 Welcome everyone. Thank you for coming to the Intel AI Lounge and joining us here for this autonomous world event. My name is Jack. I'm the Chief Architect of our Autonomous Driving Solutions at Intel. And I'm very happy to be here and to be joined by esteemed panel of colleagues who are going to engage you all in a great dialogue and discussion. There will be time for questions as well. So keep your questions in mind. Jot them down so you can ask them to us later. So first, let me introduce the panel. Next to me we have Michelle, who's the co-founder and CEO of Finemine. She's just in an interview here shortly. Finemine is a company that provides a technology platform for retailers and brands. It uses artificial intelligence as the heart of the experiences that her company's technology provides. Joe from Intel is the head of partnerships and acquisitions for artificial intelligence and software technologies. He participated in the recent acquisition of Movidius, a computer vision company that Intel recently acquired and is involved in a lot of smart city activities as well. And then finally, Suresh, who's a data scientist by trade, but now heads JDA Labs, which is researching emerging technologies and their application in the supply chain worldwide. So at the end of the day, the Internet of Things and artificial intelligence really promises to improve our lives in quite incredible ways and change the way that we live and work. Oftentimes the first thing that we think about when we think about AI is Skynet, but we at Intel believe in AI for good and that there's a lot of things that can happen to improve the way people live, work, and enjoy life. So as things in the Internet of Things become connected, smart, and automated, artificial intelligence is really going to be at the heart of those new experiences. So as I said in my role as the architect for autonomous driving, it's a common place where people think about artificial intelligence because what we're trying to do is replace a human brain with a machine brain, which means we need to endow that machine with intelligence, thoughts, context, experiences, all of these things that sort of make us human. So computer vision is a space, obviously, with cameras in your car that people often think about, but it's actually more complicated than that. How many of us have been in this situation on a two-lane road? Maybe there's a car coming towards us, there's a road off to the right, and you sort of sense. You know what? That car might turn in front of me. There's no signal, there's no real physical cue, but there's something about what that driver is doing where they're looking tells us, so what do we do? We take our foot off the accelerator, we may be hovered over the brake just in case, but that's intelligence that we take for granted through years and years and years of driving experience that tells us something interesting is happening there. So that's the challenge that we face in terms of how to bring that level of human intelligence into machines to make our lives better and richer. So enough about automated vehicles though. Let's talk to our panelists about some of the areas in which they have expertise. So first for Michelle, I'll ask many of us probably buy stuff online every day, every week, every hour with hourly delivery now. So a lot has been written about sort of the death of traditional retail experiences. How will artificial intelligence and the technology that your company has sort of rejuvenate that retail experience whether it be online or in a traditional brick-and-mortar store? One of the things that I think is common misconception when you hear about the death of the brick-and-mortar store, the growth of e-commerce, is really that e-commerce is beating brick-and-mortar in growth only. And there's still over 90% of the world's e-commerce is done in physical brick-and-mortar stores. So e-commerce while it has the growth has a really long way to go. And I think one of the things that's going to be really hard to replace is the very human element of interaction and connection that you get by going to a store. So just because a robot named Pepper comes up to you and asks you some questions, like they might get you the answer you need faster and maybe more efficiently. But I think as humans we crave interaction and shopping for certain products, especially is an experience better enjoyed in person with other people, whether that's an associate in the store or people you come with to the store to enjoy that experience with you. So I think artificial intelligence can help it be a more frictionless experience whether you're in store or online to get you from point A to buying the thing you need faster. But I don't think that it's going to ever completely replace the joy that we get by physically going out into the world interacting with other people to buy products. You said something really profound. You said that the real revolution for artificial intelligence and retail will be invisible. What did you mean by that? Yeah, so right now I think that most of the artificial intelligence that's being applied in the retail space is actually not something that shoppers like you and I see when we're on a website or when we're in the store. It's actually happening behind the scenes. It's happening to dynamically change the webpage to show you different stuff. It's happening to further up the supply chain with how the products are getting manufactured, put together, packed, shipped, delivered to you. And that efficiency is just helping retailers be smarter and more effective with their budgets. And so as they can save money in the supply chain, as they can sell more products with less work, they can reinvest in experience, they can reinvest in the brands, they can reinvest in the quality of the products. So we might start noticing those things change, but you won't actually know that that has anything to do with