>> Announcer: Live from Las Vegas, it's theCUBE! Covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel, along with its ecosystem partners. >> Welcome back to the Sands Convention Center in Las Vegas everybody, you're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante, I'm here with my cohost Justin Warren, this is day one of our coverage of AWS re:Invent 2019, Naveen Rao here, he's the corporate vice president and general manager of artificial intelligence, AI products group at Intel, good to see you again, thanks for coming to theCUBE. >> Thanks for having me. >> Dave: You're very welcome, so what's going on with Intel and AI, give us the big picture. >> Yeah, I mean actually the very big picture is I think the world of computing is really shifting. The purpose of what a computer is made for is actually shifting, and I think from its very conception, from Alan Turing, the machine was really meant to be something that recapitulated intelligence, and we took sort of a divergent path where we built applications for productivity, but now we're actually coming back to that original intent, and I think that hits everything that Intel does, because we're a computing company, we supply computing to the world, so everything we do is actually impacted by AI, and will be in service of building better AI platforms, for intelligence at the edge, intelligence in the cloud, and everything in between. >> It's really come full circle, I mean, when I first started this industry, AI was the big hot topic, and really, Intel's ascendancy was around personal productivity, but now we're seeing machines replacing cognitive functions for humans, that has implications for society. But there's a whole new set of workloads that are emerging, and that's driving, presumably, different requirements, so what do you see as the sort of infrastructure requirements for those new workloads, what's Intel's point of view on that? >> Well, so maybe let's focus that on the cloud first. Any kind of machine learning algorithm typically has two phases to it, one is called training or learning, where we're really iterating over large data sets to fit model parameters. And once that's been done to a satisfaction of whatever performance metrics that are relevant to your application, it's rolled out and deployed, that phase is called inference. So these two are actually quite different in their requirements in that inference is all about the best performance per watt, how much processing can I shove into a particular time and power budget? On the training side, it's much more about what kind of flexibility do I have for exploring different types of models, and training them very very fast, because when this field kind of started taking off in 2014, 2013, typically training a model back then would take a month or so, those models now take minutes to train, and the models have grown substantially in size, so we've still kind of gone back to a couple of weeks of training time, so anything we can do to reduce that is very important. >> And why the compression, is that because of just so much data? >> It's data, the sheer amount of data, the complexity of data, and the complexity of the models. So, very broad or a rough categorization of the complexity can be the number of parameters in a model. So, back in 2013, there were, call it 10 million, 20 million parameters, which was very large for a machine learning model. Now they're in the billions, one or two billion is sort of the state of the art. To give you bearings on that, the human brain is about a three to 500 trillion model, so we're still pretty far away from that. So we got a long way to go. >> Yeah, so one of the things about these models is that once you've trained them, that then they do things, but understanding how they work, these are incredibly complex mathematical models, so are we at a point where we just don't understand how these machines actually work, or do we have a pretty good idea of, "No no no, when this model's trained to do this thing, "this is how it behaves"? >> Well, it really depends on what you mean by how much understanding we have, so I'll say at one extreme, we trust humans to do certain things, and we don't really understand what's happening in their brain. We trust that there's a process in place that has tested them enough. A neurosurgeon's cutting into your head, you say you know what, there's a system where that neurosurgeon probably had to go through a ton of training, be tested over and over again, and now we trust that he or she is doing the right thing. I think the same thing is happening in AI, some aspects we can bound and say, I have analytical methods on how I can measure performance. In other ways, other places, it's actually not so easy to measure the performance analytically, we have to actually do it empirically, which means we have data sets that we say, "Does it stand up to all the different tests?" One area we're seeing that in is autonomous driving. Autonomous driving, it's a bit of a black box, and the amount of situations one can incur on the road are almost limitless, so what we say is, for a 16 year old, we say "Go out and drive," and eventually you sort of learn it. Same thing is happening now for autonomous systems, we have these training data sets where we say, "Do you do the right thing in these scenarios?" And we say "Okay, we trust that you'll probably "do the right thing in the real world." >> But we know that Intel has partnered with AWS, I ran autonomous driving with their DeepRacer project, and I believe it's on Thursday is the grand final, it's been running for, I think it was announced on theCUBE last year, and there's been a whole bunch of competitions running all year, basically training models that run on this Intel chip inside a little model car that drives around a race track, so speaking of empirical testing of whether or not it works, lap times gives you a pretty good idea, so what have you learned from that experience, of having all of these people go out and learn how to use these