Behind The Tech with Kevin Scott - Dr. Eric Horvitz: Chief Scientific Officer, Microsoft

Episode Date: May 28, 2020

Microsoft’s first Chief Scientific Officer, Technical Fellow and world-renowned AI researcher, Dr. Eric Horvitz, gives us an insider’s look into how the experts are approaching the fight against Covid-19. Learn how AI and bioscience are playing a critical role in gaining insights into the best way to approach this pandemic and prepare for future challenges.   Click here for full transcript of this episode. Listen to other Microsoft podcasts at aka.ms/microsoft/podcasts

I think that we know so little even today and there's so much possible that I think our grandkids will live in a very different world where biology will have played a major role in almost everything that's being touched including the materials that we build systems from and so I'm very excited about the possibilities materials that we build systems from. And so I'm very excited about the possibilities. Hi, everyone. Welcome to Behind the Tech. I'm your host, Kevin Scott, Chief Technology Officer for Microsoft. In this podcast, we're going to get behind the tech. We'll talk with some of the people who made our modern tech world possible and understand what motivated them to create what they did.

So join me to maybe learn a little bit about the history of computing and get a few behind-the-scenes insights into what's happening today. Stick around. Hello, and welcome to Behind the Tech. I'm Christina Warren, Senior Cloud Advocate at Microsoft. And I'm Kevin Scott. It is great to see you, Kevin. Well, you know, virtually anyway.

So are you getting into the rhythm working from home yet? I think it has finally gotten a little bit easier. The big challenge for us in the early days of the COVID shelter in place was having both of our kids home. So I've got a nine-year-old and and also doing our own IT support for our kids, which was not really what we had signed up for. Right, right. So how has your work changed? I know you traveled a lot. So that's obviously not happening anymore. So how's your team working?

Yeah, obviously I'm not traveling anymore, right? That's for sure. Yeah, you know, it's been interesting. Our team was mostly remote first anyway, but this is a different sort of remote experience. So it's been, I think, good in the sense that we already had experience working together from different places, but it's still different and we're all, I still think, adjusting. But one of the interesting things with Microsoft is we're focusing on trying to see how we can do advocacy and events and the things that I used to travel for online and potentially reach even more people.

So that's pretty cool. And as our listeners may know, Microsoft Research is actually playing a significant role in bringing our technical resources to the table. And so today we're going to hear from a Microsoft colleague who is leading the charge. Yeah, we're really excited to have Dr. Eric Horvitz on the show today. Eric's one of the most highly regarded AI researchers in the field with contributions in spam machine learning, perception, natural language understanding, and decision making. His efforts to understand the influences of AI on people in society, including ethics, law, and safety, really are paving the way for responsible AI practices. It's such important work. So let's hear what Eric's been up to.

Our guest today is Dr. Eric Horvitz. Eric is a Microsoft Technical Fellow and the company's first Chief Scientific Officer. As Microsoft's Chief Scientist, Eric provides leadership and expertise on a broad range of scientific and technical areas, from AI to biology and medicine, to a whole host of issues that lie at the intersection of technology, people, and society. Eric earned a PhD in AI from Stanford and is one of the field's leading innovators and luminaries. Eric also has the rare distinction of having earned his MD, also from Stanford, which gives him a unique view and understanding of the many connections between AI, biology, and biomedicine. I'm thrilled to have Eric with us today. Welcome, Eric.

Thanks, Kevin. It's great to be here. Yeah, so I'd love to start, as we always do, by understanding how it is you first got interested in science and technology. Presumably that was when you were a kid. So can you tell us a little bit about that? Yeah, I just know that I've always been sort of inspired to understand things, and I didn't distinguish between human creations, artifacts, and stuff I would see in the world. So I was confused and intrigued and interested in living things in space and time. I remember even being very, very young, asking my first grade teacher if I can know more about time. And she ended up bringing me to the library at Birch Elementary School and showing me a book about clocks.

And I said, no, I don't really mean clocks. I mean time. And I was also intrigued by light. I had this really beautiful phosphorescent, phospholuminescent nightlight in the 60s. And beautiful green light would wash the room at night in this glow. And I was just curious, what the heck was light? So, I had these basic questions. I remember having a discourse with my father about, you know, I heard a lot about God and I was curious what God was made of. And I couldn't get a good answer from adults about that. And when it comes to machines and mechanism, I took apart a flashlight, I think it was like the summer after kindergarten or so, because I remember in first grade, I was already into this and talking to friends about this. But I realized that there

was a circuit there and I found some wire. And I think I impressed my family more than myself when I ran around the house with a battery and a wire with a light bulb lighting up with my finger, under my finger. And I think this was also around the time that, again, mid-60s when there was a lot of, you know, a lot of the cartoons we were watching back then had electronic robots and Astro Boy flying around, very helpful entities. And I was curious about electronic brains. I don't know where I got that idea, but I remember having a bag of parts on my way to my grandmother's house in the back of a station wagon. Maybe this is around second or third grade, but the peanut can wires, light bulbs, I thought I could assemble an electronic brain on the way to my grandmother's house in the back of a station wagon. And didn't get, you know, still looking on that today,