artificial intelligence because it's not always in a robot that's rolling up to you in an aisle. So you mentioned the supply chain. And Suresh, that's something that we hear about a lot. But frankly for most of us I think it's very hard to understand what exactly that means. So could you educate us a bit on what exactly is the supply chain and how is artificial intelligence being applied to improve it? Sure, sure. So for a lot of us supply chain is either a term that we picked up when we went to school or we read about it every so often, but we're not that far away from it. It is in fact a key part of what Michelle calls the invisible part of one's experience. So when you go to a store and you're buying a pair of shoes or you're picking up a box of cereal, how often do we think about how did it ever make its way here? Were the constituent components, they probably came from the multiple countries and so they had to be manufactured, they had to be assembled in these plants. They had to then be moved either through vessel or through trucks. They probably have gone through multiple warehouses and distribution centers and then finally into the store. And what do we see? We want to make sure that when I go to pick up my favorite brand of cereal, it better be there. And so one of the things where AI is going to help, and we're doing a lot of active work in this, is in the notion of the self-learning supply chain. And what that means is really bringing in these various assets and actors of the supply chain. First of all, through IoT and others, generating the data, obviously connecting them and through AI, driving the intelligence so that I can dynamically figure out the fact that the ocean vessel that left China on its way to Long Beach has been delayed by 24 hours, what does that mean when you go to a foot locker to buy your new pair of shoes? Can I come up with alternate sourcing decisions? So it's not just predicting, it's prescribing and recommending as well. So behind the scenes, generating a lot of the data, connecting a lot of these actors and then really driving the smarts. That's what the self-learning supply chain is all about. Is the supply chain always international or can they be local as well? Definitely local as well. I think what we've seen over the last decades, it's kind of gotten more and more global, but a lot of the supply chain can really just be within the store as well. You'd be surprised at how often retailers do not know where their product is. Is it in the front of the store? Is it in the back of the store? Is it in the fitting room? Even that local information is not really available. So to have sensors to discover where things are and to really provide that efficiency which right now doesn't exist is a key part of what we're doing. So Joe, as you look at companies out there to partner or potentially acquire, do you tend to see technologies that are very domain specific for retail or supply chain or do you see technologies that could bridge multiple different domains in terms of the experiences we could enjoy? Yeah, definitely. So both. A lot of infant technologies start out in very niche use cases, but then there are technologies that are pervasive across multiple geographies and multiple markets. So Smart Cities is a good way to look at that, right? So let's level set really quick on Smart Cities and how we think about that. I have a little sheet here to help me. All right, so if anybody here played SimCity before, you have your little city that's a real world that sits here. So this is reality and you have little buildings and cars and they all travel around and you have people walking around with cell phones and what's happening is as we develop Smart Cities, we're putting sensors everywhere. We're putting them around utilities, energies, water. They're in our phones. We have cameras and we have audio sensors in our phones. We're placing these on light poles, which is existing, you know, sustaining power points around the city. So we have all these different sensors and they're not just cameras and microphones, but they're particulate sensors. They're able to do environmental monitoring, things like that. And so what we have is we have this physical world with all these sensors here and then what we have is we've created basically this virtual world that has a great memory because it has all the data from all those sensors and those sensors really act as ties. If you think of it like a quilt, tying a quilt together, you bring it down together and everywhere you have a stitch, you're stitching that virtual world on top of the physical world and that just enables incredible amounts of innovation and creation for developers, for entrepreneurs to do whatever they want to do to create and solve specific problems. So what really makes that possible is communications, right, and connectivity. So that's where 5G comes in. So with 5G it's not just a faster form of connectivity, right? It's new infrastructure. It's new communication includes multiple types of communication and connectivity. And what it allows it to do is all those little sensors can talk to each other again. So the camera on the light pole can talk to the vehicle driving by or the sensor on the light pole. And so you start to connect everything and that's really where artificial intelligence can now come in and sense what's going on. It can then reason, which is neat to have a computer or some sort of algorithm that actually reasons based on a situation that's happening in real time and then acts on that. But then you can iterate on that or you can adapt that in the future. So if we think of an actual use case, we'll think of a camera on a light post that observes an accident. Well, it's programmed to automatically notify emergency services that there's been an accident. But it knows the difference between a fender bender and an actual major crash where we need to send an ambulance or maybe