ALM models on a real live race car and race around a track? >> I think there's several things, I mean one thing is, when you turn loose a number of developers on a competitive thing, you get really interesting results, where people find creative ways to use the tools to try to win, so I always love that process, I think competition is how you push technology forward. On the tool side, it's actually more interesting to me, is that we had to come up with something that was adequately simple, so that a large number of people could get going on it quickly. You can't have somebody who spends a year just getting the basic infrastructure to work, so we had to put that in place. And really, I think that's still an iterative process, we're still learning what we can expose as knobs, what kind of areas of innovation we allow the user to explore, and where we sort of walk it down to make it easy to use. So I think that's the biggest learning we get from this, is how I can deploy AI in the real world, and what's really needed from a tool chain standpoint. >> Can you talk more specifically about what you guys each bring to the table with your collaboration with AWS? >> Yeah, AWS has been a great partner. Obviously AWS has a huge ecosystem of developers, all kinds of different developers, I mean web developers are one sort of developer, database developers are another, AI developers are yet another, and we're kind of partnering together to empower that AI base. What we bring from a technological standpoint are of course the hardware, our CPUs, our AI ready now with a lot of software that we've been putting out in the open source. And then other tools like OpenVINO, which make it very easy to start using AI models on our hardware, and so we tie that in to the infrastructure that AWS is building for something like DeepRacer, and then help build a community around it, an ecosystem around it of developers. >> I want to go back to the point you were making about the black box, AI, people are concerned about that, they're concerned about explainability. Do you feel like that's a function of just the newness that we'll eventually get over, and I mean I can think of so many examples in my life where I can't really explain how I know something, but I know it, and I trust it. Do you feel like it's sort of a tempest in a teapot? >> Yeah, I think it depends on what you're talking about, if you're talking about the traceability of a financial transaction, we kind of need that maybe for legal reasons, so even for humans we do that. You got to write down everything you did, why did you do this, why'd you do that, so we actually want traceability for humans, even. In other places, I think it is really about the newness. Do I really trust this thing, I don't know what it's doing. Trust comes with use, after a while it becomes pretty straightforward, I mean I think that's probably true for a cell phone, I remember the first smartphones coming out in the early 2000s, I didn't trust how they worked, I would never do a credit card transaction on 'em, these kind of things, now it's taken for granted. I've done it a million times, and I never had any problems, right? >> It's the opposite in social media, most people. >> Maybe that's the opposite, let's not go down that path. >> I quite like Dr. Kate Darling's analogy from MIT lab, which is we already we have AI, and we're quite used to them, they're called dogs. We don't fully understand how a dog makes a decision, and yet we use 'em every day. In a collaboration with humans, so a dog, sort of replace a particular job, but then again they don't, I don't particularly want to go and sniff things all day long. So having AI systems that can actually replace some of those jobs, actually, that's kind of great. >> Exactly, and think about it like this, if we can build systems that are tireless, and we can basically give 'em more power and they keep going, that's a big win for us. And actually, the dog analogy is great, because I think, at least my eventual goal as an AI researcher is to make the interface for intelligent agents to be like a dog, to train it like a dog, reinforce it for the behaviors you want and keep pushing it in new directions that way, as opposed to having to write code that's kind of esoteric. >> Can you talk about GANs, what is GANs, what's it stand for, what does it mean? >> Generative Adversarial Networks. What this means is that, you can kind of think of it as, two competing sides of solving a problem. So if I'm trying to make a fake picture of you, that makes it look like you have no hair, like me, you can see a Photoshop job, and you can kind of tell, that's not so great. So, one side is trying to make the picture, and the other side is trying to guess whether it's fake or not. We have two neural networks that are kind of working against each other, one's generating stuff, and the other one's saying, is it fake or not, and then eventually you keep improving each other, this one tells that one "No, I can tell," this one goes and tries something else, this one says "No, I can still tell." The one that's trying with a discerning network, once it can't tell anymore, you've kind of built something that's really good, that's sort of the general principle here. So we basically have two things kind of fighting each other to get better and better at a particular task. >> Like deepfakes. >> I use that because it is relevant in this case, and that's kind of where it came from, is from GANs. >> All right, okay, and so wow, obviously relevant with 2020 coming up. I'm going to ask you, how far do you think we can take AI, two part question, how far can we take AI in the near to mid term, let's talk in our lifetimes, and how far should we take it? Maybe you can address some of those thoughts. >> So how far can we take it, well, I think we often have the sci-fi narrative out there of building killer machines and this and that, I don't know that that's actually going to happen anytime soon, for several reasons, one is, we build machines for a purpose, they