basically. That's really awesome. And were your parents scientists or technical engineers? My parents were both school teachers. My mother was a kindergarten teacher. And I remember being very proud of that in kindergarten. I would tell everybody at a time when the kindergarten teacher was like the person you most looked up to that, by the way, my mom was a kindergarten teacher too. That was considered awesome by my peers at the time. And my father was a high school teacher. He did science as well as history. So where, I mean, it sounds like you had a bunch of innate curiosity, which is awesome. And like one of the themes I think we see with a lot of people who chose careers in science and technology. But did you have any role models when you were a little kid or things that were in the popular media that were inspiring you? Or did this just really come out of, you know, from your perspective, nowhere?

Lots of books. My parents had a home library filled with lots of books. We had the Merrick Library, Merrick, Long Island, where I would spend lots of time. I got to know the science sections as well as the pet section of the library pretty intensively. And mostly books at the time, and friends, some of whom had aligned interests. It's hard to think of the idea of being in the first or second grade and having a third grade, having a scientific support team. But we sort of had peers that were interested as well. In third grade, I was elected to be the chairperson of the science club, I remember. And we had all sorts of projects involving wind speed and solar energy back in those days.

But I'm not sure where some of the interest came from. It was largely curiosity and books. And later in life, of course, I had some fabulous mentors. And, you know, we all think back to our various teachers in elementary school. You know, you start in kindergarten and go to sixth grade. Each teacher has a major influence on people. And, you know, I can remember sitting at this desk in sort of a, I thought it was kind of a militaristic setting. And I asked myself on the first day of first grade, is this what school

is going to be like? I have to sit at this desk like for like, like 12 years. And I, the way that the first grade went, I was really unimpressed. And I would have given it all up if it wasn't for, and I'll call out a name, Mrs. Frank, my second grade teacher, who like completely opened the world to me. Was open to science and interested in answering questions. You know, and then you jump forward to fifth grade, Mrs. O'Hara. And these people were just brilliant teachers. Mr. Wilmot in sixth grade, where he celebrated my interests, and we had science fairs, and I actually won the science fair that year. And you have a few teachers like that, that really are like large planets that spin you off with the gravitational field into new directions. Yeah. You know, I think that's something that we, as a society, systemically underappreciate is the role of these really incredible teachers and what a massive influence they have in your life.

It's amazing. Yeah, I'm quite certain that if Mrs. Frank wasn't there in second grade, I'd be doing something very different now in the world. Yeah. Well, and it's also really interesting. I think to a certain extent, all children have this innate curiosity. So it'll sort of be interesting to talk about this later when we are chatting about AI. But in a sense, humans are learning machines machines and we sort of come into this world and we, you know, we have an innate curiosity to understand, you know, what's going on around us. to focus on with my children is to do everything that I possibly can to encourage them to lean into and celebrate their own curiosity and to support it in all of the ways that you can. Because I have this very strong belief that curiosity is one of these pivotal things that helps you be successful in life. And even when you're not talking about technology or science, you're talking about your fellow human beings and trying to develop things like compassion. I believe compassion is rooted in

curiosity. It is like you wanting to know where another person is coming from or like what they're thinking about or how they're processing their world. And so like I just – it's unbelievably important. I believe this curiosity. in your life who really celebrated that curiosity rather than thinking it was this annoying thing that was distracting them from what else they were trying to do. Yeah, it's kind of interesting. It's almost like there is something innate and basic in humans. And I've heard biologists and anthropologists talk about what makes Homo sapiens different than some other even closely related primates. And some of it is this delayed maturation.

They talk about this idea that human beings are more kid-like their whole life than closely related uh a species uh kid like referring to puppy-like curiosity that just continues on um this idea of continuing to explore versus being locked in and anything we can do to promote i think which is very much a human uh probably makes us more human than we know deep unrelenting curiosity i think can go a long way for individuals and for society as a whole. I was thinking about years ago, thinking about how much pleasure I get. It's almost like raw pleasure with getting an answer. This tension combined with a little bit of awe and mystery of a question building

and the pressure around that and how when it gets resolved into a partial answer, the gradient that you're on and the kind of pleasure you get traveling through that terrain is so deep and great. It's like one of the deepest pleasures I know. These bursts of insight. And to have to just think that in some people that might be linked to pain, or I don't want to go there. And the fact that that could come from the nurturing that led to that kind of shift of the natural pleasures of learning and growing

to a painful, I don't want to go there because I don't want to learn something new, for whatever reasons of background. It is very sad. Yeah. Well, I know even myself, like, there is a certain degree of discomfort to being fully immersed in a problem. Because, like, I don't know about you, I tend to get obsessed with questions and trying to find their answers. I remember when I was in grad school, I would be working on proving a theorem. And, you know, with some of these things I would spend days on. And I, on multiple occasions, I would be so immersed in the problem that I was trying to solve that I would dream about it. And several times I dreamt the solution to a theorem I was trying to prove. And I would wake up and like, oh my, I got it now.