multiple fire trucks, right? And then you can create iterations and that learns to become more smart. Let's say there was a vehicle that was in the accident that had a little yellow placard on it that said hazard, right? You're going to want to send different types of emergency services out there so you can iterate on what it actually does. And that's a fantastic world to be in and that's where I see AI really play. That's a great example that it's all about in terms of making things smart, you know, connected and autonomous. So Michelle is somebody who's founded a company in this space with technology that's trying to bring some of these experiences to market. There may be folks in the audience who have aspirations to do the same. So what have you learned over the course of starting your company and developing the technology that you're now deploying to market? Yeah, I think because AI is such a buzzword, right? Like you can get a .ai domain now. It doesn't mean that you should use it for everything, right? Maybe seven, 10, 15 years ago. Like these trends have happened before, right? In the late 90s it was technology and there was technology companies and they sat over here and there was everybody else. Well, that's not true anymore. Every company uses technology. Then fast forward a little bit. There was like social media was a thing. Social media was these companies over here and then there was everybody else. And now every company needs to use social media. Or actually maybe not. Maybe it's a really bad idea for you to spend a ton of money on social media and you have to make that choice for yourself. So the same thing is true with artificial intelligence and what I tell, you know, venture capitalists, I did a panel on AI for venture capitalists last week trying to help them figure out when to invest and how to evaluate and all that kind of stuff. And what I would tell other aspiring entrepreneurs is AI is a means to an end. It's not an end in itself. So unless you're a PhD in machine learning and you want to start an AI as a service business, you're probably not going to start an AI-only company. You're going to start a company for a specific purpose to solve a problem and you're going to use AI as a means to an end. Maybe, if it makes sense, to get there to make it more efficient and all that stuff. But if you wouldn't get up every day for 10 years to do this business that's going to solve whatever problem you're solving, or if you wouldn't invest in it if AI didn't exist, then adding .ai at the end of the domain is like, it's not going to work. So don't think that that will help you make a better business. That's great advice. Thank you. Suresh, as you talked about sort of the automation then of the supply chain, what about people? What about the workers? Whose jobs may be lost or displaced because of the introduction of this automation? What's your perspective on that? That's a great question. It's one that I'm asked quite a bit. If you think about the supply chain with a lot of the manufacturing plants, with a lot of the distribution centers, a lot of the transportation, not only are we talking about driverless cars, as in cars that you and I own, we're talking about driverless delivery vehicles. We're talking about drones and all of these, on the surface, it appears like it's going to displace human beings. What humans used to do, now machines will do and potentially do better. So what are the implications around human beings? So I'm asked that question quite a bit, especially from our customers. My general perception on this is that I'm actually cautiously optimistic that human beings will continue to do things that are strategic, human beings will continue to do things that are creative, and human beings will probably continue to do things that are truly catastrophic, that machines simply have not been able to learn because it doesn't happen very often. The one thing that comes to mind is when ATM machines came about several years ago, before my time, that displaced a lot of teller jobs in the banking industry, but the banking industry did not go belly up. They found other things to do. If anything, they offered more services. There were more branches that were closed, and if I were to ask any of you now if you would go back and not have 24-7 access to cash, you would probably laugh at me. So the thing is, this is AI for good. I think these things might have temporary impacts in terms of what it will do to labor and to human beings, but I think we, as human beings, will find bigger, better, different things to do, and that's just been the nature of the human journey. There's definitely a social acceptance angle to this technology, right? Many of us technologists in the room, it's kind of easier for us to understand what the technology is, how it works, how it was created, but for many of our friends and family, they don't. So there's a social acceptance angle to this. Michelle, as you see this technology deployed in retail environments, which is a space where almost every person and every country goes, how do you think about making it feel comfortable for people to interact with this kind of technology and not be afraid of the robot or the machine behind the curtain? Yeah, it's a great question. I think that user experience always has to come first. So if you're using AI for AI's sake, or for the cool factor, the wow factor, you're already doing it wrong. And what I tend to tell people who are like, oh my god, AI's down so scary, we can't let this happen. I'm like, it's already happening and you're already liking it. You just don't know, right? Because it's invisible in a lot of ways. So if you can point out those scenarios where AI has already benefited you and it wasn't scary because it was a friendly kind of interaction, you might not even realize it was there versus something that looks so different and panic driving. I think that's why the driverless