MIRA MURATI:
[MUSIC] We're working on something that will
change everything. Will change the
way that we work, the way that we interact
with each other and the way that we think and
everything really, all aspects of life. KEVIN SCOTT:
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've made our
modern tech world possible and understand what motivated them to
create what they did. 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] Today we have a super-exciting guest
with us, Mira Murati. I've had the pleasure of
working very close with Mira and her team at OpenAI
for the last several years. Even though I've had all of these opportunities
to interact with her, it was so interesting to
hear more about her story, like how she grew up, how she first became interested in first mathematics and then physics and
science and like where, like this intense
curiosity that she had from childhood
eventually led her. I think there were just some amazing nuggets
in our conversation. Just can't wait to dive
right in so let's get at it. [MUSIC] Mira Murati
is the CTO of OpenAI. She worked as an engineer
and product manager most notably helping to develop
the Tesla Model X. She joined OpenAI in
2018 as the VP of Applied AI and Partnerships and has since been
promoted to CTO. During that time,
she's helped bring AI products like ChatGPT, DALL-E and GPT-4 public
and has partnered closely with our team at
Microsoft to integrate their technology
into our products. It is so awesome to have
you on the show today, Mira, thank you so
much for joining us. MIRA MURATI:
Thank you, Kevin. Excited to be here. KEVIN SCOTT:
I'm going to learn a lot about you today that I don't know which
I'm super stoked about. I would love to
understand how you got interested in science and
technology in the first place. MIRA MURATI:
It started with math. When I was a kid I just
gravitated towards math and I would do problem sets all the time and
then we eventually did Olympiads and I loved doing
that, it was such a passion. I grew up in Albania, it's a small country
in Europe and this was during the transition from totalitarian communism
to this liberal capitalism. When I was two, the dictatorship regime fell and it was anarchy overnight. But I think one
thing that people misunderstand about
communist regimes is that, when everything is equal there is really fierce
competition for knowledge and education
is everything and so that's the setting
that I grew up in. I was just always very hungry for knowledge and the
pursuit of knowledge. But in a place where there's this constant regime change
and everything is uncertain, I gravitated more towards
the truth in science, something that felt steady
and you get to the bottom of. Also the sources of
history books or other books are questionable,
history kept changing. I think maybe just intuitive and natural
gravitation towards sciences and math
was amplified by the circumstances in
which I grew up in. From a very young age I was
super interested in math and physics and continued to
pursue them until university. KEVIN SCOTT:
Were your parents mathematicians or scientists? MIRA MURATI:
No, not really. They actually taught
literature and so it was just an
organic interest towards math and science. KEVIN SCOTT:
Coming from the West like one of the things that - I'm a little
bit older than, or a lot older than you I think. One of the things that struck me growing up where I also was interested in math and science and programming fairly early on, was that there was this
competitive nature between the liberal democracies
of the West and some of the Russian
coalition that knowledge itself, like particularly science and mathematics and
technical knowledge, were like one of these
things that were highly valued both here
and there at the time, because it was a way to like just compete in whatever contest it was
that we were playing. I don't know whether it felt
like that in Albania or not. MIRA MURATI:
Yeah, very much like that. I just love doing all these Olympiads whether it
was chemistry or biology or math and when you're a kid you don't really think about
that. It was just a passion. But looking back, I can see the circumstances and
also just keep in mind that there wasn't access
to a lot of tools or entertainment
and so a little bit was just out of boredom as well. Boredom actually I think is
a very powerful motivator to go explore and really
pursue frontiers of anything. In the first years
of my childhood, Albania was incredibly isolated like North Korea is today. There wasn't much inflow of entertainment or anything
really besides books. Books were this entire universe and back then I'd just
search everything in books. Now we've got all these
powerful tools at our fingertips and can
do anything really. KEVIN SCOTT:
What you just said, that boredom is a very useful thing, I could
not more strongly agree with. I think it's really
interesting that we seem to as a society have
decided that boredom is bad and it is a
thing to minimize. It's one of the things that I struggle with my own children. I've got a 12 and a
14-year-old and they don't have the same
capacity to be bored as I did when
I was a child. I didn't grow up in Albania. I'm sure it's probably unfair to even make this
comparison but I grew up in rural Central Virginia. We had three television channels and I was bored a lot and most of my life was to get in books and it was a
very useful thing to, I got focused very quickly on
things that were substantive. MIRA MURATI:
Yes exactly that. Exercising that ability to
stay focused on something and reflect on information or distilling this
