On August 21, 2019 more than 150 girl geeks attended tech talks at Bosch’s Sunnyvale office. Lightning talk topics included IoT, AI, and Human Machine Interaction.
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Transcript from Bosch Girl Geek X Dinner:
Angie Chang: We are really excited coming to Bosch to be listening to so many amazing Girl Geeks tonight.
Dr. Hauke Schmidt: We are very happy to host the Girl Geek dinner as a celebration of gender diversity, and I’m very proud of the team here who has put all this together.
Dr. Uma Krishnamoorthy: How many of you came here looking for headphones, acoustic systems in our demos? We’re not that company. You may have gone outside and you may have seen our car, autonomous car, so I don’t have to speak to our autonomous driving effort.
Dr. Seow Yuen Yee: Have you ever thought of how does the car know when to deploy these airbags? This is thanks to the airbags control unit in the car. It house a tiny little sensors which we call accelerometers.
Tara Dowlat: Did you guys know that at least every single one of you in this room, in your pockets or in your bags, have at least one sensor from Bosch on you? It’s a fun fact.
Dr. Yelena Gorlin: Each new generation of a battery management system looks to increase the charging speed of our device without having an effect on its lifetime.
LisaMarion Garcia: Each of these individual sectors provide us different opportunities to incorporate AI, either as a feature of a product that we sell or as part of the process of producing that product.
Dr. Shabnam Ghaffarzadegan: So our idea is asking human and machine to work together to empower their both abilities with much more perception and knowledge, and also to make a better machine to help us in our everyday life.
Sun-Mi Choi: So how many of you are using ride hailing apps to get from A to B on a regular basis? Mobility is also getting more user centric. The consumer is more and more changing from owned to shared.
Dr. Uma Krishnamoorthy: Big goals here. 2020, the goal is all of our electronic products will be connected. And in 2025, all our products are going to either possess intelligence or AI will have played a key role in their creation.
Angie Chang: Thanks for coming out tonight. I’m Angie Chang, founder of Girl Geek X. We’ve been hosting Girl Geek dinners up and down from San Francisco to San Jose for the last 11 plus years. We are really excited to be coming to Bosch to be listening to so many amazing girl geeks tonight.
Gretchen DeKnikker: I got my own microphone. You guys have no idea what that means. I’m Gretchen. Thank you. How many of you, it’s your first Girl Geek dinner? Good. Okay. So like she said, we do them every week. We also have a podcast, so pull out your phone now and go to your favorite podcast app and then rate it and then write a review or send us a message and say, “This is how it could be better.” Because we’re only doing it so it’ll be awesome for you guys. Right?
Gretchen DeKnikker: Then we also recently opened a little swag store on Zazzle. So there’s all sorts of cute things. I only have one or two cute things tonight. Cute water bottle.
Group: [crosstalk 00:02:58] …
Gretchen DeKnikker: I know. It’s ridiculous. Oh, I kind of had stuff with … There are more designs than this one. Apparently I only just brought things … But it’s a fanny pack. It’s so cute. Okay. Got it. So I’m going to try something new tonight. Who’s found a job through Girl Geek? No one? Okay, get out. Okay, has anyone got a … Oh, you did.
Audience member: No, I [inaudible 00:03:29].
Gretchen DeKnikker: Oh. No, definitely not. That’s awesome. Okay, anyone found a job lead? Oh, okay.
Audience member: I found candidates through Girl Geek.
Gretchen DeKnikker: You found candidates. Okay. So if you guys want to email us, I have these things and you can’t buy them. You can only get it from me. These adorable socks. So if you want to tell us, we would love to feature your story about finding a girl geek, a job through Girl Geek Dinner or something that you built and we want to have little community features and stuff. If you do it, you get those socks and it’s the only way in the world to get the socks.
Gretchen DeKnikker: Okay, so without further ado, how great is this space? This has been so awesome so far. You guys enjoying it? All right, so without further ado, we are bringing this gentleman right here.
Dr. Hauke Schmidt: Thank you very much, and welcome to Bosch. So my name is Hauke Schmidt. I’m the head of corporate technology research for Bosch here in North America. And I’m also the site leader for the innovation center here in Sunnyvale. A few words about the company for those of you who don’t know Bosch all too well. We have our roots in the automotive business, so we’re actually the largest automotive supplier in the world.
Dr. Hauke Schmidt: And very likely, if you open your car, there are a couple of Bosch components inside. You also might know us from household appliances or power tools. We’re also a leading IoT company, as you saw in the videos, here. And we’re driving product and services innovation in the areas of mobility, industrial, and building technologies.
Dr. Hauke Schmidt: One interesting part about Bosch is the ownership structure. We are privately held. We’re a very large multinational out of Germany and privately held. And mostly to the largest part, owned by the Robert Bosch Foundation. And the Foundation then also takes all of the profits and earnings we create and puts them to use in charitable projects. So this gives us an extra motivation to work hard and provide good results.
Dr. Hauke Schmidt: The site here, we’ve been in Silicon Valley for 20 years now. We have our 20th anniversary this year. We moved into this building one and a half years ago so this is now our new home here with a nice Bosch sign outside as well. We have about 200 scientists, engineers, and experts on site, and these experts cover a broad variety of different functions of the company. We have here everything from corporate research, venture capital technology scouting, prototyping, product development, but we also have product sales and engineering services here on site that we offer into the local industry around us.
Dr. Hauke Schmidt: For us diversity is an important thing. We have associates here from a very broad variety of different ethnic backgrounds, also from experts in a large number of different technology fields. So today we are very happy to host the Girl Geek dinner as a celebration of gender diversity and I’m very proud of a team here who’s put all this together since I’m also the executive champion at the Women at Bosch Group here on site as well.
Dr. Hauke Schmidt: Thank you. So with that ,without further ado, I would like to hand over to Uma who has her own microphone. [inaudible 00:07:21]. To kick off some of the lightning talks that we’ll listen to right now. Thank you.
Dr. Uma Krishnamoorthy: Can you hear me now?
Dr. Uma Krishnamoorthy: Okay. First welcome from my side. My name is Uma Krishnamoorthy and I am a director here at Bosch RTC. We are part of corporate. We, me and my department, are part of corporate research of the bigger Bosch. My particular groups are focused on microsensor systems technologies and multiphysics modeling and simulation areas of research.
Dr. Uma Krishnamoorthy: So today, [inaudible 00:08:10] this works, my role is very easy. It’s going to be a bit longer than the others but my role is relatively easy. I’m going to be giving your introduction to Bosch from a broader scale than what hopefully Hauke did. Then, of course, I’m going to lead into the Internet of Things and how we play a role in there.
