Mira Murati: Chief Technology Officer, OpenAI
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.