car thing is a big deal because you're so used to seeing in America, at least someone on the left side of the car in the front seat. And not seeing that is like, whoa, crazy. So I think that it starts with the experience and making it an acceptable kind of interface or format that doesn't give you that, oh my god, something is wrong here kind of feeling. Yeah, that's a great answer. In fact, it reminds me there's this really amazing study by a professor called Nicholas Epley that was published in the Journal of Social Psychology. And the name of the study was called a Mind in a Machine. And what he did was he took subjects and had a fully functional automated vehicle and then a second identical fully functional automated vehicle. But this one had a name and it had a voice and it had sort of a personality. So it had human anthropomorphic sort of characteristics. And he took people through these two different scenarios and in both scenarios he was evil and he kind of introduced a crash in the scenario where it was unavoidable. There was nothing going to happen. You were going to get into an accident in these cars and then afterwards he pulled the subject to say, well, what did you feel about that accident? First, what did you feel about the car? They were more comfortable in the one that sort of had anthropomorphic features. They felt it was safer and they'd be more willing to get into it, which is kind of not terribly surprising. But the kicker was the accident. And the vehicle that had a voice and a name, they actually didn't blame the self-driving car they were in, they blamed the other car. But in the car that didn't have anthropomorphic features, they blamed the machine. They said there's something wrong with that car. So it's one of my favorite studies because I think it does sort of illustrate that we have to remember sort of the human element to these experiences. And as artificial intelligence sort of begins to replace humans, or some of us even, we need to remember that we are still social beings and how we interact with other things, whether they be human or non-human, is important. So, Joe, you talk about sort of evaluating companies. Michelle started a company. She's gotten funding as you go out and look at new companies that are starting up. There's just so much activity. Companies that just add .ai to the name, as Michelle said. How do you cut through the noise and sort of try to get to the heart of is there any value in the technology that company's bringing or not? Definitely. Well, each company has its unique special sauce, right? And so just to reiterate, what Michelle was talking about, we look for companies that are really good at doing what they do best, whatever that may be, whatever that problem that they're solving, that a customer is willing to pay for. We want to make sure that that company is doing that. No one wants a company that just has .ai in the name. So we look for that number one. And the other thing we do is, once we establish that we have a need or we're looking at a company based on either talent or intellectual property, we'll go and we'll have to do a vetting process. And it takes a whole, it's a very long process and there's legal involved. But at the end of the day, the most important thing for the startup to remember is to continue doing what they do best and continue to build upon their special sauce and make sure that that is very valuable to their customer. And if someone else wants to look at them for acquisition, so be it, but you need to be maniacally focused on your own customer. That's kind of my two cents. I'm thinking again about this concept of sort of embedding human intelligence. But humans have biases, right? And sometimes those biases aren't always good. So how do we, as technologists in this industry, sort of try to create AI for good and not unintentionally put some of our own human biases into models that we train about, you know, what's socially acceptable or not? Anyone have any thoughts on that? There you go. I actually think that the hype about AI taking over and could destroy humanity, it's possible and I don't want to disagree with Stephen Hawking because he's way smarter than I am. But, I mean, he kind of recognizes it could go both ways. And so right now we're in a world where we're still feeding the machine. Right? And so there's a bunch of different, you know, issues that came up with humans feeding the machine with their foibles of racism and hatred and bias and humans experience shame which causes them to kind of lash out and want to put somebody else down, right? And so we saw that with Tay, the Microsoft chatbot. We saw that with even like Google's fake news, right? They're like picking sources now to answer the question in the top box that might be the wrong source. Ads that Google serves often show men, you know, high paying jobs, $200,000 a year jobs and women don't get those same ones. So if you trace that back, it's always coming back to the inputs and the lens that humans are kind of coming at it from. So I actually think that we could be in a way better place like after the six singularity happens and the machines are smarter than us and they take over and they become our overlords because when we think about the future, it's a very common tendency for humans to like fill in the blanks that what you don't know in the future with what's true today. And I was talking to you guys at lunch, we were talking about this Harvard psychology professor who wrote a book and in the book he was talking about how in the 1950s, you know, they were imagining the future in all these sci-fi stories and they have flying cars and hovercrafts and they're living in space but the woman still stays at home and everyone's white. So they forgot to like extrapolate the social things and like paint the picture in. So I think when we're extrapolating into the future where the computers are our overlords, we're