Dr. Uma Krishnamoorthy: Hauke unfortunately told you what we do, so I’m going to ask anyway. How many of you already were aware of what Bosch does and what our products are before you came to the dinner today? Oh, that’s quite a few. Okay. The reason I ask, how many of you came here looking for headphones, acoustic systems in our demos or lens solutions? We’re not that company.
Dr. Uma Krishnamoorthy: Yep, we are Bosch. Who are we? First thing, we’re very diverse and the range of products we cover is very broad. I’m going to try to cover some of it today from the perspective of IoT. I’ll start off with this slide here, market figures. Bosch, exactly as Hauke mentioned, is from– originally started by Robert Bosch in 1886. So we’re over 130 years old.
Dr. Uma Krishnamoorthy: Yeah, we’re pretty old. We started in Germany, but as you can see we’re a global company. We have been in the Americas since 1906, I believe, over 100 years old. Very, very long time, very well established manufacturing company. We’ve made a very huge reputation in creating high quality products.
Dr. Uma Krishnamoorthy: We have 268 manufacturing sites across the world. Of course, we have a lot of representation in Asia-Pacific also. I wanted to draw your attention to that number right in the middle, 409,881 associates. That’s a huge number. Just to give you an idea, you take all of the associates at Alphabet, all of the associates at Apple, combine them, multiply it by approximately two. Okay, you’re all Girl Geek so approximately 1.78. And that will be the number of associates at Bosch. This was of course from 2018, so we are huge.
Dr. Uma Krishnamoorthy: To give you an idea of scale. So what do we do? I’m going to try to answer that question with this slide. You may be aware of our products in the consumer goods business. You may have seen our dishwashers, washing machines, maybe some coffee makers, many household appliances, power tools. Very popular there and a leading supplier. We also work in energy and building technology. What is this?
Dr. Uma Krishnamoorthy: Here’s a leading manufacturer of security communication technology. We actually make energy efficient heating products. This is a bigger business in Germany maybe than here, so we’re very well known for that. Or Europe, not Germany. On top of that, Hauke already mentioned mobility solutions.
Dr. Uma Krishnamoorthy: Sixty percent of our sales come from the mobility solutions business. This includes automotive and also consumer electronics. Essentially things like sensors that go in your cell phone, smartwatches, things of that sort. We’re a leading provider of that too.
Dr. Uma Krishnamoorthy: Surprising to me, I’ve been with Bosch for four years so this was a bit of a surprise, industrial technology. We also make a variety of industrial technologies. What does this mean? If you’ve ever been to the Jelly Belly factory, on the way back from Tahoe, you know, it’s a good stop.
Dr. Uma Krishnamoorthy: So if you stop there and look around, take a tour of the factory floor, you will see Bosch equipment, packaging equipment. I believe they might have been sorting the jellybeans, but I can’t remember exactly. So we are pretty broad and you’ll see us in many places, unexpected places. That’s how broad we are.
Dr. Uma Krishnamoorthy: To give you an idea of our culture, Hauke already mentioned our founder, Robert Bosch. We strongly follow the values of our founder Robert Bosch, which comprises of quality and innovation which is what our products are known for. This may not be as well-known over here in the US, but it’s known in Germany for sure, is the aspect of social commitment.
Dr. Uma Krishnamoorthy: Robert Bosch himself gifted the Robert Bosch Hospital to the City of Stuttgart back in 1936, which stands to this day. A lot of very important medical research is done there, including, I believe … I can’t remember all the details but a variety of really good medical research is done there.
Dr. Uma Krishnamoorthy: As Hauke mentioned, we’re privately held. Ninety percent of our shares are held by this Robert Bosch Foundation and this foundation fundamentally finances work that addresses social challenges. So they focus on areas like healthcare, science, society, education, international relations, all about society and life.
Dr. Uma Krishnamoorthy: They have provided, the number’s right there. 153-ish million euros to project grants that are in these areas. So, they really put their money where their values stand. That’s the message there. As I mentioned, one of the one of the cornerstones of Bosch is our innovation. We’re worldwide but we also have a very strong commitment to innovation. We have a, I don’t have the numbers here, a very large number of associates. Believe it was in 65,000 number range of associates who work in R&D across the company.
Dr. Uma Krishnamoorthy: Some of those actually work under a separate division called corporate research, which we’ve alluded to in the past and what you see in the background here is our campus that was recently built in Germany specifically for corporate research that services all of the Bosch groups,, fundamentally, almost all of them.
Dr. Uma Krishnamoorthy: And, what you really … I would like to highlight this one sentence over here our objective. Our motto is invented for life which is pretty much self-explanatory. So everything we do is about the quality of life, enhancing the quality of life through technology. I would like to say one more thing about this. Recently–I’ll have to … Mind me if I refer to my notes. Only because our CEO recently announced that we Bosch were going to be the first carbon-neutral industrial enterprise from 2020. That is a huge statement, and we’re all committed to delivering on that.
Dr. Uma Krishnamoorthy: What we came for, that was the introduction very briefly. I’ll try to go through this pretty fast. IOT at Bosch. This is going to essentially be kicking off a series of tech talks centered around IoT for Bosch. I’m only going to set it up for them. The real speakers will come after me.
Dr. Uma Krishnamoorthy: So what does IoT mean for Bosch? As many of you know, IoT is about creating better customer experiences through connectivity. And Bosch plays a very big role in it because we make a variety of products and we’re connecting them to make our customers get a better experience out of it, fundamentally. That’s the simplest way you can think about it.
Dr. Uma Krishnamoorthy: In the process, though, what we are noticing is industries are transforming, and we are playing a key role in this transformation at Bosch. So how are we playing in this field? Just giving you a sampling over here. You may have gone outside and you may have seen our car, autonomous car, so I don’t have to speak to our autonomous driving effort, our driver assistance efforts. There’s many of those that are ongoing that are widely shared.
Dr. Uma Krishnamoorthy: But on top of our mobility efforts we also work in the smart city area. We have products in all of these areas so connecting them and providing customer experiences goes beyond mobility into smart city, into buildings, industry, industry 4.0. But one of the key things for us, for our connected Bosch systems across these domains is we are creating intelligent user centric solutions without compromising safety or data security. Those are big messages that we carry and we essentially put into all our products.
Dr. Uma Krishnamoorthy: What is Bosch’s IoT vision? Again a borrowed slide. You will see big goals here. 2020, the goal is all of our electronic products will be connected. We’re going to continue working across a variety of domains and in 2025 all our products are going to either possess intelligence or AI will have played a key role in their creation. So AI is closely tied to our IoT.
Dr. Uma Krishnamoorthy: A few examples, I’ll have to go very quick. She just told me I have five minutes left. Quick examples, home appliances. Series 8 oven. It’s an oven, yes, but it’s also a microwave, it’s also a steamer, and it’s connected. So you can bake a cake–if you have the right app–you can bake a cake in it from your phone, and I’ll leave it there.
