[0:00:00] Hello, I'm Andrew Main, and this is the OpenAI podcast. Today, our guests are OpenAI's chief scientist, Jakub Pahacki and Simon Sidor. We're going to talk about measuring AI progress, how you determine AGI, and where the next breakthrough might come from. The model was able to correctly identify that it didn't make progress on the problem. We started asking very, very seriously the question, like, are we ready as an organization for...
[0:00:26] Incredibly fast-paced progress. When we think about how we shape our research program at OpenAI, we seek to create intelligence that is very general. I want to first start off by understanding your roles. So Jakub, you're the chief researcher, chief scientist at OpenAI? Chief scientist, yes. Okay, what does chief scientist mean? So the primary thing I'm responsible for is setting the research roadmap for the company. So deciding what is a technical path we are going to bet on and what is the...
[0:00:56] The underlying long-term research that we're going to pursue. So how about you, Simon? What do you do? Random things. Random things, okay. Yeah, I mostly do IC work. I try to, well, maybe sprinkle of leadership somewhere in there. I try to do whatever is most useful. Now, you two knew each other before working at OpenAI, right? Yeah, we went to the same high school. Same high school? Yeah.
[0:01:26] Were you guys friends? I think we became best friends after we left. I think coming to the US is the kind of emotional experience that forms bonds. I think in high school, we were more like colleagues. What kind of high school produces guys like you? We went to this high school in Poland. I think we were both...
[0:01:54] Drawn there by this computer science teacher, Mr. Ryszard Szubartowski, who's had a great track record before we went there of bringing up computer scientists, programmers, with this big focus on programming competitions and pursuing excellence in just one field.
[0:02:21] Yeah, I think that was a very formative experience and a great mentor for us. Oh, wow. Yeah, definitely. I think Kedar was really going deep on programming. I think it went way beyond typical high school curriculum. There was graph theory, matrices, and all sorts of stuff like that. I actually hope that maybe with ChatGPT it's a little bit easier for people now to do this kind of deep dives because without the right mentor and without...
[0:02:51] A lot of work. It's kind of hard to replicate that experience. I've been using it to explain things like there's the Monty Hall problem where you have to choose which door. And you go into ChatGPT and you say, make a graphic interactive version of this. And all of a sudden you can see it. It can show you the different solutions if you do one thing or the other. I think it's one of these things where I'm excited about the ability to not just explain in text, but to build multimedia to do things. And it does get into the area of...
[0:03:18] There's not really a measure for that. You know, it's a use case. You know, this didn't exist before. And, you know, we talk about AGI, but we kind of have very loose definitions and whatever. And I'd love to hear kind of like how you would describe it, both from a technical and also like a layperson's understanding of it. Yeah. Well, maybe to like address the point about teaching, you know, this sort of like better.
[0:03:44] Explanation of some concept or teaching aristocratic methods. It's definitely a powerful use of ChatGPT and I think works well with a teacher like our Mr. Swartowski. But at the same time, I think the thing that he was able to provide was more emotional support and space, which I think it will be hard for AI to do alone.
[0:04:13] That's a great point. I think that gets lost a lot because sometimes here people talk about, oh, well, AI will replace education. And for me, I had teachers that maybe their facts weren't always right, but their heart was there and they were caring and they answered questions for stuff. And so I think that's a good point that these are companions to that. And I think that a teacher using these tools can be even more capable teacher. So on the subject of AGI, though, I want to hear first, like, give me your technical definition of it.
[0:04:41] Or actually, not a technical question. Give me how you would describe it to if you were talking to a younger sibling. A few years ago, when we would talk about AGI, it felt like the technology deep learning has incredible promise. But at the same time, the concept still felt a little bit abstract and far away. And so I think...
[0:05:10] You know, whether you talk about like, you know, human level intelligence, ability to converse naturally, you know, ability to, you know, solve math problems or pursue research, like they all kind of felt like in the same space. I think as technology has progressed, now we see there's like this, these are actually like quite distinct capabilities. And I think we pretty clearly are at the point where the AI is able to converse naturally.
[0:05:41] on a wide range of topics. It is able to solve math problems. I think getting a gold medal at IMO is something we've long discussed as a milestone on the path to AGI and that happened. I think solving all the problems of the National Math Olympiad is actually a little bit harder and I think it's another milestone on the path there. But I think increasingly
[0:06:09] We see that these kind of point-wise measures are less adequate, and so we turn to thinking about what is its actual impact in the world. For me personally, the thing that I think about when I think about how AI progress really impacts the world meaningfully, I first think about its potential for automating the discovery and production of new technology.
[0:06:39] I think we tend to associate new ideas, fundamental technological progress with just human ingenuity. And we measure our progress by these major milestone inventions and technological revolutions. And I think it is just hard to internalize. It is possible to automate.