painting them with our current reality which is where humans are kind of terrible and maybe computers won't be and they'll actually create this like utopia for us. So it could be positive. That's a very positive view. So do we have this all figured out? Are there any sort of big challenges that remain in our industries? So I do want to add a little bit more to the learning because I'm a data scientist by training and a lot of times I run into folks who think that everything's been figured out, everything is done, this is so cool, it's all good to go and one of the things that I share with them is something that I'm sure everyone here can relate to. So if a kindergartner goes to school and starts to spout profanity, that's not because the kid knows anything good or bad. That is what the kid has learned at home. Likewise, if we don't train machines well, its training will in fact be, you know, biased to your point. So one of the things that we have to keep in mind when we talk about this is we have to be careful as well because we're the ones doing the training. It doesn't automatically know what is good or bad unless that set of data is also fed to it. So I just wanted to kind of add to your... Good, thank you. So why don't we open it up a little bit for questions? Any questions in the audience for our panelists? There's one there, it looks like Emily will get to you soon. I had a question for the rest. Based on what you just said about us training, or you all I guess training these models and teaching them things. So I guess when you deploy these models to the public with them being machine learning and AI based, is it possible for us to retrain them and how do you kind of build in redundancies for the public kind of, you know, like throwing off your model and things like that? What are some of the considerations that go into that? Well, one thing for sure is training is continuous. So no system should be trained once deployed and then forgotten. And so that is something that we as AI professionals need to be absolutely. Because trends change as well. What was optimal two years ago is no longer optimal. So that part needs to continue to happen. And where the whole IoT space is so important is it will continue to generate relevant, consumable data that these machines can continue to learn. So how do you decide what data those good or bad to as you retrain and evolve that model over time? As a data scientist, how do you do sort of selection on data? So, and I want to piggyback on what Michelle said because she's spot on. What is the problem that you're trying to solve? It always starts from there because we have folks who come in, the CIOs, oh look, when big data was hot we started to collect a lot of the data and nothing has happened. But data by itself doesn't automatically do magic for you. So we ask, what kind of problem are you trying to solve? Are you trying to figure out what kinds of products to sell? So are you trying to figure out the optimal assortment mix for you? Are you trying to find the shortest path in order to get to your stores? And then the question is, do you now have the right data to solve that problem? A lot of times we put the science, and I'm a data scientist by training. I would love to talk about the science, but really it's the problem first, the data and the science that come after that. Thanks, good advice. Any other questions in the audience? Test, test, can you hear me? Yep. So with AI machine learning becoming more commonplace and becoming more accessible to developers and visionaries and thinkers alike, right, rather than being just a giant warehouse with a ton of machines and you get one tiny kind of machine learning, do you foresee more governance coming to play in terms of what AI is allowed to do and the decisions of what training data is allowed to be fed to AI in terms of influence? You know, you've talked about like, you know, data determining if it's AI will become good or bad, but humans being the ones responsible for the training in the first place, obviously they can use that data to influence as they speak. It's kind of the governance and the influence, right? I'll take a quick stab at it. So yes, it's going to be an open discussion. It's going to have to take place, because really they're just machines. It's machine learning. We teach it. We teach it what to do, how to act. It's just an extension of us. And in fact, I think you had a really great conversation or a statement at lunch where you talked about your product being an extension of a designer because, and we can get into that a little bit, but really it's just going to do what we tell it to do. So there's definitely going to have to be discussions about what type of data we feed. It's all going to be centered around the use case of what best solves the use case, but I imagine that that will be a topic of discussion for a long time about what we're going to decide to do. So if you want to comment on this thought of taking a designer's brain and putting it into a model somehow. Well actually what I wanted to say was that I think the regulation and the kind of governance around it is going to be self-imposed by the developer and data science community first, because I feel like even experts who have been doing this for a long time don't really have their arms around what we're dealing with here. And so to expect our senators, our congressmen and women to actually make like regulation around it is a lot because they're not technologists by training, they have a lot of other stuff going on. If the community that's already doing the work doesn't quite know what we're dealing with, then how can we expect them to get there? So I feel like that's going to be a long way off, but I think that the people who touch and feel and deal with models and with data sets and stuff every day are the kind of people who are going to get together and kind of self-regulate for a while if they're