And I would go write everything down before I forgot. And I experience that sometimes is discomfort. So I like totally understand what you're saying about this. You know, like sometimes maybe people experience a little bit of fear and anxiety when they are approaching an unknown. Right. And if they have to get used to the notion or get familiar with the idea that there are pleasurable bumps along the way and a pop towards the end when you get near a solution.

For me, it's similar. Sometimes I'll have a problem. I remember from my dissertation work, actually, at Stanford, I was really, at a time, worrying about this tension between how do you do things formally with probability and decision theory when it's intractable? And when you needed this kind of reasoning to do some good work in high stakes decision problems. And just being at loggerheads with contradiction. I remember actually where I was at the moment. I was visiting my family and cleaning the garage.

I'm not sure how they got me to do that on this day. But actually, I've had this image of looking at this stuff scattered throughout the garage and in my mind, seeing an interesting solution coming to the fore finally, just out of the blue, that became the kernel of what I ended up working on and the solution to this tension. So sometimes you run these batch jobs, which are just tantalizing in there in the background and popping up when you're driving a car or when you're cleaning a garage. But you're online and a portion of your soul is really focused on getting to an answer continually. Yeah. And I think the other thing that I will say and then start talking about your trajectory a little bit more, but I think there is a very interesting thing about this whole phenomenon that you are describing where you've got the discomfort of the unknown and this sort of tension between the thrill of discovery and the frustration of navigating a problem that you can get better at over time if you practice. So, like, the more you do it, the more that you understand that you are

going to be able to get these little victories over the problem and, like, hopefully, like, be able to get to a good solution at the end of the day. And I think as you understand that, it makes you more not just willing but eager to go seek these problems out because it really does become this amazing experience and and like very rewarding and i should say that it's not all individual um as i'm thinking about the the visceral sensations we have as we think about a problem or or ask a question and then pursue an answer urgently or over time um there's the sense that i've had i remember being looking at the stars one night as a young kid, maybe a little bit more into middle school and feeling anxiety about hanging in three space

that the sky wasn't a bowl. It was like the sun was one of these stars I was looking at. I was just hanging out there in three space and kind of an an anxiety, an angst, existential angst. And I remember this warmth when I felt like, yes, but in science, you can talk to people who are worrying about the same thing. And it's almost like a social, supportive experience. We can sort of all come together as humanity and come to the answers together. And it was kind of a warmth at that point that this wasn't just me alone sitting there hanging on a star, but it was a group. We can work together on this.

Yeah, I definitely agree that that's a really important part of how the whole scientific process works. Like the fact that there is a community that you're supporting one another. And honestly, the problems that we're trying to solve right now, and we'll talk about some of these later, are so complex that, you know, this notion that a lone genius can go do something that is, you know, like really revolutionary has always been a fiction. You know, like we're always building on what others have built before us. And in many cases,

the problems themselves that we're trying to tackle are of such vast complexity that you have to have lots and lots and lots of people working on them simultaneously in order to make real progress. So you went to Stanford. How did you decide to go to Stanford? And what was your major as an undergraduate? So as an undergraduate, everybody in my family, we all went to state schools. I think I spent an afternoon on a ping pong table filling out a form. I wasn't thinking much about college.

I just said, you know, that's what we do. I went to the State University of New York at Binghamton, which was the top school in New York when I was applying to schools. And when I got to the university, I just absolutely loved every class I was taking. And I said, I'm curious about physics and biology. Those two things were like where most of my curiosities were clustered. And so I started taking a bunch of physics classes and a bunch of biology classes, biochemistry, and so on. And at some point, I didn't want to stop looking at both. And I went to a mentor advisor whose class I loved. He taught a class in biophysics believe it or not I said

yeah this is great and I asked him I said there's no major in biophysics what do you think it would take to like do a special major in this area where I can really work with you and put together an undergraduate sequence that would really capture what you would do if you were going to study this area, even as an undergrad. And this was Professor Starzak. And we sat together and came up with a program and took it to what was called the Innovative Program Board. And a committee looked at this proposed major and they said, good to go. Now, there's a lot more classes in this and directed readings with some incredible professors. But I felt like I had the best of both worlds, chemistry, physics,

math, bio together. And then in the middle of all this, I ran into two professors as I was getting to junior and senior year. Both were just remarkable. One professor is Howard Patti, who was a professor from Stanford, actually. He did his PhD at Stanford, and his interest was emergent phenomenon. And particularly, he looked at biology from the point of view of a physicist and symbol systems. And he wrote some beautiful pieces, essays. They're still celebrated. They just not too long ago had a celebration of his career. I was immersed in Howard Patee's readings and thinkings, which were very deep and interesting

and cutting to the core of, I would call, the theoretical foundations of biology from the point of view of a physicist. And at the same time, I started talking to Robert Isaacson, who was taking a biophysics perspective on brains, looking at limbic systems in rats. I started talking to him, and he persuaded me to work in his lab, and I started looking at living neural networks. And I started getting very interested in brains. I hadn't really been thinking a lot about brains and minds since trying to build an electronic brain in like, you know, first and second grade with peanut cans and springs and wires and clay and light bulbs. So right towards the senior year, I was trying to pull together my biophysics background to looking at how brains work.

