneur, which is basically never take no for an answer and just keep forging ahead, was actually the bane of my existence, because I was considered to be too aggressive.

Denise Hummel: So here I am now. I have left to start this new firm, which basically is technology, using AI and nudge messaging to bring inclusive leadership to leaders in real time, which is super exciting. And I have to pitch for VC and I’m still running into the same issues that I was running into before, which is that as a woman founder, I have to be this assertive, take no prisoners person in order to convince VC that I have the stick-to-itiveness to get this done. But when I do, then I’m the aggressive woman, who isn’t like the quintessential female persona that they’re all looking for. So that’s a really long background question. But the actual question itself is, do you have any feedback on what we can do as women to walk that line to have that nuance between standing out and fitting in and being assertive enough to make it but not so assertive that we are the aggressive ones that no one wants to do business with?

Bret Scofield: I guess my initial thought on that is, because I think in a lot of situations, especially in enterprise and dealing with a lot of customers who are aggressive men, and I’ve tried being aggressive, assertive, et cetera. And it doesn’t feel right to me, it just doesn’t feel like Bret. And I think that’s fine because I think that I can be me and still get the message across and still be successful, and all that sort of stuff. And it feels better, it doesn’t feel like I have to be super pushy, and that sort of thing. So yeah, that’s where I come out on that because I think that you can try and be this person that you’re not, and it’s ultimately not going to come through as much as being you. So are there other thoughts on this?

Neha Narkhede speaking

Chief  Product Officer Neha Narkhede speaks on panel at Confluent Girl Geek Dinner.  Erica Kawamoto Hsu / Girl Geek X

Neha Narkhede: I can add a little bit. I’m going to channel an RBG quote on that, which is it pays to be deaf sometimes. And I say that because you got to keep going. You got to pitch your startup and it’s an extremely arduous opportunity. You don’t want to get bogged down by all this feedback, because it turns out that in order to start a company, you have to be ambitious and aggressive, and very, very persistent. So I wouldn’t worry a lot about the perception. There’s going to be feedback, I’ve gotten a lot of this feedback, “You’re too ambitious, you’re too aggressive.”, and I’m saying, “Well, thank you. I’m going to try to work on how that doesn’t come across sometimes.” But it’s absolutely necessary to sort of put your blinders on during a certain stage and just keep on going. Because you do not want to stop in your journey, because of a lot of this increased skepticism from the outside. So I’m going to just say, keep going.

Karen: Hi, I’m Karen, super lucky to ask the last question. First, thank you all for the talk. Totally loved it. This question is for Liz and Neha. I am a software engineer and I observed in my company, and maybe what a lot of companies, being an engineer, a female engineer, as you go up, you see less and less senior women engineers, actually have the data from my company. So I can’t share, but it’s like at some point, there’s a huge drop. Beyond that, you just don’t see women anymore. And in the industry, we definitely see less women architects or women CTOs as compared to other roles. So one thing I would like to know, is first, Neha, I looked at your LinkedIn. So I see you have a good career growth at LinkedIn. So at that time, what pushed you through getting to be a more senior software engineer? Same for Liz, I know you want to keep growing, being really focused on this one area. So what is your view of this problem? Yeah, I think that’s it.

Liz Bennett: Yeah, it’s definitely true. It’s kind of eerie how there’s so few women the further up the stack you get. I think, for me, I have always thought of myself as just a person. I don’t see myself, I don’t often think of myself as like a female engineer. I almost actively avoid thinking about how I’m the only woman in the room, and after years of doing that, it just kind of stopped occurring to me when it happened, and it just became a normal thing. And I think the less I think about that, the more I can just focus on being an engineer and focus on doing what I love doing, which is technical work.

Liz Bennett: I really love doing it. And I think people see that, and they see that you love it. And they see that you’re competent. I do have to go out of my way sometimes to advocate for myself. When I do it intentionally, and when I’m doing it, I’m conscious that I’m doing it. And it helps a lot when you say, “Hey, I built all of the streaming infrastructure at Stitch Fix.” People are like, “Oh, okay, she knows what she’s talking about.” You have to actually do that. There’s one thing that I think a lot about, well, I used to think about, but not so much anymore, but it’s kind of like the competency chasm that you’re talking about. I think for a lot of women, they’re seen as incompetent until they prove themselves to be competent. And for men, it’s the opposite. They’re seen as competent until they prove themselves to be incompetent.

Neha Narkhede: Sometimes.

Liz Bennett: So I think that I’m like, Okay, I have to go through this process, I have to prove myself. And I haven’t had too many problems with it. But it is something that I’ve come to learn over the years.

Neha Narkhede: I’ll add one more thing to that is, when you’re on this technology ladder, there’s going to be a point where you sort of feel like you’re running out of options. And that’s when you try to fall back to this management option, which is sort of a parallel option. And a lot of us take that because at some point, you get tired of advocating for yourself or pushing for that new opportunity. If it is the right choice you want to make, then you should take it, but if not, I would recommend, ask for things explicitly, ask for that new opportunity that, the same way Priya asked for this new opportunity on the strategy side, ask for things until you hear a clear no, because you never know where there is an opportunity where someone like you might be a good fit, but people are not quite thinking about it actively. You don’t want to wait until that sort of thing walks up to you. You want to go aggressively vouch for it and not be scared to hear a no.

Dani Traphagen: Cool. All right, thank you so much, ladies. Okay, so networking, deserts, maybe an added La Croix for the road. Whatever you’d like to do next, I hope that that was really useful for you. I know it was for me, I learned I’m a hedgehog. An informative night the whole way around. And yeah, if you have any questions for any of us, feel free to come up. The Confluent careers page is a fantastic place to check out. I hope you will. Feel free to check out our LinkedIn pages and and just go ahead and connect with us. If you have any further questions, we’re really happy to give you any advice that we can or help in any way and just actually have some friends in our community. So anything we can do. Thanks again.

Neha Narkhede: Thank you.

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Neha Narkhede and Sarah Allen

Confluent founder Neha Narkhede and Bridge Foundry founder Sarah Allen meeting at Confluent Girl Geek Dinner in 2019.  Erica Kawamoto Hsu / Girl Geek X

Why changing the face of “superstar developer” matters

Neha Narkhede began her career as a software engineer, working at Oracle and LinkedIn. She was a co-creator of Apache Kafka, a popular open-source stream-processing software platform that was created at LinkedIn. She spoke on a panel Girl Geek Dinner while she was still in engineering there. She saw a big opportunity with Kafka and convinced her fellow Kafka co-creators to start Confluent as a B2B infrastructure company in 2014 – Kafka’s event streaming is used by 60% of Fortune 100 companies today.

With only 2% of venture capital going to women entrepreneurs, Neha beat the odds and demonstrated that it’s possible to thrive as a technical leader. She served five years as the company’s Chief Technology Officer, and recently became Chief Product Officer to continue growing the brand. Confluent’s founders recently raised Series D venture funding for the company at a valuation of $2.5 billion, and they employ over 900 people.

Silicon Valley needs more Nehas! Read more.

Girl Geek X Microsoft Lightning Talks & Panel (Video + Transcript)

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Angie Chang speaking

Girl Geek X Welcome: Angie Chang kicks off a sold-out Microsoft Girl Geek Dinner at Microsoft Reactor in San Francisco, California.  Erica Kawamoto Hsu / Girl Geek X

Transcript of Microsoft Girl Geek Dinner – Lightning Talks & Panel:

Angie Chang: So hi, everyone. My name is Angie Chang and I’m the founder of Girl Geek X. I want to thank you so much for coming out tonight to the Microsoft Reactor. I’m super excited to see everyone here and to introduce you to all of Microsoft’s girl geeks, to see this amazing art and tech demos. Who here signed up for a demo? I saw a lot of people interested in demos and getting tours, so I’m really excited that you are able to do that. Thank you once again to Microsoft and to all the people who helped plan this night.

Angie Chang: How many of you this is your first Girl Geek Dinner? Wow. And how many of you consider yourself like a regular at Girl Geek Dinners? Thank you so much for coming back again and again. We do this almost every week, going to different tech companies, meeting the girl geeks, and we hope you tune into our podcast. We have a regular podcast on topics from internet security, to emotional security, to management, to working in the Silicon Valley. So please tune in on iTunes or Spotify. We also have a very active social media. So if you follow us at Girl Geek X, you can also tweet and share with Girl Geek X Microsoft tonight and we will retweet and reshare.

Angie Chang: Now I would like to introduce our first presenter. Her name is Kaitlyn Hova and she is the co-owner of Hova Labs, where they have designed and produced the Hovalin, which is a 3D printed violin. Kaitlyn.

Kaitlyn Hova: Thank you so much for having me. This is wonderful. So my name is Kaitlyn Hova. I currently work at Join and I also co-own a company called Hova Labs, where we like to make a bunch of weird projects. It’s kind of like one of those like, “If I had time, why wouldn’t I make this?” kind of companies. So it’s just me and my husband and the biggest thing that we really wanted to do was to find a way to convey what synesthesia was like in real time. Who here knows what synesthesia is? Yeah, it’s not very many people. It’s all right. So synesthesia is a neurological phenomenon in which two senses are inherently crossed, causing sensations from one sense to lead to an automatic but also involuntary experience in another. A version of this is called chromesthesia, which is when people can physically see sounds.

Kaitlyn Hova: I didn’t know this was in any way unusual until I was around 21 years old when I was in my final music theory course and our professor just mentioned, “Isn’t it crazy? That some people can see sounds?” Yeah, I ended up dropping my music degree and going into neuroscience, because that’s way more interesting, right?

Kaitlyn Hova: So, ever since then, I’ve been trying to find a way to display what synesthesia was like, because when you’re discussing it with people, it tends to end up going into the more like psychedelic conversation, and it’s not really. So, how to display it? I play violin, so we thought, “Wouldn’t it be wonderful if there was a violin that we could light up with the colors that I see in real time?” This didn’t exist, so of course you have to go to the drawing board, and the first thing on our list was, “What if we had a clear violin and we just put LEDs in that?” We couldn’t find a clear violin and if we could, it was probably too expensive.

Kaitlyn Hova: So, ended up deciding like, “Well, how hard would it be to 3D print one?” It took a year and a half to figure out how not to make a violin and then to figure out how to. I think we went through about like 30 or 40 iterations because you end up getting really desperate and saying like, “Well, what is the violin anyway?” because it’s really hard to make this. It started out as a stick with strings and then kind of grew from there.

Kaitlyn Hova: So now, here it is. Once we got our first prototype, we ended up deciding that this violin on its own, LEDs aside, was a really great product, so why not release it open source for people to 3D print their own music programs? We’re still seeing a trend in schools where music is systematically underfunded, while these same schools are getting STEM grants, so why not? Seems like a connection there. Thank you.

Kaitlyn Hova violin playing synthesia

Violinist Kaitlyn Hova plays a few songs at Microsoft Girl Geek Dinner.   Erica Kawamoto Hsu / Girl Geek X

Emily Hove: Let’s hear it for Kaitlyn. Kaitlyn, thank you so much.

Kaitlyn Hova: Thank you.

Emily Hove: This is fantastic. What a great way to start off such an inspirational evening.

Kaitlyn Hova: Thanks.

Emily Hove: So thank you very much.

Kaitlyn Hova: Cheers.

Emily Hove speaking

Program Manager Emily Hove welcomes the Girl Geek X community to Microsoft Reactors around the world, from San Francisco to London!  Erica Kawamoto Hsu / Girl Geek X

Emily Hove: Welcome, everybody. Welcome to the San Francisco Microsoft Reactor and the Girl Geek Dinner.

Kaitlyn Hova: Thank you, Chloe.

Emily Hove: My name is Emily Hove. I’m part of the global Microsoft Reactor program and we have a lot of synergies between Girl Geek and the Microsoft Reactors. Similar to the way Girl Geek inspires and connects women in technology, our Reactors are all about being community hubs and everything that is related to developers and startups, giving developers and startups the tools where they can learn, connect, and build. So, we hope you all find a night that is inspiring and where you’re able to connect and build today.

Emily Hove: If you’re interested in a little bit more about the Reactor program, we’ve got some cards around the room and they talk about some of the fantastic upcoming workshops and meetups that we have. So we’d love to encourage you to check out our calendar of events and invite you all to attend. With that, I’d like to bring up Chloe Condon, who will be our MC for the evening, and help introduce some of the inspiring people and inspiring women in technology that we have for you tonight. So Chloe, cloud developer advocate extraordinaire.

Chloe Condon: Hello. Thank you so much for coming. This is theater in the round. So I’m just going to keep walking in a circle like I’m giving a very serious keynote so you all don’t see my back. Thank you so much for coming tonight. We are so excited to have you here at the Reactor. Who’s first time at the Reactor, this event? Incredible. That is so exciting. I hope we see you here a lot more. If you want to participate in one of the Fake Boyfriend workshops that I put on here, you can build a button to get you out of awkward social situations, come see me after. We are doing those all the time here. They’re so much fun. Also ask me about my smart badge. This is a little scrolling LED badge that we’re probably going to do a workshop for pretty soon, as well. So come see me after if you’re interested at all in learning about those events and we’ll get you signed up for them.

Chloe Condon: I’m going to tell a little story before I introduce our first guest. I am so, so excited to be your MC tonight. I actually met Angie because I went to Hackbright. Do we have any Hackbright or bootcamp grads in the audience? No. Amazing. So, Angie spoke at my bootcamp and told us all about Girl Geek Dinner and I thought, “That sounds so cool. I would love to go to one someday.” So it’s literally a dream come true to be here with all of you today. This is my first Girl Geek Dinner ever, and I get to be your MC.

Chloe Condon: So, I’m so excited to introduce our first speaker tonight. She is incredible. Please, please show everybody how cool your dress is when you come up here, or I’ll be very upset. I would like to introduce Kitty who is going to tell us all about the incredible technology and fashion that she uses to make things like the amazing dress that I’m sure she’s about to tell you about. So Kitty, come on up. All right.

Kitty Yeung Microsoft Girl Geek Dinner

Microsoft Garage Manager Kitty Yeung gives a talk on “Hacking at the Microsoft Garage” at Microsoft Girl Geek Dinner.  Erica Kawamoto Hsu / Girl Geek X

Kitty Yeung: Hi, everybody. Good evening. Thank you so much Chloe for introducing me. In fact, I’m not going to talk about my dress. That’s for the demo later. I’m going to talk about actually what’s behind that, all the innovation work that we’ve been doing at Microsoft. So, I’m the manager of The Garage at Microsoft. How many of you have heard of The Garage before? Some of you, some of you I’ve met actually.

Kitty Yeung: So, this is a program that drives the innovation, drives a culture of innovation and experimentation. How do we do that? We say, “Doers not talkers.” We actually get our hands dirty. When we think about something, we act on it. These are the culture pillars for Microsoft. To a lot of us when we first see them, they saw just words, but how do we actually implement these and achieve this? We have all kinds of programs and mechanism to drive innovation in Microsoft. Hacking, we have global sites, we have internship programs, experimental outlet is how we ship projects out, and we have intrapreneurs program, and we do storytelling. So I’m going to go into each of these.

Kitty Yeung: The hacking at Microsoft has become the culture. We actually organize the world’s largest global hackathon at Microsoft, and The Garage is the organization that organizes it. Guess how many people attended this year? Globally, there were 27,000 people attending our hackathon, and everyone was excitedly bringing their great ideas to the hackathon and forming teams all around the world. Whether or not you know them, whether or not you’re from the same org, same teams, you can put your skills together and build something that you feel passionate about. We had thousands of projects every year submitted to the hackathon, and The Garage helps people not only have these ideas submitted, we help them grow their ideas into prototypes, and we help them ship.

Kitty Yeung: Satya is a big supporter of our hackathon. He walks in the tent and look at the projects. He said last year, “Bigger ideas, more customers.” So, we can hack on anything we want. So it could be small things. It could be something that we use every day. It could be something that has real impact in the society, we can really help our customers achieve their industry scale ideas. So we also work with our customers and we bring our customer come here to hack.

Kitty Yeung: The experimental outlet, we also call it a ship channel. So this is a mechanism for us to get those ideas in but also provide them with the business model, idea building, how to enter the market, and we help our employees ship those projects out. So if you go to The Garage website, you will see about 100 projects that’s already in the market, and we feature our employees who came up with those good ideas. You can see all the teams on the website, everyone who put their part time together to really achieve something. So, we also have very big projects that we collaborated with industry partners and customers.

Kitty Yeung: Intrapreneurs program is kind of a internal startup program. It involves these ideas, these teams, hackathon teams, to actually pitch their ideas to the leaders and get support. So some of these projects can grow into a feature of an existing Microsoft product, or sometimes they become a product of Microsoft.

Kitty Yeung: We also run our internship program very differently. If you are familiar with traditional internships, usually students come in and they work under one manager in a big team working on a small part of a big project. Instead, our interns come in as a team and inside a team usually we hire like 30 students per site. Silicon Valley just started our first pilot program, so we only had one team, but we have six really, really good students. Usually we’ll have teams of six to eight, and they have developers, usually a PM, and a designer, forming a complete skill set. Then business teams at Microsoft pitch their ideas to our interns and the interns pick which one they like to do, and they drive it like a startup in the company for 12 weeks. Then they can deliver the projects back to the team, or even better, we can ship it directly into the market. It’s a very, very competitive and rewarding program. So if you’re undergrad, think about applying to that internship program at The Garage.

Kitty Yeung: We also engage with storytelling, those ideas, those projects got shipped out. We tell a story, we have a PR team, and you will see a lot of news articles about Microsoft innovation. Pay attention next time when you read an article like that if they mention The Garage.

Kitty Yeung: The global sites is also our feature. We have seven global locations right now for The Garage, and we are expanding. Each location has our own ecosystem, and also, each location has our facility. We have maker spaces, we have technologies that we provide to our employees. They can do prototyping, they can bring their ideas to share with their colleagues. We do startup pitching. We do show and tell and workshops to educate our people and also give them a platform to achieve their collaborations.

Kitty Yeung: So these are the seven sites worldwide. We’re in Silicon Valley and we are now called The Garage Bay Area. And as you can imagine, we have a unique ecosystem of a lot of startups, a lot of big companies and universities. So we work with all of these people in the ecosystem and we collaborate to really build projects that can impact the world. So, as I mentioned, we work with our employees and engage with all of our business teams inside Microsoft, and we work with customers. We bring them to work on projects and hack with us.

Kitty Yeung: Here are some numbers. You can see that we have very global and diverse team, but we actually only have 20 people worldwide. So, the 20 people drive all of those activities that I just mentioned. 27,000 hackers this year is an updated number. Last year, behind that 27, there was 23,000. You can see that it’s growing every year. It’s only going to get bigger. 76 countries participate and we’ve held more than 100 interns already. With the most competitive schools around our local areas. You can find more than 100 projects that’s in the market and on the global website. 19 of them became actual Microsoft products and lots of social media posts, lots of news articles about Microsoft innovation. So, make sure you follow us on the social media.

Kitty Yeung: Some of the Bay Area’s specific projects. Seeing AI, we build a lot of projects that help the people with needs, people who have disabilities. Seeing AI is a project that we shipped a few years ago that help blind people see through technology. So you can hold a phone, the camera will detect what’s in front of you and also read it out, interpret. It can also detect facial expressions and people’s age. So it gives blind people information about their surroundings.

Kitty Yeung: Sketch 360 is a project we just shipped last year, is by an artist inside Microsoft, Michael Scherotter. He had an idea of, “Why don’t we sketch 360 pictures directly?” So, we can build like a full environmental canvas and you can draw anything you want. You can also put that into VR or AR to visualize it. We also last year shipped some apps. Spend is by MileIQ team. So, lots of local projects. We’re just going through our hackathon projects this year.

Kitty Yeung: So personally, that’s why I’m also here to do a demo. I’ve build some of the projects in The Garage to satisfy personal ambitions of anyone in Microsoft can use The Garage as a resource to build their communities, can build their projects. So I have built a lot of wearable technologies. I’m doing a demo right there. We have these different dresses with different sensors and AI, machine learning functionality, and robotic dresses that I can show you later on. But I also have a passion for quantum computing because of my physics background. I’m a physicist, actually. So, I see the need to build a community of people learning about quantum. So this is a study group that I founded in Bay Area, teaching people how quantum computing works, including physics, maths, the hardware, and software, and any employee with good ideas, they can do this. So we have a lot of employees who wanted to do, say AR tech community, they can come to The Garage and do that. Or they have passion for IOT, they can come to The Garage and do that. So, these are just some examples.

Kitty Yeung: So since Girls Geek is also sort of about career, I think this will be my last slide to show you something about your aspiration. This is a guide. So see where you are in this chart of Ikigai and see where you are and figure out what would you like to be. I think for me, I can feel Ikigai in Microsoft because I’m doing something I love, something the world needs, and something I can be paid for that’s important, and something I’m good at. So, if you can get to that sweet spot, that should be your goal. Also, think about how you’re aligned to the global goals. That’s what I can do. I highlighted some of the goals that I could do in the company as well as through my personal projects. I think I would love to expand this and I think this will be a good guide for everyone, how we can do more impactful work for the world. Thank you.