don't come from an embattled evolutionary past like we do, so their motivations are a little bit different, say. So that's one piece, they're really purpose-driven. Also, building something that's as general as a human or a dog is very hard, and we're not anywhere close to that. When I talked about the trillions of parameters that a human brain has, we might be able to get close to that from a engineering standpoint, but we're not really close to making those trillions of parameters work together in such a coherent way that a human brain does, and efficient, human brain does that in 20 watts, to do it today would be multiple megawatts, so it's not really something that's easily found, just laying around. Now how far should we take it, I look at AI as a way to push humanity to the next level. Let me explain what that means a little bit. Simple equation I always sort of write down, is people are like "Radiologists aren't going to have a job." No no no, what it means is one radiologist plus AI equals 100 radiologists. I can take that person's capabilities and scale it almost freely to millions of other people. It basically increases the accessibility of expertise, we can scale expertise, that's a good thing. It makes, solves problems like we have in healthcare today. All right, that's where we should be going with this. >> Well a good example would be, when, and probably part of the answer's today, when will machines make better diagnoses than doctors? I mean in some cases it probably exists today, but not broadly, but that's a good example, right? >> It is, it's a tool, though, so I look at it as more, giving a human doctor more data to make a better decision on. So, what AI really does for us is it doesn't limit the amount of data on which we can make decisions, as a human, all I can do is read so much, or hear so much, or touch so much, that's my limit of input. If I have an AI system out there listening to billions of observations, and actually presenting data in a form that I can make better decisions on, that's a win. It allows us to actually move science forward, to move accessibility of technologies forward. >> So keeping the context of that timeframe I said, someday in our lifetimes, however you want to define that, when do you think that, or do you think that driving your own car will become obsolete? >> I don't know that it'll ever be obsolete, and I'm a little bit biased on this, so I actually race cars. >> Me too, and I drive a stick, so. >> I kind of race them semi-professionally, so I don't want that to go away, but it's the same thing, we don't need to ride horses anymore, but we still do for fun, so I don't think it'll completely go away. Now, what I think will happen is that commutes will be changed, we will now use autonomous systems for that, and I think five, seven years from now, we will be using autonomy much more on prescribed routes. It won't be that it completely replaces a human driver, even in that timeframe, because it's a very hard problem to solve, in a completely general sense. So, it's going to be a kind of gentle evolution over the next 20 to 30 years. >> Do you think that AI will change the manufacturing pendulum, and perhaps some of that would swing back to, in this country, anyway, on-shore manufacturing? >> Yeah, perhaps, I was in Taiwan a couple of months ago, and we're actually seeing that already, you're seeing things that maybe were much more labor-intensive before, because of economic constraints are becoming more mechanized using AI. AI as inspection, did this machine install this thing right, so you have an inspector tool and you have an AI machine building it, it's a little bit like a GAN, you can think of, right? So this is happening already, and I think that's one of the good parts of AI, is that it takes away those harsh conditions that humans had to be in before to build devices. >> Do you think AI will eventually make large retail stores go away? >> Well, I think as long as there are humans who want immediate satisfaction, I don't know that it'll completely go away. >> Some humans enjoy shopping. >> Naveen: Some people like browsing, yeah. >> Depends how fast you need to get it. And then, my last AI question, do you think banks, traditional banks will lose control of the payment systems as a result of things like machine intelligence? >> Yeah, I do think there are going to be some significant shifts there, we're already seeing many payment companies out there automate several aspects of this, and reducing the friction of moving money. Moving money between people, moving money between different types of assets, like stocks and Bitcoins and things like that, and I think AI, it's a critical component that people don't see, because it actually allows you to make sure that first you're doing a transaction that makes sense, when I move from this currency to that one, I have some sense of what's a real number. It's much harder to defraud, and that's a critical element to making these technologies work. So you need AI to actually make that happen. >> All right, we'll give you the last word, just maybe you want to talk a little bit about what we can expect, AI futures, or anything else you'd like to share. >> I think it's, we're at a really critical inflection point where we have something that works, basically, and we're going to scale it, scale it, scale it to bring on new capabilities. It's going to be really expensive for the next few years, but we're going to then throw more engineering at it and start bringing it down, so I start seeing this look a lot more like a brain, something where we can start having intelligence everywhere, at various levels, very low power, ubiquitous compute, and then very high power compute in the cloud, but bringing these intelligent capabilities everywhere. >> Naveen, great guest, thanks so much for coming on theCUBE. >> Thank you, thanks for having me. >> You're really welcome, all right, keep it right there everybody, we'll be back with our next guest, Dave Vellante for Justin Warren, you're watching theCUBE live from AWS re:Invent 2019. We'll be right back. (techno music)