information further, and a lot of math is like that. You just need to sit with
a problem forever and it exercises that muscle and faith that if you sit with it, you'll discover something. KEVIN SCOTT:
For sure. I don't know about you but I've even had
hard math problems that I worked on in the
past where I was so obsessed with them that I would dream about them and I sometimes would even
wake up and I'm like, oh, finally, like I got the proof for this theorem
that I just dreamt. MIRA MURATI:
Exactly. KEVIN SCOTT:
I'm interested to hear how that interest
that you had, which sounds like it was innate,
or just sort of in the air and culture just from the
circumstances of how you grew up. But how did it get nurtured? Some of this stuff is hard, and so did you have
mentors or teachers? Were the schools good? Maybe I should ask
a different way. At some point whenever you are trying to do something
substantive, things get hard enough
where you get stuck. How did you get
yourself unstuck? MIRA MURATI:
For me, my teachers, when I was growing up, they were extremely
supportive and it was unusual circumstances because I think today maybe less of
that would be available. But back then, I don't know. Maybe they saw something
in me and they really wanted to help me pursue my interests and often in class I'd do completely
different problem sets because I was bored
with the usual curriculum. I would still sit
there with everyone, but they were very supportive of me doing something
entirely different. I was also lucky that my sister is a year-and-a-half
older than me. When I'd get bored with myself, I would go and look
into her books. Then when I'd do her books
and then I would find other books and my teachers
were very helpful with that. I think that was probably
the most helpful thing. Like I always knew there
was something else. There was more to pursue, there was more to learn
and then when I was 16, I was fortunate to get a scholarship to study abroad
in Vancouve, Canada, where I did my last two
years of high school. That was a big opportunity
to get outside of Albania and study in an
international school with people from many
different countries. That was a great
opportunity for me. KEVIN SCOTT:
Where did computers enter the picture for you? MIRA MURATI:
It was quite late, I would say. Maybe when I was a teenager in Albania and
Internet was slow, but I already thought
about intelligence a lot more through math and solving problems
and just like, the scenario of how the
world works and trying to explain a lot of things
through math or physics even. But I was always interested
in how the brain works and intelligence more theoretically
and at abstract levels. But I would say that
the art of what I pursued was more in
the theme of trying to apply my knowledge and
trying to apply technology to really hard problems that in some way makes
our lives better. When I was in college, I was studying engineering because I thought this
was the best way to apply my knowledge to actually solving real problems
in the world. When I was studying engineering, I was very interested in
pursuing ways to bring sustainable transport
to the world and also just sustainable
energy in general. My senior project actually was building this hybrid racecar. It was fun, but also we
wanted to do something that felt really hard and so
instead of batteries, we used supercapacitors and really trying to push
what was possible, and obviously that was not something that you could
build in production, but it was pushing science
and seeing what's possible. That's why thereafter
I went to work at Tesla and I was
really passionate about sustainable energy and doing my part in bringing sustainable
transport to the world. That was a very exciting time about 10 years ago at Tesla. KEVIN SCOTT:
That's awesome. What type of engineering did you study? Were you an electrical engineer, mechanical engineer,
something different? MIRA MURATI:
I studied mechanical engineering. A lot of hands-on stuff; software, but also hands-on. KEVIN SCOTT:
What was your favorite thing about, because you're doing
something very different now, like mechanical
engineering is quite a bit different than running a
software engineering team, and like, I love
mechanical engineering. It's funny enough, like I built my entire career on
software engineering, but most of what I do in my free time is mechanical engineering
and mechanical design. What attracted you to that in the first place other
than the sustainable, that it was a lever on doing something in
sustainable energy, and how how was that different
than what you do now? MIRA MURATI:
I think back then I probably saw it as a more tangible way to change things and it
didn't feel abstract. It felt very tangible. You make a change and
you see it and you see how it affects reality. I was always a thinker, I would explore different
things. It was hard. Mechanical engineering is hard, but it's also very fulfilling and there was
always a software component, so like in a hybrid car, you've got the entire system. It's not just the mechanical
engineering part, there's always the
software component, the electrical
engineering component. It's a little bit of everything and I always was
attracted to complex systems. When I was at Tesla, I got more interested in autopilot
and the promise of it and also what we could do with
AI and computer vision to completely change the
way that we travel. That got me more and
more interested in AI and what it could
do in the world, what changes it could bring. I didn't necessarily want