I ended up reading a couple of books. One was Herb Simon's book called Sciences of the Artificial. And another book was Michael Arbib's book, Brains, Machines, and Mathematics. And both were very motivating to me in terms of the questions that were being asked. And so I ended up applying to graduate programs which combined neurobiology with an MD. I thought, why not get into the human, have this human dimension to understand the clinical world.

Someday we'll understand brains. So I ended up getting a bunch of acceptances and had to choose among places that had up getting a bunch of acceptances and had to choose among places that had more of a mix of things and flexibility around your degree. And that would be very classical MD-PhD work and very focused. And ended up on a set of intuitions thinking through Stanford might have more of the mix that I was looking for, but I wasn't sure. Because by the time I ended up going to grad school, I was really zooming towards computation at a time where you wouldn't be thinking as an undergrad about getting into the 80s about artificial intelligence. You'd be thinking about neurobiology, neuroscience, biophysics. And so when I hit Stanford, there I was interested in getting going on neurobiology, being thrown, believe it or not, into a medical school class with a cadaver

where I ran into some close colleagues who actually had similar interests to mine. And at Stanford, you could wander off to main campus, which was just a bike ride away. So I spent a lot of time in my first year taking classes in computer science, artificial intelligence, philosophy of mind, and cognitive psychology, along with the regular medical school classes. And towards the second year, I said, you know something, I need to, I don't think neurobio is going to have the right mix for me in my pursuit of my core curiosities about what the freak was going on with minds, with human brains and the brains

of vertebrates and other animals on the planet. And that the fastest path to insights would be through computer science. And I remember one of the moments I was thinking about what I'd been doing in my laboratory work that I became very good at, doing unit recordings, looking at small circuits, listening to the ticks on the speaker in a darkened room, looking at the oscilloscope on interesting questions about how particular subsystems worked,

the thermoregulation subsystem in a rat, for example. And thinking that what I was doing all those years was sticking a thin wire into a chip and trying to infer an operating system in the application code, you know, and even the hardware by listening into the Morse code of a single gate. And I felt like that would be a waste

of good time on the planet. And I remember thinking it was a major shift to say, I'm going to give up the pursuit of a neurobiology, neuroscience PhD, and I'm going to move over now to go all in on what I came to know as artificial intelligence research, history, depth, you know, with the methods, I wanted to really master it. And it's sort of an interesting time in the 80s for AI. So one of the things we've chatted about on the podcast before is the fact that AI has had this distinct cycle of booms and busts over the years that at the Dartmouth workshop in 1955,

the program that McCarthy and these luminaries put together was way more ambitious than they, in reality, were going to be able to accomplish. And that we have had several of these cycles where the enthusiasm and the expectation for what we were going to be able to accomplish is sort of far exceeded our ability, which leads to these AI winters where you've got a bust and, you know, like people sort of go sour on the whole discipline. And as I remember it, the 80s, I forget what time in the 80s, but by the time I got to grad school,

we were well into an AI winter where it was no longer this fashionable thing in graduate programs. When you got into AI, was it right before the AI winter that we had? Or was Stanford some way a unique island where the enthusiasm for the field was undiminished over time?

I've often said that that proposal is written so well and it's so aspirational that if you submitted it today to DARPA or National Science Foundation, you'd probably get a high grant scoring to be funded. Just go for it. I mean, as written. So back to your question, when I first jumped in, it was 84. It was kind of a warm time and getting hotter. It was the time where the rule-based expert system, these production systems that do, for example, backward chaining through these modular human expert rules

were becoming quite popular. And I remember one of my first meetings was IJCAI 1985 at UCLA. And it was just an amazing time of excitement and inspiration with thousands of attendees. It felt like NeurIPS feels today. But 84, 85, 86, there was a kind of a collapse of interest and a bunch of startups going out of business that had been funded during the earlier time when it was discovered it was just kind of hard to build these systems and maintain them. And maybe these logical systems weren't as powerful and as promising as people had thought they'd be and weren't as easy to use or build.

I've looked over the history quite carefully. And what's called AI winter for us, when I say us, it was I and students at Stanford that were studying similar topic areas. And we had very close friends that we met at conferences and workshops at MIT and CMU and a few other places that created this invisible college of grad students that were looking for a different way to do things. And in many ways, we were up against the glowing cinders, and you might call them ashes, of what had been really exciting just two or three years before, typically pioneered by the people who are our mentors and advisors, which created some tension. And what we were looking for was going back to the basics

and building on the shoulders of the great statisticians and probabilists and folks who had done inference and optimization over decades. We discovered that the AI of the time in the early 80s to mid-80s was defining itself as, no, no, no, no, that's operations research. Or no, no, no, there are too many numbers there. Numbers aren't symbols. We want to make it like symbols. There was lots of tension there.