Dr. Uma Krishnamoorthy: This app is apparently not available everywhere but it is there, the technology is there. Mobility, you already mentioned that powertrains is one of the big areas we contribute in for the automotive business. Electric powertrains is our big area of work now. One thing I’ll show here is we are taking it beyond just electrification of cars, we’re actually moving into other powertrain systems for other vehicles such as two wheelers and trucks.
Dr. Uma Krishnamoorthy: Another aspect here is beyond just building EV vehicles, we’re also looking at connecting these vehicles. So anybody using an EV vehicle cares about charging them. So we actually have an app. Bosch has an app that’ll let you find up to 20,000 charging stations, which is very convenient, in five countries. I believe that will be increasing as this gets used more.
Dr. Uma Krishnamoorthy: Last but not least, the example automated valet parking. This came out recently. I had a beautiful video on this. It took too long so I’ll just tell you in two sentences. Automated valet parking. It’s like a mini autonomous vehicle that you can use in a parking garage.
Dr. Uma Krishnamoorthy: You bring your car to the garage, you walk out of it, hit the park button on your phone, the car will go park itself. When you are done with your dinner or whatever else, you come back to the garage. Say pick up the car. The car will drive itself to you. You can get in it and go home. That’s the idea and it’s actually real and they already rolled it out. So, that’s an example of some of the innovation we contribute to.
Dr. Uma Krishnamoorthy: Now I’ll be talking to you about some of the elements of IoT, not for very long. We have tech talks following me, they’ll go into all the details. So here, I’m going to talk briefly about transformation from the things to IoT. I’ve already mentioned that we make a lot of things here at Bosch across many domains. But one of the fundamental things we do is in the hardware. Sensors is a big area for Bosch, we are one of the enablers–sensors are the enablers for the Internet of Things and we’re one of the leaders in building micro sensors. Bosch Sensor Tech, in fact, is the part of Bosch that builds them, and you’ll be hearing a lot more about that from Tara right after me.
Dr. Uma Krishnamoorthy: Sensors are the data collectors. They are your direct connection to your products, they collect the states of your products, whatever they are. Then, another aspect of it that is kind of hidden, but is very important as batteries. So we need batteries to charge all of our things and our sensors and our phones, everything else. So that’s another aspect that we will be talking about soon. Yelena will be talking about it, I believe.
Dr. Uma Krishnamoorthy: Bosch has a strong background in the hardware aspect of manufacturing and in sensors products. So we understand that, the cause and effect. That’s our core business. So, what else is there to be done in IoT? It’s all about the connectivity. So once you have the data, you have to connect to it. We have the data collectors.
Dr. Uma Krishnamoorthy: So the next thing you need is to analyze the data and to create some–once you acquire the data you want to provide some, I guess models, right, and some plans on essentially understanding the data and to potentially predict what’s going to happen for whatever system you’re working with. So that’s where our AI comes into play right, and LisaMarion will be talking about that. She’s part of our BCA, Bosch Center for Artificial Intelligence.
Dr. Uma Krishnamoorthy: Then, finally, it all comes down to the user and the user interface. So that portion will be handled. It’s an important portion but that portion will be handled by … Panpan and Shabnam will be talking about that. They’re a part of our human machine interface, we used to call the interactions, human machine interaction group.
Dr. Uma Krishnamoorthy: So fundamentally we are integrating our hardware with AI, our IoT products and our sensors and that’s in a very, very high-level picture of what Bosch does in IoT. I’m going to stop there and hand the microphone on to Tara. So Tara and Seow Yuen Yee will be talking about sensors next and they will introduce the next speakers. So thank you very much.
Tara Dowlat: Hi everyone, my name is Tara Dowlat and I’m part of Bosch Sensor Tech. I’m part of the team that focuses on consumer electronic sensors and I’m an Account Manager, part of sales team.
Dr. Seow Yuen Yee: Hi everyone, my name is Seow Yuen. If it’s hard to pronounce you can call me SY. I’m the senior research engineer here in the corporate research. I’m part of Uma’s team. What I do is I make sensors and these sensors go to your car, your home and your phone. So I’ll tell you more about it later.
Tara Dowlat: So, did you guys know that at least every single one of you in this room in your pockets or in your bags have one sensor and most like the majority of you guys had least one sensor from Bosch on you? It’s a fun fact. Let me tell you that sensors are all around us. We might notice it, we might not, but these tiny, tiny little devices are actually pretty commonly used.
Tara Dowlat: They’re made out of micro electromechanical systems. They go also known as MEMS. These devices are made out of silicon. Silicon is the same exact material we use for semiconductor chips and they are used for really complex circuits or switches that we use in our industry today.
Tara Dowlat: If you look at the picture to the right side over here, this shows the structure of a MEMS and you can see that within a thickness of a hair line how many tiny little springs we’re able to fit in there. That’s a MEMS structure for you and typically these devices are within millimeter square. So we can see that how detailed and small these structures are and I find it personally very impressive.
Dr. Seow Yuen Yee: How are sensors made? The process starts with the silicon ingot that you can see on the left there and then it is later cut into thin slices that we call the silicon wafers. So this is an example of the silicon wafers. By itself it is not useful until we are able to process on it to make intricate features. We are able to do this thanks to our Bosch colleagues Franz Laermer and Andrea because they invented the deep reactive ion etching in 1996.
Dr. Seow Yuen Yee: It is now known as the Bosch Process because it has the ability to create a high aspect ratio profile in the silicon wafers. How high is a high aspect ratio and how tiny is tiny? Here’s an example that is the width of these trenches as five micron wide and the height–the deep is 50 micron deep. So you can imagine how small all these features are.
Dr. Seow Yuen Yee: Accelerometer, we’ll tell you later about it. It’s an example of a type of sensors that we are able to create using this process and Tara will tell you more about the sensors and other sensors, about accelerometers other sensors.
Tara Dowlat: So just as SY mentioned, we have a family of classical sensors known as motion sensors. We have magnetometers, accelerometers, gyroscopes, the combination of two that would be an IMU or you put all of the three together it’s known as nine degree of freedom or absolute orientation.
Tara Dowlat: But why do we care about these sensors in general? What’s the application or how do they improve our lives? Well the most classical approach was the use of sensors and automobiles. You guys might have heard about ABS, ESP or even tire pressure monitoring system on newer cars. These are sensor applications. Without the sensors on your cars, you guys would not have these safety functionalities.
Tara Dowlat: Let me ask you this. If you had the choice between a sports car, a sedan, or SUV for safety of your family which class of car would you guys probably pick?