(upbeat music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE conversation. >> Hello, and welcome to theCUBE Studios here in Palo Alto, California. We have a special Around theCUBE segment, Unpacking AI. This is a Get Smart Series. We have three great guests. Rajen Sheth, VP of AI and Product Management at Google. He knows well the AI development for Google Cloud. Dr. Kate Darling, research specialist at MIT media lab. And Professor Barry O'Sullivan, Director SFI Centre for Training AI, University of College Cork in Ireland. Thanks for coming on, everyone. Let's get right to it. Ethics in AI as AI becomes mainstream, moves out to the labs and computer science world to mainstream impact. The conversations are about ethics. And this is a huge conversation, but first thing people want to know is, what is AI? What is the definition of AI? How should people look at AI? What is the definition? We'll start there, Rajen. >> So I think the way I would define AI is any way that you can make a computer intelligent, to be able to do tasks that typically people used to do. And what's interesting is that AI is something, of course, that's been around for a very long time in many different forms. Everything from expert systems in the '90s, all the way through to neural networks now. And things like machine learning, for example. People often get confused between AI and machine learning. I would think of it almost the way you would think of physics and calculus. Machine learning is the current best way to use AI in the industry. >> Kate, your definition of AI, do you have one? >> Well, I find it interesting that there's no really good universal definition. And also, I would agree with Rajen that right now, we're using kind of a narrow definition when we talk about AI, but the proper definition is probably much more broad than that. So probably something like a computer system that can make decisions independent of human input. >> Professor Barry, your take on the definition of AI, is there one? What's a good definition? >> Well, you know, so I think AI has been around for 70 years, and we still haven't agreed the definition for it, as Kate said. I think that's one of those very interesting things. I suppose it's really about making machines act and behave rationally in the world, ideally autonomously, so without human intervention. But I suppose these days, AI is really focused on achieving human level performance in very narrowly defined tasks, you know, so game playing, recommender systems, planning. So we do those in isolation. We don't tend to put them together to create the fabled artificial general intelligence. I think that's something that we don't tend to focus on at all, actually if that made sense. >> Okay the question is that AI is kind of elusive, it's changing, it's evolving. It's been around for awhile, as you guys pointed out, but now that it's on everyone's mind, we see it in the news every day from Facebook being a technology program with billions of people. AI was supposed to solve the problem there. We see new workloads being developed with cloud computing where AI is a critical software component of all this. But that's a geeky world. But the real world, as an ethical conversation, is not a lot of computer scientists have taken ethics classes. So who decides what's ethical with AI? Professor Barry, let's start with you. Where do we start with ethics? >> Yeah, sure, so one of the things I do is I'm the Vice-Chair of the European Commission's High-Level Expert Group on Artificial Intelligence, and this year we published the Ethics Guidelines for Trustworthy AI in Europe, which is all about, you know, setting an ethical standard for what AI is. You're right, computer scientists don't take ethical standards, but I suppose what we are faced with here is a technology that's so pervasive in our lives that we really do need to think carefully about the impact of that technology on, you know, human agency, and human well-being, on societal well-being. So I think it's right and proper that we're talking about ethics at this moment in time. But, of course, we do need to realize that ethics is not a panacea, right? So it's certainly something we need to talk about, but it's not going to solve, it's not going to rid us of all of the detrimental applications or usages of AI that we might see today. >> Kate, your take on ethics. Start all over, throw out everything, build on it, what do we do? >> Well, what we do is we get more interdisciplinary, right? I mean, because you asked, "Who decides?". Until now it has been the people building the technology who have had to make some calls on ethics. And it's not, you know, it's not necessarily the way of thinking that they are trained in, and so it makes a lot of sense to have projects like the one that Barry is involved in, where you bring together people from different areas of expert... >> I think we lost Kate there. Rajen, why don't you jump in, talk about-- >> (muffled speaking) you decide issues of responsibility for harm. We have to look at algorithmic bias. We have to look at supplementing versus replacing human labor, we have to look at privacy and data security. We have look at the things that I'm interested in like the ways that people anthropomorphize the technology and use it in a way that's perhaps different than intended. So, depending on what issue we're looking at, we need to draw from a variety of disciplines. And fortunately we're seeing more support for this within companies and within universities as well. >> Rajen, your take on this. >> So, I think one thing that's interesting is to step back and understand why this moment is so compelling and why it's so important for us to understand this right now. And the reason for that is that this is the moment where AI is starting to have an impact on the everyday person. Anytime I present, I put up a slide of the Mosaic browser from 1994 and my point is that, that's where AI is today. It's