[0:07:10] Most of this process, it is possible to have a big computer that is coming up with ideas that fundamentally change our understanding of the world. And I actually think that is not that far away. And so thinking about that, you know, what separates us from that and what are the consequences of such technology is my first thought. I just ordered a little Mac studio because I want to take the open source model GPT-OSS and I want to just let it run nonstop.
[0:07:37] Because that idea, just the idea of letting it generate and do stuff 24-7 is fascinating to me. But you're talking about a scale of basically automating science at a huge scale. And so what kind of discoveries, what kind of things do we think might be the first things we could see from that? When we think about how we shape our research program at OpenAI, we seek to create intelligence that is very general. We drive towards this automated researcher as a priority.
[0:08:06] We don't really think of it as let's take these specific domains and let's deploy this ecology there. I think that is a way to make faster point-wise progress, but I think the potential for the really big discoveries and the most meaningful technology advancement comes from this generality. Although still, I think we see the technology...
[0:08:33] It's kind of easier to apply in some domains than others. I think especially in places that combine a large amount of reasoning with a lot of domain knowledge and intuition seem very amenable to these systems. I think in particular we see pretty incredible results on medicine, which is very encouraging. I have high hopes about that.
[0:09:05] I think naturally being a company of AI researchers, we think a lot about automating our own work. I think it is also kind of a... I don't think it's a... If AI can indeed reach a point where you can automate AI research, then that is probably a very important thing to automate. And similarly thinking about how it can help with automating research on AI alignment and safety.
[0:09:33] I'm obviously impressed by the IMO, IOI results. I was actually about to add that in the past when we were talking about the IMO of Jakob, that was a few years ago, and we were still trying to even figure out what our definition of AGI might be. One kind of concept we were considering is something like solving all the problems on the Math Olympiad.
[0:10:01] And why did that feel appropriate? It's just like, okay, if you have a model of such a superior mathematical reasoning, then it should be able to disrupt a bunch of different domains that can be mathematically modeled. I mean, I'm in general just... I think maybe this podcast is just a good opportunity to share a little bit more of an inside perspective. I was astounded by the...
[0:10:31] So sometimes I see those headlines where people say that the economic impact of AI is only like 3% or 5%, right? And those headlines are often accompanied by comments like, well, so AI is slowing down or people are overhyping AI so much and it's only like 3%. So what's up with that, right?
[0:10:59] And when I see headlines like this, I remember maybe 10 years ago, I was working on natural language processing with deep learning. And back then, it just didn't really work. I remember Jakub once came to test one of the technologies we were working on, and that was trying to detect sentiment of sentences. And he was trying, this movie is bad.
[0:11:28] Correctly classified as negative. This movie is good. Correctly classified as positive. And then he would say, this movie is not bad. And the model is like, oh, negative. Yeah, yeah. Right? So that was 10 years ago, right? And since then, like, okay, like we slowly got like...
[0:11:44] We slowly started solving tasks like this, solving tasks like decide is this word a noun, a verb, that was like sentiment neuron. Then we had GPT-1, GPT-2 started producing like a paragraph of text that made sense, right? That was such a breakthrough. Right now it feels so simple, but back then it was such a breakthrough. Then we had like GPT-3, GPT-4. GPT-4 was like, to me, like kind of...
[0:12:12] Like, let's say my personal AGI moment, because it would sometimes say things that surprised me. And I was like, like, can this model actually surprise me? Right. It's still back then, like chat GPT for my personal use kind of felt a little bit more like a nuance and kind of like maybe slightly better Google, but like, like what's the big deal. And then like suddenly we get to deep research and, and this can actually like answer questions too early.
[0:12:41] Rarely make things up. That felt useful. And then finally, now we have models that can compete in programming competitions, which was very hard-earned for me personally, and even more so for Jakub, obviously. The pace of progress, just from the perspective of somebody working on this technology, is absolutely amazing. So when you see that 3%,
[0:13:05] Like I raised you, like 10 years ago, if we had to quantify it, it would probably be like 0.00001% or something, right? So like really, I think those numbers need to be put in perspective, right? And there is no reason not to believe that like in a year it will be 10%, in two years it will be 20% and so on and so forth.
[0:13:24] Yeah, I've heard it said that if you looked at like a graph of the economy from, let's say, like, you know, World Wide Web, you know, early 90s forward, and you said, point to the internet happening to the economy. You can't find the point. There's no point to go, oh, okay, Tim Berners-Lee announced this, whatever. And I think AI is a lot like that, where people go, oh, we've only measured this one. Our measures are hard. It's hard to know that, you know, one, who's using it, how they're using it.
[0:13:48] And you brought up a very good point too about if you've been following it for a while, I remember training like a very simple next character predictor on my computer and it was terrible, right? One, I'm using a small computer, but even then, and then you got, you know, the sentiment analysis, you're playing with bird and it's kind of, you know, you get a little bit better, but then GPT-2 comes out and I read every single output on GitHub. Every single output GPT-2 came out because I'm like, there is something going on with this.