Tara Dowlat: Okay. Let me tell you. Twenty years ago that was not the concept. SUVs and safety were not two words used in the same sentence. Actually these cars were known to be rolling over on the road and actually not safe at all. So what changed since then? The use of a gyroscope on the car is enabling them to stay stable on the road and not roll over. That makes them safe.
Tara Dowlat: Within 20 years or so the market and perception has changed so much that all of you guys think SUV is the best choice to go with. That’s the use of sensor. But, also the modern applications. Take autonomous driving, everybody in the news is talking about it. Autonomous driving would have not been possible without sensors or even more commonly used applications like Park Assist when you tell your car please park it for me in this tight spot. That’s using your sensors in the car, or when you’re trying to drive on the road and hopefully you guys are paying attention and it’s not dismissing the traffic or texting but more modern cars have this functionality that it actually tells you please slow down there’s an object in front of you. Don’t switch lane there’s an object next to you. These are the functionalities that modern cars have because of use of sensors in them.
Dr. Seow Yuen Yee: Applications that Tara mentioned there’s one more applications that should be familiar to all of you which is the airbags deployment. From 1987 to 201,8 more than 50,000 lives has been saved by airbags according to the US Transportation–Department of Transportation. Have you ever thought of how does the car know when to deploy this airbags?
Dr. Seow Yuen Yee: This is thanks to the airbags control unit in the car and in this control unit it has a tiny little sensors which we call accelerometers. When there’s movement like this impact in your car during the accident this [inaudible 00:29:32] this sudden impact.
Dr. Seow Yuen Yee: So let me show you the video of how it works. The accelerometer chip here contains of two parts, that’s the circuit chip and the MEMS sensors. In the MEMS sensors you can see the blue part is the movable part and the red part is the stationary part.
Dr. Seow Yuen Yee: When there’s movement in your car the blue part will move relative to the red part and from there it caused the relative capacitance change between these two parts. This capacitance change can then be sent to the airbag unit here which will deploy the airbags. For that it will protect you.
Dr. Seow Yuen Yee: The sensing part itself takes around 15 to 30 milliseconds time to sense it and the airbags will deploy from 60 to 80 milliseconds. So that’s how fast it is that can deploy to protect you.
Tara Dowlat: So, a more modern recent application for sensors are consumer electronics, specifically smartphones or tablets. You guys have might noticed over the past few years that actually the cameras have improved quite a bit in terms of picture quality. I hate to take all the credit for the sensors but they did play a part.
Tara Dowlat: You guys have might noticed that when you’re trying to take a picture you’re trying to zoom in and historically I was one of the people that would move the camera back and forth trying to get the best photo and then making sure that my picture’s not blurry. Well today the cameras do that for you and part of it is because of the image stabilization and the sensors that they use with the cameras. That’s one of the applications that uses a sensor.
Tara Dowlat: But another more commonly used one. When you go from horizontal to vertical on your phone when you’re looking at pictures and videos this is something that probably most of us use every day. That’s a use of a sensor on your phone. Or this one I’m a personal huge fan–navigation.
Tara Dowlat: I’m always lost and somehow people trust me to put me in charge of direction. But the reality of it is with my phone, if there is no magnetometer on it I’m looking at the direction and I don’t know if it says right is it really my right or my left.
Tara Dowlat: But a magnetometer on my phone would be able to tell me where is the true north and at what point do I need to truly turn right or left. That’s a really helpful application for most of us that we probably use and don’t commonly notice that it’s a sensor on there.
Dr. Seow Yuen Yee: One other thing is as you all know that GPS hardly works inside the building. In the case of an emergency, especially in tall buildings, it is very critical for the emergency first responder to know exactly where you are and this includes what floor you are in. The GPS do not give you this kind of information but our Bosch pressure sensor comes to rescue.
Dr. Seow Yuen Yee: Because of the as you increase the elevation, the altitude the air pressure decreases and this tiny change of pressure can be sensed by our Bosch pressure sensors. So let me show you another video of how the pressure sensor works. Again in the package it has two chip where there’s a circuit chip and the MEMS sensors.
Dr. Seow Yuen Yee: This time the MEMS sensors consist of a pressure sensitive membrane and on which there is four resistors which are connected in a wisdom bridge formation. As there’s the pressure change the shape of the membrane changes due to the pressure and the resistance is changed due to the change of the membrane.
Dr. Seow Yuen Yee: This resistance change is measured as water changes which ranged from one to five and this water changed correlates to the pressure and this pressure would tell you which elevation you are in. The information from this will be sent to the first responder and they will come to rescue you.
Tara Dowlat: Just as SY mentioned, pressure sensor belongs to another family of sensors that are getting quite commonly adapted nowadays, they belong to environmental sensors. That includes temperature, humidity, gas, or a combination of all those together as one single sensor.
Tara Dowlat: But how did they become so popular nowadays? Well, we are all health aware nowadays. I think most of you guys might be interested, but by show of hands how many of you guys track how many steps you’ve taken or how many stairs have you climbed today? Majority of you. Well, I guess most of us has invested in either a fitness band or a smartwatch or look at it on our phones.
Tara Dowlat: When you go under health application it tells you how many steps you’ve taken. That’s an accelerometer on your phone or on your device. Or if you’re interested in knowing how many climbs of stairs you’ve climbed today. Well, that’s a pressure sensor for you that gives that app information. But it’s not just about humans.
Tara Dowlat: So, I recently heard about a cool application from one of our potential customers that they are trying to put this step tracking option on their chicken. You would wonder why. But, I guess when you go to these stores you notice that there is like advertisement for eggs that are range free and organic, that extra dollar amounts that they are charging is justified because these chickens are taking more steps.
Tara Dowlat: The more steps they take, the healthier your chicken. But today we’re here for IoT and how does the sensor relate to IoT. How does that impact me as an individual? How does it change the quality of my life? I can take the example of a smart home. This belongs to the IoT category. Without the use of all these sensors, smart homes would not be possible. Let’s focus on my case specifically and I think some of you guys might relate.
Tara Dowlat: I’m here with you in the evening or the afternoon today. I will spend some time to drive home and during this drive I would be probably sitting in traffic, it’s hot and I’m thinking I wish when I get home that my Roomba has cleaned the floor. So IoT would be able to enable that.
Tara Dowlat: I wish that the AC has been running for the past 30 minutes because I’m somewhat environmental friendly but not extremely. I still like a cool room. So I’ll take that and I can make sure that a cup of coffee is waiting for me while I watch my last show before I go to bed. That’s a smart home for you.
Tara Dowlat: For IoT to be enabled we need to make sure that all these sensors are effectively and efficiently communicating. But then it becomes a matter of power consumption. That’s why Yelena would introduce battery management, which is a really important topic here at Bosch for us. Thank you.