Chloe Condon: Okay. Wait. You cannot leave the stage without sharing this dress. I’m going to make you model it. It is so incredible. So, do you want to say a little bit about it first?

Kitty Yeung: Okay. This is one of my designs, among the other ones I brought. All of these prints are my own paintings. This is a painting of Saturn and I wanted to simulate Saturn on the dress. How do I do that? Because Saturn has a ring, so why don’t I make a ring that when I rotate it will show Saturn. It also has an angle detector. There’s an accelerometer in here. So if it achieves a certain angle it will light up like the stars.

Chloe Condon: Amazing, amazing.

Kitty Yeung: Thank you.

Chloe Condon: Thank you so much. When you wear such a fabulous dress, we should have had a catwalk. I’m so sorry everyone. Amazing. Thank you so much, Kitty. I really, really love that and I loved that final slide. I took pictures of it so I can look at it later and map out my own plan. I am so excited to introduce our next guest that is going to tell us all about machine learning. Priyanka, come on up to the stage. I have a little … do you need a clicker? Amazing. Here you go.

Priyanka Gariba speaking

Head of TPM for AI Priyanka Gariba gives a talk on “Leading a large scale and complex machine learning program at LinkedIn” at Microsoft Girl Geek Dinner.  Erica Kawamoto Hsu

Priyanka Gariba: Hi, everyone. First off, I’m not showing off anything as cool as what the other women did, but I also want to say this is my first time here at Girl Geek Dinner and I think this is amazing. Look at the energy, like room full of women. How many times in a day do we get to see that, or even a month, right? So thank you for having me. My name is Priyanka Gariba and I lead Artificial Intelligence Technical Program Management group at LinkedIn. My talk for today is going to be how we are scaling machine learning at LinkedIn. We are one of the large and complex program that has been funded by our engineering group.

Priyanka Gariba: So, I’ve structured my talk into four different areas. I’ll give a quick introduction on LinkedIn and some of the products that are really powered very heavily by machine learning. I will then get into the problem statement of what we are trying to do in order to scale machine learning. Then talk a little bit about our technology, and then wrap it up with sure, we can scale with building a solution and with technology, but there’s also an aspect of people, and so how do we scale that, and what is LinkedIn doing about it? Okay. All right. With that, let’s get started with the vision and mission for LinkedIn.

Priyanka Gariba: Our vision is to create economic opportunity for every single member in the global workforce. Our mission is, the way we are going to realize it is of course by connecting world’s professional to make them more productive. Let’s take an example of this room itself, right? So many cool things that were shown up, so many cool people, so many cool women that we spoke to. Just imagine if we were connected to one another, there’s so much value we can bring in each other’s life, and LinkedIn can help us do that. So, how are we trying to realize our vision and our mission is through some of our products.

Priyanka Gariba: I’m hoping and I think everyone here is at least having a profile on LinkedIn, and if you’re not connected to the cool women here in the room, I encourage that before you leave, definitely connect with one another. But some of the products that really help us do that is People You May Know. This is a product line that really helps us build our connections. It understands, there is a recommendation system that runs behind it, there is machine learning models that run behind it, very heavily AI powered, and it really allows us to know who are the people, like minded people, that we need to be connected to, and the value we can bring in each other’s life by just having that connection.

Priyanka Gariba: Then of course there is Feed. Everybody who goes on LinkedIn as a platform is going to see Feed as the first product. Jobs is another product, which is very heavily powered by machine learning behind it. Why am I talking about all these products? AI at LinkedIn is like oxygen, and one thing that all these products have in common is AI. With that, what that means is we know that machine learning is everywhere. It’s powering every single product line that we build, it’s helping us bring the best experiences to all our members across the board. So, because of that one reason, we know that what we need to do is we need to enable more people to do machine learning at LinkedIn.

Priyanka Gariba: So, there are two pieces to my talk. One, which I think I’ll dive into more than the second one, is going to be technology. There’s one way we can scale technology, is by building a solution. How do we enable our machine learning engineers to really build and deploy models faster so that the experiences that they can bring to all the members is at a faster rate. The second one is by scaling people.

Priyanka Gariba: So, to tap into the exact problem that we are trying to solve, let’s look at our machine learning development life cycle. It’s as simple as any software development life cycle, right? Basically a machine learning engineer has an idea, there’s something you want to solve for, what is the first couple of things that they would do? They’ll think about what are the machine learning features that are available to them? How do you crank up all these features together? Try and test it in an offline model, train with some datasets, and once you value it and feel comfortable that this is something good, the next big piece is going to be actually serving it in production and then seeing results through AB testing and all of that.

Priyanka Gariba: I’m not going to dive too much into this. This really just is an extension of that life cycle. Basically you start with an idea and then there are different functions along the way. There is a product management, there’s dev, and the way we really make decisions on product is very heavily powered by our AB testing platform. We make ramp decisions only based on that. Once we see the results, only then do we believe that that is a model that we want to ramp further to our members.

Priyanka Gariba: Why talk about all of this? Why talk about the life cycle, right? If all these products are being built at LinkedIn and if so many people are doing it and all the teams are doing this, what that means is every single team is doing and deploying models in a very different way. There are many, many technologies, they are all on different stacks, it’s not standardized across the board, and one thing we encourage at LinkedIn is for people to move around within teams. So today if you want to work on a Feed team, tomorrow you want to work on a Job Recommendation team, how do you do that? Your stack is different. Half the days are going to be spent in just ramping up.

Priyanka Gariba: So, we introduced something called as Productive Machine Learning. Really our goal is to enable end to end experience of machine development life cycle to be more robust, reliable, and consistent, and standardized. The experience we are looking for is for an ML engineer, all you have to worry about is come up with an idea, and then there is everything else is opaque for you. There is a big box and you don’t have to worry on how you move from one phase to the other. Ideation to machine learning features to training to scoring to serving it in the introduction. You don’t have to worry about this and how are we going to do that.

Priyanka Gariba: So, we’ve put together this program, it’s to give you context, this is a really large scale program, about 6,200 engineers across the board working on it, different geolocations. The way we are structuring it is by talking about three different phases.

Priyanka Gariba: Model creation, going back to that life cycle that you saw, everything from ideation to training and evaluating your model comes under model creation. So we have multiple components that blend into that. Then the next piece for us is deployment. Once you believe that your model is really good and ready for serving, you deploy it in production. The third piece, this is not really a phase, but something that cuts across, is making sure your quality is accurate. Meaning features that you used for your offline training are very similar to what you see in online. So online, offline consistency.

Priyanka Gariba: So, I just wanted to, because I had 10 minutes, I just wanted to give you a flavor of this big undertaking that we are doing at LinkedIn and also give you a little bit of flavor of how we are structured. Typically, every time we build something, we follow a traditional model. You have a leader, you have multiple managers, you have engineers, and you come up with a goal on a project and everyone works together. This one, we wanted to do something different. What we did is, let’s bring every single person in LinkedIn who is really passionate about solving this problem.

Priyanka Gariba: So put together what’s your team, we had everyone across the board, in different geolocations too. There is someone who will be infrastructure heavy. There is someone who is a machine learning engineer who can help us really give us inputs when we are building the solution that it’s really going to work for them. Then there’s product managers, CPMs, engineers, across the board, but it’s really all of these coming together, forgetting the boundaries of management, realizing that there is one goal that we have, is to get an end to end machine learning life cycle ready, was the key thing for us. I already mentioned that, team of teams, we’re geolocated. That is also one reason why we wanted to do that, is we wanted engineers across the board because if we were solving a problem just for headquarters, which is in Mountain View, we will not be solving for everyone at LinkedIn.

Priyanka Gariba: Then of course with any product that you build in any company, there is a big piece of adoption. So, for us, the strategy that we have used is that let’s, the three big phases that we spoke about, let’s build small components underneath it and let’s allow every product team to pick up a component and adopt that depending on what their pain point is. So, for example, if a Feed team is really struggling with how do you train a model, then what we wanted to offer them is pick up that component and get adopted on that. Once you buy the idea, then slowly and gradually navigate into the adoption of the other components too. This helped both ways. This helped us get real early feedback from our customers and users, and then it also allowed us to load balance. So we could develop things while something was already being tested and we were getting that iteration loop from our users.

Priyanka Gariba: So, I spoke about the technology, and I spoke about the solution. The second thing that LinkedIn is doing, and I’m just giving a very high level preview of this, is in order for us to democratize AI or to make it readily available and to enable more engineers to do that, there’s a program that LinkedIn’s kicked off, it’s called AI Academy. There are three different types of courseworks of program, AI 100, 200, 300. As you graduate from one to the other, really the intensity of the techniques and machine learning increases. So AI 100 is really just getting a flavor of what AI is, what machine learning is, and get you familiarized with it. And then 200 you start understanding how do you build a model, and three is when you actually build your own model and put it in production. I can talk all about this and I’m happy to talk about it later on, but this is just a preview, and there’s a lot of blogs and things that we’ve already put on LinkedIn.

Priyanka Gariba: This is another blog for Productive Machine Learning for those of you who are interested in reading more about it, and I’ll share my slides as well. That’s it. Just a quick flavor. I had 10 minutes, so I thought at least I’ll come up here and talk to you and give you a flavor of what we are doing to democratize machine learning at LinkedIn. But happy to, I don’t know if I have time for questions, but I can take questions later on as well. Thank you.

Priyanka Gariba: Okay. I can take a question or two if … After. Okay. All right. Sure.

Chloe Condon: Thank you so much. All right. So, next up, I will take that from you. Next up we have a very special treat, but before I introduce our very special guest, I’m going to show you my favorite LinkedIn feature. How many people have added someone on LinkedIn tonight? Okay. Well now you’re going to add more people. So, if you go to your LinkedIn app in the very top in the search bar, there is a barcode, a scanning barcode, and if you click on that, instead of having to type out the person’s name and awkwardly ask for spelling, you can just scan their barcode tonight. So you can share that secret tip that I learned recently from someone else at a meet up that I now pass onto you to make spelling people’s names less awkward. So definitely scan everyone’s badge here tonight. My best advice always in tech is to meet as many people as you can, and tell your story and share their stories while you’re here tonight with all these amazing people.

Chloe Condon: I am going to welcome our very, very special guest for tonight, Charlotte. Come on down. We are so excited to welcome Charlotte Yarkoni to the SF Reactor. Here you go.

Charlotte Yarkoni speaking

Corporate Vice President, Cloud + AI Division, Charlotte Yarkoni gives a warm welcome at Microsoft Girl Geek Dinner.  Erica Kawamoto Hsu

Charlotte Yarkoni: Thank you. I need to start out and tell you guys, I’m sick. I really, really apologize for my voice. I’ve been told I don’t look as bad as I sound, so I thought it’d still be okay to show up, but hopefully you’ll manage to go with me this evening. It was important for me to come. So again, I hope you can work with me on the sound quality. But my problem is as I’m watching everybody on stage, I wanted one of these mics so I can put it down, cough, and anywhere I go I’m going to … somebody’s in my blast radius. So, if I come over here and stand by the post, please don’t be offended.

Charlotte Yarkoni: Anyways, good to be here tonight. Thank you guys all for coming. I thought what I would do is first share with you a little bit about my journey of being a woman in tech and what that’s meant to me in my career. I do need a clicker. My telepathic PowerPoint clicking slides are not on today due to the head cold. So, I actually go talk a lot to universities. I go to some high schools. I love talking to young girls about STEM, but I always kind of have to ground in. Let me tell you what tech looked like when I was in middle school and high school.

Charlotte Yarkoni: This was it, by the way. There were no smartphones, there were no tablets, there were no laptops. I remember when Asteroids came out and me and my brothers thought it was amazing. Right? So that’s kind of where we were. Then this was our social network. There was no Twitter, there was no WeChat, there was no Snapchat. It was pretty much a bonfire in somebody’s field when their parents were out of town in the town I grew up in. So, that’s kind of where I come from.

Charlotte Yarkoni: I actually, I grew up in South Carolina. I was super fortunate to get a scholarship to come to UC Berkeley. I’m pretty sure I’m the only person from South Carolina to ever go to Berkeley. I was actually part of an inaugural program at the time called Electrical Engineering or Computer Science, or EECS as it was known. This is what code looked like when I was coding. Has anybody ever written in Lisp? Anyone? Did anyone? Yeah. Kicking it old school. All right. So, that was sort of my education, if you will, and my real foray into tech.

Charlotte Yarkoni: Then, I got out of college and started working and figuring out how to use technology as an applied science, not just in an academic sense, and this was kind of the world I was in. Actually cell phones came out and yes, that’s what they looked like for those of you that weren’t born then, because I know there’s a few of you here. Windows 95 was all the rage, right? You remember that? Then we get to today and it’s just a very, very different world.

Charlotte Yarkoni: One of the things that I love about technology is the fact that it has actually opened up all of our worlds, in so many ways that we can have so much more impact. We can instantly connect to people that we could never connect to 30, 40, 50 years ago. I’m not that old, I’m just framing my comments. But you think about that and it’s not just connecting to those people, it’s the access to information that you also have immediately at your fingertips. It’s amazing. It’s amazing that what you can harness with that kind of resources at your fingertips.

Charlotte Yarkoni: The challenge is, though, it comes with a responsibility, and I will tell you, at Microsoft, and GitHub, and LinkedIn, we spend a lot of time on that. In fact, it’s not just about innovating, it’s about innovating with purpose, and really making sure that you’re actually leaving the world in a better place than you found it before you introduced your solutions. So it’s those unintended consequences that you have to be very thoughtful about. As we continue to get more and more technology at our disposal, how do we use it for good? That kind of brings me to really, what’s my role.

Charlotte Yarkoni: Today in my role is, at Microsoft, I run a group called Commerce and Ecosystems. You can tell I’m not a marketing person, so there you go. But I’m really here. I focus on answering three questions. The first is, how do people actually discover who we are and what we do in our products and services? And Microsoft’s a very big company, it’s a global landscape. We offer lots of different products and services across our portfolio, but there are a lot of ecosystems and communities that actually don’t know who we are and what we do.

Charlotte Yarkoni: Five years ago it was a lot about open source, and I remember I actually went to … I started at Microsoft about three years ago and I went to an open source conference. By the way, I grew up in open source, so my background actually started out in Unix and moved to Linux. I never wrote a piece of code in .NET. Would probably look and feel a little bit like Lisp to me, honestly, if I tried to do it now. So when I came to Microsoft, I went to a familiar conference, and people were like, “Why are you here, man? Azure doesn’t run Linux.” I’m like, “What are you talking about? Yeah, it does.” People need to know, right? So we had to go fix that.

Charlotte Yarkoni: Second thing I focus on is after you discover us, how do you engage with us in a way that’s meaningful to you? And most of that is online. People don’t always want to have to go somewhere to learn how to do something. They will now have to sign up for a week long course, right? Necessarily to know how to build a solution using the technology that they have. So we spend a lot of time and energy focused on that and what’s the set of tooling or resources that we can offer.

Charlotte Yarkoni: Then the final point is, how do we just get easier to do business with our customers and partners? That’s where the commerce piece comes in and it’s all about what are some of the new business models we need to create to actually, how do we run all those capabilities across all our products and all our channels today? So there is a good bit of engineering that comes in each one of these aspects, but there’s also a lot of business work that I have to focus on. And again, it comes with that overarching layer of responsibility, is to how do we think about continuing to make progress in a positive way so we can have a positive impact on the communities we serve.

Charlotte Yarkoni: So that’s kind of who I am, and I think what we’re going to do at this stage is a little bit of like an AMA, and I’m really hoping you guys don’t ask me too many questions because the more I talk I think the worse I sound, but I will try to answer everything for sure. I was going to have Chloe join me, and I was going to have Shaloo Garg join me. So, just as a reminder of both, Chloe and Shaloo are part of my team and they’re part of the drive discovery effort. So I’ll let you guys, you guys will talk a little bit more about yourselves, I’m sure, but I’m going to turn it over to our master of ceremonies. Kick us off. Do you want that mic or you want–

Chloe Condon: Sure. Mics all round here.

Charlotte Yarkoni: This one may be contaminated.

Chloe Condon: All right. I wouldn’t want to catch the virus, the Charlotte virus. Amazing. So, I figure we’ll have a seat. Have a seat wherever. We had a bunch of people submit questions earlier in our fishbowl, thank you so much for all of the questions that we got earlier. So, what I figured I would do is we would start with an introduction with Shaloo. Would you like to tell everyone who you are, what you do?

Shaloo Garg, Chloe Condon, Charlotte Yarkoni

Microsoft girl geeks: Senior Cloud Developer Advocate Chloe Condon, Corporate Vice President for Cloud + AI Charlotte Yarkoni, and Managing Director of Silicon Valley’s Microsoft for Startups Shaloo Garg answer audience questions with candor at Microsoft Girl Geek Dinner.  Erica Kawamoto Hsu

Shaloo Garg: Yeah. Absolutely. Firstly, thank you guys so much for coming here today. It means a lot. My name is Shaloo Garg and I lead the startup business growth for Silicon Valley for Microsoft, and entire California as well. It’s an exciting space to be in, and part of Charlotte’s team and part of what we do is not only engage with founders and CTOs and CIOs here of startups, but also drive meaningful partnerships, which is … this is Silicon Valley, there are a lot of partners here, how do we work with them to drive awareness of how Microsoft can help entrepreneurs there? So good to be here.

Chloe Condon: Amazing. Thank you so much. I have these randomly selected questions here.

Shaloo Garg: Those are a lot of questions.

Chloe Condon: It’s a lot of questions. I don’t know if we’re going to get through all of them. We may do kind of a rapid inside the actor’s studio type of lightning round at the end here. But I love this first one. I chose this one first and this is for Charlotte. It says, “What’s it like being an executive at one of the top companies? Do you have a life?” Great phrasing, whoever wrote this.

Charlotte Yarkoni: I’d like to think I have a life. Yes, I do have a life. I have two children, both girls, one–

Chloe Condon: Great. Are they coding already?

Charlotte Yarkoni: One is 23, just graduated. She went to Reed College, and by the way, back to Berkeley, I thought when I went to Berkeley from South Carolina, I was an enlightened liberal. And when I dropped my daughter off at Reed College, I felt like I was the most conservative person on the planet. I was a little worried about my life choices at that point. But she graduated there in linguistics and she actually is starting school this week, getting her master’s at University of Washington.

Charlotte Yarkoni: She would be very offended if I called her a developer or an engineer, yet she spends a lot of time writing programs and are doing statistical analysis on languages because she focuses on Russian, Japanese, Spanish language and language heritage.

Chloe Condon: Wow.

Charlotte Yarkoni: So, that’s my oldest. My youngest is 13, and a prolific gamer and developer. Python is her language of choice. She has lots of opinions about every other language.

Chloe Condon: As she should.

Charlotte Yarkoni: It kind of takes me longer these days to set up an environment for her to code in than it does for her to whip out a new game that she’s thinking about. So, I’m pretty sure she’s going to end up somewhere in the engineer community as a professional at one point. I also have three horses. I ride. I grew up three day eventing, for those of you who know what that is. Now that I’m older and have kids, I wondered what my parents were thinking when they let me do that. But I still ride and I still compete. Then I do my day job.

Chloe Condon: That is a fun fact.

Charlotte Yarkoni: I think the thing about today’s technology is, the good and the bad is it allows you to be accessible all the time. So, you can actually, you have to know how to be at the right place at the right time, which is usually the conflict that occurs, but you are able to go do what you need to do personally and do things professionally as you go. So that’s something I’m really, I feel privileged by who I work for in the industry I’m in and the technologies that we’ll be bringing for all the working moms out there.

Chloe Condon: Wow. That’s actually a great segue into the next question, which I’ll direct to Shaloo first, which is, how do you relax and unwind? Like with how long and tough your day jobs are, how do you get to chill?

Shaloo Garg: So, best is tennis. I love playing tennis and that’s how I unwind, and when I go out and play tennis, I try not to take my cell phone with me or my kids. So I have a 13-year-old daughter too, and a nine-year-old son who quite a handful.

Charlotte Yarkoni: Do you have any Serena moments on the court?

Shaloo Garg: I do. But that’s how I unwind, which is just completely unplug, just a moment of Zen and just go out there and hit it.

Chloe Condon: I’m very similar. I craft. I like to do like things with my hands and not look at a screen and just build something fun, like a costume or something that lights up. And you’re riding horses.

Charlotte Yarkoni: Yeah, but I could not build a costume. So, we each have our strengths.

Chloe Condon: Hit me up for Halloween. We’ll get you guys–

Charlotte Yarkoni: I’m going to hit you up for Halloween. Okay.