And at times, I know for me in particular I specifically I sought out new advisors at the time and moved over to working with George Danzig who was an operations research leader who if people know his work he's fabulous, but his intellectual contributions include the simplex method for optimization. Yep. And Ronald Howard, who had defined the phrase decision analysis and was really interested in thinking through how do you build systems that can help clarify thinking and bring together multiple factors under uncertainty. And what I found in George Danzig and Ron Howard, decision theorist and an optimization probabilist and folks like Brad Efron and stats, where they were looking across campus at the AI people

and thinking like, what the hell are those people thinking? And so what I started doing, I felt like, and it wasn't just me, there was a few of us in, I had a close colleague, David Heckerman, Michael Wellman at MIT,

Orin Etzioni at CMU, and others, started to think through, like, what were the big questions in AI? Even going back to the 1950s documents and before, and how could we start to build on what we knew was the science of optimization, decision-making,

action under uncertainty, high-stakes consideration of preferences, trade-offs, and started pushing in a direction that at first was considered quite foreign and outside of AI, not AI. A very distinguished professor told me that, you know, after listening to me talking about bounded rationality with using probability as the basic fabric and decision theory, he said, you know what? You have something we call physics envy, which is, I guess, referring to Freudian notions of another kind of envy. And, you know, you really need to look at symbols and high-level manipulation of predicates, go back to theorem proving. You're really wasting your time with these numerical methods. They call them numerical.

Even as we were coming up with abstractions like Bayesian networks and influence diagrams, which are higher-level constructs, representations. And I remember at the time we were joking about getting bumper stickers as grad students, rebellious grad students, driving around campus that we're going to say, numbers are symbols too. It was that bad in those days. Well, it's really interesting though because what you all were collectively doing, sort of steering things away from the symbol manipulation like systems of logic

sorts of research and getting things into this more, you know, sort of statistical framework has basically set the course for artificial intelligence over the past three decades. I mean, like most of what we talk about now when we're talking about artificial intelligence is statistical machine learning of some flavor or another. And that's really sort of a stunning thing to have that foundational piece persist for as long as it has. I don't know whether you all were cognizant of what you were

doing, but it's a really big deal that the field pivoted in that way. Yeah, rather than being cognizant, we felt like we were outsiders with some really important ideas to share. There was a panel at AAAI in 1984, I recall, where several people were almost booed off stage as we tried to bring up this idea of uncertainty in AI, principles of uncertainty. And that next year, in 1985, we decided to take that panel and make it into a workshop

we called Uncertainty in AI, UAI, which was a separate Roman community. And I remember the moment this outlandish thing happened in 2007, I was invited to be the president of AAAI. We joked, we being the former Invisible College, that the revolution was complete. And it really felt that way. Like, all of a sudden, we said, you know, look, I remember like just in like 2010 or so, like, you know, AAA, I always said, it's like UAI.

It's like UAI. It's like a big UAI now. It's everything. So let's, you know, since I want to make sure we get to some of the like really interesting stuff that we've been doing, we've been doing recently, let's fast forward all the way to some of the AI work that you've been doing over the past handful of years, which I think is of, again, really foundational importance. Like maybe even more important than this shift that you all agitated for and sort of realized when you were grad students. And that is sort of thinking about AI in the human context.

So as these technologies have become unbelievably more powerful, like especially over the past like 10 or 15 years, and their applicability to problem solving in the real world has never been higher. We are now being faced with a whole bunch of questions about what's the ethics of applying this particular algorithm or technique in this scenario? How do we make sure that these systems are doing things in unbiased ways. What is fairness in these systems? What are the things that we shouldn't use AI for? Where are the places where AI should always have decision-making systems where there should always be a human in the loop?

And so you've done, I think, some of the really most important work in the field at Microsoft and in these organizations that you have helped to start and are involved in the leadership of, like the Partnership for AI, on thinking about what the ethical responsibility frameworks are for doing AI in a modern world. How did you decide that that was something that was going to be such an important focus for you? I've always been interested in high-stakes decision-making, decisions that really make a difference in the world. This is why I wanted probability and utility theory to have a formal foundation for these actions and recommendations, applications in healthcare.