to become a car person. I always had this curiosity for different things and
I was very curious about how AI would
affect the way that we interact with machines and how we interact with
information in general. At the time, I got
really interested in spatial computing
and just interacting with information and
complex concepts in a completely different way
than we interact even today, really, with a keyboard
and the mouse, which is just so limited. I thought that AI and
computer vision would help us really change this interface of interacting with information. I imagined virtual reality or augmented reality where
you can almost touch molecules or you can get a sense for Chaos Theory or
gravitational waves, and that is such an
intuitive understanding of complex concepts versus
when you read it on a page. It's almost like it's as
intuitive as grabbing a ball and getting a sense of projectile motion
even if you don't know the laws of physics. I thought, this can really
change the way that we learn and the way we absorb the world. KEVIN SCOTT:
That feels so true to me. I think one of the things that I really appreciate
about the modern world that we live in right
now is that you have things like YouTube, where if you are trying
to understand a thing, there are so many people trying
to explain that thing in so many different ways that
if you are determined enough, you can find someone
explaining the thing in exactly the right way for your particular brain to
understand it quickly. That was always my struggle. I could learn very quickly, but I don't think I learn exactly the same way
that other people learn. If I can get the right
conceptual hook on something, then I've got it and I can
even understand the things that before I got the hook
were too complicated. It's one of the things
actually that really excites me about what it is that you-all are doing in OpenAI with these agents because the agents, if you are trying to get it
to explain something to you, it's infinitely patient
and it's adaptable. It will explain things to you in the way that
you need it to explain things to you if you're
willing to have a conversation and tell it
what it is that you need. That feels very powerful to me. MIRA MURATI:
I completely agree. It's one of the things that
I'm most excited about with these large language
models and just generally deploying the AI
systems that we're building in the real world. KEVIN SCOTT:
Let's go back for a minute before we get on to all of the
exciting AI stuff, which I'm sure is
what everyone wants to hear us talk about. I want to hear a little
bit about Tesla. What was it like
working there and like you had a pretty
big responsibility there at the end where you were the head product manager
for the Model X, which is one of
the most amazing, innovative vehicles that
anyone's ever created. For you not thinking of
yourself as a car person, like you helped make one of
the most disruptive cars that the world has seen maybe
in the past 40, 50 years. Tell us a little bit about that. MIRA MURATI:
Tesla was an incredible place and in some ways actually, I find it quite similar
to OpenAI now where you have - obviously
it was much bigger and working on something
very different - but this high density
of very talented, smart people that are just so passionate about
what they're doing. It's almost like a
spiritual pursuit. Everyone believed so hard in what they were doing and that being the most
important thing. That is just so
powerful when you're working on really hard problems. In the case of Tesla, it's transforming an
entire industry versus creating many new ones as
well as transforming them. It was incredibly hard, but also just
invigorating and so fun and I learned so much
in a short amount of time. I don't think it's
normal to build a car from zero to one in
just three, four years. It's a very short time. These things usually have
this very long lifecycle or timelines in terms of design and prototyping
and production and so on. One of the things that
I learned at Tesla was there's always
some different way, even if it seems impossible, there is always a different way. In products in general, there's these two
ways of building products where you have the
really, really polished stuff. Then this way of
hacking and iterating and getting a lot
of feedback from your user base and
customer centricity iterating quickly on that. Tesla, I would say, was
in-between, doing both. That was incredible,
just the first time of operating like that in an industry
that is so established. I learned a lot as perceived
from just the power of being creative and
thinking originally. Just really changing everything and questioning what you know, and questioning why things
are done a certain way. That was a place where I started getting
really interested into the power of AI and how it would change
everything that we do. In a sense, in my career, it was the place that really catalyzed my interest
in working in AI. Then of course, after working in VR and AR, I just thought, intelligence is really
the fundamental property of how the world is
going to change. Then I got more and
more interested on just the application
side of it. But really understanding what general intelligence meant and how we could build it and how we make things go well for the world
if we do build it. KEVIN SCOTT:
Before we move on to AI, what's if you can share an interesting technical problem or technical thing that you learned on the Model X, something that was tricky or
interesting or different? MIRA MURATI:
So many things I could talk about
the Falcon doors. That could be problematic.