Dr. Yelena Gorlin: Hi, my name is Yelena Gorlin and I work in corporate research. As Tara and Seow Yuen just mentioned, we will now switch topics and I will introduce a research topic that we have here at Bosch. It focuses on batteries and specifically battery management systems.
Dr. Yelena Gorlin: Before going into the details of the topic, I wanted to take a moment and quickly introduce to you my home department in order to give you an idea what type of associates are working on the project and also what is our overarching purpose for the everyday work that we do.
Dr. Yelena Gorlin: My home department at Bosch is called energy technologies and we have three areas of research competency and they include electrochemical, modeling, characterization and controls, automatic computation and additive manufacturing. As you can imagine, the associates involved in these areas come from a diverse research background and we actually have research experience from leading academic institutions, both in the US and Germany.
Dr. Yelena Gorlin: We’re specifically strong in the areas of chemical engineering, system controls, material science, and electrochemistry. What unites us all is our interest to work on future energy technologies with the goal of reducing the global carbon footprint.
Dr. Yelena Gorlin: Recently we came up with a new motto for ourselves and it’s putting low-carbon options on the global energy menu. Our department sees the topic of battery management systems, both as a contributor to de-carbonization of our society and also as an enabler to our connected future. But you’re probably now wondering what exactly is a battery management system and how can it be so important to our future.
Dr. Yelena Gorlin: So as the name already gives it away and as I mentioned in the beginning, battery management systems have to do with batteries. Probably all of us in this room have been in a situation that seemed quite dire simply because our phone or maybe our smartwatch, our computer or our car has run out of its battery.
Dr. Yelena Gorlin: In such a situation, we were probably wishing that we could recharge our battery as quickly as possible to bring the device back to life. Well, it turns out it’s not so difficult to recharge a battery very fast once in its life. But what is difficult is to be able to offer consistent fast charging without introducing any aging effects.
Dr. Yelena Gorlin: As you probably have guessed, one of the important functions of the battery management system is to offer precisely this capability at battery management system or as we call it BMS for short controls the operation of the battery. So how fast it charges and discharges and each new generation of a battery management system looks to increase the charging speed of our device without having effect on its lifetime.
Dr. Yelena Gorlin: You can imagine that advances in this area can reduce our anxiety about how long our devices can last and as a result contribute to electrification of our society both in IoT and mobility sector and contribute to its de-carbonization. Now I hope I was able to convince you that battery management systems are very important and very significant to our future and I wanted to take a step back again and bring you to my department and our approach to this future product.
Dr. Yelena Gorlin: At its core, our approach draws on the expertise available within the department, and we rely on the different areas of background, especially in research. As I mentioned, we have chemical engineers, we have control engineers, we have material scientists and electric chemists and we primarily combine three areas and its electrochemical modeling, experimental characterization, and controls.
Dr. Yelena Gorlin: Our typical project workflow starts with the development of an electrochemical model and involves a variety of equations and parameters. We then design and execute experiments to measure these specific parameters and combine them together with a model to form what is known as parametrized model.
Dr. Yelena Gorlin: This parametrized model serves as the basis for the next generation BMS and is used to generate new control algorithms. These control algorithms are what is going to allow us to charge our devices, so our watches, our phones, our computers, and our cars at faster speeds and therefore increase our confidence in all of these IoT components and contribute to the development of our connected future.
Dr. Yelena Gorlin: Thank you very much for your attention. I will now pass the mic to LisaMarion who will tell you about artificial intelligence.
LisaMarion Garcia: Hi, everyone. My name is LisaMarion I work at the Bosch Center for Artificial Intelligence here in Sunnyvale. So, we have a lot of opportunities for AI at Bosch. As my previous colleagues have mentioned, we cover a wide variety of different sectors from mobility, industrial, building, and consumer goods. Each of these individual sectors provide us different opportunities to incorporate AI, either as a feature of a product that we sell or as part of the process of producing that product.
LisaMarion Garcia: As Uma had mentioned before, that is a major goal for Bosch, to by 2025 have all our products either possess some artificial intelligence as part of their features that we provide to the consumers or as we produce them we are using AI.
LisaMarion Garcia: What we need to introduce AI into our products or our processes is–what gets discussed mostly when people are talking about artificial intelligence tends to be focused on the algorithms more. So that’s basically how you actually train a system to be able to learn by itself, how a car can drive itself, for example.
LisaMarion Garcia: We do work on that in-house as well. The Bosch Center for Artificial Intelligence has a pretty sizable research team that is currently working on state-of-the-art research topics. But additionally to actually get it from an idea, from a theoretical idea, into a product we need both compute resources, which we of course have access to, and most importantly, we need data.
LisaMarion Garcia: So, one of the advantages that being such a large company gives us, especially a company that covers so many different sectors is that we have access to a bunch of different types of data. BCAI overview, I guess. Our general mission is to help reach that goal, obviously, of introducing AI into the different areas.
LisaMarion Garcia: I’ve already covered our research team. We also have an enabling team which are–you can kind of think of them as AI evangelists. They go out to the different business units and kind of teach them about what machine learning is, how it can help in their products, what kind of data they need to be collecting if they want to be able to gain relevant insights from it.
LisaMarion Garcia: Then we have the services team which is where I work. We focus more on applied AI. So what we do is we consult with various business units within Bosch who have use cases or interested in introducing machine learning into their products or processes and we basically help them take that from an idea to a reality.
LisaMarion Garcia: We cover these four different areas. I’m going to briefly describe kind of each one. We have a bunch of different projects ongoing right now. But for an example in the manufacturing domain, something that we do is we work with optical inspection, which is where we put a camera in the production line at Bosch’s many plants and we basically collect images of the parts as they come through and try to perform or try to train a model to do automated part inspection. So basically being able to tell if a part is passing or failing by just looking at an image of it.
LisaMarion Garcia: In the engineering space, we do some work around gaining insights from data that is collected as we develop a new sensor, for example, for a new product or if we are trying to add kind of a smart home type of functionality to an existing appliance that Bosch already makes.
LisaMarion Garcia: For supply chain management and controlling we have a financial forecasting platform that basically looks at all of Bosch’s financial data and can make predictions about future sales. Then intelligence services, which I’m going to go into slightly more detail on since that is more of what I have worked on recently.
LisaMarion Garcia: So AI for mobility is obviously a hot topic. We have two main groups at Bosch that are working on that. We have for my friends that work in the autonomous driving space you may be familiar with the L3 to L5 kind of designations.
LisaMarion Garcia: So we have a driver assistance functions which are going to be your L3 and below. Those are things like automated braking when you detect a hazard on the road or lane keeping. Kind of those functionalities that already exist in your car. We also have autonomous driving group, which is the car outside, which would be the car driving itself.