good-hearted people, right? We talk about AI for good. Some people are bad. Those people won't respect those covenants that we come up with, but I think that's the place we have to start. So really you're saying I think for data scientists and those of us working in this space we kind of have a social, ethical or moral obligation to humanity to ensure that our work is used for good. No pressure. Okay. None taken. Sure. Good. Any other questions? I just wanted to talk about the second part of what you said. We've been working with a company that builds robots for the store. A store associated, if you will. And one of their very interesting findings was that the greatest acceptance of it right now has been at car dealerships. Because when someone goes to a car dealer and we all have had terrible experiences doing that, that's why we try to buy it online. But just this perception that a robot would be unbiased, that it will give me information without trying to push me one way or the other. So there's that perception side of it too that isn't the governance part of your question, but more the bias perception side of what you said, which I think is fascinating how we're already trained to kind of think that this is going to have an unbiased opinion, whether or not that's true. Right? That's fascinating. Very cool. Thank you, Suresh. Any other questions in the audience? No? Michelle, I could ask, you've got a station over there that talks a little bit more about your company, but for those that haven't seen it yet, could you tell us a little bit about, you know, what is the experience like or how is the shopping experience different for someone that's using your company's technology than what it was before? Free advertising, I would love to. No, but actually I started this company because as a consumer I found myself going back to the user experience piece just constantly frustrated with the user experience of buying products one at a time and then getting zero help, and then here I am having to like Google how to wear a white blazer to not look like an idiot in the morning when I get dressed with my white blazer that I just bought and I was excited about. And it's a really simple thing which is like, how do I use the product that I'm buying? And that really simple thing has been just abysmally like handled in the retail industry because the only tools that the retailers have right now are manual. So in fashion, some of our fashion customers like John Barbados is an example we have over there. It's like a designer for high-end men's clothing. And John Barbados is a person. Like it's not just a named company. He's an actual person and he has a vision for what he wants his products to look like and the aesthetic and the style and there's like a rock star vibe. To get that information into the organization, he would share it verbally with PDFs, things like that. And then his team of merchandisers would literally go manually make outfits on one page and then go make an outfit on another page with the same exact items and then products would go out of stock and they'd go around in circles and that's a terrible, terrible job. So the conversation earlier about people losing jobs because of artificial intelligence, I hope people do lose jobs and I hope they're the terrible jobs that no one wanted to do in the first place because the merchandisers that we help, like the one from John Barbados literally said she was like weeks away from quitting and she got a new boss and said, if you don't fix this part of my job, I'm out of here. And he had heard about us, he knew about us and so he brought us in to solve that problem. So I don't think it's always a bad thing because if we can take that kind of rote, boring, repetitive task off of humans' plates, what more amazing things can we do with our brain that is only human and very unique to us and how much more can we advance ourselves in our society by giving the boring work to a robot or a machine. That's fantastic. So Joe, when you talk about smart cities it seems like people have been talking about smart cities for decades and often people cite funding issues, the regulatory environment or a host of other reasons why these things sort of haven't happened. Do you think we're on the cusp of breaking through there or what challenges still remain for filling that vision of a smart city? I do, I do think we're on the cusp. I think a lot of it has to do largely actually with 5G connectivity, the ability to process and send all this data that needs to be shared across the system. I also think that we're getting closer and more conscientious about security which is a major issue with IoT making sure that our end devices or our edge devices and those things out there sensing are secure. And I think interoperability is something that we need to champion as well and make sure that we basically work together to enable these systems. They're very, very difficult to create little tiny walled gardens of solutions in a smart city. You may corner a certain part of the market but you're definitely not going to have that ubiquitous benefit to society if you establish those little walled gardens. So those are the areas I think we need to focus on and I think we are making serious progress in all of them. Michelle, you mentioned earlier that artificial intelligence is kind of all around us in lots of places and things we do on a daily basis but we probably don't realize it. Could you share a couple of examples? Yeah, so I think everything you do online for the most part, literally anything you might do, whether it's Googling something or you go to some article, the ads might be dynamically picked for you using machine learning models that have decided what is appropriate based on you and your treasure trove of data that you have out there that you're giving up all the time and not really understanding you're giving up. Yeah, exactly. So that's basically