Chloe Condon: This one says, “What would be your advice for your past self coming straight out of college?” I love that question.

Charlotte Yarkoni: Who you asking?

Chloe Condon: Anyone can jump in. Yeah.

Shaloo Garg: I think coming out of college, I wish I was more aware of getting a coach or a mentor, which I was not aware. And during my career I sort of looked upon women leaders and requested them to be mentors and coaches. So what I try to do now is go out and coach and mentor women or young girls myself. So, I realize that they may be in the same situation as I was in, which is, “Hey, I can ask a woman leader to say, ‘Would you mind spending 30 minutes with me?'” But they don’t ask. Right? So I preemptively do that in schools, colleges here in Silicon Valley. Actually right up our Market Street office, that’s another office of ours, every month, I host open office hours for young women who are out there, budding entrepreneurs. It doesn’t have to do anything with Microsoft. So, as soon as you walk in the door, it doesn’t have to be, “Hey, you have to sign up to work with us,” but it’s just coaching, and I love it. So, wish I had that, but a part of me is just giving back, just making sure that someone out there is benefiting.

Chloe Condon: Yeah, that’s great advice. Charlotte.

Charlotte Yarkoni: I think, for me, one of the things that it’s taken me a long time to appreciate and I really, I encourage everybody to have some thought about this for their own journey, both personally and professionally, resilience is such an important thing. When I look back on my career, I feel, again, very privileged to have worked in all the places and spaces that I have. But the successes I had weren’t one success right after the other. It was a success built off of quite frankly, a mountain of failures and trials to get there. It was about taking those learnings and applying and getting better. I think a lot of what we do as an industry is about solving a problem, solving an opportunity, and getting better as we go, and iterating, and it’s really hard to do that as a person.

Charlotte Yarkoni: I’m going to go out on a limb and assume all you people here are somewhat overachievers. So every time that you have a failure, you want to prosecute the failure and you want to prosecute yourself, and that’s okay as long as you make it a constructive thing and learn from it, and the older you get and the more experienced you get, the more you start to really embrace and almost be proud of those failures for what they taught you, because you wouldn’t be wherever you are without it. That’s just a fact. I don’t know that I appreciated that in my younger age. I was certainly an overachiever and thought I knew a lot more than I knew at the time. I know that’s shocking, but it’s true. But as I went through my career, it was a process for me to understand how to really get value in the mistakes, how to really give value in the failures, and use them to move forward.

Charlotte Yarkoni: I just would encourage everybody, get out there and try. That’s step one and step two, is make sure you learn and embrace the mistakes, right? And it is about that of resilience that will just make you so much of a better person whatever you decide to do, however you decide to do it.

Chloe Condon: My advice would be, I don’t think I knew right when I graduated what I wanted to do with the rest of my life. I wish I had taken a little time to travel or maybe to explore different industries and fields that maybe I wanted to dip my toe in. Because I think what the wonderful thing about working in tech is you don’t have to commit to doing the same thing for your entire life. You can always change and learn a completely new technology or … There was a tweet that I think I retweeted this morning, which was, “Your job that you have in five years may not even exist. So try not to plan out your life too strategically,” and I think that’s really wonderful advice because technology is growing at a rapid rate and we may be working for something we don’t even know exists yet. The new, I don’t know, a new iPhone. Who knows?

Chloe Condon: Great. Next question that I have is, I love this one, “What’s the best book you’ve read this year?” Does anyone have one? I know mine. I can go first while people think.

Shaloo Garg: Go, go for it.

Chloe Condon: I read a book. Oh no, you go first because I want to make sure I get her name right, the author’s name right.

Shaloo Garg: So I think the life-changing moment for me was the book that I read by Eckhart Tolle. It’s called The Power of Now, and it teaches you a lot about what Charlotte talked about, failure. It also teaches you how to stay engaged but not attached, which is you’re really passionate about something that you’re doing. Keep that passion, but don’t get so emotionally sucked into it that you break down. So it also teaches you mindfulness and awareness. And then how to be an A player, which is you’re mindful, you’re aware of what you’re doing, but guess what? You got to go and get it. So I thought that was completely life-changing for me because I learned quite a bit in terms of just being strong, being very passionate about what I do, but not emotional, and then just chasing it, chasing the ball and just chasing the heck out of it.

Charlotte Yarkoni: Mine’s an oldie but a goodie, because my youngest was doing a book report on this one, the Life of Pi.

Chloe Condon: That’s a good one.

Charlotte Yarkoni: I just loved that. I haven’t read it in many years and so she brought it home and I brought out my copy so we could read it together. It is just an amazing book.

Chloe Condon: That is on my list. You said yours was The Power of Now?

Shaloo Garg: Power of Now.

Chloe Condon: Okay. Write that one down, everyone. I recently read Just the Funny Parts by Nell Scovell, she’s a female comedy writer, and I found … it’s an autobiographical piece. She used to write for Saturday Night Live, David Letterman, and it’s a completely male dominated field. It was the first time I had read about an industry other than tech that was similarly structured and formatted and it talked about, she’s a comedy writer, so it comes from this place of empathy and humor, and I would highly recommend it. She helped write Sheryl Sandberg’s book. She also wrote a lot of Obama’s jokes, I found out in that book. So, a lot of the things that made us chuckle from Obama came from her.

Chloe Condon: So, next one is, “Who has influenced you most in your life and why?”

Charlotte Yarkoni: That one’s actually really hard. I will tell you both my parents passed away in the last year. They were quite older. I’m the youngest of a large family. Pretty sure I was an accident, so, it’s okay. But you spend a lot of time reflecting on your nuclear family when those kinds of things happen, and they happen inevitably to everyone. So I definitely think my parents had a large influence on my life. I think my teachers had a large influence on my life. I’m the proud product of the public education system of South Carolina, which I think at the time I was growing up was like 49th in the country. But I went from there to UC Berkeley, which was an amazing school. And I had some amazing teachers to help me learn how to learn, is what I got from that.

Charlotte Yarkoni: I’ve been super fortunate to have some great mentors and what I would call guidance counselors throughout my career, that I still do lunch with and dinners with and catch up with. So, I feel like I’ve had a lot of influences and I do think for the last 20 plus years, though, my kids have probably taught me more humility and patience and resilience and all the other virtues we speak so highly of. They’ve probably been the biggest forcing function in my life in recent years.

Chloe Condon: What about the horses?

Charlotte Yarkoni: The horses are my sanity. I will tell you, we moved to Australia for a couple of years and I couldn’t take my horses with me and I was, my husband will tell you, I was a miserable person for the time I was gone.

Chloe Condon: I’m picturing you writing postcards back to your horses at home.

Charlotte Yarkoni: I came home. I came home every two months to see them.

Chloe Condon: Aww. How about you, Shaloo?

Shaloo Garg: So, parents, but I think my mom. So I lost my parents at a very young age. I remember when thinking back growing up, so I was born in India, but I grew up in Middle East, and I grew up in a community where there was lot of domestic violence and girls were not allowed to go to school. And so there were a lot of changes that were happening around me. In fact, while growing up, I went to 14 different schools between elementary, middle, and high school. So you can imagine moving from Saudi Arabia to Iraq, to Kuwait during the war zone time. But I remember going through all this, my mom always taught me and my sister is that, if there’s ever a problem in life and there is a simpler solution, and there is a hard solution, guess what? Pick the hardest one, because it’s going to make you go through that process, whereas a simpler one, you’re just going to take it and just sit with it and you’re not going to learn anything. So I do look back and I think that she’s had an amazing influence on me.

Shaloo Garg: And as Charlotte said, my kids, I keep learning from them every single day. They teach me so many things in terms of if I get upset about something, they’ll just say, “Hey mom, just relax. This is just a small thing, just move on.” I think that’s how I keep learning more and more. And of course, amazing coaches and mentors and some really amazing female leaders who I look upon to.

Chloe Condon: I would have to agree. My mother passed away when I was 16, but she was a costume designer, graphic designer, creative arts person, and I try to bring my creative arts training and background into all the technology that I do and create. So I think that was probably the biggest influence on me, would have to be my mom as well.

Chloe Condon: What is the biggest challenge we are facing in tech currently? A tough one.

Charlotte Yarkoni: I actually think our biggest challenge as a society is climate change. I think technology can be a solution for that. So, that’s an indirect answer to a direct question, but I would say that is the thing that I would love to see all of us, I don’t care what you’re doing, where you’re working, but to start having serious thoughts about how we can go reverse decades of adverse effect on the planet. It helps everybody, and I do think the real accelerants are going to lie not just in changing our behavior and our consumption, but also in having technology help us. I don’t think we’ve really gone there yet as a society at large. So for me, it’s something I’m kind of anxious to push along however I can in whatever small way that I can. I think that’s how I think about it.

Charlotte Yarkoni: With technology, you have things like quantum, which is just amazing. The beauty of working somewhere like Microsoft is we are spending a ton of research and we have really crazy people, crazy smart people working on this, and every now and then if I have to go give a talk and I need to give my five minutes of quantum computing update for the cloud, I always ask, “Are there any theoretical physicists in the audience? Because if there are, I’m not going to do this because you know way more than me,” kind of thing.

Chloe Condon: Come on up.

Charlotte Yarkoni: But it’s amazing, and in essence you take what sits in a data center the size of a football field today and you can run it in what’s in the size of a refrigerator in your house. But, the cooling you need to do that is extraordinarily more than the power we’re consuming today, and the impact that will have, by the way, if it’s not done right, either we’re not producing it correctly and/or we’re not cooling it correctly, can have a devastating effect. So how do we think about things like that, these new trends with this aspect of sustainability around the climate, I think is super important. So I apologize, I kind of rambled on that answer, but I actually think this one’s a really important one.

Chloe Condon: I agree. I actually met someone at Open Source Summit recently who works on our IOT team here at Microsoft in Redmond, and his job on the IOT team is to help offset our carbon emissions from our server center. So I thought, “That’s such an important, important way for us to help make the environment a better place with Microsoft.” So, yeah.

Charlotte Yarkoni: Absolutely, and the lady who runs our data centers, her name is Noelle, she’s a peer of mine. I love her dearly. She’s just an amazing woman. She actually grew up as a chemical engineer.

Chloe Condon: Wow.

Charlotte Yarkoni: A lot of her time on how do we run our data centers is spent in areas that you and I wouldn’t know how to go solve, because it is about how do you think about power? How do you think about new sources like geothermal and things like that. I think it’s great. I think it’s great we’re thinking that way, but we got to do more.

Chloe Condon: Yeah.

Shaloo Garg: I think the biggest challenge is the knowledge or the lack of awareness behind power of technology. So, I often see this, I keep bringing up edtech as a very common example, and in fact, here in the Valley, edtech is right now the hottest topic in the social impact circle. I can guarantee you, when I throw the word school out here and I ask you to just close your eyes and think of, tell me what you think of. You’re going to think of a building. You’re going to think of kids running, a blackboard, and a teacher. But that’s not what education is only. Education can be a seven-year-old girl sitting in Uganda who’s not allowed to go to school, but she can sit at home and do schooling at home using an iPad, right? Just because she’s a girl, she’s not allowed to go to school.

Shaloo Garg: That is the power of technology, and it kills me every single day when I read about places like Somalia and Syria, and so many other places, where easily companies, and Microsoft does amazing job, that’s one thing I’m really proud to be, which is be part of this company. We do amazing work globally in enabling this. I think we need to continue to talk about the power of technology, which we do in our jobs and outside our jobs, but we need more and more people to go out there and coach people and say, “Hey guys, education is just not about textbooks. It can be digital education powered by technology.” I think that to me is the biggest challenge right now, which is lack of awareness.

Chloe Condon: Yeah, accessibility and access to that is so important.

Charlotte Yarkoni: Can I interrupt this broadcast? Do we have any recruiters in the audience? Because I think we have our newest recruit. She did an awesome walk-in by the way.

Chloe Condon: Love the pants. Great pants. This is a very fun question. What emoji do you use most often?

Charlotte Yarkoni: I don’t use them correctly, as my children … I always send them stuff–

Chloe Condon: It’s the horse one, right?

Charlotte Yarkoni: … and they’re like, “Why did you send me this? Do you know what this means?” I’m like, “No. No.”

Chloe Condon: I think that’s part of your job as a mom, right?

Charlotte Yarkoni: Well, I have gotten in this habit of sending random ones just to freak my kids out.

Chloe Condon: Love it.

Charlotte Yarkoni: I usually am pretty clean at work with the okay and the goofball face, and the smiley face, but it cracks me up because we were just having this discussion the other day, because I sent something that apparently I shouldn’t have sent as a parent.

Chloe Condon: It’s like a secret hidden emoji language.

Charlotte Yarkoni: It really is.

Chloe Condon: Yeah.

Charlotte Yarkoni: And you, what do you use?

Chloe Condon: I would say it’s a tie between the sobbing emoji and the laugh crying emoji, because I don’t have any other two emotions other than those two extremes. There’s no in between for me. I’m either hysterically laughing or hysterically crying.

Charlotte Yarkoni: What do you use, Shaloo?

Shaloo Garg: Smile and laughter, and that’s it. For the kids, with the kids, I’ll just use hearts, and sometimes my daughter says, “Mom, just stop using those… You’re embarrassing me, mom.”

Chloe Condon: Yeah. What are the most important decisions you face every day? Or what is the most important decision you face every day?

Shaloo Garg: How to make founders successful, and especially in a market like this. I just love it. It’s an upstream market, constantly challenging ourselves. What else can we do? What else can we do in this market? I absolutely love it. It is challenging. It’s extremely challenging.

Chloe Condon: It’s a huge question.

Shaloo Garg: It’s a huge question. I’ve been with the company for eight months and when I joined initially, I was a bit nervous. I was like, “Great, I’m so excited about this job,” and when I went out there, talked to founders, everyone was like, everyone gave me a standard response, “Well, yeah, okay.” But now slowly and slowly we’ve started building it as part of the narrative that we haven’t only the meetings, which is how do we help the founders, and if we switched that, our jobs become much more easier, which is, “I’m here to help you and this is how I can help you.” So I think that to me is absolutely the most fun part.

Chloe Condon: Yeah.

Charlotte Yarkoni: By the way, as part of my team, that’s a great answer for these little startups. I think my job is really making the set of decisions that best serve our customers, our partners, best serve the team. It’s always a balance, right? We have so much we’ve got to get done. We love innovating, we love getting new capabilities out there, making sure that we’re doing that with the right sense of urgency and the right balance for the teams delivering them. Most of my day, in any one of my teams that I look at, is just making the right calls to make sure that we’re doing right by the community, as both our community that’s working on it and the communities we’re trying to serve.

Chloe Condon: Yeah. I would say for me it’s how to get people excited to learn, and what is going to get them having fun. Because I think we work all day, we work like an eight-hour plus day sometimes in front of machines using technology, and what are fun creative ways to get people excited about that and to build really cool, amazing things together that can solve these big questions and problems like the environment and getting accessibility to folks who don’t have the access to this technology. So, it’s always fun to enable that power to people.

Chloe Condon: How much time do we have? Do we want to do maybe one or two more questions? One more question. Okay, cool. Let’s see. I think this is a really good … Actually, I would love to end with your advice to all of our amazing women in this audience, and men in the audience. What would be your advice to someone who’s looking to move up in their career and have a successful career as a person in tech?

Charlotte Yarkoni: I think being you is the most important part. Whatever that means, right? Just be your most authentic self. It’s a hard thing to do. It’s a hard thing in our industry. It’s a hard thing in super competitive environments like here in San Francisco. Seattle is very similar in that regard. I have found people get the most reward and have the most success when they’re actually themselves, whatever that means. I also think being the authentic you will not just make you better, it will actually make whatever team you’re on better. It will make whatever company you’re at better, it will make whatever product or service you’re working on better. Just be you and be proud to be you.

Chloe Condon: I love that.

Shaloo Garg: So, I would say do what you’re passionate about because when you’re passionate, you bring your best. Do not be afraid to take risk, and I know this sounds like a cliche, but really challenge yourself. If there is a risk, if you want to do something and it looks very risky, just go ahead and do it. Maximum, you’re going to fail, but you’ll learn something from it. If you come out victorious, that’s great. Then the last thing I would say is just trust yourself and just believe in your instinct that you’re doing good for the business, you’re doing good for the company, you’re also doing good for those startups or customers or whoever your stakeholders are, and just go chase it. If you keep it straight and if you keep what I call the compass straight, there’s going to be lots of amazing learning in the process.

Chloe Condon: My advice is actually a great segue into our mingling and happy hour section. Mine would be to talk to as many people as you can in this industry. If you have the opportunity to get coffee with someone you really idolize or a mentor, or someone who’s doing what you want to be doing in this industry, having conversations, I think, is so wonderful and you are all about to use that LinkedIn feature that I just taught you, and meet some really amazing people. So make connections and network and yeah, have the most amazing time.

Chloe Condon: I want to thank both of our…

Shaloo Garg: Thank you.

Chloe Condon: … panelists today. Round of applause for Shaloo and Charlotte.

Charlotte Yarkoni: Thank you for hosting.

Chloe Condon: Of course. Thank you to to Kitty. Thank you to Priyanka. Thank you to everyone, to Kaitlyn who’s not here, but oh my gosh, that amazing, amazing musical performance we had to start off the evening. Please, enjoy yourselves. I think we still have some beverages and snacks here, so have a wonderful time. Make sure you get some swag and stickers and we will be around to chat. All right. Thanks everyone.

Microsoft girl geeks, Microsoft Reactor fun

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Episode 19: Switching Job Functions

Angie Chang: Welcome to the Girl Geek X podcast, connecting you with insights from women in tech. This is Angie, founder of Girl Geek X and Women 2.0.

Sukrutha Bhadouria: Hi. This is Sukrutha. I’m, by day, an engineering manager.

Gretchen DeKnikker: This is Gretchen, and I’ve been working in tech for over 20 years.

Rachel Jones: This is Rachel, the producer of this podcast. And we’re the team behind Girl Geek X. This podcast brings you the best of Girl Geek X events, dinners, and conferences, where we’ve been elevating women in tech for over 10 years.

Angie Chang: And today we’ll be discussing switching job functions.

Rachel Jones: We’ve covered career transitions before. So how might this topic be a little different?

Gretchen DeKnikker: Well, career transitions tend to be any… I guess it would be a subset, right? So, career transitions are you’re switching companies, you’re getting a promotion, you’re… whatever. And the job function is, I was doing X, and I’m now going to do Y. Whether or not that’s with the same company or a new company, you’re moving really far out of your comfort zone, and you’re about to try something really new.

Sukrutha Bhadouria: Yeah, you could be working in the exact same area, but your role could be totally different too.

Rachel Jones: Have any of you, during your career, switched job functions?

Sukrutha Bhadouria: The only job function that I ended up switching that was really noticeable was from being an engineer to a manager. But, while I was an engineer, too, I changed job functions a few times. I was primarily a backend engineer, and then I switched to being a frontend engineer, and then full stack. I feel like, after you work in a particular job function for some time, you’re working… playing to your strengths, so you start to lose the distinction between doing, really, more of what you’re good at, versus being comfortable and then complacent. So, just the fear of not constantly wanting to be too comfortable is what forced me to look around and switch around.

Gretchen DeKnikker: I think there’s only one time that I went from one company to the next where I didn’t switch job functions. I had more of an operational role, and then I went back to business school. And I thought I was wanting to go into marketing, but I realized as I was doing a marketing internship, that business development was a lot more interesting. And business development has a lot of operational functions, right? You’re dealing with accounting, and sales, and product, and engineering, and you’re kind of working all of those things together. So, if you look at my resume, it makes no sense. But the core functions and sort of making the pieces work together is what I do. And that always comes with a different title or a different scope of what’s most important, but I end up working with all of the pieces in different ways.

Sukrutha Bhadouria: Angie, you changed job functions quite a bit too. You were a web designer, and then you also did product management. You were doing so many various roles in the education startup you were in, as well.

Angie Chang: Yeah, I think there were… I was pausing on this, because I feel like I change careers a lot, and so it didn’t fall in this category of job functions. And I’ve been at, like Gretchen, tiny startup companies with less than 50 people. And I’ve been there for only a few years. So I don’t feel like I’ve… in the situation where people at the biggest companies worked there for decades, and then they’re switching their job functions or after years of doing a job. So, for me, I didn’t really identify with this switching job functions. But we’re right. Because, we were trying to differentiate between career transitions and job functions. Yes, I’ve definitely, happily, jumped and tried new things. I went from product management, to marketing, to editor in chief, to… I think that, like Gretchen said, at startups, you give yourself names, and they’re not totally serious sometimes. Or sometimes they are. And you wind up doing so many things.

Sukrutha Bhadouria: What about you, Rachel? Tell me what changes you’ve experienced in your career.