LisaMarion Garcia: Some collaborations that this group has done with BCAI that I’ve been involved with have been lane keeping. So if you see the top image, we basically take a semantic segmentation map of a scene and basically use that to keep the car on the road. We also do hazard detection.
LisaMarion Garcia: So if you look at these two images in the middle, the one on the left is mostly clear windshield, the one on the right the windshield has been obscured with some droplets of water. A human looking at these two images can clearly tell that they’re the same scene. We basically our brains have a really good way of mentally deleting the information that you don’t need.
LisaMarion Garcia: It’s very difficult for a computer to do the same thing. That’s one of the main challenges when we’re training algorithms to be able to see, for example, for driving a cart. So we’ve done some work around helping either make the model itself more robust to these kinds of disturbances or basically just having some kind of a sense so that the car knows when one or more of the cameras has been had its vision obscured.
LisaMarion Garcia: Then the last topic, which I wanted to cover in slightly more detail, is the data privacy compliance topic. So I’m not sure how many of you are aware of the GDPR regulation. Yes, okay, a lot of nodding. So that’s a really important law that was passed by the EU which basically … The general gist of it is that any company that is collecting personally identifiable information from people without their consent basically needs to delete that data every six months or somehow you scrub the personally identifiable information.
LisaMarion Garcia: For our automotive topics, that mainly covers human faces and license plates. So what we did to help our business units and prevent them from throwing away their data every six months is we developed a tool using deep learning to be able to identify, locate the faces and license plates in the data that was generated by the proprietary Bosch sensors and blur those out of the image.
LisaMarion Garcia: So basically what we are doing is helping them generate training data that they can use long term and also store, which will help them basically consistently validate their work over time. So, yeah, just AI for your AI. That’s kind of the overview of what Bosch is doing in regards to AI. I have kind of mostly talked about how we spread AI internally and now I’m going to bring the user back into the conversation and pass off to my colleagues to talk about human machine collaboration. Thank you.
Dr. Shabnam Ghaffarzadegan: Hi, my name is Shabnam. I’m a research scientist here at Bosch working in human machine interaction group and I’m very excited to be here with my colleague Panpan Xu who is our group too.
Dr. Panpan Xu: Hello everyone. I’m Panpan, I’m also working on the human machine collaboration topic at Bosch Research. So today Shabnam will first give an introduction of what are the topics we have been working on.
Dr. Shabnam Ghaffarzadegan: The topic we are really excited to work here at Bosch is human machine collaboration. If you think about everyday life there’s so many tasks that human is so good at but machine usually has so much trouble doing them. Also there are so many tasks, let’s say repetitive tasks, that machine might be so good at doing them very accurately but human would be having so much trouble to perform them in a short amount of time.
Dr. Shabnam Ghaffarzadegan: So our idea is asking human and machine to work together to empower their both abilities to make a superhuman with much more perception and knowledge and also to make a better machine to help us in our everyday life. Here at Bosch, we do focus on many core technologies such as robotic manipulation, text mining, audio analytics and visualization. We do apply these technologies to so many different use cases such as IoT industry 4.0, smart home, and smart cars.
Dr. Shabnam Ghaffarzadegan: How we do? So here first I’m going to introduce you how AI can help humans. So our goal is empowering human capabilities. What we do in our group is that we take different modalities that we see in the environment such as visual clues, text and audio and speech that we hear around ourselves and we combine this information with domain knowledge, context knowledge and user knowledge and we translate them to some specific applications such as personal assistants, conversational AI, and augmented reality.
Dr. Shabnam Ghaffarzadegan: As I mentioned, our goal is empowering human with domain specific AI. Here our focus on one of the use cases we work that I focus on personally, which is intelligent audio analytics. If you think of course the speech is one of the main … No, it’s okay. We can continue hearing that. It’s fine.
Dr. Shabnam Ghaffarzadegan: Okay, what I wanted to say was that if you think about speech, of course, it’s one of the main input and the way of communicating with outside world as a human, right, but there are so many other sounds that we can hear in the environment such as the sample of sounds you just heard. Right?
Dr. Shabnam Ghaffarzadegan: By these sounds you can guess kind of what kind of environment you were at. Were you at the beach or where you at a restaurant, right, just by listening to the noise in that environment or you can guess what kind of machine are you operating. Is that machine is working in a right mode or is it broken? Right?
Dr. Shabnam Ghaffarzadegan: So here in our group we focus on signal processing and machine learning techniques to discover three kind of sounds. The first one is environmental sounds. As you heard, is it beach, is it in the office, is it in a restaurant? The second one would be machine sounds. Right?
Dr. Shabnam Ghaffarzadegan: We hear, we listen to the different machines in the environment and we try to recognize if they’re malfunctioning or working in the right state. And finally human sound, but non-speech human sound. Imagine you might be coughing or sneezing and that might be a clue that you might have some health issues and you might want to go to a doctor. Right?
Dr. Shabnam Ghaffarzadegan: So the audio analytics field is kind of newer compared to vision or speech technology that already exists so we have so many challenges at this field and the main one would be lack of data as always existing artificial intelligence and also we need to be really robust toward the other different kind of noise and environments that we are at.
Dr. Shabnam Ghaffarzadegan: Here’s some of the use cases we work on. The first one we can focus on physical security and automation. You think that in most places the physical security systems are based on cameras but there might be so many situations cameras might fail. Let’s say, if it’s dark at night or if it’s foggy so the camera might not see what’s happening in environment. But also there are some events that camera is visual clues are not able to capture them.
Dr. Shabnam Ghaffarzadegan: Let’s say gunshot. Right? With a camera if the gunshot is not in the visual field you can’t basically [inaudible 00:54:23]. So, our idea is including microphone to a camera to understand more information about our environments. In this case, such as gunshot, glass break, and a smoke alarm can be sounds that can alarm our physical security system.
Dr. Shabnam Ghaffarzadegan: The next use case is industry 4.0. As I mentioned, we would like to put microphone in our plants and listen to the machines that working on those plants. For this, this is a very easy step to move toward industry 4.0 since the only thing we need to do is basically we put a MEMS microphone on these devices and just listen to them to see if they are operating correctly or not.
Dr. Shabnam Ghaffarzadegan: The third one would be an automotive sensing and diagnosis. Of course, autonomous cars, they are hot topics these days and they are having so many sensor already on them such as radar, camera. But we believe that autonomous cars needs to have the hearing sense as well. One of the important use case would be for example hearing emergency vehicles if there is siren happening for example police car or ambulance so these autonomous cars needs to understand these sounds and act accordingly.
Dr. Shabnam Ghaffarzadegan: Another use case can be listening to your car parts, for example, your car engine. If you go to repair shop so many of the very experienced repair shops they just listen to your engine and they would guess if you have a problem, so this is our idea to do that automatically.