anything online. I'm trying to think of it in the real world. I think that to your point earlier about the supply chain, just picking a box of cereal off the shelf and taking it home, there was no artificial intelligence in that at all but the supply chain behind it, so the supply chain behind pretty much everything we do even in television, right? Like how media gets to us and gets consumed at some point in the supply chain. There's artificial intelligence playing in there as well. So Suresh, in a supply chain where we can get same day even within the hour sort of delivery, how do you get better than that? What's coming that's innovative in the supply chain that will be new in the future? Well, so that is one example of it but you'd be surprised at how inefficient the supply chain is even with all the advances that have already gone in whether it's the physical advances around building modern warehouses and modern manufacturing plants or whether it's through software and others that really help schedule things and optimize things. What has happened in the supply chain just given how they've evolved is they're very siloed. So a lot of times the manufacturing plant does things that the distribution folks do not know. The distribution folks do things that the transportation folks don't know and then the store folks know nothing other than when the truck pulls up that's the first time they find out about things. So where the great opportunity in my mind is in the spaces that I'm in is really the generation of data, the connection of data and finally the deriving the smarts that really help us improve efficiency. There's huge opportunity there. And again we don't know it because it's all invisible to us. Good. Let me pause and see if there's any questions in the audience. I think we got one there. Thank you. Hi guys, you alright? I just had a question about ethics and the teaching of ethics. As you were saying we we feed the artificial intelligence whereas in a scenario which is probably a little bit more attuned to automated driving in a car crash scenario between doing crash into two people or three people I would be choosing two whereas the scenario maybe actually is better to crash the car and kill myself. That thought would never go through my mind because I'm human. My rule number one is self-preservation. So how do we teach the computer this sort of side of it? Is it actually the AI ethic going to be better than our own ethics? How is that? Yeah it's a great question I think. I think the opportunity is there as Michelle was talking earlier about maybe when you cross that chasm and you get this new singularity maybe the AI ethics will be better than human ethics because the machine will be able to think about greater concerns than perhaps other than ourselves. But I think just from my point of view working in the space of automated vehicles I think it is going to have to be something that the industry and societies and societies are different in different geographies in different countries we have different ways of looking at the world cultures value different things so I think technologists in those spaces are going to have to get together and sort of agree amongst the community from a social contract theory standpoint perhaps in a way that's going to be acceptable to everyone who lives in that environment. I don't think we can come up with a uniform model that would apply to all spaces but it's got to be something though that we all as members of a community can accept and say yep that would be the right thing to do in that situation. And that's not going to be an easy task by any means which is I think one of the reasons why you'll continue to see humans have an important role to play in automated vehicles so that the human could take over in exactly that kind of scenario because the machines perhaps aren't quite smart enough to do it or maybe it's not the smart to the processing capability it's maybe that we haven't as technologists and ethicists gotten together long enough to figure out what are those moral and ethical frameworks that we could use to apply to those situations. Any other thoughts from the panel? Yeah, I wanted to jump in there real quick. Absolutely questions that need to be answered but let's come together and make the solution that needs to have those questions answered, right? So let's come together first and fix the problems that need to be fixed now so that we can build out those types of scenarios we can now put our brain power of work to decide what to do next. There was a quote I believe by Andrew Ng from Baidu he was saying concerning questions about what's going to happen in the future with AI are we going to have AI overlords or anything like that and instead it's kind of like worrying about overpopulation at the planet Mars. Because maybe we're going to get there someday and maybe we're going to send people there and maybe we're going to establish a human population on Mars and then maybe it will get too big and we'll have problems on Mars but right now we haven't landed on the planet and I thought that really does a good job of that overall concern about AI taking over. So when you think about AI being applied for good and Michelle you talked about don't do AI just for AI's sake have a problem to solve. I'll open it up to any of the three of you what's the problem in your life or in your work experience that you'd love somebody out here would go solve with AI? I have one, sorry I want to take this real quick. There's roads blocked off and it's raining and I have to walk a mile to find a taxi in the rain right now after this to go home. I would love for us to have some sort of ability to manage parking spaces and determine when and who can come into which parts of the city and when there's a spot downtown I want my autonomous vehicle to know which one's available and go directly to that spot and I want it to be queued in a certain