Rachel Jones: I think one transition that I made early in my career that was kind of subtle is when I was still working with students in youth media. And when I first started, my role was really focused on the tech side of production with them, just showing them how to use the cameras and how to edit. And I switched to being more on the storytelling side. And even thinking about approaching media from that different perspective and how you teach technical things versus how you teach a kind of softer skill, I really had to think about the work that I was doing in a completely different way, even though I was working in the same space with the exact same students and creating the same kind of content. Yeah, my whole approach had to change completely.

Angie Chang: Lerk-Ling Chang is Vice President of Strategic Ventures at Guidewire. She leads Guidewire’s venture investing efforts and drives acquisitions and strategic partnerships. We heard about her transition from product to corporate strategy at the Guidewire event.

Lerk-Ling Chang: I did product management at Guidewire for… I guess it was probably about 12 years… and then decided to switch out of that role into something completely different, focusing on corporate strategy. What that means, initially, was two things. Strategic partnerships. And then second is acquisitions. It’s been kind of fun doing that, because I worked on acquisitions and as an investment banker before. But at that time, you kind of run numbers. You kind of say, “Hey, you can cut costs here, and you can add here, or you can increase revenues by 10%, 20%,” but you don’t really know what it looks like.

Lerk-Ling Chang: Now, I’m on the other side of the table, where we have to go through systematically to understand… Hey, can we really grow revenues, work with all the different teams around the company to understand how to plan an integration, and make sure the acquisition actually comes to fruition. So, I’ve been involved in all the five acquisitions that we’ve done, and it’s been a really interesting experience seeing that. And now I’ve had the opportunity, as part of this, to now lead up our venture investments, which are going to be starting up and doing a lot more of. So, taking the initiative when you see something that’s a problem that you think you can help fix, taking the initiative to suggest solutions, and then working with people to see if that can actually come to fruition…. That has helped quite a bit.

Angie Chang: Wow. 12 years. That’s really impressive. When we were at Guidewire… listening to the women at Guidewire talk about how long they’ve been working there, I think we were all just amazed and had a lot of respect for people that can spend a decade or more at a company and continue to grow.

Gretchen DeKnikker: And respect for the company that could retain that many amazing… because that panel at that event was amazing. You guys should check out the Guidewire YouTube.

Sukrutha Bhadouria: Yeah. I remember it wasn’t uncommon, at least 10 years ago, to stay at your company for that long. But, now it’s becoming less and less common. When companies are able to retain their employees for that long, it’s obviously because they have a program or they have the environment where people can switch around job functions, which is what keeps the profession exciting. It’s interesting, the way Lerk-Ling was moving around in job functions the way she did.

Gretchen DeKnikker: I mean, I think it makes total sense, right? She had the investment banking background, but then she did product. And now she’s doing corporate strategy. And corporate strategy is all about product. Right? And understanding… Is this going to be additive, and understanding how the potential acquisition is going to incorporate into an existing product. So, the transition makes perfect sense, and it’s so cool that she was able to make that. And I think it takes a certain amount of bravery. Especially when you’re 12 years into something. You’re very comfortable. You’re a total expert at it to be, “Okay, I’m going to go do this other thing. And I’m going to have to have a beginner’s mindset, and I’m going to have to make mistakes. And have to probably make mistakes that other people are going to see, but I’m going to stick with it”

Sukrutha Bhadouria: What I find really interesting is her seizing this opportunity to see what are the areas she can make changes in. Often times, we don’t really look for areas that we can make changes in, make a difference in, because we’ve been in a particular role or a job function for a while. You stop looking outside of your area. That really fascinated me, and I know it’s going to push me to continue to do that. Because I’ve done some of it, but not at the degree that I would like to. What did you think, Angie?

Angie Chang: Yeah, I liked how she is very assertive and ambitious, and trying to look around corners, and seeing how she can lend her expertise and grow her domain of expertise. And, hopefully, it’s not so scary. I remember… At an Elevate conference, when Shawna Wolverton, the SVP of Product at Zendesk, was talking about all the jobs that she’s had. And how, taking them, you wouldn’t think that being a handbag designer would actually benefit your career, but it actually gave her a lot of really important insights that helped her in her career later on. And I think, similarly, a lot of people talk about the time they spent as a bank teller, or as a waitress, or as a barista, and how that experience has really helped them later in their careers. So, yeah, always having those different job functions can be very beneficial for your overall journey in life.

Sukrutha Bhadouria: Yeah. It’s always when you look back, you see the pieces of the puzzle that fit, that at the time you didn’t think they would fit. So, having different experiences definitely makes you better at whatever next role you take. Because, you have a closer chance to a full picture.

Rachel Jones: I think one thing that I took away from Lerk-Ling’s quote is just how when you’re switching job functions, it doesn’t even have to be something that’s completely unfamiliar and brand new. A lot of times, you’re using the same skill set that you’ve always had. You’re just applying it in a different way to have a different kind of impact. [inaudible 00:10:49] That was interesting to me.

Sukrutha Bhadouria: Full stack software engineer, Samantha Puth, shared her experience with her colleague, Cathy, as they moved between job functions, during our recent dinner with Amplitude.

Samantha Puth: Initially, we had created this really safe space to learn and be challenged. But over time, we realized that we became too comfortable and too complacent, and that in itself was the scariest thing. Being comfortable is not necessarily a bad thing, but being complacent means you’re stagnating your career. And we really try to prevent that.

Samantha Puth: So, that’s how we started getting to know each other, and we tried to discuss, “How can we keep improving our career? How do we keep growing together?” It’s hard to find advocates that are going to push you to do more. And as my manager was trying to do it, I still felt like I needed more. So, from there, I personally tried a few different things. Cathy tried similar things, where we moved to different parts of the product, different parts of the tech stack. And I, myself, as a traditionally more frontend engineer, did a rotation in DevOps for a quarter. And while I learned a lot, I just didn’t feel like it was super sustainable.

Samantha Puth: So, we knew the inevitable was coming. But that didn’t make it any easier. And as scary as it was, we were more fearful for the fact that our careers may be stagnating and we were missing out on valuable opportunities. So, with that fear in mind, that job is to really dive down deep and figure out what it is that we want. What is it that keeps us happy? What sustains this fulfillment as a developer? So, over lots of deliberation, on cocktail hours, happy hours, and wine, we came up with this. This was our need. We needed to find an ability or an opportunity to continually learn while providing a lot of impact. We knew we were the kind of people who would get bored if we weren’t being challenged. Yet, we were the kind of people who didn’t feel valued or fulfilled, if we weren’t proving to ourselves that we had an impact for those around us, as well as our customers.

Samantha Puth: So, that led us to Amplitude, where we’ve been actively trying to measure whether or not we’re actually doing this. This goal is something that we’re trying to keep each other accountable for. Or as I like to say it, accountabilibuddies who like to drink wine.

Gretchen DeKnikker: Hers really resonated with me, because I do get bored pretty quickly. I like to build things. I like to create something from nothing. I make this dumb analogy that if you put me in it a junk yard and said, “Take all these weird parts and make an engine,” eventually I would make an engine. And then once it ran fairly consistently, I would be bored. And I’d be, “Okay, so somebody else needs to come in and soup it up, and make it go fast and make it whatever.” But I get bored. And so, I think that’s probably the reason why I’ve always kind of switched functions to keep it interesting and to keep myself challenged. Because I’m one of those people who gets kind of self-destructive, if I’m not being challenged. I was the kid who was always in trouble, because I’d be talking or whatever else, because I was bored in class.

Sukrutha Bhadouria: That was me too. But I found that there were moments that I was letting myself be complacent before I got bored. So, you can be enjoying what you’re doing, but you’re just not getting any better… playing to your strengths.

Gretchen DeKnikker: And there’s nothing wrong with being complacent, if you are drawing what you need from some other part of your life, right? My life has always sort of centered around my job. But if you’ve got a hobby, or a volunteer thing, or your family, or whatever it is that you focus your time on… Right? Then, have a job where you can be a bit complacent, right? Because you’re growing in other areas of your life, and you need one part to be simple on some level.

Sukrutha Bhadouria: My thought is that I don’t want to let it reach that point where I’m like, “Oh my gosh. I’ve been bored for a whole year.” You know what I mean? I don’t want it to reach that stage that I’m reacting so late. Yeah, I think I’ve gotten better at looking for signs when I’m starting to go into that complacent stage. Because usually, for me, what it turns into is me feeling like I’m not getting appreciated at work, or I’m not getting my due. And it coincides with me starting to feel like I haven’t been challenging myself enough. Because it takes time to even make a move in your job functions, you want to get ahead of that, is what I feel.

Angie Chang: I was thinking of what Gretchen said about it being okay to be complacent. So, I’ve had… two times I was a product manager, and I know that in the Silicon Valley everybody was like, “You should be product manager. It’s very respectful, [inaudible 00:15:57].” So, I did it for two different jobs, and I knew it was kind of… I’m not saying complacent. But I feel like, maybe, that was not the ideal fit for me. It was weird because, I was like, “I’ve been very entrepreneurial. I shouldn’t do this, being CEO.” But, as Gretchen was saying, if there’s other things in your life that you are doing that light your fire and that you’re interested in… I was doing Women 2.0 on the side and Girl Geek dinners. So to me, I think, that was my saving grace through working at a job in tech… is that Women in Tech aspect on the side.

Rachel Jones: I was kind of thinking the same thing when Gretchen made that comment. For me, a lot of times when I do feel complacent at work, my response isn’t to go in that same job and think of a new function, but to find something outside of work to give me that fulfillment. That’s why I do podcasting outside of work… feeling like, yeah, I can still use a part of my skill set that’s exciting that I don’t get to use in my job. So that’s interesting to explore… not just within your strict career, but what things you can add to it on the outside to fulfill that need to challenge yourself. So, when you decide that it’s time to make a change, how can you go about doing that?

Sukrutha Bhadouria: Yeah. So, in terms of advice, I like Samantha’s approach where she and her friend Cathy… They were really deliberate about making sure that they talked through all the ideas of what they should do next and finally came up with what it is that they wanted to switch over to. I mean… Having someone you’re doing it with makes it a lot easier. Especially because they were looking specifically for opportunities where they were able to provide impact, while also learning and challenging themselves. And I think that sounds like a really good way to do it. I’m sure there are various ways. [Inaudible 00:17:56]

Angie Chang: I think from some of the ways that I’ve seen… For example, women in Product really succeed is when they work in Product, they create these Facebook groups or communities, these women in product meetups. They become little organizers of these different cities all around… I think, the Bay area and beyond. And it gives them a leadership opportunity, and also the chance to talk to other women in Product, and kind of share their experiences, figure out how to navigate the interview process, the different hurdles and challenges. They’re getting together. And that’s been really helpful for people’s careers.

Sukrutha Bhadouria: And this stage is when you want to have a network, or mentors. They don’t have to be someone who is far ahead of you in your career, but it could be someone who’s just had those experiences before you have, where you can bounce ideas off of them. So, that’s why we, at every Girl Geek Dinner, we’re recommending to everyone to make their network before they actually need it.

Gretchen DeKnikker: I think the first thing you need to do is figure out what you like and what you don’t like about your current job. And then kind of doing what Sukrutha was suggesting of talking to friends. Definitely talking to former bosses who sort of understand your strengths and weaknesses and understand you as a whole person, in a way that your colleagues or your friends don’t necessarily understand. And just sort of talking through, “I don’t really like doing this. I’d rather not do it anymore. I do like doing these two things,” and talking to as many people as you can who can give you ideas of, “Well, that same skill set would actually apply to this,” or “Have you considered moving into a role like this one?” And that’s where the best advice for me has come from… is particularly from former bosses who probably know, probably better than you do, what you’re really good at.

Sukrutha Bhadouria: I think that’s a great tip. I wouldn’t have thought of asking a former boss, but that’s… That’s really cool. I know I’ve asked a product manager I’ve worked with, because she was sort of working the [inaudible 00:20:08] role as me where she would be able to see what I was good at and what I wasn’t shining at and be able to give me similar advice. That’s really cool. I would think about that.

Gretchen DeKnikker: Yeah, that’s usually my starting point. When I’m frustrated, or bored, or just thinking, “What’s next?” And, of course, if you have an Angie… An Angie’s always very helpful with this. [crosstalk 00:20:33].

Angie Chang: You mean somebody who tells you all the things you could be that are two levels or one level above you? Whenever I hear women talk about, “Oh, I gave someone advice, free advice, I might be like, “You should charge for that,” or, “You should be an angel investor, or a partner, get tax credit.” [crosstalk 00:20:53]

Sukrutha Bhadouria: Why not?

Angie Chang: People are like, “No, I have to take a class. I have to just be [inaudible 00:21:00]

Gretchen DeKnikker: Angie’s next career transition is going to be into Life Coach, though.

Angie Chang: I mean… I guess that’s one way to categorize it. But I feel like… I don’t know. I think that’s the way we should be, just helping each other out. At these accountabilabuddy wine sessions, just help each other, elevate each other. So, for example, when I was looking for roles, I would always look for something very tactical and creative, and people would be like, “You totally deserve to be a director somewhere.” I’m like, “What? No.” And like, “I can see it.” And like, “No.” And so, you know, it’s daring to think that they’d… You always need that person, at least one, in your corner telling you that, you know, [crosstalk 00:21:43].

Gretchen DeKnikker: You need lots of them. Because, you’re not going to, generally, do it on your own. You’re not going to be like, “I’m so ready for that,” and be surrounded by people who don’t think you are. It’s normally your network pulling you, and being like, “Oh, come on, girl.”

Sukrutha Bhadouria: We’ll give you that push you need.

Angie Chang: We are like the best wing women, I think… for each other.

Gretchen DeKnikker: We are actually. Yeah. [inaudible 00:22:07]

Rachel Jones: [inaudible]

Gretchen DeKnikker: I don’t think any men- [crosstalk 00:22:13] Yeah. Even if we do, it totally bears repeating. It’s awesome.

Angie Chang: I think that a common trait of women is we can help each other out, almost more than we can help ourselves. But in that practice of helping out another woman, you’re like, “Well, I just gave Catherine that advice. I need to give myself that same kind of advice.” [crosstalk 00:22:39].

Sukrutha Bhadouria: When you’re mentoring someone else, you’re assisting someone else, and helping yourself as well.

Rachel Jones: So, one piece of advice that I have… But I think, yes, for myself, as much as other people… is to just try it. Because, I think I hold myself back a lot of times when there’s something new that I want to do. Just like, “I didn’t go to school for this. So, I didn’t have this many years of experience like the other people doing it.” And yeah, I just won’t even try because of that. But that doesn’t make sense. Just taking that first step of trying it, instead of holding yourself back based on the experience you don’t have.

Gretchen DeKnikker: Angela Buckmaster shared her own insights in navigating job function changes during our dinner with Poshmark, where she’s the Director of Community Operations.

Angela Buckmaster: I actually have been at Poshmark for a little over six years now. And so, I found Poshmark through a friend who still works here. We were friends through middle school and high school. And she heard that I had graduated college and was looking for my first big girl job. And she was on the community team here and said, “Hey, you should come interview.” So I did. And at the time, we had basically one role, which was Community Associate. And through that we wore a lot of different hats, as is typical at a startup. And from there, we kind of started to build into different teams. And so, from there I moved into the support team, still under the community umbrella. And I did some management for a couple of years. And through that, I noticed that I started having more and more of an interest in our KPIs and our SLAs. And I wanted to know why are they the way they are, how can we make them better, and to really understand them on a deeper level.

Angela Buckmaster: And so, I started speaking to my manager, and LyAnn, our SVP, and just letting them know, “I’m really interested in this. I would love to move into more of a data driven role.” The time wasn’t right… right at that moment. But I kept telling them, and I kept trying to get into projects that I could kind of dip my toes into the analytics area. Until the day came when LyAnn approached me, and she said, “Okay, the role is here. Let’s do it.” So, I happily went into that… more of an analytics role on the community team, which was awesome. I got to stay with my community family and did that for about a year. And then LyAnn approached me with another opportunity and said, “Hey, let’s build out this team.” So now I have the three areas. I have a data analytics team, a product knowledge team, and a training team.

Angela Buckmaster: And so, I’ve learned a lot over six years, right? I’ve learned that you can’t just keep your dreams to yourself. I think something I really believe is… Whatever you think about and you talk about all the time, is what you are or what you will become. And so I was very open, and I kept telling people about my dream. And I truly believe that that’s why it happened, because if you don’t speak up, no one knows. Right? So, that’s my little tip. I would encourage you all to just be very open about your passions and your dreams.

Gretchen DeKnikker: The thing that Angela points out is that she expressed her interest long before it was available. And I think sometimes it’s hard to know… To be like her, you have to know what your dream is. But, I think having a really strong relationship with your boss and saying, “Oh, that’s really… I really liked working on that thing.” And letting them know your interests and your preferences helps them, especially at a fast growing company like Poshmark, really build out those goals.

Gretchen DeKnikker: As a manager, you’re always in your head, especially in a really fast moving organization. You’re always thinking about what your next hire is going to be, and how that’s going to change the team, and what the skill set is going to be. And you’re just moving these players around on a board constantly. And having that bit of information, as a manager, is hugely important. So, even if it feels weird for you to express that, or they’ll think you don’t like your current job… It’s really a gift to your manager to tell them the things that you like.

Angie Chang: I think that’s a really good reminder for managers, also… to know where their reports want to be in a few years. So they can keep that in mind, as roles open up.

Sukrutha Bhadouria: It’s really hard though. Because, sometimes the manager is more wanting to keep the person who was great for that team or with that for that project, when that project may not be good for their growth anymore. So–

Gretchen DeKnikker: Yeah, but that’s not a good manager. Right?

Sukrutha Bhadouria: No, it’s not.

Gretchen DeKnikker: You definitely want one that–

Sukrutha Bhadouria: Not everybody has… realizes that they don’t have a good manager, until it’s too late.

Angie Chang: Right. And a lot of managers… not to knock the manager… learn that… learning curve of the first few years of their career. So, there’s always that chance that you don’t have a manager to help guide you, and you have to be outspoken like Angela.

Sukrutha Bhadouria: I’ve seen managers who have been managers [inaudible 00:27:53], and not provide that insight to the person who’s reporting to them. So, as that person is looking for changes, you need to manage up really well. You want to look up to your manager. If they’re not supporting you or not helping you, you need to realize that early.

Gretchen DeKnikker: And, I don’t think that her story is that much of an uncommon one, as far as joining a very fast growing company early. But, what she really had going for her, beyond just being able to speak up and say, “These are the things that I enjoy doing,” or “These are things I’d like to try,” is having someone who was in her corner that was championing her the entire time. Right? Like her… the person who keeps coming to her with these opportunities… You don’t leave a manager like that, right? If they’re going to keep growing you within a company… You don’t hear of people, especially in a company that stage where Poshmark is, of someone being there for six years. But why would she leave? She’s got the wind at her back and all the support that… at least from this little bit that we know, that she needs. And she’s growing. So, if you wonder why people only stay for 2 point whatever years in Silicon Valley, it’s because they don’t get that.

Rachel Jones: What stuck out to me was Angela’s process as she was waiting for something to become available. She didn’t just announce her intentions and sit back. But she mentions kind of dipping her toes into projects that let her get close to what she was trying to do. So, really just taking any opportunity to really demonstrate to people around her what she was interested in. And give her the kind of experience, so that when something did open up, she was really poised to take it. I think that level of initiative and intention is definitely something to strive for.

Rachel Jones: Do we have any final thoughts on switching job functions?

Sukrutha Bhadouria: I liked the suggestion, Gretchen, that you had where you said, “You should talk to a former boss who knows your strengths and anyone who knows your strengths,” to discuss what your next opportunity should be. I also like the idea of constantly looking out for opportunities where you can learn and make a dent. So, I feel like I should constantly be doing that. So, you don’t want to only be learning, but not have an impact. Because then, unfortunately, you’re not moving the needle, and that that energy is probably better spent where you can always make a change.

Gretchen DeKnikker: I think you just have to be willing to be courageous and understand that it would be hard to make a transition. And you’ll be out of your depth, but that ultimately you’ll be so proud of yourself, one… once you’ve like gone through it all. But also that you’re learning and you’re growing, and not just sort of sitting comfortably out of fear.

Angie Chang: But, I think also just this idea… People always say, the FOMO feeling of you’re missing out and things. Trying new things, though, is good…. And just taking new opportunities to see how it goes. You can always go back. If that’s not your company, someplace else. [inaudible].

Rachel Jones: So, one thing that I would say, just for people who might not work at an early stage company where there’s tons of flexibility to try different things. Or maybe you just don’t have a manager where it’s safe to announce your intentions like that. Yeah, if you’re feeling stagnant, also just think about maybe some outside of work channels that might be able to fulfill the things that you’re looking for.

Angie Chang: Thanks for listening to this episode of the Girl Geek X Podcast. Please rate and review us on your favorite podcasting app, and we’ll be back soon with more advice from women in tech.