at the very beginning stages of how we can impact people, even though it's been around for 70 years. And what's interesting about ethics, is we have an opportunity to do that right from the beginning right now. I think that there's a lot that you can bring in from the way that we think about ethics overall. For example, in our company, can you all hear me? >> Yep. >> Mm-hmm. >> Okay, we've hired an ethicist within our company, from a university, to actually bring in the general principles of ethics and bring that into AI. But I also do think that things are different so for example, bias is an ethical problem. However, bias can be encoded and actually given more legitimacy when it could be encoded in an algorithm. So, those are things that we really need to watch out for where I think it is a little bit different and a little bit more interesting. >> This is a great point-- >> Let me just-- >> Oh, go ahead. >> Yeah, just one interesting thing to bear in mind, and I think Kate said this, and I just want to echo it, is that AI is becoming extremely multidisciplinary. And I think it's no longer a technical issue. Obviously there are massive technical challenges, but it's now become as much an opportunity for people in the social sciences, the humanities, ethics people. Legal people, I think need to understand AI. And in fact, I gave a talk recently at a legal symposium, and the idea of this on a parallel track of people who have legal expertise and AI expertise, I think that's a really fantastic opportunity that we need to bear in mind. So, unfortunately us nerds, we don't own AI anymore. You know, it's something we need to interact with the real world on a significant basis. >> You know, I want to ask a question, because you know, the algorithms, everyone talks about the algorithms and the bias and all that stuff. It's totally relevant, great points on interdisciplinary, but there's a human component here. As AI starts to infiltrate the culture and hit everyday life, the reaction to AI sometimes can be, "Whoa, my job's going to get automated away." So, I got to ask you guys, as we deal with AI, is that a reflection on how we deal with it to our own humanity? Because how we deal with AI from an ethics standpoint ultimately is a reflection on our own humanity. Your thoughts on this. Rajen, we'll start with you. >> I mean it is, oh, sorry, Rajen? >> So, I think it is. And I think that there are three big issues that I see that I think are reflective of ethics in general, but then also really are particular to AI. So, there's bias. And bias is an overall ethical issue that I think this is particular here. There's what you mentioned, future of work, you know, what does the workforce look like 10 years from now. And that changes quite a bit over time. If you look at the workforce now versus 30 years ago, it's quite a bit different. And AI will change that radically over the next 10 years. The other thing is what is good use of AI, and what's bad use of AI? And I think one thing we've discovered is that there's probably 10% of things that are clearly bad, and 10% of things that are clearly good, and 80% of things that are in that gray area in between where it's up to kind of your personal view. And that's the really really tough part about all this. >> Kate, you were going to weigh in. >> Well, I think that, I'm actually going to push back a little, not on Rajen, but on the question. Because I think that one of the fallacies that we are constantly engaging in is we are comparing artificial intelligence to human intelligence, and robots to people, and we're failing to acknowledge sufficiently that AI has a very different skillset than a person. So, I think it makes more sense to look at different analogies. For example, how have we used and integrated animals in the past to help us with work? And that lets us see that the answer to questions like, "Will AI disrupt the labor market?" "Will it change infrastructures and efficiencies?" The answer to that is yes. But will it be a one-to-one replacement of people? No. That said, I do think that AI is a really interesting mirror that we're holding up to ourselves to answer certain questions like, "What is our definition of fairness?" for example. We want algorithms to be fair. We want to program ethics into machines. And what it's really showing us is that we don't have good definitions of what these things are even though we thought we did. >> All right, Professor Barry, your thoughts? >> Yeah, I think there's many points one could make here. I suppose the first thing is that we should be seeing AI, not as a replacement technology, but as an assistive technology. It's here to help us in all sorts of ways to make us more productive, and to make us more accurate in how we carry out certain tasks. I think, absolutely the labor force will be transformed in the future, but there isn't going to be massive job loss. You know, the technology has always changed how we work and play and interact with each other. You know, look at the smart phone. The smart phone is 12 years old. We never imagined in 2007 that our world would be the way it is today. So technology transforms very subtly over long periods of time, and that's how it should be. I think we shouldn't fear AI. I think the thing we should fear most, in fact, is not Artificial Intelligence, but is actual stupidity. So I think we need to, I would encourage people not to think, it's very easy to talk negatively and think negatively about AI because it is such a impactful and promising technology, but I think we need to keep it real a little bit, right? So there's a lot of hype around AI that we need to sort of see through and understand what's real and what's not. And that's really some of the challenges we have to face. And also, one of the big challenges we have, is how do we educate the ordinary person on the street to understand what AI is, what it's capable of, when it can be trusted, and when it cannot be trusted. And ethics gets of some of the way there, but it doesn't have to get