Dr. Shabnam Ghaffarzadegan: Finally to give you some idea how we perform these acts. So basically we do use microphones to get this raw audio input from the environment. This information, we do some signal processing to enhance this signal to remove some environmental noise that we don’t want them and we do use domain knowledge, meaning that we do look into what kind of environment we are performing.
Dr. Shabnam Ghaffarzadegan: Are we in a factory? Are we in a house? Are we in a car? Based on that we extract some features and finally we do machine learning and AI to detect what kind of audio events was in the environment. Next my colleague, Panpan, she will explain now how human can help AI.
Dr. Panpan Xu: So, here comes the other side of story, how can human help make AI more intelligent and more reasonable to the humans. So, our approach is actually very much human in the loop method for big data analysis which we call visual analytics. Visual analytics is actually a technique which combines technologies from many different fields and one of these field is data mining.
Dr. Panpan Xu: With data mining we basically trying to gain insights from data with automatic algorithms and identify the patterns inside it. The other technique is visualization. Basically, we can draw the chart to show different trends and patterns detected by the data mining algorithms and then show or present to the users.
Dr. Panpan Xu: Most important part is user interaction. Actually, in this user centric approach we want to really take in users’ input or users’ knowledge into the data analysis process so it does not appear as a black box choose users. So, one use case that is very much related to this visual analytics topic is expandable AI.
Dr. Panpan Xu: Basically, in most of the cases we use AI as a black box. Basically the machine learning model takes the input and then produce some output to–For example, in autonomous driving we take the video input from the camera and then the steering wheel will take the corresponding directions or in medical diagnostics solutions the AI usually take an image and then tells the doctor or the patient what kind of disease it is.
Dr. Panpan Xu: But this kind of black box approach is usually not much reliable or people do not really want to use the machine learning model as a black box. So, with visual analytics we can present the explanation to the users actually and then the user can provide feedback to the model and continuously improves model until the model becomes transparent or explainable for the users.
Dr. Panpan Xu: Why this is important as I explained, we have these fairness issues because we want to know AI is making its decisions based on some meaningful features instead of other features like gender which can make this model unfair to certain populations and also we want to make this model robust.
Dr. Panpan Xu: On the other hand. There’s also this GDPR regulation which requires every decision made by AI to be explainable to the humans. So the user have the right to assess explanation to the decision made by an algorithm.
Dr. Panpan Xu: So now let’s go in on our deeper technical dive to look at a recent research paper we have published at ACM [inaudible 01:00:04] this year and which is about interpretable and steerable sequence learning. And that has application in many different AI fields like text mining or medical diagnostic sensor.
Voiceover: Recurrent neural networks have shown impressive performance in modeling sequence data. They have been successfully used in a lot of applications, sentiment analysis, machine translation, speech recognition and so on. However, they are considered as black boxes since it is very difficult to explain their predictions. Without explainability it could cause trust and ethics issues.
Voiceover: How can I trust the predictions coming out of a black box? These problems will limit the applications of these deep learning models in various decision-making scenarios. For example, a data scientist has developed a sequence prediction model to predict the risks of future problems of a car based on its historical faults.
Voiceover: However, the mechanics and repair shops may find it difficult to choose the right maintenance strategy with just prediction results. Sometimes they even suspects that the modeling is wrong. The need for explanation is pervasive in such decision-making processes. The predictive model serves as a smart analysis module rather than an automatic end-to-end solution.
Voiceover: Our idea is to explain the predictions by providing similar examples. Such case based reasoning strategy is commonly used in our daily life. For example, why classify a restaurant review, “Pizza is good but service is extremely slow” as negative? This is because it is similar to two prototypical negative sentences, good food but worse service and service is really slow.
Voiceover: We use sequence encoder R which encodes the input sequence into a fixed length embedding vector H. The model learns K prototype vectors that are most representative in the embedding space. We compute these similarities between H and the prototype vectors. The similarity scores are used as a source for prediction. To ensure that the prototypes are readable, we project the prototype vectors to their closest training samples every few epics.
Voiceover: To further improve interpretability, we’ve simplified the prototype sequences using a beam search based algorithm. To utilize expert knowledge, we design an interaction scheme which allows human users to incorporate their domain knowledge into the model. We build interpretable and steerable sequence models for vehicle fault predictions, sentiment analysis, protein classification, and heartbeat classification.
Voiceover: You can get explanations to the accurate predictions on the fly.
Dr. Panpan Xu: [inaudible 01:02:57]. I would like to thank [inaudible 01:03:03] for the very nice voiceover of the video. So, if you have any questions about the paper you can search it online. So there’s the title below at the bottom of this slide. So, now let’s move on to the next topic and see how Bosch is enabling a new area of mobility with our presenter Sun-Mi here.
Sun-Mi Choi: Hello. Also from my side I guess I’m the last turn. I hope you guys are still with me.
Group: Yes. [crosstalk 01:03:44] …
Sun-Mi Choi: That was a little bit too silent. Are you still with me?
Sun-Mi Choi: Okay, good. Thank you. I know it’s late. My name is Sun-Mi Choi. So please just call me Sunny. I’m Sunny from Sunnyvale so it’s easy to remember. I’m responsible for business development strategy within a newly established group. We are probably the youngest group within Bosch. We are eight months old so we were born beginning of this year and probably also the smallest group and we are called progressive mobility players, short PMP.
Sun-Mi Choi: I will tell a little bit more about it later but basically what we do is focus on new mobility startups because we see the mobility world is changing a lot. A lot of new players are entering the market and we are focused on two players which are new electric vehicle manufacturers and at the same time also on mobility service providers.
Sun-Mi Choi: Today we’ve heard a lot about innovative amazing technologies, learning about sensors, learning about battery management solutions, artificial intelligence, and human machine collaboration. I’ve been with Bosch seven years but I didn’t know that we had so much capability in-house. I just moved here beginning of this year so it’s amazing to see how much capabilities we have.
Sun-Mi Choi: I would like to bring in a little bit of a different perspective. Basically bringing a little bit the market perspective customer needs to explain and verify why these capabilities are so important for Bosch and also for the future of mobility.
Sun-Mi Choi: So, before I start, I would like to give a little bit of a bigger picture of why the mobility is changing and what are the driving forces behind.
Voiceover: Our world is changing and this change is visible across the globe. More than 50% of our population now lives in cities. These cities are growing, as is the share of older people in them, while space to live is becoming ever more precious. More and more goods and people need to be transported, pushing the traffic infrastructure to its limits and increasing pollution and noise levels.