Rachel Jones: This podcast is produced by me, Rachel Jones, with event recording by Eric Brown and music by Diana Chow. To learn more about Girl Geek X or buy tickets to our next dinner, visit Girlgeek.io, where you can also find videos and transcripts from all our events.

Angie Chang: Thanks to our sponsor, Amplitude. Amplitude is a leader in product analytics, providing digital product intelligence that helps companies ship great customer experiences for systematic business growth. Amplitude has defined the future of how companies interact with data build better products. This podcast is also sponsored by Poshmark. Poshmark is currently the largest social commerce marketplace for fashion. Anyone on the platform can buy, sell, and share their personal style with millions of other users. Poshmark brings together a vibrant community every day and encourages them to express themselves and share their love of fashion. This podcast is also sponsored by Guidewire. Guidewire believes that P&C Insurance plays a vital role in protecting people and businesses and enabling society to function. Guidewire specializes in serving P&C Insurance, exclusively with a focused commitment that puts customer success above all else. Their core competency is software development, and Guidewire holds themselves accountable for ensuring that the customers have the right technology to execute on their promises and policyholders over the long term.

Why changing the face of the “superstar developer” matters

Neha Narkhede began her career as a software engineer, working at Oracle and LinkedIn. She was a co-creator of Apache Kafka, a popular open-source stream-processing software platform that was created at LinkedIn. She spoke on a panel Girl Geek Dinner while she was still in engineering there. She saw a big opportunity with Kafka and convinced her fellow Kafka co-creators to start Confluent as a B2B infrastructure company in 2014 – Kafka’s event streaming is used by 60% of Fortune 100 companies today.

Changing the face of the “superstar developer” matters for all of us

Confluent founders Jay Kreps, Neha Narkhedee, Jun Rao

With only 2% of venture capital going to women entrepreneurs, Neha beat the odds and demonstrated that it’s possible to thrive as a technical leader. She served five years as the company’s Chief Technology Officer, and recently became Chief Product Officer to continue growing the brand. Confluent’s founders recently raised Series D venture funding for the company at a valuation of $2.5 billion, and they employ over 900 people.

Silicon Valley needs more Neha’s

In the 21st century, tech companies have made entrepreneurs cool again – an acceptable career path with ambitious MBAs heading to tech instead of finance. Facebook’s Mark Zuckerberg and Salesforce’s Marc Benioff have started billion-dollar companies, with press coverage of their every sentence. Hospitals are named after them. NVIDIA’s Jensen Huang’s name is on the newest Stanford engineering building. These highly visible entrepreneurs impact the next generation of inventors and engineers.

The women of Silicon Valley haven’t made the same impact, with the exception of famous spouses. Facebook’s Sheryl Sandberg has a strong chance to make an outsized impact outside her current professional role, we shall see what she does in the future. Many accomplished, super-smart women of Silicon Valley don’t gloss nearly as many magazine covers or present as many conference keynotes. What is the story behind Amazon’s MacKenzie Bezos and her hand in building the world’s biggest business?

It’s time to stop hiding behind humility and enable the mechanisms to lift up technical women leaders, entrepreneurs and investors. That means, have a marketing/PR budget to power the promotion of your women leaders and ensure their press coverage. We need more buzzy business magazine covers with diverse faces:

Meg Whitman, Limor Fried, Yoky Matsuoka, Katrina Lake, Audrey Gelman, Arlan Hamilton
Magazine covers starring (from top left): Meg Whitman, Limor Fried, Yoky Matsuoka, Katrina Lake, Audrey Gelman, Arlan Hamilton

Neha is tracking to be the next cloud computing leader. VMware’s Diane Greene sat on Alphabet’s board (she’s also on the boards of Intuit and Stripe) and led Google Cloud as CEO until 2018. In her final Google blog post, she wrote: “I want to encourage every woman engineer & scientist to think of building their own company someday. The world will be a better place with more female founder CEOs.

The adage “You can’t be what you can’t see” means we need more women leading at the highest levels, and more technical women in the spotlight, gracing magazine covers, giving talks and interviews. We need to invest in their startups, buy from women-led businesses, and hire and retain more women in male-dominated industries.

Shining a spotlight on women in tech

Just as Grace Hopper Celebrations fill employers’ recruiting university pipelines, we need technical women to succeed at mid and senior levels as well – to be retained in addition to being hired, encouraged and recognized, paid fairly and promoted.

We need to fix the leaky pipeline in addition to hiring new grads.

Melinda Gates recently told Harvard Business Review: Go to your company and say we’re going to open more internships at different levels. How do we create pathways in?”

Angie Chang and Sukrutha Raman Bhadouria, co-founders of Girl Geek X

At Girl Geek X, we have been putting women onstage for over a decade at their companies’ dinners for networking and learning.

We love watching women progress in their career journeys, whether it’s working in big tech company, or at a startup.

Join us at an upcoming Girl Geek Dinner!

Sponsor a Girl Geek Dinner to organize one at your company / employer!

Watch the video from Confluent Girl Geek Dinner featuring Neha Narkhede, Bret Scofield, Liz Bennett, Priya Shivakumar, and Dani Traphagen on YouTube. Please subscribe to our Girl Geek X channel on YouTube for videos from our events.

This article was first published on LinkedIn Pulse by Angie Chang.

(Top Photo by: Erica Kawamoto Hsu / Girl Geek X)

Girl Geek X OpenAI Lightning Talks and Panel (Video + Transcript)

Like what you see here? Our mission-aligned Girl Geek X partners are hiring!

Gretchen DeKnikker, Sukrutha Bhadouria

Girl Geek X team: Gretchen DeKnikker and Sukrutha Bhadouria kick off the evening with a warm welcome to the sold-out crowd to OpenAI Girl Geek Dinner in San Francisco, California.   Erica Kawamoto Hsu / Girl Geek X

Transcript of OpenAI Girl Geek Dinner – Lightning Talks & Panel:

Gretchen DeKnikker: All right, everybody, thank you so much for coming tonight. Welcome to OpenAI. I’m Gretchen with Girl Geek. How many people it’s your first Girl Geek? All right, okay. Lots of returning. Thank you for coming. We do these almost every week, probably like three out of four weeks a month. Up and down the peninsula, into the South Bay or everywhere. We also have a podcast that you could check out. Please check it out, find it, rate it, review it. Give us your most honest feedback because we’re really trying to make it as awesome as possible for you guys. All right.

Sukrutha Bhadouria: Hi, I’m Sukrutha. Welcome, like Gretchen said, Angie’s not here but there’s usually the three of us up here. Tonight, please tweet, share on social media, use the hashtag GirlGeekXOpenAI. I also, like Gretchen, want to echo that we love feedback, so any way you have anything that you want to share with us. Someone talked about our podcast episodes today. If there’s any specific topics you want to hear, either at a Girl Geek Dinner or on our podcast, do share that with us. Either you can find us tonight or you can email us. Our website is girlgeek.io and all our contact information’s on there. Thank you all. I don’t want to keep you all waiting because we have amazing speakers lined up from OpenAI, so.

Sukrutha Bhadouria: Oh, one more quick thing. We’re opening up sponsorship for 2020 so if your company has not sponsored a Girl Geek dinner before or has and wants to do another one, definitely now’s the time to sign up because we fill up pretty fast. We don’t want to do too many in one month. Like Gretchen said, we do one every week so definitely would love to see a more diverse set of companies–continue to see that like we did this year. Thank you, all. Oh, and over to Ashley.

Ashley Pilipiszyn speaking

Technical Director Ashley Pilipiszyn emcees OpenAI Girl Geek Dinner.   Erica Kawamoto Hsu / Girl Geek X

Ashley Pilipiszyn: All right, thank you.

Sukrutha Bhadouria: Thanks.

Ashley Pilipiszyn: All right. Hi, everybody.

Audience: Hi.

Ashley Pilipiszyn: Oh, awesome. I love when people respond back. I’m Ashley and welcome to the first ever Girl Geek Dinner at OpenAI. We have a … Whoo! Yeah.

Ashley Pilipiszyn: We have a great evening planned for you and so excited to see so many new faces in the crowd but before we get started, quick poll. How many of you currently work in AI machine learning? Show of hands. All right, awesome. How many of you are interested in learning more about AI machine learning? Everybody’s hands should be up. All right. Awesome. We’re all on the right place.

Ashley Pilipiszyn: Before we kick things off, I’d like to give just a brief introduction to OpenAI and what we’re all about. OpenAI is an AI research lab of about 100 employees, many of whom you’re going to get to meet this evening. Definitely, come talk to me. Love meeting you. We’ve got many of other folks here, and our mission is to ensure that safe, artificial general intelligence benefits all of humanity.

Ashley Pilipiszyn: To that effect, last year we created the OpenAI Charter. The charter is our set of guiding principles as we enact this mission and serves as our own internal system of checks and balances to hold ourselves accountable. In terms of how we organize our research, we have three main buckets. We have AI capabilities, what AI systems can do. We have AI safety, so ensuring that these systems are aligned with human values. We have AI policy, so ensuring proper governance of these systems.

Ashley Pilipiszyn: We recognize that today’s current AI systems do not reflect all of humanity and we aim to address this issue by increasing the diversity of contributors to these systems. Our hope is that with tonight’s event, we’re taking a step in the right direction by connecting with all of you. With that, I would like to invite our first speaker to the stage, Brooke Chan. Please help me welcome Brooke.

Brooke Chan speaking

Software Engineer Brooke Chan from the Dota team gives a talk on reinforment learning and machine learning at OpenAI Girl Geek Dinner.  Erica Kawamoto Hsu / Girl Geek X

Brooke Chan: Yeah. Hello. Is this what I’m using? Cool. I’m Brooke Chan. I was a software engineer on the Dota 2 team here at OpenAI for the past two years. Today, I’m going to talk a little bit about our project, as well as my own personal journey throughout the course of the project.

Brooke Chan: We’re going to actually start at the end. On April 13th, we hosted the OpenAI Five Finals where we beat the TI8 world champions OG at Dota 2 in back-to-back games on stage. TI stands for The International, which is a major tournament put on by Valve each year with a prize pool upwards of $30 million. You can think of it like the Super Bowl but for Dota.

Brooke Chan: There have been previous achievement/milestones of superhuman AI in both video games and games in general, such as chess and Go, but this was the first AI to beat the world champions at an eSports game. Additionally, as a slightly self-serving update, OG also won the world championship this year at TI9 just a few weeks ago.

Brooke Chan: Finals wasn’t actually our first unveiling. We started the project back in January of 2018 and by June of 2018, we started playing versus human teams. Leading up to finals, we played progressively stronger and stronger teams, both in public and in private. Then most recently, right before finals, we actually lost on stage to a professional team at TI8, which was the tournament that OG later went on to win.

Brooke Chan: Let’s go back to the basics for a minute and talk about what is reinforcement learning. Essentially, you can think of it as learning through trial and error. I personally like to compare it to dog training so that I can show off pictures of my dog. Let’s say that you want to teach a dog how to sit, you would say sit and just wait for the dog to sit, which is kind of a natural behavior because you’re holding a treat up over their head so they would sit their butt down and then you would give them that treat as a reward.

Brooke Chan: This is considered capturing the behavior. You’re making an association between your command, the action and the reward. It’s pretty straightforward for simple behaviors like sit but if you want to teach something more complicated, such as like rolling over, you would essentially be waiting forever because your dog isn’t just going to roll over because it doesn’t really understand that is something humans enjoy dogs doing.

Brooke Chan: In order to kind of teach them this, you instead reward progress in the trajectory of the goal behavior. For example, you reward them for laying down and then they kind of like lean over a little bit. You reward them for that. This is considered to be shaping rewards. You’re like teaching them to explore that direction in order to achieve ultimately your goal behavior.

Brooke Chan: Dota itself is a pretty complicated game. We can’t just reward it by purely on winning the game because that would be relatively slow so we applied this technique of shaped rewards in order to teach the AI to play the game. We rewarded it for things like gold and kills and objectives, et cetera. Going more into this, what is Dota?

Brooke Chan: Dota is a MOBA game which stands for multiplayer online battle arena. It’s a little bit of a mouthful. It’s a game that was developed by Valve and it has an average of 500,000 people playing at any given time. It’s made up of two teams of five and they play on opposite sides of the map and each player controls what’s considered a hero who has a unique set of abilities.

Brooke Chan: Everyone starts off equally weak at the beginning of the game, which means that they’re low levels and they don’t have a lot of gold and the goal is that over the course of a 30 to 60-minute game, they earn gold and become stronger and eventually, you destroy your opponent’s base. You earn gold and experience across the map through things like small fights or like picking people off, killing your enemy, taking objectives, things like that. Overall, there’s a lot of strategy to the game and a lot of different ways to approach it.

Brooke Chan: Why did we pick Dota? MOBAs in general are considered to be one of the more complex video games and out of that genre, Dota is considered the most complex. Starting off, the games tend to be pretty lengthy, especially in terms of how RL problems typically are, which means that strategy tends to be hard with a pretty delayed payoff. You might rotate into a particular lane in order to take an objective that you might not be able to take until a minute or a minute and a half later. It’s something that’s kind of like hard to associate your actions with the direct rewards that you end up getting from them.

Brooke Chan: Additionally, as opposed to games like Go and chess, Dota has partial information to it, which means that you only get vision around you and your allies. You don’t have a full state of the game. You don’t know where your enemies are and this leads to more realistic decision-making, similar to our world where you can’t like see behind walls. You can’t see beyond what your actual vision gives you.

Brooke Chan: Then, finally, it has both a large action and observation space. It’s not necessarily solvable just by considering all the possibilities. There’s about 1,000 actions that you can take at any given moment and the state you’re getting back has the value size of about 20,000. To put it in perspective, on average, your game of chess takes about 40 moves and Go takes about 150 moves and Dota is around 20,000 moves. That means that the entire duration of a game of chess really wouldn’t even get you out of the base in Dota.

Brooke Chan: This is a graph of our training process. On the left, you have workers that all play the game simultaneously. I know it’s not super readable but it’s not really important for this. Each game that they’re playing in the top left consists of two agents where an agent is considered like a snapshot of the training. The rollout workers are dedicated to these games and the eval workers who are on the bottom left are dedicated to testing games in between these different agents.

Brooke Chan: All the agents at the beginning of the training start off random. They’re basically picking their actions randomly, wandering around the map doing really awfully and not actually getting any reward. The machine in green is what’s called the optimizer so it parses in all of these rollout worker games and figures out how to update what we call the parameters which you can consider to be the core of its decision-making. It then passes these parameters back into the rollout workers and that’s how you create these continually improving agents.

Brooke Chan: What we do then is we take all of these agents and we play them against all the other agents in about 15,000 games in order to get a ranking. Each agent gets assigned a true skill, which is basically a score calculated on its win-loss records against all the other agents. Overall, in both training and evaluation, we’re really not exposing it to any kind of human play. The upside of this is that we’re not influencing the process. We know that they’re not just emulating humans and we’re not capping them out at a certain point or adding a ceiling on it based on the way that humans play.

Brooke Chan: The downside of that is that it’s incredibly slow. For the final bot that we had play against OG we calculated that it had about 45,000 years of training that went into it. Towards the end of training, it was consuming about approximately 250 years of experience per day. All of which we can really do because it’s in simulation and we can do it both asynchronously and sped up.

Brooke Chan: The first time they do get exposed to human play is during human evaluations. They don’t actually learn during any of these games because we are taking an agent, which is a snapshot and frozen in time and it’s not part of the training process. We started off playing against our internal team and our internal team was very much not impressive. I have us listed as 2K MMR, which is extremely generous. MMR means matchmaking rating which is a score that Valve assigns to the ranked play. It’s very similar to true skill. 2K is very low.

Brooke Chan: We were really quickly surpassed. We then moved on to contract teams who were around like 4K-6K MMR and they played each week and were able to give us feedback. Then in the rare opportunities, we got to play against professional teams and players. Overall, our team knew surprisingly little about Dota. I think there are about four people on our team who had ever played Dota before and that’s still true post-project, that no one really plays Dota.

Brooke Chan: This leads us to our very surprising discovery that complicated games are really complicated and we dug ourselves into this hole. We wanted a really complicated game and we definitely got one. Since the system was learning in a completely different way than humans, it became really hard to interpret what it was actually trying to do and not knowing what it was trying to do mean we didn’t know if it was doing well, if it was doing poorly, if it was doing the right thing. This really became a problem that we faced throughout the lifetime of our project.

Brooke Chan: Having learned this, there was no way to really ask it what it was thinking. We had metrics and we could surface like stats from our games but we were always leveraging our own intuition in order to interpret what decisions it was making. On the flip side, we also had human players that we could ask, but it turned out it was sometimes tough to get feedback from human players.

Brooke Chan: Dota itself is a really competitive game, which means that its players are very competitive. We got a lot of feedback immediately following games, which would be very biased or lean negatively. I can’t even count the number of times that a human team would lose maybe like, “Oh, this bot is terrible” and I was like, “Well, you lost. How is it terrible? What is bad about it?” This would create this back and forth that led to this ultimate question of is it bad or is it just different? Because, historically, humans have been the source on how to play this game. They make up the pro scene, they make up the high skill players. They are always the ones that you are going to learn from. The bots would make a move and the humans say it was different and not how the pros play and therefore, it’s bad. We always had to take the human interpretation with this kind of grain of salt.

Brooke Chan: I want to elaborate a little bit more about the differences because it goes just beyond the format of how they learn. This game in general is designed to help humans understand the game. It has like tooltips, ability descriptions, item descriptions, et cetera. As an example, here’s a frozen frame of a hero named Rana who’s the one with the bright green bar in the bottom left. She has an ability that makes you go invisible and humans understand what being invisible means. It means people can’t see you.

Brooke Chan: On the right, what we see is where we have like what the AI sees and it’s considered their observation space, it’s our input from the game. We as engineers and researchers know that this particular value is telling you whether or not you’re invisible. When we hit this ability, you can see that she gets like this little glow to her which indicates that she’s invisible and people understand that. The AI uses this ability and sees that the flag that we marked as invisible goes from 0 to 1 but they don’t see the label for that and they don’t really even understand what being invisible means.

Brooke Chan: To be honest, learning invisibility is not something trivial. If you’re walking down the street and all of a sudden, you were invisible, it’s a little bit hard to tell that anything actually changed. If you’ve ever seen Sixth Sense, maybe there’s some kind of concept there, but additionally, at the same time, all these other numbers around it are also changing due to the fact that there’s a lot of things happening on the map at once.

Brooke Chan: Associating that invisibility flag, changing directly to you, activating the ability is actually quite difficult. That’s something that’s easy for a human to do because you expect it to happen. Not to say that humans have it very easy, the AI has advantages too. The AI doesn’t have human emotions like greed or frustration and they’re always playing at their absolute 100% best. They’re also programmatically unselfish which is something that we did. We created this hyper parameter called team spirit which basically says that you share your rewards with your buddy. If you get 10 gold or your buddy gets 10 gold, it’s totally interchangeable. Theoretically, in a team game, that should be the same case for humans but inherently, it’s not. People at its core are going to play selfishly. They want to be the carrier. They want to be winning the game for the team.

Brooke Chan: All these things are going to influence pretty much every decision and every behavior. One pretty good example we have of this is called buybacks. Buybacks is a mechanic where when you die in the game, you can pay money in order to immediately come back to life and get back on the map. When we first enabled the AI to do this, there was a lot of criticism that we got. People were saying, “Oh, that’s really bad. They shouldn’t be wasting all their money” because the bots would always buy back pretty much immediately.

Brooke Chan: Over time, we continue doing this behavior and people kept saying, “Oh, that’s bad. You should fix it.” We’re like, “Well, that’s what they want to do.” Eventually, people started seeing it as an advantage to what we had, as an advantage to our play style because we were able to control the map. We were able to get back there very quickly and we were able to then force more fights and more objectives from it.

Brooke Chan: As a second self-serving anecdote, at TI9, there were way more buybacks way earlier and some people pointed this out and maybe drew conclusions that it was about us but I’m not actually personally going to make any statement. But it is one example of the potential to really push this game forward.

Brooke Chan: This is why it was difficult to have human players give direct feedback on what was broken or why because they had spent years perfecting the shared understanding of the game that is just like inherently different than what the bots thought. As one of the few people that played Dota and was familiar with the game and the scene, in the time leading up to finals, this became my full-time job. I learned to interpret the bot and how it was progressing and I kind of lived in this layer between the Dota community and ML.

Brooke Chan: It became my job to figure out what was most critical or missing or different about our playstyle and then how to convert that into changes that we could shape the behavior of our bot. Naturally, being in this layer, I also fell into designing and executing all of our events and communication of our research to the public and the Dota community.

Brooke Chan: In designing our messaging, I had the second unsurprising discovery that understanding our project was a critical piece to being excited about our results. We could easily say, “Hey, we taught this bot to learn Dota” and people would say, “So what? I learned to play Dota too. What’s the big deal?” Inherently, it’s like the project is hard to explain because in order to understand it and be as excited as we were, you had to get through both the RL layer which is complicated, and the Dota layer which is also complicated.