us all of the way there. We need good old-fashioned good engineering to make people trust in the system. >> That was a great point. Ethics is kind of a reflection of that mirror, I love that. Kate, I want to get to that animal comment about domesticating technology, but I want to stay in this culture question for a minute. If you look at some of the major tech companies like Microsoft and others, the employees are revolting around their use of AI in certain use cases. It's a knee-jerk reaction around, "Oh my God, "We're using AI, we're harming the world." So, we live in a culture now where it's becoming more mission driven. There's a cultural impact, and to your point about not fearing AI, are people having a certain knee-jerk reaction to AI because you're seeing cultures inside tech companies and society taking an opinion on AI. "Oh my God, it's definitely bad, our company's doing it. "We should not service those contracts. "Or, maybe I shouldn't buy that product "because it's listening to me." So, there's a general fear. Does this impact the ethical conversation? How do you guys see this? Because this is an interplay that we see that's a personal, it's a human reaction. >> Yeah, so if I may start, I suppose, absolutely there are, you know, the ethics debates is a critical one, and people are certainly fearful. There is this polarization in debate about good AI and bad AI, but you know, AI is good technology. It's one of these dual-use technologies. It can be applied to bad situation in ways that we would prefer it wasn't. And it can also, it's a force for tremendous good. So, we need to think about the regulation of AI, what we want it to do from a legal point of view, who is responsible, where does liability lie? We also think about what our ethical framework is, and of course, there is no international agreement on what is, there is no universal code of ethics, you know? So this is something that's very very heavily contextualized. But I think we certainly, I think we generally agree that we want to promote human well-being. We want to compute, we want to have a prosperous society. We want to protect the well-being of society. We don't want technology to impact society in any negative way. It's actually very funny. If you look back about 25-30 years ago, there was a technology where people were concerned that privacy was going to be a thing of the past. That computer systems were going to be tremendously biased because data was going to be incomplete and not representative. And there was a huge concern that good old-fashioned databases were going to be the technology that will destroy the fabric of society. That didn't happen. And I don't think we're going to have AI do that either. >> Kate? >> Yeah, we've seen a lot of technology panic, that may or may not be warranted, in the past. I think that AI and robotics suffers from a specific problem that people are influenced by science fiction and pop culture when they're thinking about the technology. And I feel like that can cause people to be worried about some things that maybe perhaps aren't the thing we should be worrying about currently. Like robots and jobs, or artificial super-intelligence taking over and killing us all, aren't maybe the main concerns we should have right now. But, algorithmic bias, for example, is a real thing, right? We see systems using data sets that disadvantage women, or people of color, and yet the use of AI is seen as neutral even though it's impinging existing biases. Or privacy and data security, right? You have technologies that are collecting massive amounts of data because the way learning works is you use lots of data. And so there's a lot of incentive to collect data. As a consumer, there's not a lot of incentive for me to want to curb that, because I want the device to listen to me and to be able to perform better. And so the question is, who is thinking about consumer protection in this space if all the incentives are toward collecting and using as much data as possible. And so I do think there is a certain amount of concern that is warranted, and where there are problems, I endorse people revolting, right? But I do think that we are sometimes a little bit skewed in our, you know, understanding where we currently are at with the technology, and what the actual problems are right now. >> Rajen, I want your thoughts on this. Education is key. As you guys were talking about, there's some things to pay attention to. How do you educate people about how to shape AI for good, and at the same time calm the fears of people at the same time, from revolting around misinformation or bad data around what could be? >> Well I think that the key thing here is to organize kind of how you evaluate this. And back to that one thing I was saying a little bit earlier, it's very tough to judge kind of what is good and what is bad. It's really up to personal perception. But then the more that you organize how to evaluate this, and then figure out ways to govern this, the easier it gets to evaluate what's in or out . So one thing that we did, was that we created a set of AI principles, and we kind of codified what we think AI should do, and then we codified areas that we would not go into as a company. The thing is, it's very high level. It's kind of like the constitution, and when you have something like the constitution, you have to get down to actual laws of what you would and wouldn't do. It's very hard to bucket and say, these are good use cases, these are bad use cases. But what we now have is a process around how do we actually take things that are coming in and figure out how do we evaluate them? A last thing that I'll mention, is that I think it's very important to have many many different viewpoints on it. Have viewpoints of people that are taking it from a business perspective, have people that are taking it from kind of a research and an ethics perspective, and all evaluate that together. And that's really what we've tried to create to be able to evaluate things as they come up. >> Well, I