(laughter) KEVIN SCOTT:
Maybe at a high level, we can talk about that. That is an interesting
design choice to make. Obviously a brand new
thing and as an engineer, I don't know the details
of the implementation, but I can imagine
how difficult it was to make that feature
of the car work, technically. Did you
all have a sense for, I'm sure there're just dozens of these things in a car where like some designer has this idea
that I want to do this thing, then some engineer has to go decide or figure out how
to make the thing work. Just in general, how do you
balance those two things? MIRA MURATI:
There are a lot of things about the Model X that felt just really pushing the envelope and just they
had never been done before, or especially in that kind of car. The doors were a
feature like that, or the HVAC system,
the HEPA filter. It always required
bringing together the whole team or the parts that would
be working together. Design, engineering,
manufacturing, the software side of a team, or maybe if it was relevant the electrical engineers and really bringing together
all the pieces. You could design it
together versus hand it off and then go back and forth or design something that
could not be manufactured. That was very powerful
in working with teams that have different
backgrounds, domain expertise, figuring out how to design something that has
never been done before, adopting new ideas, but also very quickly killing old ideas and moving
on to the next one. Just figuring out
the right problem to work on at the right time. KEVIN SCOTT:
I think that is an incredibly
important thing. This idea of you do your
work and then throw it over the wall to the next person or a team and the change that
has to go do the next thing is that there's a
certain efficiency that you can get from
doing things that way. But if you're trying to
make something brand new, it's very difficult to have these waterfall
processes like that. There's so many jokes about, like one of the things that I
was going to ask you about as a mechanical
engineer is, hey, did you spend any time
in the machine shop? Because there's this tension between
mechanical engineers and machinists, like, "you gave me this print and
there's no way to make it." Or, there's the tension in software engineering between the product managers
and the engineers, the product manager says
"we're going to go do this thing" and the
engineers are like "are you crazy?" It usually works better when everybody is in
the conversation. It's super interesting, to hear you say that's
how you all did your work. MIRA MURATI:
Totally. It's funny that you mentioned it because
as a mechanical engineer, I was often machining my own
parts just to understand the constraint
limitations and also just the challenges of doing it. It was very similar
to Tesla where the design engineers were
often on the floor fitting, testing the parts, and just working very closely with
manufacturing engineers. I think that like you said, it's key to innovating at scale past a certain size
of company. It's difficult to
innovate if you're just throwing things
over the wall and bureaucracy can
kick in, or processes. As they grow, companies
can lose their vision and stop pursuing new ideas. But if you cut through
that and minimize the layers of processes
and things or hoops that you have
to jump through to get something done
or bring some new idea, then I think it's much easier. So that was something actually
quite critical looking back that I learned
working at Tesla. KEVIN SCOTT:
I was listening a long while ago to an interview that Elon was doing where he was describing this
thing that was happening, not with the Model X, but another one of the automobiles
where they were having a really challenging time
getting something manufactured. As soon as he started
asking the right questions, it turned out that the problem
wasn't solving the problem of how to make this particular thing
actually manufacturable. It was like, why did this thing exist at all? Like it was just completely unnecessary in such
a way that they got designed and the real fix wasn't like go solve
the nasty hard problem. Because the thing itself was a little bit arbitrary
and it's like change the initial
conditions and then the problem gets
easier to solve. I think that is one of
the things I admire a lot about Elon is like
this first principles. They always like being
able to step back and ask the right questions about why are we doing a thing
the way that we're doing it? What is necessary
and what is not? MIRA MURATI:
I mean, I think this is incredibly
important - stepping back, I mean, having the
ability to be immersed in details and dig
deep when you need to, but also stepping
back and asking the right questions and having this high degree
of adaptability in the team and tolerance
for ambiguity. Because especially when people
are extremely experienced, they have a certain
way of doing things, and so you need to be adaptable and also believe and disbelieve
things at the same time. Those are hard qualities and traits to sit together. KEVIN SCOTT:
Then there's just something about big organizations, like organizations
should only be big if the nature of the
problem that they're solving for their stakeholders
requires you to be big. Because bigness, it is almost
a flavor of entropy that forces some stuff to happen where just because of the
complexity of the whole, like no one has all of the details in their head and