Brooke Chan: Through planning our events, I realized this was something we didn’t really have a lot of practice on. This was the first time that we had a lot of eyes on us belonging to people with not a lot of understanding of reinforcement learning and AI. They really just wanted to know more. A lot of our content was aimed at people that came in with the context and people that were already in the field.

Brooke Chan: This led me to take the opportunity to do a rotation for six months on the communications team actually working under Ashley. I wanted to be part of giving people resources to understand our projects. My responsibilities are now managing upcoming releases and translating our technical results to the public. For me, this is a pretty new and big step. I’ve been an engineer for about 10 years now and that was always what I loved doing and what I wanted to do. But experience on this team and growing into a role that didn’t really exist at the time allowed me to tackle other sorts of problems and because that’s what we are as engineers at the core, we want to be problem solvers.

Brooke Chan: That’s kind of my takeaway and it might seem fairly obvious but sometimes deviating from your path and taking risks let you discover new problems to work on. They do say that growth tends to be at the inverse of comfort so that means that the more you push yourself out of your comfort zone and what you’re used to, the more you give yourself opportunities for new challenges and discovering new skills. Thank you.

Lilian Weng

Research Scientist Lilian Weng on the Robotics team gives a talk on how her team uses reinforcement learning to learn dexterous in-hand manipulation policies at OpenAI Girl Geek Dinner.  Erica Kawamoto Hsu / Girl Geek X

Lilian Weng: Awesome. Cool. Today, I’m going to talk about some research projects with that at OpenAI robotics team. One big picture problem at our robotics team is to develop the algorithm to power general-purpose robots. If you think about how we humans are living this world, we can cook, we lift to move stuff, we add some more items with different tools. We fully utilize our body and especially our hands to do a variety of tasks. To some extent, we are general-purpose robots, okay?

Lilian Weng: That’s, we apply the same standard to our definition of such a thing. A general-purpose robot should be able to interact with a very complicated environment of the real world and able to manipulate all kinds of objects around it. However, unfortunately, most consumer-oriented robots nowadays are either just toys or very experimental or focus on specific functionalities and they are robots like factory arms or medical robots. They can interact with the environment and operating tools but they’re really operated by humans so human controls every move or they just play back a pre-programmed trajectory. They don’t really understand the environments and they cannot move autonomously.

Lilian Weng: In our projects, we’re taking a small step towards this goal and in this we try to teach a human-like robot hand to do in-hand manipulation by moving the objects. This is a six-phase block with OpenAI letters on it, move that to a target orientation. We believe this is an important problem because a human-like robot hand, it’s a universal effort. Imagine we can control that really well, we can potentially automate a lot of tasks that are currently done by human. Unfortunately, not a lot of progress have been made on human-like robot hand due to the complexity of such a system.

Lilian Weng: Why it is hard? Okay. First of all, the system has very high dimensionalities. For example, in our robot, which is as you can see this cool illustration. Shadow dexterity hand, it has 24 joints and 20 actuators. The task is especially hard because during the manipulation, a lot of observations are occluded and they can be noisy. For example, your sensor reading can be wrong and your sensor reading can be blocked by the object itself. Moreover, it’s virtually impossible to simulate your physical world 100% correctly.

Lilian Weng: Our approach for tackling this problem is to use reinforcement learning. We believe it is a great approach for learning how to control robots given that we have seen great progress and great success in many applications by reinforcement learning. You heard about OpenAI Five, the story of point AlphaGo and it will be very exciting to see how reinforcement learning can not only interact with this virtual world but also have an impact on our physical reality.

Lilian Weng: There is one big drawback of reinforcement learning model. In general, today, most of the models are not data efficient. You need a lot of training sample in order to get a good model trained. One potential solution is you build a robot farm. You just collect all the data in parallels with hundreds of thousands of robots but imagine just given how fragile a robot can be. It is very expensive to build and maintain. If you think of another problem, a new problem, or you want to work with new robots, it’s very hard to change. Furthermore, your data can get invalidated very quickly due to small changes in your robot status.

Lilian Weng: As that, we decided to take the sim2real approach, that is you train your model every single simulation but deploy that on physical robots. Here shows how we control the hand simulation. The hand is moving the object to a target orientation. The target is shown on the right so whenever the hand achieved the goal, we just sample a new goal. It just keeps on doing that and we cap the number of success at 50.

Lilian Weng: This is our physical setup. Everything is mounted in this giant metal cage. It’s like this big. The hand is mounted in the middle. It’s surrounded with a motion caption system. It’s actually the system that people use for filming special effects films, like the actor has dots on their bodies, kind of similar. This system tracks the five fingertip positions in the 3D space. We also have three high-resolution cameras for capturing images as input to our vision model. Our vision model predicts positional orientation of the block. However, our proposal sim2real approach might fail dramatically because there are a lot of model difference between simulation and reality. If your model all refer to the simulation, it can perform super poorly, the real robots.

Lilian Weng: In order to overcome this problem, we decided to take … we use reinforcement learning, okay. We train everything simulations so that we can generate technically, theoretically infinite amount of data. In order to overcome the sim2real difference, we use domain randomization.

Lilian Weng: Domain randomization refer to an idea of randomized different elements in simulation so that your policy can be exposed to a variety of scenarios and learn how to adapt. Eventually, we expand the policy to able to adapt to the physical reality.

Lilian Weng: Back in … This idea is relative news. I think they first proposed it in 2016. The researchers try to train a model to control drone like fly across furnitures or the indoor scenarios. They randomized the colors and texture of the walls and furnitures and without seeing any real-world images, they show that it performs pretty well in reality.

Lilian Weng: At OpenAI, we use the same approach to train a better model to protect the position orientation of the objects. As you can see some of the randomization looks totally unrealistic but somehow it worked very well when we feed the model with real images. Later, we also showed that you can randomize all the physical dynamics in simulations and this robot trained with domain randomization worked much better than the one without.

Lilian Weng: Let’s see the results. Okay. I’m going to click the … You really struggle a little bit at the first goal. Yes, okay. The ding indicates one success. This video will keep on going until goal 50 so it’s very, very long but I personally found it very soothing to look at it. I love it.

Lilian Weng: I guess that’s enough. This is our full setup of the training so in the box A, we generate a large number of environments in parallels in which we randomize the physical dynamics and the visual appearance. Based on those, we train two models independently. One is a policy model which takes in the fingertip position and object pose and the goal and output, a desired joint position of the hand so that we can control the hand. Another model is the vision that takes in three images from different camera angles and output the position orientation of the object.

Lilian Weng: When we deploy this thing into the real world, we combine the vision prediction based on the real images together with a fingertip position tracked by the motion capture system and feed that into our policy control model and output action so that then we just send it to the real robot and everything starts moving just like the movie shown. When we train our policy control model, we’ve randomized all kinds of physical parameter in the simulator such as masses, friction coefficient, motor gain, damping factor, as well as noise on the action, on observation. For a revision model, we randomized camera position, lighting, material, texture, colors, blah, blah, blah, and it just worked out.

Lilian Weng: For our model’s architecture, I’ll just go very quickly here. The policy, it’s a pretty simple recurrent unit. Has one layer of really connective layer and the LSTM. The vision model is a straightforward, multi-camera setup. All the three cameras share this RestNet stack and followed by a spatial softmax.

Lilian Weng: Our training framework is distributed and synchronized PBO, proximal policy optimization model. It’s actually the same framework used for training OpenAI Five. Our setup allowed us to generate about two years simulated experience per hour, which corresponds to 17,000 physical robots, so the gigantic robot factory and simulation is awesome.

Lilian Weng: When we deploy our model in reality, we noticed a couple of strategies learned by the robot like finger pivoting, sliding, finger gaiting. Those were also commonly used by human and interestingly, we never explicitly give it words or encouraged those strategies. They would just emerge autonomously.

Lilian Weng: Let’s see some numbers. In order to compare different versions of models, we deployed the models on the real robots and count how many successes the policy can get up to 50 before it dropped the block or time out. We first tried to deploy a model without randomization at all. It got a perfect performance in simulation but look, you can see it’s zero success median. Super bad on the real robot.

Lilian Weng: Then we’re adding domain randomization. The policy becomes much better because 13 success medians, maximum 50. Then we used RGB cameras in our vision model to track the objects. The performance only dropped slightly, still very good. The last one, I think this one’s very interesting because I just mentioned that our policies are recurrent units so like LSTM, it has internal memories.

Lilian Weng: Well, interesting, see how important this memory is so we replaced this LSTM policy with a FIFO or NAS and deployed that on robot and the performance dropped a lot, which indicates that memory play an important role in the sim2real transfers. Potentially, the policy might be using the memory and try to learn how to adapt.

Lilian Weng: However, training in randomized environments does come with a cost. Here we plot the number of success in simulation as a function of simulated experiencing measured in year. If you don’t apply randomization at all, the model can learn to achieve 40 success with about three years simulated experience but in order to get to same number like 40 success in a fully randomized environment took 100 years.

Lilian Weng: Okay, to quick summary. We’ve shown that this approach, reinforcement learning plus training simulation plus domain randomization worked on the real robot and we would like to push it forward. Thank you so much. Next one is Christine.

Christine Payne speaking

Research Scientist Christine Payne on the Music Generation team gives a talk on how MuseNet pushes the boundaries of AI creativity, both as an independent composer, and as a collaboration tool with human artists.  Erica Kawamoto Hsu / Girl Geek X

Christine Payne: Thank you. Let’s see. Thank you. It’s really great to see all of you here. After this talk, we’re going to take a short break and I’m looking forward to hopefully getting to talk to a lot of you at that point. I’ve also been especially asked to announce that there are donuts in the corner and so please help us out eating those.

Christine Payne: If you’ve been following the progress of deep learning in the past couple years, you’ve probably noticed that language generation has gotten much, much better, noticeably better in the last couple of years. But as a classical pianist, I wondered, can we take the same progress? Can we apply instead to music generation.

Christine Payne: Okay, I’m not Mira. Sorry. Hang on. One moment, I think we’re on the wrong slide deck. All right, sorry about that. Okay, trying again. Talking about music generation. You can imagine different ways of generating music and one way might be to do a programmatic approach where you say like, “Okay, I know that drums are going to be a certain pattern. Harmonies usually follow a certain pattern.” You can imagine writing rules like that but there’s whole areas of music that you wouldn’t be able to capture with that. There’s a lot of creativity, a lot of nuance, the sort of things that you really want a neural net to be able to capture.

Christine Payne: I thought I would dive right in by playing a few examples of MuseNet, which is this neural net that’s been trained on this problem of music generation. This first one is MuseNet trying to imitate Beethoven and a violin piano sonata.

Christine Payne: It goes on for a while but I’ll cut it off there. What I’m really trying to go with in this generation process is trying to get long-term structure so both the nuance and the intricacies of the pieces but also something that stays coherent over a long period of time. This is the same model but instead trying to imitate jazz.

Christine Payne: Okay, and I’ll cut this one off too. As you maybe could tell from those samples, I am more interested in the problem of composing the pieces themselves, so sort of where the notes should be and less in the actual quality of the solemnness and the timbre. I’ve been using a format that’s called MIDI which is an event-based system of writing music. It’s a lot like how you would write down notes in a music score. Like this note turns on at this moment in time played by this instrument maybe at this volume but you don’t know like this amazing cellist actually made it sound this way so I’m throwing out all of that kind of information.

Christine Payne: But the advantage of throwing that out is then you can get this longer-term structure. To build this sort of dataset, it involves a little bit of begging for data. I’ve had a bunch of people like BitMidi and ClassicalArchives were nice enough to just send me their collections and then a little bit of scraping and also MAESTRO’s Google Magenta’s dataset and then also a bunch of scraping online sets.

Christine Payne: The architecture itself, here I’m drawing really heavily from the way we do language modeling and so we use a specific kind of neural net that’s called a transformer architecture. The advantage of this architecture is that it’s specifically good at doing long-term structure so you’re able to look back not only at things that have happened in the recent past but really, you can look back like what happened in the music a minute ago or something like that, which is not possible with most other architectures.

Christine Payne: In the language world, I’d like to think of this, the model itself is trained on the task of what word is going to come next. It might initially see just like a question mark so it knows it’s supposed to start something. In English, we know like maybe it’s the or she or how or some like that. There’s some good guesses and there’s some like really bad guesses. If we know now the first word is hello then we’ve kind of narrowed down what we expect our next guesses should be. It might be how, it might be my, it’s probably not going to be cat. Maybe it could be cat. I don’t know.

Christine Payne: At this point, we’re getting pretty sure–like a trained model should actually be pretty sure that there should be a good 90% chance the next word is name and now it should be like really 100% sure or like 99.5% sure or whatever that the next word is going to be is. Then here we hit kind of an interesting branching point where there are tons of good answers so lots of names could be great answers here, lots of things could also be really bad answers so we don’t expect to see like some random verbs, some random … There are lots of things that we think would be bad choices but we get a point here to branch in good directions.

Christine Payne: The idea is once you have a model that’s really good at this, you can then turn it into a generator by sampling from the model according to those probabilities. The nice thing is you get the coherent structure. When you get a moment like this, you know like I have to choose … In music, it’s usually like I have to choose this rhythm, I have to choose … like if I choose the wrong note, it’s just going to sound bad, things like that. But then there are also a lot of points like this where the music can just go in fun and interesting different directions.

Christine Payne: But of course, now we have the problem of how do you translate words, how do you translate this kind of music into a sequence of words that the model can do. The system that I’m using is very similar to how MIDI itself works. I have a series of tokens that the model will always we see. Initially, it’ll always see the composer or the band or whoever wrote the piece. It’ll always see what instrument to expect in the piece or what set of instruments.

Christine Payne: Here, it sees the start token because it’s at the start of this particular piece and a tempo. Then as the piece begins, we have a symbol that this C and that C each turn on with a certain volume and then we have a token that says to wait a certain amount of time. Then as it moves forward, the volume zero means that first note just turned off and the G means the next note turns on. I think we have to wait and similarly, here the G turns off, the E turns on and we wait. You can just progress through the whole set of music like this.

Christine Payne: In addition to this token by token thing, I’m helping the model out a little bit by giving it a sense of the time that’s going on. I’m also giving it an extra embedding that says everything that happens in this purple line happens in the same amount of time or at the same moment in time. Everything in blue is going to get a different embedding that’s a little bit forward in time and so forth.

Christine Payne: The nice thing about an embedding or a system like this is that it’s pretty dense but also really expressive. This is the first page of a Chopin Ballade that is like actually encapsulates how the pianist played it, the volumes, the nuances, the timings, everything like that.

Christine Payne: The model is going to see that sequence of numbers like that. Like that first 1444 I think means it must mean Chopin and the next one probably means piano and the next one means start, that sort of thing. The first layer for the model, what it has to do is it needs to translate that number into a vector of numbers and then it can sort of learn a good vector that’ll represent so it’ll get a sense of like this is what it means to be Chopin or this is what it means to be like a C on a piano.

Christine Payne: The nice thing you can do once … The model will learn. Like initially it starts out with a totally random sense so it has no idea what those numbers should be but in the course of training, it’ll learn better versions of that. What you can do is you can start to map out what it’s learned for these embeddings. For example, this is what it’s learned for a piano scale like all the notes on a piano and it’s come to learn that like all of these As are kind of similar, that the notes relate to each other. This is like moving up on a piano. It’s hard to tell here but it’s learned little nuances like up a major third is closer than like up a tritone or stuff like that. Like actually really interesting musical stuff.

Christine Payne: Along with the same thing, given the fact that I’m always giving it this genre token and then the instrument token, you can look at the sort of embeddings it’s learned for the genres itself. Here, the embedding it’s learned for all these French composers. Ends up being pretty similar. I actually like that Ravel wrote like in the style of Spanish pieces and then there’s the Spanish composer that’s connected to him so like it makes a lot of good sense musically. Similarly, like over in the jazz domain, a lot of the ones. I think there are a couple of random ones that made no sense at all. I can’t remember now off the top of my head. It’s like Lady Gaga was connected to Wagner or something like but mostly, it made a lot of great sense.

Christine Payne: The other kind of fun thing you can do once you have the style tokens is you can try mismatching them. You can try things like literally taking 0.5 of the embedding for Mozart plus 0.5 of the embedding of jazz and just like adding them together and seeing what happens or in this case what I’m doing is I’m giving it the token for Bon Jovi, instruments for bands, but then I’m giving it the first six notes of a Chopin Nocturne. Then the model just has to generate as best it can at that point.

Christine Payne: You’ll hear at the start of this, it’s very much how the Chopin Nocturne itself sounds. I’ve cut off the very, very beginning of it but you’ll hear–so that left-hand pattern is going to be like straight out of Chopin and then well, you’ll see what happens.

Christine Payne: Sorry, it’s so soft but it gets very Bon Jovi at this point, the band kicks in. I always loved it like Chopin looks a little shocked but I really love that it manages to keep the left-hand pattern of the Nocturne going even though it’s like now thinks it’s in this pop sort of style.

Christine Payne: The other thing I’ve been interested in this project is in how musicians and everyone can use generators like this. If you go to our OpenAI blog you can actually play with the model itself. We’ve created, along with Justin and Eric and Nick, a sort of prototype tool of how you might co-compose pieces using this model. What you can do is you can specify the style and the instruments, how long a segment you want the model to generate and you hit start and the model will come back with four different suggestions of like how you might begin a piece in this style. You go through and you pick your favorite one, you hit the arrow again to keep generating and the model will come up with four new different ways. You can continue on this way as long as you want.

Christine Payne: What I find kind of fun about this is you’re actually really … like it feels like I’m composing but not at a note by note level and so I was really interested in how humans will be able to, and musicians will be able to guide composing this way. Just kind of wrapping up, I thought I would play an example of … This is one guy who took both GPT-2 to write the lyrics, which I guess is hence the Covered in Cold Feet and then MuseNet to do the music. It’s a full song but I’ll just play the beginning of it that he then recorded himself.

Christine Payne: (singing)

Christine Payne: Visit the page to hear the whole song but it’s been really fun to see those versions. The song, I ended up singing it the entire day. It gets really catchy but it’s been really fun to see musicians start to use it. People have used it to finish composing symphonies or to write full pieces, that sort of thing.

Christine Payne: In closing, I just wanted to share I’ve gone through this crazy path of two years ago being a classical pianist to now doing AI research here and I just wanted to … I didn’t know that Rachel was going to be right here. Give a shout out to fast.ai. She’s the fast.ai celebrity here but yeah. This has been my path, been doing it. These are the two courses I particularly love, fast.ai and deeplearning.ai and then I also went through OpenAI’s Scholars program and then the Fellows Program. Now I’m working here full-time, but happy to talk to anybody here if they’re interested in this sort of thing.

Christine Payne: The kind of fun thing about AI is that there’s so much that’s still wide open and it’s really helpful to come from different backgrounds where you bring a … It’s amazing how if you bring a new perspective or a new insight, there are a lot of things that are still just wide open that you can figure out how to do. I encourage anyone to come and check it out. We’ll have a concert. Thank you.

Mira Murati speaking

RL Team Manager Mira Murati gives a talk about reinformatiion learning and industry trends at OpenAI Girl Geek Dinner.   Erica Kawamoto Hsu / Girl Geek X 

Mira Murati: Hey, everyone, I’m Mira Murati and I’ll talk a little bit about the advancements in reinforcement learning from the lens of our research team here at OpenAI. Maybe I’ll kick things off by just telling you a bit about my background and how I ended up here.

Mira Murati: My background is in mechanical engineering but most of my work has been dedicated to practical applications of technology. Here at OpenAI, I work on Hardware Strategy and partnerships as well as managing our Reinforcement Learning research team alongside John Schulman, who is our lead researcher. I also manage our Safe Reinforcement Learning team.

Mira Murati: Before coming to OpenAI, I was leading the product and engineering teams at Leap Motion, which is a company that’s focused on the issue of human machine interface. The challenge with the human machine interface, as you know, is that we’ve been enslaved to our keyboard and mouse for 30 years, basically. Leap Motion was trying to change that by increasing the bandwidth of interaction with digital information such that, just like you see here, you can interact … Well, not here, with the digital space in the same natural and high bandwidth way that you interact with your physical space. The way you do that is using computer vision and AI to track your fingers in space and bring that input in virtual reality or augmented reality in this case.

Mira Murati: Before that, I was at Tesla for almost three years leading the development and launch of the Model X. That’s enough about me. I’ll touch a bit about on the AI landscape as a whole, just to offer a bit of context on the type of work that we’re doing with our Reinforcement Learning team. Then I’ll talk a bit about the impact of this work, the rate of change in the field as well as the challenges ahead.