manner to where I'm next in line and I know I would love for someone to go solve that problem. There's been some development on the infrastructure side for that kind of solution we have a partnership Intel does with GE and we're putting sensors that have it's an IoT sensor basically it's called Citi IQ it has environmental monitoring audio visual and it allows this type of use case to take place so I would love to see iterations on that I would love to see sorry there's another one in particular about growing up I lived in Southern California right on the hill right against the hills a housing development against the hills and there was not a factory but a bunch of oil derricks back there I would love to have a sensor that sense the particulate in the air to see if there was too many fumes coming from that oil field into my into my yard grown up as a little kid I would love for us to solve problems like that so that's the type of thing that we'll be able to solve those are the types of innovations that we'll be able to take place so I'm going to sit down on that one and let someone else take it I'm really glad you said the second one because I was like thinking what I'm about to say is totally going to trivialize Joe's pain and I don't want to do that but cancer is my answer because there's so much data in health and all these patterns are there waiting to be recognized there's so many things we don't know about cancer and so many indicators that we could capture if we just were able to amass the data and take a look but I knew a great brilliant company that was using artificial intelligence specifically around image processing to look at CAT scans and figure out what the leading indicators might be in a cancerous scenario and they pivoted to some way more trivial way more trivial problem which is still a problem and not to trivialize parking and whatnot but it's not cancer and they pivoted away from this amazing opportunity because of the privacy and the issues with HIPAA around health data and I understand there's a ton of concern with getting into the wrong hands and hacking and all of this stuff I get that but the opportunity in my mind far outweighs the risk and the fact that they had to change their business model and change their company essentially like broke my heart because they were really onto something yeah that's a shame and it's funny you mentioned that Intel has an effort that we're calling the cancer cloud what we're trying to do is provide some infrastructure to help with that problem and the way cancer treatments work today is if you go to a university hospital let's say here in Texas how you interpret that scan and how you respond and apply treatment that knowledge is basically just kept within that hospital and within that staff and so on the other side of the country somebody could go in and get a scan maybe that scans brand new to that facility and so they don't know how to treat it but if you had an opportunity with machine learning to sort of be able to compare scans from people not only just in this country but around the world and understand globally all of the hundreds of different treatment paths that were applied to that particular kind of cancer I think how many lives could be saved because then you're sharing knowledge with what treatment courses of treatment worked you know but it's one of those things like you say sometimes it's the regulatory environment or it's other factors that sort of hold us back from applying this technology to do some really good things it's a great example any other questions in the audience I have one so this goes on off of the HIPAA question and you were talking about dynamically displaying ads earlier what does privacy look like in a fully autonomous world anybody can answer that one are we still private citizens how about from a supply chain standpoint and you can learn a lot about somebody in terms of the products that they buy and I think to all of us we sort of know maybe somebody is tracking what we're buying but it's still kind of creepy when we think about how people could potentially use that against us so how do you from a supply chain standpoint approach that problem yeah it's something that comes up in my life almost every day because one of the things we'd like to do is to understand consumer behavior you know how often am I buying what kinds of products am I buying you know what am I returning and so for that you need a transactional data you really get to understand the individual now that then starts to get into this area of privacy do you know too much about me and so a lot of times what what we do is data is clearly anonymized so all we know is customer A has this tendency customer B has this tendency and that then helps retailers you know offer the right products to these customers but to your point there are those privacy concerns and I think issues around governance issues around ethics issues around privacy these will continue to be ironed out I don't think there's an answer a solid answer for any of these just yet and it's largely a reflection of society how comfortable are we with how much privacy right now I believe we put the individual in control of as much information as possible that they are able to release or not right and so a lot of like you said everyone's anonymizing at the moment but it's really that may change as society's values change slightly and we'll be able to adapt as necessary why don't we try to stump the panel anyone have any ideas on things in your life you'd like to be solved with AI for good any suggestions out there and we could then hear from our data scientists and technologists and folks here any ideas no alright good alright well thank you everyone really appreciate your time thank you for joining Intel here in the AI lounge at Autonomous World we hope you've enjoyed the panel and we wish you a great rest of your event here at South by Southwest thank you