Back in the 80s and early 90s, our goal was just to get something to work. But even during those times, we saw interesting challenges with moving these systems into the actual world of usage. Like doctors who wanted to understand, like understand why the system made a recommendation, who would say things like, no, that can't be right. Can it explain itself to me? And coming up with methods to do explanation, even in the late 80s, seeing how important that would be, this human connection. I remember working on a project where I was NASA Mission Control in Houston in my last

year of my dissertation work, looking at some high-stakes decisions, time-critical decisions with propulsion control people. And I realized that it wasn't just making recommendations, it was figuring out what to display to people to help them make a decision. So there's this open-world issue that became very important to me as part of understanding the bigger role of AI in larger human settings. To me, it was more or less obvious in high stakes areas,

you had to consider these things. And then when I was become AAA president, it was a time where there was lots of initial discussions about the singularity coming, and there was both utopian and dystopian views being debated. And so I decided to make the theme of my presidency, AI in the Open World. And there's an open world of how do you put a system that's limited into more complex worlds and give it the ability to understand its own abilities and to be really much more omnipresent about the reality of helping humans out and or controlling

a system that has a function in the world not just this narrow wedge of of expertise on a certain particular classification topic or prediction and a second theme was thinking more deeply about the influences of our projects in the world. And this was 2008. And when it came to that part, I remember in my presidential address, which each president gives an address, I talked about the technical aspects and then the social and societal aspects. And I called together a group of about 25 people to create a study that I called the long-term futures of AI and its influences. And we had three groups meeting, and we ended up doing something very interestingly and analogous to the Asilomar meeting that the biologists had held in 1975. I think it was looking at recombinant DNA. But we ended up doing a workshop, a three-day workshop at Asilomar.

We all came back from our breakout groups to do reports. And it was the first time I heard this phrase from the short-term acute challenges group. We had a long-term group, a short-term problems group, and then an ethics and legal team as part of this effort. But it really drew me in and got me excited because I heard this phrase, criminal AI. I said, wow, what is criminal AI? And this group reported their findings about the malevolent use of AI by state and non-state actors and where it could go. We had another team. I explicitly asked a team to look at, could they take something as you might call fanciful,

but interesting as Isaac Asimov's laws of robotics, which folks have read about in his robot series, and actually codify them in a system with modern AI techniques so that the system could be proven to be reliable and responsible to human beings and to society. And we had a great breakout session on that topic. And then we looked at ethics and legal issues. So that whole experience and working with the community on these topics, which resonated deeply with me me for my interest in seeing these technologies do well in real hard decision problems and recommendations

further pulled me into thinking more deeply about the role that we would have as researchers, as scientists, as professionals, and as companies in thinking through not just the technology itself, which was growing in its power and its usage in commerce, as well as in areas like defense and healthcare, but to really consider deeply what we might do as a growing field,

including technical issues, social issues, looking at the human dimension. You know, if people are using these systems, how do you design them in a way not just where they might explain their reasoning if people want to know what's going on, but how do you understand how to apply the technology in a way that will complement expertise that's already available from human beings? How do you think through longer-term futures where the technology begins to shape the nature of work and the nature of the tasks people do in particularly

named jobs, like I'm a doctor, I'm a lawyer, I repair automobiles, to understand what it all would mean so that we would know how the role of humans would be co-evolving with the role of machines. And then it's just been so rewarding to see the rise of a whole field that's studying problems that are also rising in issues, for example, around the bias of systems that are trained on data that comes from cultures and societies that have all sorts of nuanced histories that lead to sampling issues and data that represents the societies from which it came and systems that you can build from that data that will amplify existing inequities in society.

So to see, you know, there's actually a rising field now of people that are pointing out these examples and coming up with ways to better visualize and understand and address them. it seems very likely to me that we are going to want to apply our most powerful technology platforms, including modern machine learning, to over the course of the next several decades. And I think perhaps the most interesting area or sort of intersection is what's happening right now in biology and how that intersects with the work that's going on in AI and high-performance computing. And, you know, it was sort of an interesting intersection and has been growing increasingly interesting over the past handful of years. And now it's just sort of acutely interesting and urgent and necessary because of all of the work that we need to do to adapt ourselves to handling the COVID-19 pandemic.

So you might not have realized how prescient you were when you were choosing to get a PhD in AI and a medical degree. But you're in this interesting position now where you have this background and point of view and visibility into this intersection. And I'd love to hear your thoughts about where you think things might be going over the next handful of years. Yeah, it's pretty impressive to see the connections

between computer science and ideas of abstraction, modularity, the ability to simulate in computer science, and where it's touching on biology. that I studied with Howard Patee on looking at biology as a physical system that had certain interesting properties that most of the world might look at as magic or in a different category, but in reality is a very interesting set of mechanisms that even relies on, for example, hierarchical abstraction the way our programs do. Yeah, so one of the things that you and I saw recently, like we went to chat with Drew Indy at Stanford, and one of the things that he said in that conversation that was so interesting to me is that now that we understand, and we're still early days,

but we understand a little bit of how to program or reprogram biology to do different things than what the biological systems do on their own. that you have yeast, which are basically little breweries. They are biological organisms that transform things like carbohydrates into carbon dioxide and alcohol, for instance. But his assertion was you've got these yeasts that we could reprogram to brew a whole wide range of compounds that we could use, for instance, as we pursue sustainability. So, like, things that might be alternatives to things that we would synthesize with petrochemicals, for instance. And, like, that's a really exciting idea. You know, we are in the early days of a major revolution in our ability to manipulate the physical world. Biology has figured this out in beautiful ways, has built