so we'd like, you can find yourself trapped in
you know just feverishly, working as hard as you can on the details of something. If you could pull
all the way back, you would just find that the
thing that you're working so hard on is completely
unnecessary. Since one of the great
things about the size at OpenAI is at right now is you still institutionally
and the complexity of things you can - You have less of that weird entropy that
happens to big organizations. The thing that I've
found is you just have to fight against it. It's super hard. Because if you're not pushing
back against this thing, you're just letting people entirely optimize for
the narrow thing, it just metastasizes into
confusion basically, and people optimizing
for the wrong thing. MIRA MURATI:
So then momentum just carries on. KEVIN SCOTT:
Yeah. Let's talk about AI. Let's start with, how did you make the transition
from Tesla to open AI? Because you were in very
early. From the beginning. It wasn't obvious at the start that like--
not obvious at all. MIRA MURATI:
Not at all. KEVIN SCOTT:
You get to where you're at now. What made the leap? MIRA MURATI:
After I worked in VR and AR, and was really intent on defining the new interface
for special computing, back then it was
a bit too early, I think, too early
for VR and AR. But at that time we actually got really interested in how AI can help us redefine the way that we interact with
the world and we absorb information and
the things that we produce and how it
affects creativity. Just this entire
concept of amplifying our intelligence and
what that means. I was really interested in learning more and seeing
where this can go, this idea of pushing
intelligence as a fundamental property
that can have this very broad
universal impact. At the time, I was
unsure whether -- what the chances of
that are to go all the way to artificial
general intelligence. But I was just very interested in figuring out how far
we could pursue it, and it really seemed like maybe the last thing
that we'd ever work on. It seemed like the
most important thing that I could work on. It was important to
me to work on it at a place that cared about making sure that it
goes well for the world. I joined OpenAI when
it was a non-profit and the mission of the company
was then and still is, to make sure that building
AGI goes well for everyone in the
world and people can benefit from what it will bring. Obviously since then,
for practical reasons, we've evolved the structure
of the company to have it be limited partnership
with a capped profit. It still maintains the same
mission and the non-profit oversees the mission
of the company. But I just pursued my curiosity and what felt like the most important thing
to me at the time. KEVIN SCOTT:
Which I like honestly I think is super good career
advice for anyone. Being able to make choices
about what you do, where you believe the thing
that you're working on is the most important thing you can make a contribution to. I think people don't think
deliberately enough about. MIRA MURATI:
I think it's so important because, when you're working on
really hard things, it's that passion, that innate curiosity is the thing
that can pull you through. KEVIN SCOTT:
Yeah. A hundred percent I mean just really glad you
said that because I say this to people
all the time. If you're working on
a really hard problem with a bunch of really smart, highly motivated
people, it's hard. Like most days you're failing. You go in and. MIRA MURATI:
Exactly. KEVIN SCOTT:
You're trying something and it doesn't work, and you're frustrated
with yourself and you're frustrated with the
people around you, and there are only
a very small number of things that you can have that will help you do that day after day after day until you actually solve the problem, and you get something
that matters. If you quit before you
solve the problem, then you haven't
solved the problem, you've got nothing
but this accumulated frustration that you've had. I think one of the
very few things that you can have that
will get you through is, you have to believe that it's the most important thing
that you could be doing. You have to believe that it matters, like money's not enough. Your mom wanting you to
do it isn't enough. It looking good on your resume
isn't enough. You have to just deeply believe that it's the most important thing
you could be doing. MIRA MURATI:
Yeah, exactly. It's hard to find that
faith and belief. You almost have to
experiment a bit through, I mean your entire life and sometimes to just really
find what that is, that really
brings you this satisfaction. KEVIN SCOTT:
Yeah. Yeah, and at some point you also have to figure out what your mechanism is for dealing with
that frustration of friction and failure
because it's tough. I'm sure this is for everything that
you've done because you seem to have repeatedly chosen
to do very hard things. I know for me I
repeatedly choose to do, the most important
thing is almost always the hardest thing you
could choose to do, and so just being able to sustain that over
time because at some point too you probably had enough success from your career at Tesla where you
could have chosen, just from a success perspective to not do the hardest thing, well, I can go do something
slightly easier than try to make an AGI
in a non-profit, right? (laughter)
It sounds impossibly hard. MIRA MURATI:
When you put it like that, yes in fact. KEVIN SCOTT:
One of the things that I think has helped OpenAI be very successful is you have
really excellent people, folks who are in their
particular domain, whether it's
figuring out how to, wring numeric performance
out of a GPU or if it's someone who
understands how to do safety and alignment
work or whether it's someone who understands how to architect a deep neural network, someone who understands
distributed systems. You have, just people
who are at the very top of their game in each
one of those areas, and you also have this mission, how do you go solve this incredibly
complicated problem that not just OpenAI, but humanity's been
sort of thinking about for thousands of
years and how do you make that a reality and
how do you do it in a way where it creates massive
benefits for humanity. But you've got this third
thing that's interesting, which is a way to keep people focused on moving
forward and progress. You can have the mission and you can have all of
these smart people, but they could be running in
1,000 different directions, and their work could
not be accruing to a thing that's
making progress. And I think that's sort of
the extraordinary third thing that you all have
been able to do, and I don't know whether you share that same perspective, I'm just sort of curious on your take or what that
missing element is, because lots of labs
are out there with really smart people
spending a lot of money and they've got an interesting
intellectual mission but they still haven't been
able to make this sort of progress that
you all have made. MIRA MURATI:
It's incredibly hard. Like you say, you can have these incredibly talented
people in high density and they are
innately curious and they're forever in pursuit of
discovering something new, but that needs to compound, you need to have all the
smart people working together on kind of
similar or same bets, and you want to motivate people, you don't hire smart people, tell them what to do and -- you want them to
be motivated and aligned enough to work
on the same thing. At OpenAI I think one of the most important
things that we managed to do well was take a bet or take a
couple of bets on the things that we
believed the most and get alignment on those
very early on and even at the stage of
recruiting people actually and bringing them in, that's most important and making sure they're really
aligned on those things. It's hard to say no, especially when there
is so much opportunity, it could be working on all
these different ideas. It's incredibly hard to say no, and you doubt yourself. It might take a while for
these bets to pan out, the scaling laws and
focusing on one large model, a ton of data, which now it's obvious, but back then, not so much. Getting alignment on
that is incredibly hard, but I think it goes back
to this idea of figuring out how you work on the right
problem at the right time, and having faith in that. KEVIN SCOTT:
Yeah, I want to double-click on this notion of it's
hard to say no. It's incredibly hard to say no, because the thing that you're
faced with as CTO of OpenAI, and I have had a lot of this
over the past two decades, is you will have the smartest people in the
world coming to you with very good ideas that you think are interesting and you're a curious
person and you're like, that's amazing, I love this. And then, you know that that idea is not on the
path that you're pursuing and it might not be the next most important
thing to go work on if you're choosing the next most important
thing and just saying no, and you're also a good person and the people who
you work with, are good people and you don't want to disappoint them and you
don't want them to be sad, and so it's a real
art form I think, and it's two parts, it's like having the confidence
and the courage to say no, yourself, when you also have your own
uncertainties like, 'am I wrong, am I
making the right call?' and then being able to
deliver the no where, it's not a no,
it's sort of a no, but it's no, here's this other thing that I think if you do
that it will be even more interesting and create
more impact, it's hard MIRA MURATI:
Exactly, it is extremely hard. Together with that goes
building the muscle as an organization to learn new things quickly or learn
what's not going to work very quickly and adopt
what's going to work very quickly and kill
the old ideas quickly. It is hard to kill
things that are already maybe working but they're not working as well as something
new that you could be doing. KEVIN SCOTT:
Yeah, well, look, I think that's another thing that you all do really well, and it's very important is choosing when to
stop doing things. Like, for instance, you-all had like an
incredibly great demo a handful of years ago of a robotic hand that
could single hand solve a Rubik's cube
and it was a demo that was trying to get a reinforcement
learning system to learn a robotic kinematic model. It's technically
interesting work. It's a super cool demo, but like you-all decided, this isn't on the path, so we're going to stop
working on this and that's a hard decision for me because
that was a lot of work for someone and it was like their favorite
thing in the world. It's like at the end
people may quit because, you stop doing this thing and that's the thing they wanted
to work on so they're going to go find some
other place to go work on it but it's important.
