[MUSIC]
>> 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.
[MUSIC]
 >> 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 have 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.
 [MUSIC]
>>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 11-year-old who, like,
have the great fortune of being able to do some of their
schoolwork remotely. But it also meant having them at
home, that my wife and I all of a sudden were teaching
assistants once again, and also doing our own IT support
for our kids, which was not really what we had signed up for. (Laughter.)
>> Right, right.
>> So how has your work changed?
I know you traveled a lot. So like, that's
obviously not happening anymore. So, how's your team working?
>> Yeah, obviously I am not traveling anymore, right?
 (Laughter.) 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, you
know, with Microsoft is we're focusing on trying to see
how we can do advocacy and events, and the things that I
use 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 that span
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.
[MUSIC]
>>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, it’s -- 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 being very, very young asking
my first-grade teacher if I could know more about time.
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’m also intrigued by light. I had this really
beautiful phosphorescent -- phospholuminescent nightlight
in the ‘60s, beautiful green light would wash the room at
night in this glow. I was curious, what the heck was
light? So, I had these basic questions. I remember
having a discourse with my father about some -- I heard a
lot about god. 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. 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 in 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 cartoons we were watching back then had
electronic robots and Astro Boy flying around and very
helpful entities. I was curious about electronic brains. I
don’t know where I got that idea. But I remember having
a bag of parts and on the way to my grandmother’s house
in the back of the 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 the
station wagon. And didn’t get --
 >> That’s so cool.
>> You know, still working on that today, basically.
>>That’s really awesome. And were your parents scientists or technical engineers?
>> My parents were both schoolteachers.
My mother was a kindergarten teacher. 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. 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. 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 or third grade
having a scientific support team, but we sort of had
peers that were interested as well. In third grade, I
became -- I was elected to be the chairperson of the
science club, I remember. We had all sorts of projects
involving wind speed and solar energy back in those days.
But I’m not sure, you know, where some of the interest
came from. It was largely curiosity and books. And later
in life, of course, I had some fabulous mentors. You
know, we all think back to our various teachers in
elementary school, you know? You start in kindergarten,
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 -- what I thought was kind of a
militaristic setting. And I asked myself on the first day
of first grade, “Is this what school’s going to be like? I
have to sit at this desk, like, for like 12 years?”
(Laughter.) And the way that first grade went, I was
really unimpressed. 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. Wilmott in sixth grade, where
he celebrated my interests and we had science fairs. I
actually won the science fair that year. And you have a
few teachers like that who really are like
large planets that spin you up into their gravitational
field into new directions.
 >> Yeah, 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 -- 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 you know, it’s also really
interesting. So, would you -- 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 and we sort of come into this world and
we have an innate curiosity to understand what’s going on
around us. And the thing, you know, for me as a parent
that I have really tried 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, 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, and so wonderful to hear that
you had these teachers early 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. I’ve heard biologists and
anthropologists talk about what makes homo sapien sapiens
different than some other even closely related primates.
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 species -- “kid-like” referring
to puppy-like curiosity that continues on. 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 -- 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
years ago, 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 think that in some people that might be
linked to pain, “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, 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 would -- some of these things, I’d 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, like, several times, I dreamt the
solution to a theorem I was trying to prove. And I
would wake up and, like, “Oh, I got it now!” And I’d go
write everything down before I forgot. And, like, that is
-- it is, like, I experience that sometimes as
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 they have to get used to the notion that -- or get familiar with the idea
that-- there are pleasurable bumps along the way, and a
pop toward the end when you get near a solution. You
know, 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 the 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 I actually had this image of
looking at this, you know, 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 get -- you
might run these batch jobs which are just tantalizing
and in the background and they’re popping up when you’re
driving the car or when you’re cleaning a garage. But
you’re online and you’re sort of -- you know, a portion
of your soul is really focused on getting to an answer continually.
>> Yeah, and I think, you know, the other
thing that I will say and then we should 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,
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, like, very rewarding.
>> And I should say that it’s not all individual. As I’m thinking about the visceral
sensations we have as we think about a problem or ask a
question and then pursue and answer urgently or over time,
there’s this sense that I’ve had. I remember looking at
the stars one night as a young kid, maybe a little bit
more into middle school, and feeling anxiety about
hanging in 3-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 3-space kind of an
anxiety, 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.
It’s kind of almost like a social supportive experience
where we can all come together as humanity and come to the
answers together. 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.
>> Yes, 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, like, 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 are 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.
>> Right.
>> Yeah. So how did you -- so you went to Stanford.
How did you decide to go to Stanford?