beautiful mechanisms that synthesize, that do incredible acts of chemistry, physics that are robust, self-replicating, the spinning out of shapes and structures through embryogenesis, just these magical, because you don't understand them, capabilities are coming into focus now through the lens in part of computation, physics, biochemistry. But perhaps the biggest insights in terms of the lens and the capabilities and the opportunities, I think are at the intersection of metaphors and concrete mechanisms from computer science

staring directly at biology and looking at the information theoretic aspects of what goes on in biology and then thinking about how these things can be, these systems can be harnessed in new ways, as well as borrowing ideas from biology and thinking through how we build systems, how we design materials and so on. But your comment focused more on like, how can we better modulate, moderate, design biological systems to do new acts of creation with applications in biology, applications in healthcare, applications in material science, applications in neuroscience. I think that we know so little even today, and there's so much possible that I think our

grandkids will live in a very different world where biology will have played a major role in almost everything that's being touched, including the materials that we build systems from. And so I'm very excited about the possibilities. It's bringing me back to my roots of biophysics now combined with computer science and artificial intelligence. So I'm happy to be in this new role. And as we've been talking, to start thinking deeply, working with partners like Drew Endy, David Baker, York Sea League, so many people that really are lit up now with thinking in this way of looking at what we call

a rising field of synthetic biology. How do you program biology? How do you guide it in new ways? How do you understand and control cancer? It's a runaway program. And we can just go topic by topic and think through what the engineering paradigm shifts we might need to design new kinds of robust and predictable functionalities in biological systems. You know, even something like the magic of something like the ribosome. We've all learned about the ribosome in basic biology classes. Oh, it's this interesting coalescence of RNA and protein in a structure that takes symbols and builds effectors and structures. It's one of these key, we'll call it a key pivot point of what makes biology biology you know storing up coincidences and insights in

long pieces of tape called dna the ability to take those codes as they've been learned and to transform those codes into structure and function and then to have experience re-encoded through evolutionary processes back into that tape. But this idea of a ribosome, you know, what do we understand and what can we better understand about this hinge point between information and the physics of life, I think has a lot to say about many things that we do. And, you know, the way that you just described just described that, I hope that that is one of the exciting and inspirational things that kids today will sort of see. In fact, if someone had as eloquently described genetics and the mechanism of the ribosome

when I was a high school student, as you just did, I may have chosen to pursue a different field. But I think we are at this incredibly inspiring moment where, you know, not only will our, you know, kids and grandkids live in a, you know, much different world because of what we are able to do with our new understanding of how to leverage biology to help make people healthier and help maybe make our physical world more sustainable. But I think they actually are going to be the ones who take inspiration now in what's possible, and they're going to go build this world. And that is super exciting. Yeah, absolutely.

Cool. So we are just about out of time, but I've got one last question that I wanted to ask you. So I'm always interested about what scientists and technologists who are themselves inherently curious about the world, what do you do for hobbies? What's a thing that people might not know about you? That's interesting. Well, I try to get exercise,

and people might not know that I'm a hacker, which is I just stopped playing, but I've been playing ice hockey in the Greater Seattle Hockey League for a number of years on a team called the Hackers. It seems that most people, most of our opponents think that we're actually of hacker on the ice. So I find it's one place where I turn off everything except really focusing on teamwork and where the puck is and being out of breath and how fit I am. And I've loved those kinds of sessions, being out on the ice. I just decided to step off the team when I was getting busier and busier.

And I was actually one afternoon very grumpy at an all-hands meeting at Microsoft. And I just come back from a game from Everett at two in the morning. And I decided, you know what, I just can't do this anymore. So instead, I took up inline skating now, believe it or not, you know, but I wanted to be serious about it. So I, during NeurIPS, for example, this year in Vancouver, I went to this custom blade shop and I had like these marathon blades made and I committed to being in the Berlin inline skating marathon on September 26th. And just last night, I was worried about this. They popped up a message saying they've canceled it and they'll be in touch with us. But I was

training for the Berlin inline skating marathon down the streets up and down here with these new, with these Vancouver blades that are just like magnets on the asphalt. But I do like to get out and get my mind focused on just clearing it with running or skating or paddleboarding, other kinds of things I enjoy reading. Just coming off a really interesting book. I tend to read Science Magazine every week, and there's a great book review they do of books coming out in the sciences. And I just finished this book called Becoming Wild by Carl Safina, which is looking at animal culture, looking at, for example, sperm whales. And it's just really amazing to read about the interaction of the importance of culture, stuff that's passed down

among animals versus being in their genetic code for thousands of years, tens of thousands of years, and different cultures, even with the same species living side by side, different dialects that are spoken by whales, for example. I've always been interested in the, this gets into the AI and the open world question, but even brains in the open world, how did human beings, how did our minds, our nervous systems co-evolve with our culture, co-evolve with tools like language? And so I found lots of interest there

in that recent book that I read with core questions that I have about the role of our external world and our tools like languages with the shapes and operation of homo sapien nervous systems. That's awesome. So this has been a great conversation, Eric. Thank you so much for taking time out to chat with us today. And I really, as usual, enjoyed hearing more about what you're thinking. Yeah, great catching up, Kevin. I'm looking forward to continuing our discussions. Awesome.