Really important. MIRA MURATI:
Yeah. Exactly. At the time it was a very big bet
for the company was making. And we had that and DOTA. We had this inflection
point that okay what are we
trying to learn? How does this fit in on our path to AGI and
is there a better way? Choosing to stop working on it, thinking there's a better way. KEVIN SCOTT:
You've said a lot of very important profound
things like you just said something that I think is also very important is what
are we trying to learn? I mean if more people asked
that question deliberately, we would have a
much better world and people would
have more success. But I mean, that is
in essence, I think, one of
the things that you-all have always had
pretty good focus on. It's you're not doing activity for the
sake of activity or like doing activity for the sake of proving that you're smart. It's we have a specific thing we're trying to learn through these things that we're doing and it doesn't have to be AI. It could be product
design or it could be like parenting or whatever. What are you trying to learn through this thing
that you're doing. Let's talk a little
bit about, I mean, you-all have had
unbelievable total run, but in particular the past year or even the past six
months have been, I think, shocking to
a bunch of folks. I've been following what
you-all have been doing for a while and so what happened
the past six months I mean, it was surprising to me. But not quite as shocking
to folks who, saw nothing, nothing and then all
of a sudden ChatGPT emerges and it becomes the most interesting
thing in the world. Talk a little bit
about that journey because I think ChatGPT
is just one point on a long set of efforts that you-all
have been working on and it's not even
the last thing, so that's the other
thing people probably aren't internalizing that it is a point on a curve and
more things are coming so how have you-all
thought about that in the context of how
the public's reacting? MIRA MURATI:
The first time that we thought about deploying this model that was just
in research territory, was this insane idea. It wasn't normal back
then to go deploy a large language model
in the real world and, what is the business case? What is it actually
going to do for people? What problems is
it going to solve? Like we didn't really
have those answers. But we thought if we make
it accessible in such a way that it's easy to use
and it is cheap to use. It is highly optimized you don't need to know all the bells and whistles of
machine-learning, just accessible then
maybe people's creativity would just bring to life new products and
solutions and we'll see how this technology could help
us in the real world. Of course, we had a hypothesis, but really it was just
putting GPT-3 in the API. The first time
that we saw people interact with this large
model and the technology that we were building
and that for so many years just been
building in the lab without this real-world context and feedback from people out there so that was the
first time it was this leap of faith that it was going to
teach us something, we were going to learn
something from it and hopefully we could feed it
back into the technology. We could bring back that
knowledge, that feedback, and figure out how to use it to make the
technology better, more reliable, more aligned, safer, more robust
when it eventually it gets deployed in the
real world and I always believed that
you can't just build this powerful technology in
the lab with no contact with reality and hope that
somehow it's going to go well and it's going to be safe and beneficial
for all and somehow you do need to figure out
how to bring society along, both in gathering that
feedback and insight, but also in adjusting society to this change and the best way to do
that is for people to actually interact
with the technology and see for themselves instead of telling them or just sharing
scientific papers. That was very important and it took us a couple
of years to get to the point where we were not just releasing improvements
to the model through the API, but in fact, the first interface that was more consumer-
facing that we played around with was
DALL-E, DALL-E labs. Where people could
just input a prompt in natural language and then you'd see these
beautiful, original, amazing images come up and then really for
research reasons, we were experimenting with
this interface of dialogue, where you go back and
forth with the model in ChatGPT and dialogue is
such a powerful tool. The idea of Socratic dialogue
and how people learn. You can correct one another and/or ask questions,
get really into deep, deeper truth and so we thought
if we put this out there, even with the existing
models, we will learn a lot. We will get a lot of
feedback and we can use this feedback to actually make our upcoming model that
at the time was GPT-4, safer and more aligned so there was the motivation
and of course, as we saw in just a few days
it became super popular and people just loved interacting
with this AI system. KEVIN SCOTT:
One of the reasons why just me personally, I've been excited
about the work that you-all are doing is
this notion that you want to really allow a lot of non-expert people to be
able to play around with the technology and to imagine how they can use it
for things that they think are important
is super important to me and maybe a little bit
of same is true for you. But like I grew up, not in like one of the coastal innovation
centers where things like these AI
systems get created. You did not have computer
scientist or engineer parents and the problems that people have in rural
Central Virginia and I'm guessing the problems that
people have in Albania, some of them are common
across the board, but some of them are
like very different and some of them you can't even imagine if your
entire worldview is like, I went to Stanford, I got a job at one of the biggest technology
companies in the world and I'm building
this technology and I have to imagine all
of its possible uses. You just can't even
imagine what life is like for someone from Albania or rural Virginia and
so I think it's really unbelievably
important to have these things be platforms that aren't just getting built in a lab where all the
consequential decisions get made without any contact
with the real world. I mean, this is the last thing I want to chat about before we run out of time. But it creates this
very hard problem of how you do responsible AI. Because you get this big benefit of lots of people participating, but then you get this big bucket of things that you have to
go solve at the same time to make sure that it's not
creating a whole bunch of harm so talk a little bit about how you-all