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 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. 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 do a special major in this area where I can
really work with you in putting 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 called the
Innovative Program Board and a committee looked at this
proposed major and they said, “Good to go.” Now there were a
lot more classes in this and directed readings with some
incredible professors. But I felt like I had the best of
both worlds. I had chemistry, physics, math, bio together.
And in the middle of all this, I ran into two professors
as I was getting to junior year and senior year who both
were just remarkable. One professor is Howard Pattee, 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 and essays that are still celebrated.
They not too long ago had a celebration of his career.
I was immersed in Howard Pattee’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. So I started talking to him, he
persuaded me to work in his lab. I started looking at
these 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 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, into 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
understanding the clinical worlds. Someday, we’ll
understand brains.” So, I ended 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 other that would be very classical MD/PhD work, and
very focused. And I ended up –on a set of
intuitions—thinking through that 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 toward computation at a time where you
wouldn’t be thinking as an undergrad, you know 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. At Stanford, you can wander off to
the 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 toward 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 brains of
vertebrates and other animals on the planet.” And 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 doing unit recordings, looking at small
circuits, listening to the ticks on a 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 trying to infer an
operating system and the application code, 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 was—I came to know as
artificial intelligence research, history, depth -- you
know, all the methods-- I wanted to really master it.
>> And, you know, it’s sort of an interesting time in the
‘80s for AI. So, you know, 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, you know, the Dartmouth workshop in 1955, the
program that McCarthy and these luminaries put together
was way more ambitious than they -- you know, in reality
were going to be able to accomplish. And, you know,
that we have had several of these cycles where the
enthusiasm and the expectation for what we were going to
be able to accomplish has 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 like as I remember it, like the
‘80s, I forget what time in the ‘80s, but like by the
time I got to grad school we were -- we were on the,
like, well into an AI winter, where it was no longer like
this fashionable thing in graduate programs. So, you --
when you got into AI, was it right before the AI winter
that we had or, you know, was Stanford in some way like a
unique island where the enthusiasm for the field was
undiminished over time?
 >> Well, first of all, I want to make a comment on that 1955 proposal.
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
the National Science Foundation, you’d probably going to
a high grant score and be funded, so 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, 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 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 thought
they’d be, and weren’t as easy to use or build. I
looked over the history quite carefully and you know,
what’s called “AI Winter” for us -- and I say us -- it’s
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 -- you might call them ashes -- of what had been
really exciting just two or three years before, typically
pioneered by the people who were 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 and 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 manipulate symbols.” There was lots of tension
there, and at times, for me in particular, I specifically
sought out new advisors at the time and moved over to
working with George Dantzig, who was an Operations
Research leader who, if people know his work, his
fabulous personality, but his intellectual contributions
include the Simplex Method for optimization.
 >> Yeah.
>> 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 Dantzig and Ron Howard,
decision theorist and optimization probabilist, and folks
like Brad Efron in stats, were they were looking across
campus at the AI people and thinking, like, like, “What
the hell are those people thinking?” (Laughter.) And
so what I started doing, I felt like -- and I wasn’t just
me -- there’s a few of us in -- I had a close colleague,
David Heckerman, Michael Wellman at MIT, Oren Etzioni at
CMU, and others. We 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 kind of the science of
optimization, decision-making, action under uncertainty,
high stakes consideration of preferences, tradeoffs, and
start pushing in a direction that at first was considered
quite foreign and outside of AI—"Not AI.” A very
distinguished professor told me 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’.”
(Laughter.) 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 called 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 were going to say “Numbers are symbols, too.”
It was that bad in those days.
 >> You know, it’s really interesting, though, because what you all were
collectively doing, you know, sort of steering things away
from this symbol manipulation, like systems of logic, you
know, sorts of research and getting things into this
more, you know, statistical framework has basically set
the course for artificial intelligence over the past
three decades. Most of what we talk about now when we’re
talking about artificial intelligence is statistical
machine learning of some flavor or another. And like
that’s really sort of a stunning thing to like have that
foundational piece persist for as long as it has. I mean,
so like I don’t know whether you all were cognizant of
what you were doing, but like it’s a really big deal that
the field pivoted that way.
>> 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 growing community. 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 in like 2010 or so, you know, AAAI we said, 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 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, like, 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 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?