All right, so that was Kevin's chat with Eric Horvitz, who is Microsoft's first chief scientific officer. I loved hearing his story about his journey getting into tech and medicine, which is such an interesting combination, as well as your broader discussion about ethics in AI and where that future holds. Yeah, I think one of the remarkable things about Eric, and this has been true for so many of the guests on the podcast and so many of the people that I have the good fortune to know working in technology, is that one of the things that motivates them and that has motivated them since they were really young children is this

voracious curiosity and desire to understand what's going on in the world. And it was really, really great to hear Eric talk about that part of his life. And, you know, I think part of the reason why he chose to do things the way that he did them in terms of how he approached his education and how he has spent most of his research career is because he just refused to decide on one thing or the other. He's like, why can't it be all of the above? And that's really what we need more of when we're thinking about AI,

especially as this technology is having a greater and greater impact on society and our future. No, I couldn't agree more because it brings a really good kind of way of looking at the world that you might not get otherwise. Yeah, I mean, one of the really fortuitous things about having Eric here at Microsoft and having him play such an important role at Microsoft over the years is that when a moment like now arises where we really do have to think more comprehensively than ever before about what this intersection is between biology and artificial intelligence,

it's pretty convenient to have one of your foremost AI experts actually be a medical doctor as well. It's sort of only at Microsoft, I guess. It really is. And I mean, you didn't really get into this too much in your conversation, but I just want to ask you, especially since you've written so much about AI and since you've been having these conversations with people like Eric who are experts in biology, what role do you think AI might be able to play kind of going forward as we're looking at how to combat this

and maybe even other potential viruses or health concerns? Yeah, I think we're seeing it have a really tremendous impact already. So, you know, as we have dug in with a bunch of the researchers and a bunch of the medical professionals and biotechnologists over the past handful of months, it's already the case that they're using the tools of machine learning and artificial intelligence in relatively sophisticated ways.

So it may be on one end of the spectrum using natural language technology to better extract critical information out of our unstructured health records that are, you know, for many, many, many years now have been handwritten notes or, you know, notes that are taken and input into a medical record system. But it's still, you know, sort of this unstructured data that we really do need to be able to establish more structure around so that we can do the types of deep analytics that we need to do to understand things. like SARS, coronavirus too, and really try to widely disseminate what effective therapies are

that people are applying over time. And so, funny enough, natural language processing and natural language understanding, which are these classic techniques from artificial intelligence, have huge relevance there. You also have seen the work that people have been doing, and we've talked about this some on the podcast, in using deep neural networks to do medical diagnostics. And so I'm wearing a ring from a company called Aura right now that measures your body temperature and your pulse and a whole bunch of things about your movement. And I think this company originally intended to have these rings help you manage your all-in health, like whether you're sleeping well enough or whether you're getting enough exercise and activity. But it may be the case that the data that's gathered by devices like this are going to be really useful when you are able to train sophisticated deep neural networks with them in detecting diseases like COVID-19,

hopefully before you're gravely ill and have time to go get yourself treated so that you can jump back to robust good health as quickly as possible. And then on the very far end of the spectrum, which has been some of the most surprising bits for me to see over the past handful of years, is how the tools of AI, in particular deep reinforcement learning, are almost becoming like a new calculus for the basic sciences.

So you had calculus come about as this analytic framework for better describing and understanding the phenomena in the real world in the 18th century. And you got most of modern science from having a tool like that and I'm seeing now with AI deep neural networks and machine learning deep reinforcement learning

these new self-supervised learning techniques that we've developed over the past handful of years are being applied in science in sort of the same way that you might imagine calculus was many years ago to more accurately and faithfully model the phenomena in the physical world so that you can better understand them.

And that might be helping to accelerate a molecular simulation that's trying to understand how the spike glycoprotein in the coronavirus is interacting with your epithelial tissue and invading cells and infecting you with this horrible disease. And we are already seeing how machine learning and AI, these new techniques are being used to accelerate those simulations and to get to more accurate results.

So I think there's going to just be almost like a landslide of activity and building momentum over the next handful of years as these two worlds, artificial intelligence and biology, start to intersect in a more profound way. And I think we're going to spend a bunch of time this season on Behind the Tech talking to some of these innovators who are in the biosciences using these tools and these innovative ways to help make us all healthier and bring better healthcare outcomes to as many people as humanly possible and to use biology in ways that we really weren't even conceiving of a few decades ago.

That's great. I'm glad. And what's great about this, I think it gives us hope and we need hope right now. I know that I'm certainly feeling hopeful. Well, that's a wrap for us today. As always, please reach out anytime at BehindTheTech at Microsoft.com. We'd really like to hear from you.

How are you faring during these times? We'd love for you to share some of your stories about how you're innovating, how you're hacking, and finding ways to stay connected with technology. We'd love to hear from you. Thanks for listening. See you next time.