love that constitution angle. We'll have that as our last final question in a minute, that do we do a reset or not, but I want to get to that point that Kate mentioned. Kate, you're doing research around robotics. And I think robotics is, you've seen robotics surge in popularity from high schools have varsity teams now. You're seeing robotics with software advances and technology advances really become kind of a playful illustration of computer technology and software where AI is playing a role, and you're doing a lot of work there. But as intelligence comes into, say robotics, or software, or AI, there's a human reaction to all of this. So there's a psychology interaction to either AI and robotics. Can you guys share your thoughts on the humanization interaction between technology, as people stare at their phones today, that could be relationships in the future. And I think robotics might be a signal. You mentioned domesticating animals as an example back in the early days of when we were (laughing) as a society, that happened. Now we all have pets. Are we going to have robots as pets? Are we going to have AI pets? >> Yes, we are. (laughing) >> Is this kind of the human relationship? Okay, go ahead, share your thoughts. >> So, okay, the thing that I love about robots, and you know, in some applications to AI as well, is that people will treat these technologies like they're alive. Even though they know that they're just machine. And part of that is, again, the influence of science fiction and pop culture, that kind of primes us to do this. Part of it is the novelty of the technology moving into shared spaces, but then there's this actual biological element to it, where we have this inherent tendency to anthropomorphize, project human-like traits, behaviors, qualities, onto non-humans. And robots lend themselves really well to that because our brains are constantly scanning our environments and trying to separate things into objects and agents. And robots move like agents. We are evolutionarily hardwired to project intent onto the autonomous movement in our physical space. And this is why I love robots in particular as an AI use case, because people end up treating robots totally differently. Like people will name their Roomba vacuum cleaner and feel bad for it when it gets stuck, which they would never do with their normal vacuum cleaner, right? So, this anthropomorphization, I think, makes a huge difference when you're trying to integrate the technology, because it can have negative effects. It can lead to inefficiencies or even dangerous situations. For example, if you're using robots in the military as tools, and they're treating them like pets instead of devices. But then there are also some really fantastic use cases in health and education that rely specifically on this socialization of the robot. You can use a robot as a replacement for animal therapy where you can't use real animals. We're seeing great results in therapy with autistic children, engaging them in ways that we haven't seen previously. So there are a lot of really cool ways that we can make this work for us as well. >> Barry, your thoughts, have you ever thought that we'd be adopting AI as pets some day? >> Oh yeah, absolutely. Like Kate, I'm very excited about all of this too, and I think there's a few, I agree with everything Kate has said. Of course, you know, coming back to the remark you made at the beginning about people putting their faces in their smartphones all the time, you know, we can't crowdsource our sense of dignity, or that we can't have social media as the currency for how we value our lives or how we compare ourselves with others. So, you know, we do have to be careful here. Like, one of the really nice things about, one of the really nice examples of an AI system that was given some significant personality was, quite recently, Tuomas Sandholm and others at Carnegie Mellon produced this Liberatus poker playing bot, and this AI system was playing against these top-class Texas hold' em players. And all of these Texas hold 'em players were attributing a level of cunning and sophistication and mischief on this AI system that clearly it didn't have because it was essentially trying to just behave rationally. But we do like to project human characteristics onto AI systems. And I think what would be very very nice, and something we need to be very very careful of, is that when AI systems are around us, and particularly robots, you know, we do need to treat them with respect. And what I mean is, we do make sure that we treat those things that are serving society in as positive and nice a way as possible. You know, I do judge people on how they interact with, you know, sort of the least advantaged people in society. And you know, by golly, I will judge you on how you interact with a robot. >> Rajen-- >> We've actually done some research on that, where-- >> Oh, really-- >> We've shown that if you're low empathy, you're more willing to hit a robot, especially if it has a name. (panel laughing) >> I love all my equipment here, >> Oh, yeah? >> I got to tell ya, it's all beautiful. Rajen, computer science, and now AIs having this kind of humanization impact, this is an interesting shift. I mean, this is not what we studied in computer science. We were writin' code. We were going to automate things. Now there's notions of math, and not just math cognition, human relations, your thoughts on this? >> Yeah, you know what's interesting is that I think ultimately it boils down to the user experience. And I think there is this part of this which is around humanization, but then ultimately it boils down to what are you trying to do? And how well are you doing it with this technology? And I think that example around the Roomba is very interesting, where I think people kind of feel like this is more, almost like a person. But, also I think we should focus as well on what the technology is doing, and what impact it's having. My best example of this is Google Photos. And so, my whole family uses Google