Mira Murati: As you know, the future has never been bigger business. Every day we wake up to headlines like this and a lot of stories talking about the ultimate conversions where all the technologists come together to create the ultimate humankind dimension, that of general artificial intelligence. We wonder what this is going to do to our minds and to our societies, our workplaces and healthcare. Even politicians and cultural commentators are aware of what’s happening with AI to some extent, and politicians like this, to the extent that there’s a lot of nations out there that have published their AI strategies.

Mira Murati: There is definitely a lot of hype, but there is also a ton of technological advancement that’s happening. You might be wondering what what’s driving these breakthroughs. Well, so a lot of advancements in RL are driving the field forward and my team is working on some of these challenges through the lens of reinforcement learning.

Mira Murati: Both Brooke and Lilian did a great job going over reinforcement learning so I’m not going to touch too much upon that, but basically, to reiterate, it is you’re basically learning through trial and error. To provide some context for our work, I want us to take a look at …

Mira Murati: Oh, okay. There’s music. I wanted to take a look at this video where first you see this human baby, nine months old, how he is exploring the environment around him. You see this super high degrees of freedom interaction with everything around him. I think this is four hours of play in two minutes. In some of the things that this baby does like handling all these subjects, rolling around all this stuff, this is almost impossible for machines to do as you saw from Lilian’s talk.

Mira Murati: Then … Well, he’s going to keep going, but let’s see. Okay, now that … What I want to show you is … Okay, this is not working, but basically, I wanted you to show you that by contrast, so you have this video game over there where you would see this AI agent that’s basically trying to cross this level and makes the same mistakes over and over again. The moral of the story is that AI agents are very, very limited when they’re exploring their environment. Human babies just nine months old have this amazing ability to explore their environment.

Mira Murati: The question is, why are humans so good at understanding the environment around them? Of course, humans … We have this baby running in the playground. Of course, humans are very good at transferring knowledge from one domain to another, but there is also prior knowledge from evolution and also, from your prior life experiences. For example, if you play a lot of board games and I asked you to play a new one that you have never seen before, you’re probably not going to start learning that new game from scratch. You will apply a lot of the heuristics that you have learned from the previous board game and utilize those to solve this new one.

Mira Murati: It’s precisely this ability to abstract, this conceptual knowledge that’s based on or learned from perceptual details of real life that’s actually a key challenge for our field right now and we refer to this as transfer learning.

Mira Murati: What’s the state of things? There’s been a lot of advancements in machine learning and particularly in reinforcement learning. As you heard from the talks earlier, new datasets drive a lot of the advancements in machine learning. Our Reinforcement Learning team built a suite of games, thousands of games, that in itself you think playing video games is not so useful, but actually, they’re a great test bed because you have a lot of problem-solving and also content that’s already there. It comes for free in a way.

Mira Murati: The challenge that our team has been going after is how can we solve a previously unseen game as fast as a human, or even faster, given prior experiences with similar games. The Gym Retro dataset helps us do that. I was going to say that some of the games look like this but the videos are not quite working. But in a way, the Gym Retro dataset, you can check it out on the OpenAI blog, emphasizes the weaknesses of AI which is that of grasping a new task quickly and the ability to generalize knowledge.

Mira Murati: Why do all these advancements matter and what do the trends look like? It’s now just a bit over 100 years after the birth of the visionary mathematician Alan Turing and we’re still trying to figure out how hard it’s going to be to get to general artificial intelligence. Machines have surpassed us at very specific tasks but the human brain sets a high bar for what’s AI.

Mira Murati: In the 1960s and ’70s, this high bar was a game of chess. Chess was long considered the summit of human intelligence. It was visual, tactical, artistic, intelligence, mathematical, and chess masters could remember every single game that they played, not to mention that of their competitors, and so you can see why chess became such a symbol of mastery or a huge achievement of the human brain. It combined insight and forward planning and calculation, imagination, intuition, and this was until 1996, when the Deep Blue machine, chess machine from IBM was able to beat Garry Kasparov. If you had brought someone from the 1960s to that day, they would be completely astonished that this had happened but in 1996, this did not elicit such a reaction because in a way, Deep Blue had cheated by utilizing the power of hardware of Moore’s law. It leveraged the advancements in hardware to beat Garry Kasparov at chess.

Mira Murati: In a way, this didn’t show so much the advancements in AI, but rather that chess was not the pinnacle of human intelligence. Then the human sights were set on the Chinese game of Go, which is much more complex and just with brute force, you’d be quite far from solving Go, the game of Go with brute force and where we stand with hardware today. Then of course, in 2016, we saw the DeepMind’s AlphaGo beat Lee Sedol in Korea and that was followed by advancements in AlphaGo Zero. OpenAI robotics team of course, used some of the algorithms developed in the RL team to manipulate the cube and then we saw very recently, obviously, the Dota 5v5 beat the world champions.

Mira Murati: There’s been a very strong accelerating trend of advancements pushed by reinforcement learning in general. However, there’s still a long way to go. There are a lot of questions with reinforcement learning and in figuring out where the data is coming from and what actions do you take early on that get you the reward later. Also issues of safety, how do you learn in a safe way and also how do you continue to learn once you’ve gotten really good? Think of self-driving cars, for example. We’d love to get more people thinking about this type of challenges and I hope that some of you will join us in doing so. Thank you.

Amanda Askell speaking

Research Scientist Amanda Askell on the Policy team gives a talk on AI policy at OpenAI Girl Geek Dinner.  Erica Kawamoto Hsu / Girl Geek X

Amanda Askell: Okay, can everyone hear me? Cool. We’ve had like a lot of talks on some of the technical work that’s been happening at OpenAI. This talk is going to be pretty introductory because I guess I’m talking about what is quite a new field, but as Ashley said at the beginning, it’s one of the areas that OpenAI focuses on. This is a talk on AI policy and I’m a member of the policy team here.

Amanda Askell: I realize now that this picture is slightly unfortunate because I’m going to give you some things that look like they’re being produced by a neural net when in fact this is just an image because I thought it looked nice.

Amanda Askell: The core claims behind why we might want something like AI policy to exist in the world are really simple. Basically, AI has the potential to be beneficial. Hopefully, we can agree with this. We’ve had lots of talks showing how excellent AI can be and things that it can be applied to. AI also has the potential to be harmful so I’ll talk a little bit about this in the next slide but you know we hear a lot of stories about systems that just don’t behave the way that they’re creators intended to when they’re deployed in the world, systems that can be taken over by people who want to use them for malicious purposes. Anything that has this ability to do great things in the world can also be either misused or lead to accidents.

Amanda Askell: We can do things that increase the likelihood that AI will be beneficial so hopefully, that’s also fairly agreed-upon. But also that this includes making sure that the environment the AI is developed in is one that incentivizes responsible development. They’re like nontechnical things that we can do to make sure that AI is beneficial.

Amanda Askell: I think these are all like really simple and this leads to this idea that we should be doing some work in known technical fields just to make sure that AI is developed responsibly and well. Just to like kind of reiterate the claims of the previous slide, the potential benefits of AI are obviously kind of huge and I feel like to this audience I don’t really need to sell them but we can go over them. You know language models provide the ability potentially to assist with writing and other day-to-day tasks.

Amanda Askell: We can see that we can apply them to large complex problems like climate change potentially. This is the kind of like hope for things like a large scale ML. We might be able to enable like innovations In healthcare and education so we might be able to use them for things like diagnosis or finding new treatments for diseases. Finally, they might drive the kind of economic growth that would reduce the need to do work that people don’t find fulfilling. I think this is probably controversial. This is one thing that’s highly debated in AI ethics but I will defend it. I’ve done lots of unfulfilling work in my life and if someone could just pay me to not do that, I would have taken that.

Amanda Askell: Potential harms like language models of the same sort could be used to like misinform people by malicious actors. There are concerns about facial recognition as it improves and privacy. People are concerned about automation and unemployment if it’s not dealt with well. Like does this just lead to massive unfairness and inequity? Then people are also worried about things like decision making and bias. We already see in California that there’s ML systems being used for things like decisions about bail being set but also historically, we’ve used a lot of systems for things like whether someone gets credit. I mean so whether your loan’s approved or not given that there’s probably a huge amount of bias in the data and that we don’t know yet how to completely eliminate that, this could be really bad and it could increase systemic inequity in society, so that’s bad.

Amanda Askell: We’re also worried about like AI weapons and global security. Finally, just like a general misalignment of future AI systems. A lot of these are just like very classic examples of things that people are thinking about now, but this should just … We could expect this to be the sort of problems that we just see on an ongoing basis in the future as systems get more powerful.

Amanda Askell: I don’t think AI is like any different from many other technologies in at least some respects here. How do we avoid building things that are harmful? Doing the same kind of worries just apply to like the aviation industry. Planes can also be taken over by terrorists. Planes can be built badly and lead to accidents. The same is true of like cars or pharmaceuticals or like many other technologies with the potential to do good, it can end up … There can be accidents. It can be harmful.

Amanda Askell: In other industries we invest in safety, we invest in reducing accidents, we invest in security, so that’s like reducing misuse potential, and we also invest in social impact. In case of aviation, we know are concerned about things like the impact that flying might have on the climate. This is like the kind of third sort of thing that people invest in a lot.

Amanda Askell: All of this is very costly so this is just a kind of intro to like one way in which we might face problems here. I’m going to use a baking analogy, mainly because I was trying to think of a different one and I had used this one previously and I just couldn’t think of a better one.

Amanda Askell: The idea is, imagine you’ve got a competition and the nice thing about baking competitions, maybe I just have watched too many of them, is like you care both about the quality of what you’re creating and also about how long it takes to create it. Imagine a baking competition where you can just take as much time as you want and you’re just going to be judged on the results. There’s no race, like you don’t need to hurry, you’re just going to focus purely on the quality of the thing that you’re creating.

Amanda Askell: But then you introduce this terrible thing, which is like a time constraint or even worse, you can imagine you make it a race. Like the first person to develop the bake just gets a bunch of extra points. In that case, you’re going to be like well, I’ll trade off some of the quality just to get this thing done faster. You trade off some quality for increased speed.

Amanda Askell: Basically, we can expect something similar to happen with things like investment in areas like the areas that I talked about in the previous slide, where it’s like it might be that I would want to just like continue investing and making sure that my system is secure essentially like forever. I just never want someone to misuse this system so if I was given like 100 years, I would just keep working on it. But ultimately, I need to produce something. I do need to put something out into the world and the concern that we might have is that competition could drive down the incentive to invest that much in security.

Amanda Askell: This, again, happens across lots of other industries. This is like not isolated to AI and so there’s a question of like, what happens here? How do we ensure that companies invest in things like safety? I’m going to argue that there are four things. Some of the literature might not mention this one but I think it’s really important. The first one is ethics. People and companies are surprisingly against being evil. That’s good, that’s important. I think this gets not talked about enough. Sometimes we talk like the people that companies would just be totally happy turning up at like 9:00 a.m. to build something that would cause a bunch of people harm. I just don’t think that people think like that. People are … I have fundamental faith in humanity. I think we’re all deeply good.

Chloe Lin software engineer OpenAI Girl Geek Dinner

Software Engineer Chloe Lin listens to the OpenAI Girl Geek Dinner speakers answer audience questions.  Photo credit: Erica Kawamoto Hsu / Girl Geek X

Amanda Askell: It’s really great to align your incentives with your ethical beliefs and so regulation is obviously one other component that’s there to do that. We create these regulations and industry norms to basically make sure that if you’re like building planes and you’re competing with your competitor, you still just have to make your planes. You have to establish that they reach some of … Tripped over all of those words.

Amanda Askell: You have to establish that they reach some level of safety and that’s what regulation is there for. There’s also liability law and so companies have to compensate who are harmed by failures. This is another thing that’s driving that incentive to make sure your bake is not going to kill the judges. Well, yeah, everyone will be mad at you and also, you’ll have to pay a huge amount of money.

Amanda Askell: Finally, the market. People just want to buy safe products from companies with good reputations. No one is going to buy your bake if they’re like, “Hang on, I just saw you drop it on the floor before you put it into the oven. I will pay nothing for this.” These are four standard mechanisms that I think are used to like ensure that safety is like pretty high even in the cases of competition between companies in other domains like aviation and pharmaceuticals.

Amanda Askell: Where are we with this on AI? I like to be optimistic about the ethics. I think that coming to a technology company and seeing the kind of tech industry, I’ve actually been surprised by the degree to which people are very ethically engaged. Engineers care about what they’re building. They see that it’s important. They generally want it to be good. This is more like a personal kind of judgment on this where I’m like actually, this is a very ethically engaged industry and that’s really great and I hope that continues and increases.

Amanda Askell: With regulation, currently there are not many industry-specific regulations. I missed an s there but speed and complexity make regulation more difficult. The idea is that regulation is very good when there’s not an information asymmetry between the regulator and the entity being regulated. It works much less well when there is a big information asymmetry there. I think in the case of ML, that does exist. It’s very hard to both keep up with like, I think for regulators keeping up with contemporary ML work is really hard and also, the pace is really fast. This makes it actually quite difficult as an area to build very good regulation in.

Amanda Askell: Liability law is another thing where it’s just like a big question mark because like for ML accidents and misuse, in some cases it’s just unclear what existing law would say. If you build a model and it harms someone because it turns out that there was data in the model that was biased and that results in a loan being denied to someone, who is liable for that harm that is generated? You get easier and harder cases of this, but essentially, a lot of the kind of … I think that contemporary AI actually presents a lot of problems with liability law. It will hopefully get sorted out, but in some cases I just think this is unclear.

Amanda Askell: Finally, like market mechanisms. People just need to know how safe things are for market mechanisms to work well. In the case of like a plane, for example, I don’t know how safe my planes are. I don’t go and look up the specs. I don’t have the engineering background that would let me actually evaluate, say, a new plane for how safe it is. I just have to trust that someone who does know this is evaluating how safe those planes are because there’s this big information gap between me and the engineers. This is also why I think we shouldn’t necessarily expect market mechanisms to do all of the work with AI.

Amanda Askell: This is to lead up to this … to show that there’s a broader problem here and I think it also applies in the case of AI. To bring in a contemporary example, like recently in the news, there’s been concern. Vaping is this kind of like new technology that is currently not under the purview of the FDA or at least generally not heavily regulated. Now there’s concern that it might be causing pretty serious illnesses in people across the US.

Amanda Askell: I think this is a part of a more broad pattern that happens a lot in industries and so I want to call this the reactive route to safety. Basically, a company does the thing, the thing harms people. This is what you don’t want on your company motto. Do the thing. The thing harms people. People stop buying it. People sue for damages. Regulators start to regulate it. This would be really uninspiring as your company motto.

Amanda Askell: This is actually a very common route to making things more safe. You start out and there’s just no one who’s there to make sure that this thing goes well and so it’s just up to people buy it, they’re harmed, they sue, regulators get really interested because suddenly your product’s clearly harming people. Is this a good route for AI? Reasons against hope … I like the laugh because I’m like hopefully, that means people agree like no, this would be terrible. I’m just like well, one reason, just to give like the additional things of like obviously that’s kind of a bad way to do things anyway.

Amanda Askell: AI systems can often be quite broadly deployed almost immediately. It’s not like you just have some small number of people who are consuming your product who could be harmed by it in a way that a small bakery might. Instead, you could have a system where you’re like I’ve built the system for determining whether someone should get a loan. In principle, almost every bank in the US could use that the next day and that’s –The potential for widespread deployment makes it quite different from technologies where you just have a really or like any product where you have just like a small base of people.

Amanda Askell: They have the potential for a really high impact. The loan system that I just talked about could, basically, could in principle really damage the lives of a lot of people. Like apply that to things like bail systems as well, which we’re already seeing and even potentially with things like misinformation systems.

Amanda Askell: Finally, in a lot of cases it’s just difficult to attribute the harms and if you have something that’s spreading a huge amount of misinformation, for example, and you can’t directly attribute it to something that was released, this is concerning because it’s not like this route might work. This route actually requires you to be able to see who caused the harm and whenever that’s not visible, you just don’t expect this to actually lead to good regulation.

Amanda Askell: Finally, I just want to say I think there are alternatives to this reactive break things first approach in AI and this is hopefully where a lot of policy work can be useful.

Amanda Askell: Just to give a brief overview of policy work at OpenAI. I think I’m going to start with the policy team goals just to give you the sense of what we do. We want to increase the ability of society to deal with increasingly advanced AI technology, both through information and also through pointing out mechanisms that can make sure that technology is safe and secure and that it does have a good social impact. We conduct research into long-term issues related to AI and AGI so we’re interested in what happens when these systems become more powerful. Not merely reacting to systems that already exist, but trying to anticipate what might happen in the future and what might happen as systems get more powerful and the kind of policy problems and ethical problems that would come up then.

Amanda Askell: Finally, we just help OpenAI to coordinate with other AI developers, civil society, policymakers, et cetera, around this increasingly advanced technology. In some ways trying to break down these information asymmetries that exist and it can cause all of these problems.

Amanda Askell: Just to give a couple of examples of recent work from the teams to the kind of thing that we do. We released a report recently with others on publication norms and release strategies in ML. Some of you will know about like the GPT-2 language release and the decision to do staged release. We discussed this in the recent report. We also discussed other things like the potential for bias in language models and some of the potential social impacts of large language models going forward.

Amanda Askell: We also wrote this piece on cooperation and responsible AI development. This is related to the things I talked about earlier about the potential for competition to push this bar for safety too low and some of the mechanisms that can be used to help make sure that that bar for safety is raised again.

Amanda Askell: Finally, since this is an introduction to this whole field, which is like new and emerging field, here are examples of questions I think are really interesting and broad but can be broke down to these very specific applicable questions. What does it mean for AI systems to be safe, secure, and beneficial and how can we measure this? This includes a lot of traditional AI ethics work, like my background is in ethics. A lot of these questions about like how you make a system fair and what it means for a system to be fair. I would think of that as falling under the what is it for a system to be socially beneficial, and I think that work is really interesting. I do think that there’s just this broad family of things there are like policy and ethics and governance. I don’t think of these as separate enterprises.

Amanda Askell: Hence, this is an example of why. What are ways that AI systems could be developed that could be particularly beneficial or harmful? Again, trying to anticipate future systems and ways that we might just not expect them to be harmful and they are. I think we see this with the existing technology. Maybe it’s like trying to anticipate the impact that technology will have is really hard but like given the huge impact that technology is now having, I think trying to do some of that research in advance is worthwhile.

Amanda Askell: Finally, what can industry policymakers and individuals do to ensure that AI is developed responsibly? This relates to a lot of the things that I talked about earlier, but yeah, what kind of interventions can we have now? Are there ways that we can inform people that would make this stuff all go well?

Amanda Askell: Okay, last slide except the one with my email on it, which is the actual last slide. How can you help? I think that there’s this interesting, this is just like … I think that this industry is very ethically engaged and in many ways, it can feel like people feel like they need to do the work themselves. I know that a lot of people in this room are probably engineers and researchers. I think the thing I would want to emphasize is, you can be really ethically engaged and that doesn’t mean you need to take this whole burden on yourself.

Amanda Askell: One thing you can also do is advocate for this work to be done, either in your company, or just anywhere where people are like … in your company, in academia or just that your company is informed of this stuff. But in general, helping doesn’t necessarily have to mean taking on this massive burden of learning an entire field yourself. It can just mean advocating for this work being done. At the moment, this is a really small field and I would just love to see more people working in it. I think advocacy is really important but I also think another thing is you can technically inform people who are working on this.

Amanda Askell: We have to work closely with a lot of the teams here and I think that’s really useful and I think that policy and ethics work is doing its best, basically, when it’s really technically informed. If you find yourself working in a position where a lot of the things that you’re doing feel like they are important and would benefit from this sort of work, like helping people who are working on it is a really excellent way of helping. It’s not the only thing that you can do is spend half of your time doing the work that I’m doing and the others on the team are doing. You can also get people like us to do it. We love it.

Amanda Askell: If you’re interested in this, so thank you very much.

Brooke Chan, Amanda Askell, Lilian Weng, Christine Payne, Ashley Pilipiszyn

OpenAI girl geeks: Brooke Chan, Amanda Askell, Lilian Weng, Christine Payne and Ashley Pilipiszyn answer questions at OpenAI Girl Geek Dinner.  Erica Kawamoto Hsu / Girl Geek X 

Audience Member:  I have a question.

Amanda Askell: Yes.

Audience Member: For Amanda.

Amanda Askell: Yes.

Audience Member: Drink your water first. No, I think the ethics stuff is super interesting. I don’t know of a lot of companies that have an ethics department focused on AI, and I guess one thing that I’m curious about is, like you pointed out like your papers but like, and I know you talked about educating and all this other stuff but what are you guys…do? Do you know what I mean? Other than write papers.

Amanda Askell: Yeah.

Ashley Pilipiszyn: Oh, Christine.