think about that. MIRA MURATI:
Yeah that's well put. these trade-offs and minimizing. And you
can't have zero risk, but really minimizing
those harms and actually really being
able to respond quickly and iterate quickly on being able to maybe
make changes to the models themselves or introduce tools or policies basically
to contain those homes. That's really difficult
because often we're doing all of this
in the public eye. We don't have the privilege
of doing it behind closed doors and so obviously with that comes
a certain responsibility. But I think actually there
is no other way to do it. I think it's the only
way to get it right. It does need to be
in the public eye and it needs to be in this continuous
iterative cycle because the rate of technological
advancement right now is insane. If you hold the systems
back in the lab, the difference between
if we had never released GPT3 or
3.5 and we had just gone out with GPT4 on Chat GPT that would have shocked
the world, it already did. We had this continuous
development cycle. I think that's really important. But one of the things is
from each deployment, from every time that we put out a model, we
learned something. We learn something
about maybe the safety of our systems in the early development
cycle or impose training or in the product cycle, safety is really deeply
embedded and integrated at each stage of developing and deploying these models
and we're constantly changing what we're doing because we're just constantly
learning new things. Every week, I would say we're
learning something new. Whether it's how you think about the data that
you're selecting and filtering and analyzing the data early on or about the RL, Reinforcement
Learning with human feedback process that makes these models more
aligned or classifiers that we use in production or the tools that we're
making available for developers to have control and be able to be in the
driver's seat and steer of these models. All these pieces along
the life cycle of taking research to production. KEVIN SCOTT:
It's a complicated set of things to manage
these tradeoffs. But I agree with you. I don't know if there is any other reasonable
alternative and I think the trick is having lots
and lots of inputs that are coming into you like where
you can hear what's working, what's not working, what is the scrutiny, which of the problems that seemed substantial
or not and which of the things that people are seeing in some weird permutation of how they're trying
to use the product that you never
imagined or intended. MIRA MURATI:
Exactly. KEVIN SCOTT:
It creates - It is on the one hand very exciting. But it's also like a huge
responsibility I think. MIRA MURATI:
It is. We're working on something that will change everything, it will change the
way that we work, the way that we interact
with each other, and the way that we think, and everything really,
all aspects of life. KEVIN SCOTT:
Yeah, I have one last question for you that I ask everybody
who's on the podcast. I know you probably
have no free time given the intensity of the
past really year. But I ask everyone what they
do outside of work for fun. MIRA MURATI:
I love reading and I love going for hikes. Hiking is one of
my favorite things to do, being in nature. KEVIN SCOTT:
We live in a good place for hiking, which is good. Awesome. Well, thank
you so much Mira for taking time out of an incredibly busy schedule
to have this conversation. I've learned a ton
and just enjoyed this conversation
and enjoy being able to work with you on
a regular basis. MIRA MURATI:
Awesome. I do too. Thanks so much. KEVIN SCOTT:
Wow, that was a fascinating conversation
with Mira Murati. As close partners, I get to work with Mira and her
team all the time, helping to develop some of the big AI systems that they're building
and then figuring out how to safely deploy those unbelievably
sophisticated AI systems into the products
that we're building. But I learned a ton about Mira today that I
didn't know before. I knew she was from Albania, but I had known relatively
little about how she first got interested in science and technology in
the first place. It was so great to hear
about her teachers, always being ahead, and having those teachers who were
nurturing the curiosity that she had her going through her sister's textbooks when she got bored with the stuff
that she was working on. I think she said a year
and a half older than she was and then when she got
bored with her sister's stuff, figuring out what else
there was to learn. I think you heard at a bunch of places in our
conversation like that, what am I going
to go learn next? Why's that thing
important to learn? And this belief that
there's always something more to
go learn is one of the things I think
that has driven Mira to such success and that the teams that she's responsible
for leading to success. I think it's a good piece of career advice for all
of us to be just very intentional about how we're thinking about the activity
that we're doing right now as an opportunity to learn something that will
help us get better and better at our jobs and
to be more purposeful about how we invest more of our energy in something
into the future. It was awesome to hear about
her experience at Tesla, which I think has really shaped
how she does her job as a leader and how she tackles these complicated things where there are multi-disciplinary, intersectional teams, where you have to
pull a lot of people together with a lot of
different points of view to do some of these
super-complicated things. Just hearing her talk about her passion for intelligence
and what that means for how we are
going to interface with complicated bits of technology and how they really have been thinking for
a long while about how they take what
they do and package it in a way where lots of
people can use it and where you really can unlock
the imagination and the curiosity of a
lot of other people. You're empowering them to
use this technology to do the interesting things
from their points of view. Anyway, that was just a
fascinating conversation. There were more tidbits in there. I found myself during
the conversation remarking on several
points where she said something almost in passing
that I thought were real super valuable
nuggets of wisdom. I hope everybody gets a
chance to reflect on what this conversation really means. And that's all the time
we have for today. Big thanks to Mira
Murati for joining us. If you have anything you'd
like to share with us, please email us anytime at
BehindTheTech@Microsoft.com. You can follow us on YouTube and on any of
the usual places that you go get your
podcasts goodness and until then, we'll
see you next time.