Voiceover: But the world is waking up. Regulations are calling for stricter limits and cleaner solutions. A transformation has started, powered by new technologies and services. In a world where everything is connected, mobility is being re-imagined. Solutions like traffic management combined with cleaner and more efficient power trains and the benefits brought by automated driving will make our cities sustainable and livable.
Voiceover: Bosch is driving this change and shaping the future. The future of mobility.
Sun-Mi Choi: [inaudible 01:07:03] trends they are not new for you. But it’s still very important to understand the fundamental driving forces behind it because this actually has a really big impact on Bosch. Because as we learned from Uma, the mobility part makes 60% of our revenue and all of these changes make a huge change or an impact also our business model if we want to maintain sustainable for the future.
Sun-Mi Choi: So air pollution, congestion, urbanization, and also what we see a changing consumer behavior, all of these factors are really shaping a new focus for us in the mobility area, which we call electrified, automated, connected, and also shared and personalized, which you probably experience and also live every day.
Sun-Mi Choi: At the same time, mobility is also getting more user centric. The consumer is more and more changing from owned to shared. So how many of you are using ride hailing apps to get from A to B on a regular basis? So I see not everyone, but I see a lot of hands raised. So this has become an integral part of how we move from A to B because it brings convenience, especially in congested cities.
Sun-Mi Choi: Also, consumers become more individual and personalized and more importantly, they always want to stay connected. This all relates to mobility and new players, startups see this change and these trends as basically opportunities to come into the mobility market. Because now new capabilities are required and this disrupts the whole mobility value chain also from our Bosch perspective.
Sun-Mi Choi: So what does it mean for us? We also need to understand what these new players are about to develop, what is their thinking. How do they approach innovation? That’s why as mentioned in the beginning we are focusing on new EV based customers.
Sun-Mi Choi: So probably a lot of you know Tesla in this area. So really young companies who are starting vehicles from scratch or the second customer segment is mobility service based customers. So, all companies who provide mobility as a service, the ride hailing apps, car sharing and so on.
Sun-Mi Choi: What we see is that they have quite of a different DNA, they have different requirements. That means also for Bosch, we need to understand the requirements and adjust also the way how we approach customers. Because these young customers, they act differently, they drive innovation differently than the VW or Mercedes driver that we’ve been dealing with for the past hundred years.
Sun-Mi Choi: So it’s time to change and it has also a big transformational impact on us. So, we see in the shared space, for example, the one customer segment we are focusing on is huge change. If you look at an annual number of ride hailing rides you see a tremendous growth over the past four years. It’s been grown more than 60%.
Sun-Mi Choi: From a user perspective, you also see a good reason why they are switching from ownership to shared. One of the reasons is because 96% of the time your asset stands idle. The car is parked, you’re at work, it stands idle for eight, nine, 10 hours while you sleep also. This this is a waste of assets.
Sun-Mi Choi: So people are looking for alternative modes to move, alternative modes how to utilize their assets in a most, more efficient way. So also this is one indication for why people are moving towards shared. Last but not least, from an investor perspective, if you look at how much investments have flown into this area over the past four years only more than 80 billion US dollar have been invested into the ride hailing market.
Sun-Mi Choi: This is humongous. This is likely to grow further. So, this shared mobility will happen. So how do these new customers take, what are the pain points, what are the requirements? These are just some of the requirements or pain points that we identify when speaking to the customer. So operational costs for these ride hailing companies is a sure thing.
Sun-Mi Choi: How can we become profitable? How can I optimize my operations? Second point is how can I ensure safety and security for their passengers, especially when we go towards robo taxis, it will not have a driver anymore being able to control the ride. So we need technology to basically operate and also ensure the safety even without a driver.
Sun-Mi Choi: Third is there are so many players arising, I need to differentiate. If I want to survive in this market I need to have a good differentiation point. So personalization, how to ensure that your ride is individual and a really great experience is one important differentiator that we have identified.
Sun-Mi Choi: For all these pain points, for all these requirements that we see, it kind of makes sense where you bring now the puzzle pieces together of the capabilities that we’ve seen from sensors which connect the cars, can connect the car and the user and a lot of other use cases that we’ve learned today.
Sun-Mi Choi: Battery management solutions is super important because we see a strong push towards electrification pushed by the government. Also end users are looking for environment friendly solutions. Also a lot of these ride hailing companies tend to establish their own EV fleets.
Sun-Mi Choi: So range anxiety and also improving the battery lifetime what we learned today are super, super crucial for the customers in the market. Autonomous driving was something that was mentioned. So a lot of these companies are also going towards robo taxis. So artificial intelligence is also human machine collaboration to really ensure that there is a safe and also unique experience between the human and the machine will be very relevant.
Sun-Mi Choi: When we look at the customer and the market and the customers, we see that these capabilities will be important for the future to come. So I’m very proud to see that we are working on these very future-oriented topics. This is the way how we would like to tackle the new era of mobility.
Sun-Mi Choi: So basically in summary, with these capabilities enable the vision of our mobility customers not only the new ones, of course, also the existing customer base. Second, we want to innovate and co-create with these customers together. Because even though we have the best technology that might be requirements that we may not have seen so we need the customer input to even more improve the technology and also the use case.
Sun-Mi Choi: Last but not least, important point is really to understand and translate what the customers tells it to us into technology. That’s why it’s a good collaboration to have technology and also sales and the market proximity close to each other so that there is always an inter-linkage and a bridge between technology and also market need.
Sun-Mi Choi: So, we’ve talked a lot about AI, about new customers, about innovation, but I think it’s also important to really close with the core, with the tradition to not forget about the core business and also the roots where this company is found on. So two values from Robert Bosch, the founder, since 1886, have been that he says, “I have always acted according to the principle that I would rather lose money than trust.”
Sun-Mi Choi: So the trust to the customers, to the market, providing safety is one really crucial element. Second point for doing business also with our customers is integrity. Integrity of the promises we make to our customers in regards to quality and also in terms of the promises that we make to them. This to the founder and the values still hold today our prioritizing this versus just having a short-term transitory profit.
Sun-Mi Choi: So I would like to remind us all of us when we speak about future topics to think about the core values as well because these are important. This is how I would like to close the presentation. Thank you very much for the one hour attention. So you have been an amazing crowd.
Sun-Mi Choi: I went a little bit over time, so thanks a lot for your patience. I think we had great presentations here today. I would like to thank all of you on behalf of the whole team for coming to our Sunnyvale site, for showing interest in our portfolio, in our technologies. And we would be happy to see you again, also to mingle and network after and to see if we have some collaboration opportunities.
Sun-Mi Choi: Last but not least, of course, I would like to thank all the staff, the presenters, and all the people who have helped to support making this event happen. It was a lot of work. So let’s have a nice evening and please don’t leave too quickly. Thank you very much.