Like, how do we make sure that these systems are doing
things in unbiased ways? Like, what is fairness in
these systems? Like, what are the things that we
shouldn’t use AI for? Like, 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 sort of involved in the
leadership of, like the Partnership for AI, on thinking
about what the -- you know, the ethical and
responsibility frameworks are for doing AI in a modern
world. So, like, 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 went to
probability and utility theory, to have formal foundations
for these actions and recommendations -- applications in
healthcare. And, you know, 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, 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
NASA mission control in Houston—the last year in 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 becoming the
AAAI president, it was a time where there were 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 or controlling a system
that has a function in the world, not just this narrow
wedge 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. This is 2008. And when it came to that part, in my
presidential address -- each president gives an address --
I talked about the technical aspects and 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
subgroups 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. We ended up doing a workshop -- and
three-day workshop at Asilomar, where we all came back
from our breakout groups to do reports. It was the first
time I heard this phrase from this 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. I remember this phrase, “criminal AI.” And 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 specifically asked a team to look at
could they take something as -- might call it 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
responsibility to human beings and to society. And we
had a great breakout session on that topic. And we looked
at ethics and legal issues. So, that whole experience and
working with the community on these topics, which
resonated deeply with me, per 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 you people are using
these systems, how do you design them in a way not just
to -- 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, 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 society 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, 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.
>> So, the thing that I want to chat about now is sort of the future. Like, the
things that it seems very likely to me that we’re 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, you know,
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 with 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 like you’re in this interesting position now
where you have this background and point of view of
visibility into like this intersection. I'd love to hear
your thoughts about where you think things might be going
over the next handful of years.
>> Yes, 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. And even back to the early ideas that I
studied with Howard Pattee 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,
higher-level abstraction, the way our programs do.
>> Yeah, so the -- you know, one of the things that you and I
saw recently, like we went to chat with Drew Endy at
Stanford. And, you know, one of the things that he said
in that conversation thought was so interesting to me is
that now that we understand -- and like we’re still early
days, but like 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. And like
one of the things that he mentioned is that you know, you
have yeast, which are basically little breweries, like
they are biological organisms that transform, you know,
things into, you know, like carbohydrates into carbon
dioxide, you know, and alcohol, for instance. But,
like, you can -- you know, his assertion was, like, you’ve
got these yeasts that we could reprogram to brew a whole
wide range of compounds that we can 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 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 and physics that are
robust, self- replicating, the spinning out of shapes and
structures through embryogenesis. Just these magical –
because we don’t understand them -- capabilities are
coming into focus now through the lens, in part, of
computation, physics, biochemistry. But perhaps the
biggest insight 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 how can we better modulate,
moderate, design biological systems to do new acts of
creation with applications in biology and applications in
healthcare and 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. As we’ve been talking, to start thinking
deeply, working with partners like Drew Endy, David
Baker, Georg Seelig, 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? You know, it’s a
runaway program. And you can just -- 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 reencoded 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 that, like, I hope
that that is one of the exciting and inspirational things
that kids today will sort of see. Like, 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 this incredibly
inspiring moment where, you know, not only will our kids,
grandkids live in a 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 like I
think they actually are going to be the ones who you know
take inspiration now on what’s possible, and they’re
going to go build this world. And, like, 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, you know, themselves
inherently curious about the world, like, what do you do
for hobbies? Like, 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 of our opponents think that we’re actually
a different kind of hacker. (Laughter.) But, 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. 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’d just come back from a
game from Everett like at 2:00 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. But I wanted to be serious about it. So, 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 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 paddle
boarding. 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. I
just finished this book called Becoming Wild by Carl
Safina. Which is looking at animal culture and looking
at, for example, sperm whales, and it’s just really
amazing to read about the interaction of - or 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 -- and this gets
into the AI in the open world question, but even brains
in the open world. How do human beings -- how did our
minds, nervous systems co-evolve with our culture,
co-evolve with tools like language? And so I found lots
of interest there, you know, 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 sapiens 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. I really, as usual, enjoyed hearing more about what you’re thinking.
 >> Yeah, well, great catching up, Kevin.
Looking forward to continuing our discussions.
>> Awesome.
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>> 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, you know, tech and medicine, which
is such an interesting combination, as well as your kind
of broader discussion about ethics in AI and kind of
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, like, 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 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. (Laughter.)
>> 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 and machine
learning and artificial intelligence in relatively
sophisticated ways. So, it may be, you know, 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
notes that are taken and input into medical records
system. But it's still, you know, it's 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, you know,
understand things, for instance, like the progression of
symptoms of a pathogen like SARS coronavirus 2, and like
really try to widely disseminate what effective therapies
are that people are applying over time. And so, you
know, funny enough, natural language processing and
natural language understanding, which are these classic
techniques from artificial intelligence, have huge
relevance there. You also see in the work that people
have been doing, and we've talked about this some of the
podcasts, in using deep neural networks to do medical
diagnostics. 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 a robust, good health as quickly as
possible. And then on the, you know, sort of 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, like 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, like, 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 a -- 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 in these innovative ways to
help make us all healthier, and bring better healthcare
outcomes, and to as many people as humanly possible and,
you know, to use biology in ways that we really weren’t
even conceiving of a few decades ago.
 >> That's great. I'm glad, 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@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!
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