Amanda Askell: Which one? Yeah, so I think at the moment there’s like a few kind of rules. I can say what we do but also what I think that people in these roles can do. So in some cases it can be like looking at what you’re building internally. I think we have like the charter and so you want to make sure that everything that you’re doing is in line with the charter. Things like GPT-2 and release decisions, I think of as a kind of like ethical issue or ethical/policy issue where I would like to see the ML community build really good norms there. Even if people don’t agree with what OpenAI try to do with its release decisions, it was coming from a place of trying to build good norms and so you can end up thinking about decisions like that.

Amanda Askell: That’s more of an example of something where you’re like it’s not writing a paper, it’s just like thinking through all of the consequences of different publication norms and what might work and what might not. That’s like one aspect, that’s the kind of like internal component. I think of the external component as like, on the one hand it’s just like writing papers so just being like what are the problems here that people could work on and in a lot ways that’s just like outreach, like trying to get people who are interested in working on this to work on it further. For that, there’s a few audiences, so you might be interested in attracting people to the field if you think that there are these like ongoing problems within both companies and maybe with other relevant actors. Like maybe you also want people going into government on this stuff.

Amanda Askell: But also just like the audience can be internal, to make people aware of these issues and they can also be things like policymakers, just inform of the kind of structure of the problem here. I think of it as having this kind of internal plus external component and you can end up dividing your time between the two of them. We spend some time writing these papers and trying to get people interested in these topics and just trying to solve the problems. That’s the nice thing about papers is you can just be like, what’s the problem, I will try and solve it and I’ll put my paper of an archive. Yeah, and so I think there’s both of those.

Amanda Askell: It’s obviously fine for companies to have people doing both, like if you haven’t and I think it’s like great if a company just has a team that’s just designed to look at what they’re doing internally and if anyone has ethical concerns about it, that team can take that on and own it and look at it. I think that’s a really good structure because it means that people don’t feel like … if you’re like just having to raise these concerns and maybe feel kind of isolated, that’d be bad but if you have people that you know are thinking about it, I think that’s a really good thing. Yeah, internal plus external, I can imagine different companies liking different things. I hope that answers the question.

Rose: My question is also for Amanda. So the Google AI Ethics Board was formed and disbanded very quickly kind of famously within like the span of less than a month. How do you kind of think about that like in the context of the work that OpenAI is doing and like how do you think about like what they failed at and like what we can do better?

Amanda Askell: This was a really difficult case so I can give you … I remember personally kind of looking at this and being like I think that one thing that was in it … I don’t know if people know the story about this case but basically, it was that Google formed a board and they were like, “We want this to be intellectually representative,” and it garnered a lot criticism because it had a person who was head of the Heritage Foundation, so a conservative think-tank in the US, as one of its members, and this was controversial.

Amanda Askell: I remember having mixed views on this, Rose. I do think it’s great to … Ultimately, these are systems that are going to affect a huge number of people and that includes a huge number of people who have views on how they should be used and how they should affect them. They’re just very different from me and I want those people to be represented and I want their views on how they do or do not want systems to affect them to be at the table. We talked earlier about the importance of representativeness and I genuinely believe that for people who have vastly different views for myself. If they’re affected by it, ultimately, their voice matters.

Amanda Askell: At the same time, I think I also … there’s a lot of complicating–you’re getting my just deeply mixed emotions here because I was like, there’s a strange sense in which handpicking people to be in the role of a representative of a group where you’re like, I don’t know, we select who the intellectual representatives are also struck me as somewhat odd. It’s a strange kind of … It set off my old political philosophy concerns where I’m like, “Oh, this just doesn’t …” It feels like it’s imitating democracy but isn’t getting there. I had and I was also just like plus the people who come to the table and there are certain norms of respect to lots of groups of people that just have to be upheld if you’re going to have people with different views have an input on a topic.

Amanda Askell: I think some of the criticisms were that people felt those norms had been upheld and this person had been insulting to key groups of people, the trans community and to immigrants. Largely, mixed feelings where I was just like I see this intention and it actually seems to me to be a good one, but I see all of these problems with trying to execute on it.

Amanda Askell: I can’t give an awesome response to this. It’s just like yeah, here it is, I’ve nailed it. It’s just like yeah, these are difficult problems and I think if you came down really strongly on this where it was like this was trivially bad or you were like this was trivially good, it just feels no, they were just like there are ways that I might have done this differently but I see what the goal was and I’m sympathetic to it but I also see what the problems were and I’m sympathetic to those. Yeah, it’s like the worst, the least satisfying answer ever, I guess.

OpenAI Girl Geek Dinner audience women in AI.

OpenAI Girl Geek Dinner audience enjoys candor from women in AI.  Erica Kawamoto Hsu  / Girl Geek X

Audience Member: Hi, I have a question for Brooke. I’m also a fan of Dota and I watched TI for two years. My question is, if your model can already beat the best team in the world, what is your next goal?

Brooke Chan: Currently, we’ve stopped the competitive angle of the Dota project because really what we wanted to achieve was to show that we could get to that level. We could get to superhuman performance on a really complex game. Even at finals, we didn’t necessarily solve the whole game because there were a lot of restrictions, which people brought up. For example, we only used 17 out of the you know 100 and some heroes.

Brooke Chan: From here, we’re just looking to use Dota more as a platform for other things that we want to explore because now we know that it’s something that is trainable and can be reused in other environments, so yeah.

Audience Member: Hi, my question is about what are some of the limitations of training robots in a simulator?

Lilian Weng: Okay, let me repeat. Question is, what’s a limitation of training the robot-controlled models in the simulation? Okay, there are lots of benefits, I would say, because in simulation, you have the ground rules. You know exactly where the fingertips are, you know exactly what’s the joint involved. We can do all kinds of randomization modification of the environment. The main drawback is we’re not sure what’s the difference between our simulated environment and reality. Our eventual goal is to make it work in reality. That’s the biggest problem. That’s also what decides whether our sim2real transfer going to work.

Lilian Weng: I will say one thing that confuse me or puzzles me personally the most is when we are running all kinds of randomizations, I’m not sure whether it’s getting us closer to the reality because we don’t have a good measurement of what the reality looks like. But one thing I didn’t emphasize a lot in the talk is we expect because we design all kinds of environment in the simulation and we asked the policy model to master all of them. There actually emerges some meta learning effect, which we didn’t emphasize but with meta learning, your model can learn how to learn. We expect this meta learning in fact to empower the model to handle something they’d never seen before.

Lilian Weng: That is something we expect with domain randomization that our model can go above what it has seen in the simulation and eventually adapt to the reality. We are working all kinds of technique to make the sim2real thing happen and that’s definitely the most difficult thing for robotics because it’s easy to make things work in simulation. Okay, thanks.

Audience Member: I was just curious as kind of another follow-up question to Brooke’s answer for earlier but for everybody on the panel too. What do you consider to be some of the longer-term visions for some of your work? You did an impressive thing by having Dota beat some real people but where would you like to see that work go or what kinds of problems do you think you could solve with that in the future too, and for some other folks on the panel too?

Brooke Chan: Sure, I would say that pretty honestly when we started the Dota project we didn’t actually know whether or not we would be able to solve it. The theory at the time was that we would need a much more powerful algorithm or a different architecture or something in order to push it kind of all the way. The purpose of the project was really to demonstrate that we could use a relatively straightforward or simple algorithm in order to work on this complex game.

Brooke Chan: I think going out from here, we’re kind of looking into environments in general. We talked about how Dota might be one of our last kind of games because games are still limited. They’re helpful and beneficial in that you can run them in simulation, you can run them faster but we want to kind of also get closer to real-world problems. Dota was one step to getting to real-world problems in the parts that I talked about like the partial information and the large action space and things like that. Going on from there, we want to see what other difficult problems you could also kind of apply this sort of things to. I don’t know if other people …

Christine Payne: Sure. In terms of a music model, I would say kind of two things I find fascinating. One is that I really like the fact that it’s this one transformer architecture which we’re now seeing apply to lots of different domains. The fact that it can both do language and music and it’s really kind of interesting to find these really powerful algorithms that it doesn’t care what it’s learning, it’s just learning. I think that that’s going to be really interesting path going forward.

Christine Payne: Then, also, I think that music is a really interesting test for like we have a lot of sense as humans so we know how we would want the music to go or we know how the music affects us emotionally or there’s all this sort of human interaction that we can explore in the music world. I hear from composers saying well, they want to be able to give the shape of the music or give the sense of it or the emotion of it, and I think there’s a lot of space to explore in terms of it’s the same sort of thing we’ll want to be able to influence the way any program is going to be, how we’ll be interacting with a program in any field but music is a fun area to play with it.

Ashley Pilipiszyn: Actually, as a followup, if you look at all of our panelists and everything everyone presented too, it’s not just human and AI interaction, but human and AI cooperation. Actually, for anyone who followed our Dota finals event as well, not only did we have a huge success but, and for anyone who is a Dota fan in the crowd, I’d be curious if anyone participated in our co-op challenge. Anyone by chance? No, all right. That’s all right.

Ashley Pilipiszyn: But actually, being able to insert yourself as being on a team with OpenAI Five and I think from all of our research here we’re trying to explore the boundaries of, you know what does human AI cooperation look like and I think that’s going to be a really important question going forward so we’re trying to look at that more.

Speaker: And we have time for two more questions.

Audience Member: Thank you. Just right on time. I have a question for you, Christine. I was at a conference earlier this year and I met this person named Ross Goodwin who wrote using a natural language processing model that he trained a screenplay. I think it’s called Sunspring or something like that. It’s a really silly script that doesn’t make any sense but it’s actually pretty fun to watch. But he mentioned that in the media it’s been mostly–the credit was given to an AI wrote this script and his name was actually never mentioned even though he wrote the model, he got the training data. What is your opinion on authorship in these kinds of tools that … also the one you mentioned where you say you’re actually composing? Are you the composer or is the AI the composer? Should it be like a dual authorship?

Christine Payne: That is a great question. It’s a difficult question that I’ve tried to explore a little bit. I’ve actually tried to talk with lawyers about what is copyright going to look like? Who owns pieces like this? Because in addition to who wrote the model and who’s co-composing or co-writing something, there’s also who’s in the dataset. If your model is imitating someone like are they any part of the author in that?

Christine Payne: Yeah, I mean I have my own sort of guesses of where I think it might go but every time … I think I’m a little bit [inaudible 01:37:11] in terms of the more you think about it, the more you’re like this is a hard problem. It’s really, like if you come down hard on one side or the other because clearly, you don’t want to be able to just press go and have the model just generate a ton of pieces and be like I now own all these pieces. You could just own a ridiculous number of pieces, but if you’re the composer who has carefully worked and crafted the model, crafted … you write a little bit of a piece, you write at some of the piece and then the model writes some and you write some. There’s some interaction that way, then sure, that should be your piece. Yeah, I think it’s something that we probably will see in the near future, law trying to struggle with this but it’s an interesting question. Thanks.

Audience Member:  Okay, last question. Oh no.

Ashley Pilipiszyn: We’ll also be around so afterwards you can talk to us.

Audience Member: This is also a followup question and it’s for everyone on the panel. Could you give us some examples of real-life use cases of your research and how that would impact our life?

Ashley Pilipiszyn: An example.

Christine Payne: It’s not an easy one to close on. You want to take it. Go for it.

Lilian Weng: I will say if eventually we can build general purpose robots, just imagine we use the robot to do a lot of dangerous tasks. I mean tasks that might seem danger to humans. That can definitely reduce the risk of human labors or doing repeated work. For example, on assembly line, there are some tasks that involve human hands, but kind of boring. I heard from a friend that there are a lot of churn or there’s a very high churn rate of people who are working on the assembly line, not because it’s low pay or anything, most because it’s very boring and repetitive.

Lilian Weng: It’s not really good for people’s mental health and they have to–like the factory struggle to hire enough people because lots of people will just leave their job after a couple months or half a year. If we can automate all those tasks, we’re definitely going to leave others more interesting and creative position for humans to do and I think that’s going to overall move the productivity of the society. Yeah. That’s still a very far-fetched goal. We’re still working on it.

Amanda Askell: I can also give a faraway thing. I mean I guess my work is,, you know with the direct application, I’m like, “Well, hopefully, ML goes really well.” Ideally, we have a world where all of our institutions are actually both knowledgeable of the work that’s going on in ML and able to react to them really well so a lot of the concerns that people have raised around things like what happens to authorship, what happens to employment, how do you prevent things like the misuse of your model, how can you tell it’s safe? I think if policy work goes really well then ideally, you live in a world where we’ve just made sure that we have all of the kind of right checks in place to make sure that you’re not releasing things that are dangerous or that can be misused or harmful.

Amanda Askell: That just requires a lot of work to ensure that’s the case, both in the ML community, and in law and policy. Ideally, the outcome of great policy work is just all of this goes really smoothly and awesomely and we don’t have any bad things happen. That’s like the really, really modest goal for AI policy work.

Brooke Chan: I had two answers on the short-sighted term, in terms of just AI being applied to video games, AI in video games historically is really awful. It’s either really just bad and scripted and you can beat it easily and you get nothing from it or it’s crazy good because it’s basically cheating at the game and it’s also not really that helpful. Part of what we found out through the Dota project was people actually really did like learning with the AI. When you have an AI that’s at your skill level or slightly above, you have a lot of potential, first of all, to have a really good competitor that you can learn from and work with, but also to be constantly challenged and pushed forward.

Brooke Chan: For a more longer-term perspective, I would leverage off of the robotics work and the stuff that Lilian is doing in terms of the system that we created in order to train our AI is what is more general and can be applied to other sorts of problems. For example, that got utilized a little bit for the robotics project as well and so I feel it’s more open-ended in that sense in terms of the longer-term benefits.

Christine Payne: Okay and I’ll just wrap up saying yeah, I’ve been excited already to see how musicians and composers are using MuseNet. There are a couple examples of performances that have happened now of MuseNet pieces and that’s been really fun to see. The main part that I’m excited about is that I think the model is really good at just coming up with lots and lots of ideas. Even though it’s imitating what the composers might be doing, it opens up possibilities of like, “Oh, I didn’t think that we could actually do this pattern instead.” Moving towards that domain of getting the best of human and the best of models I think is really fun to think about.

Ashley Pilipiszyn: So kind of how I started the event this evening, our three main research areas are really on these capabilities, safety, and policy. You’ve been able to hear that from everyone here. I think the big takeaway and a concrete example I’ll give you is, you think about your own experience going through primary education. You had a teacher and you most likely … you went to science class then you went to math class and then maybe music class and then art class and gym. You had a different teacher and they just assumed, probably for most people, you just assumed you’re all at the same level.

Ashley Pilipiszyn: How I think about it is, we’re working on all these different kind of pieces and components that are able to bring all of these different perspectives together and so a system that you’re able to bring in the math and the music and the gym components of it but also able to understand what level you’re at and personalize that. That’s kind of what I’m really excited about, is this human AI cooperation component and where that’ll take us and help unlock our own capabilities. I think, to quote from Greg Brockman, our CTO, that while all our work is on AI, it’s about the humans. With that, thank you for joining us tonight. We’ll all be around and would love to talk to you more. Thank you.

Speaker: We have a quick update from Christina on our recruiting team.

Ashley Pilipiszyn: Oh, sorry.

Christina Hendrickson: Hey, thanks for coming again tonight. I’m Christina. I work on our recruiting team and just briefly wanted to talk to you about opportunities at OpenAI. If you found the work interesting that you heard about from our amazing speakers tonight and would be interested in exploring the opportunities with us, we are hiring for a number of roles across research, engineering and non-technical positions.

Christina Hendrickson: Quickly going to highlight just a couple of the roles here and then you can check out more on our jobs page. We are hiring a couple roles within software engineering. One of them, or a couple of them are on robotics, so that would be working on the same type of work that Lillian mentioned. We are also hiring on our infrastructure team for software engineers, as well, where you can help us in building some of the world’s largest supercomputing clusters.

Christina Hendrickson: Then the other thing I wanted to highlight is one of our programs. So we are going to have our third class of our scholars program starting in early 2020. We’ll be opening applications for that in a couple weeks so sneak peek on that. What that is, is we’re giving out eight stipends to people who are members of underrepresented groups within engineering so that you can study ML full-time for four months where you’re doing self-study and then you opensource a project.

Christina Hendrickson: Yeah, we’re all super excited to chat with you more. If you’re interested in hearing about that, we have a couple recruiting team members here with us tonight. Can you all stand up, wave? Carson there in the back, Elena here in the front, myself. Carson and I both have iPads if you want to sign up for our mailing list to hear more about opportunities.

Elena Chatziathanasiadou waving

Recruiters Christina Hendrickson and Elena Chatziathanasiadou (waving) make themselves available for conversations after the lightning talks at OpenAI Girl Geek Dinner.  Erica Kawamoto Hsu / Girl Geek X

Christina Hendrickson: Thank you all again for coming. Thanks to Girl Geek X. We have Gretchen, Eric, and Erica here today. Thank you to our speakers: Brooke, Amanda, Lilian, Christine, Ashley, and thank you to Frances for helping us in organizing and to all of you for attending.

Ashley Pilipiszyn: Thank you, everybody.

Our mission-aligned Girl Geek X partners are hiring!

When is it time to leave a job?

It'll get better next quarter... When it's time to quit your job

Deciding when it’s time to move on is less complicated than it seems. Here are some road-tested questions to ask yourself.

When you start feeling some kinda way, things aren’t quite right, a little off… maybe you know why, maybe you don’t. What you do know is that you’re not as happy as you think you should or could be, and you’re looking for a sign from the heavens that lets you know you’re on the right path.  Of course, divine intervention is rare and most of the time we have to figure it out for ourselves.

When you find yourself wondering if it’s time to move on, run a very simple experiment.  For a few weeks, record how you feel in the morning. Is it, “Ah man, I gotta get moving, I’ve got a lot on my calendar today!” and you hit the ground running, or is it more like “Ugh, I’ve got so much on my calendar today. I need five more minutes before I can get up and face it!” as you hit the snooze button for the third time?

If you’re having more bad days than good, pick a date in the future by which you think it will be better – 30 days, 90 days, or whatever – and measure.  Write yourself a quick email explaining what you think will change and schedule it to ping back. Pressures from big projects or a changes on the team are natural times of frustration and discord, but at some point those things will resolve themselves if they are going to. So if despite a project wrapping up or even a new positive thing happening, you’re still waking up meh more often than yay!, then it’s time to make a plan and move on.

But it’s not that simple, right?

“This is just temporary. It will get better after this project/quarter/release/new hire.”

Maybe. People tend attribute unhappiness to specific external pressures. That’s why you write the email and schedule it to arrive after that project/quarter/release is over. Tell yourself what you think is going to be different and see if it is. My experience is that it’s always something. The assumed source of my malaise changes but the feelings of discontent remain the same.

“I can’t leave my team. They need me. I can’t just desert them.”

Here’s the cold truth: everyone will leave at some point. Yes, you’re close with your colleagues, but those friendships can live on. Yes, it might create some temporary challenges while they find someone to replace you, but you have to put your needs first because no one else is going to. “Take one for the team” is rare heroic feat, not your life default. Would you expect your coworkers to put your career goals ahead of theirs?

“I am really loyal to this company/founder/mission.”

Here’s another hard truth: your company can’t love you back. It’s not a human. And there are no prizes awarded at any point later in life for soldiering on for weeks and months (or for those late nights and weekends). The people will all move on, and all you will be left with is memories of a unhappy time, maybe a few extra pounds, some missed events with friends and family, and a promise to yourself not to do that again.

“I’m scared.”

You’ll be scared in six months too. Change is scary. Before I’d make big changes, I used to read and reread the Parable of the Trapeze for motivation. It describes that feeling of terror as you jump from one bar to the next. You see the next bar swinging toward you, you know that you’ve made the jump before, but you’re still scared to let go of the bar, terrified you will freefall before your hand connects with the new bar. It’s always going to be scary, so get it over with.

I stayed at my first startup two years too long. I felt what I now understand was a misguided sense of loyalty to the company and the people. And yet none of those people are in my daily life now and the job was so long ago (15+ yrs) it’s not even on my resume anymore, and the company was acquired and no longer exists. In the end, all that I accomplished in those two years was to stunt my own learning and career growth.

Take the leap.

Write yourself that email and then sign up for the next Girl Geek Dinner.

Come get some inspiration and motivation, you need and deserve it. And who knows, by the time your email pings back, you might have a lead on your next happy adventure.

About the Author

gretchen deknikker

Gretchen DeKnikker is COO at Girl Geek X. From founding employee to founder, she’s been launching and scaling enterprise software companies since way back in the last century.Most recently, she led SaaStr from a simple blog to the world’s largest global community of 100K+ B2B founders, execs and investors, and previously co-founded SocialPandas, back by True Ventures. Gretchen attended DotCom University double majoring in Boom and Bust and holds an MBA from UC Berkeley. In her spare time, she’s a diversity and inclusion advocate who loves bacon, bourbon and hip hop.