Photos, and they don't know that underlying it is some of the most powerful AI in the world. All they know is that they can find pictures of our kids and their grandkids on the beach anytime that they want. And so ultimately, I think it boils down to what is the AI doing for the people? And how is it? >> Yeah, expectations become the new user experience. I love that. Okay, guys, final question, and also humanization, we talked about the robotics, but also the ethics here. Ethics reminds me of the old security debate, and security in the old days. Do you increase the security, or do you throw it all away and start over? So with this idea of how do you figure out ethics in today's modern society with it being a mirror? Do we throw it all away and do a do-over, can we recast this? Can we start over? Do we augment? What's the approach that you guys see that we might need to go through right now to really, not hold back AI, but let it continue to grow and accelerate, educate people, bring value and user experience to the table? What is the path? We'll start with Barry, and then Kate, and then Rajen. >> Yeah, I can kick off. I think ethics gets us some of the way there, right? So, obviously we need to have a set of principles that we sign up to and agree upon. And there are literally hundreds of documents on AI ethics. I think in Europe, for example, there are 128 different documents around AI ethics, I mean policy documents. But, you know, we have to think about how are we actually going to make this happen in the real world? And I think, you know, if you take the aviation industry, that we trust in airplanes, because we understand that they're built to the highest standards, that they're tested rigorously, and that the organizations that are building these things are held account when things go wrong. And I think we need to do something similar in AI. We need good strong engineering, and you know, ethics is fantastic, and I'm a strong believer in ethical codes, but we do need to make it practical. And we do need to figure out a way of having the public trust in AI systems, and that comes back to education. So, I think we need the general public, and indeed ourselves, to be a little more cynical and questioning when we hear stories in the media about AI, because a lot of it is hyped. You know, and that's because researchers want to describe their research in an exciting way, but also, newspaper people and media people want to have a sticky subject. But I think we do need to have a society that can look at these technologies and really critique them and understand what's been said. And I think a healthy dose of cynicism is not going to do us any harm. >> So, modernization, do you change the ethical definition? Kate, what's your thoughts on all this? >> Well, I love that Barry brought up the aviation industry because I think that right now we're kind of an industry in its infancy, but if we look at how other industries have evolved to deal with some thorny ethical issues, like for example, medicine. You know, medicine had to develop a whole code of ethics, and develop a bunch of standards. If you look at aviation or other transportation industries, they've had to deal with a lot of things like public perception of what the technology can and can't do, and so you look at the growing pains that those industries have gone through, and then you add in some modern insight about interdisciplinary, about diversity, and tech development generally. Getting people together who have different experiences, different life experiences, when you're developing the technology, and I think we don't have to rebuild the wheel here. >> Yep. >> Rajen, your thoughts on the path forward, throw it all away, rebuild, what do we do? >> Yeah, so I think this is a really interesting one because of all the technologies I've worked in within my career, everything from the internet, to mobile, to virtualization, this is probably the most powerful potential for human good out there. And AI, the potential of what it can do is greater than almost anything else that's out there. However, I do think that people's perception of what it's going to do is a little bit skewed. So when people think of AI, they think of self-driving cars and robots and things like that. And that's not the reality of what AI is today. And so I think two things are important. One is to actually look at the reality of what AI is doing today and where it impacts people lives. Like, how does it personalize customer interactions? How does it make things more efficient? How do we spot things that we never were able to spot before? And start there, and then apply the ethics that we've already known for years and years and years, but adapt it to a way that actually makes sense for this. >> Okay, like that's it for the Around theCUBE. Looks like we've tallied up. Looks like Professor Barry 11, third place, Kate in second place with 13. Rajen with 16 points, you're the winner, so you get the last word on the segment here. Share your final thoughts on this panel. >> Well, I think it's really important that we're having this conversation right now. I think about back to 1994 when the internet first started. People did not have that conversation nearly as much at that point, and the internet has done some amazing things, and there have been some bad side effects. I think with this, if we have this conversation now, we have this opportunity to shape this technology in a very very positive way as we go forward. >> Thank you so much, and thanks everyone for taking the time to come in. All the way form Cork, Ireland, Professor Barry O'Sullivan. Dr. Kate Darling doing some amazing research at MIT on robotics and human psychology and like a new book coming out. Kate, thanks for coming out. And Rajen, thanks for winning and sharing your thoughts. Thanks everyone for coming. This is Around theCUBE here, Unpacking AI segment around ethics and human interaction and societal impact. I'm John Furrier with theCUBE. 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