[0:14:13] And that's how I ended up working at OpenAI was because I was this obsessive person about that. And then with access to GPT-3, I kept saying, oh, this is really this path that's moving forward. But it's kind of crazy now because if six weeks go by and a benchmark hasn't been broken, people are like, oh, we hit the wall. We hit the wall. And I would say part of the problem, though, is that benchmarks in some ways feel like you'll see modest improvement on them. I've heard some of the benchmarks have problems and some of them actually have wrong answers. And it's impossible to get 100% if you answer them correctly.
[0:14:42] But also we talk about the term internally. I've heard people talk about this as, you know, saturation. Do you want to talk about that, Per? Yeah, I think there's a few issues that we're hitting with benchmarks right now. Yeah, I mean, a pretty clear one is saturation. And that is just the models genuinely reaching a point where, you know, for the kind of standardized forms of measuring intelligence or ability, like they are at human level for a lot of them.
[0:15:13] If you're able to perform amongst the top on these very hard high school competitions where we have the best competitors from around the world, it just becomes quite hard to have this very constrained measurement. Previously, when we were looking at GPT-1, GPT-2, GPT-3, GPT-4...
[0:15:44] Scaling, paradigm. The benchmarks were really just measuring the rising of the tide. I think now the field has developed a lot of more data-efficient ways to train for specific abilities. It doesn't mean train on these benchmarks, but you can train models that are disproportionately good at math compared to their ability to write, for example.
[0:16:10] And so they will do better on math benchmarks, but it's no longer as representative of their overall intelligence in other topics. I think these two issues combined, I think we really have to think about the reward utility of these models and especially their ability to discover new insights. Yeah, I guess that's a thing that sort of kind of gets overlooked is that you can build a model that's a really good test taker, but that model may not really...
[0:16:39] Be that useful for work. Ideally, your model should score well on tests, but just because a model got these scores doesn't mean you're going to find it personally useful. And I certainly think that's a challenge we're at now where when people say, is a model good or bad? It's kind of like saying, you know, you're trying to create a blanket assessment when there is 100 different use cases for it. You know, is a model good or bad? Maybe it's great at creative writing. Maybe it's bad at math. Maybe it's great at math and bad at creative writing. And that becomes a really big challenge. And we've talked about this with.
[0:17:09] I think the reason we've been excited about these competitions like the International Math Olympiad and Informatics Olympiad is that they are a pretty interesting example of a test that is constrained, doesn't require that much knowledge.
[0:17:35] But really test their ability to think about a problem hard for an hour or two or three. And we have very good evidence that these problems are hard. There's a lot of people that try to solve them and compete at solving them. And it matters to them. So I think this is for models that excelled at knowing a lot of things, but not necessarily
[0:18:07] Thinking very hard in the past, that really seemed like the right milestone to be working towards. Now, as I understand it, the model that scored gold medal level on that wasn't using a calculator. It wasn't using other tools. It wasn't using some of the frameworks. It was doing it purely through reasoning. Yeah, that's right. For the International Math Olympiad, the model was not.
[0:18:31] And again, it was like two years ago, you ask it to multiply two four-digit numbers, it would fail. Yeah, but definitely for this kind of contest, it is of course in a limited domain of math, but it really is about fairly creative thinking, not about applying a formula. I guess that's part of the challenge, though, is that...
[0:18:54] Once you start moving outside of math, it gets to be harder. You can start to come up with things like humanity's last exam, which I think is a pretty neat test. But you find that certain models, after they learn a certain kind of tool use, kind of figure out maybe sort of how to solve these problems better. And I would wonder, what kind of benchmarks are we going to need? What are you looking at to say, okay, this is how I can kind of get an objective measure of a capability?
[0:19:24] Co-workers here, Anna Makanju. And I was telling her about IMO. I was excited about some progress. And she's like, what's IMO? And that kind of was a very important moment for me because I do realize that some of those benchmarks, we kind of live in a bubble a little bit. For me, that competition...
[0:19:49] Feels important and especially like the computer science counterpart, IOI, because it was a big part of my life. And so it's true for many coworkers here. But actually like for an average person, like working in other fields or maybe not as interested in mathematics or computer science, maybe they're interested in history or something. A lot of speaks like five languages too. So I could see for her a different metric based on that would be interesting. Yeah. So I think like one thing that like that.
[0:20:18] It's not a perfect metric, but it at least helps keep us honest and helps keep us escape the bubble is just chat GPT usage, right? Because everybody uses chat GPT and they use it for all sorts of use cases. And obviously there's like a lot of pitfalls to using that as a metric, but at least it avoids that particular problem where like there are just some things that I'm more familiar with and other people might appreciate other things. And this gives you like a very wide coverage.
[0:20:46] Yeah, and there, too, you have subsets of users, people who are building GPTs and doing more complicated stuff. You mentioned before, too, the fact that the model will reason longer, and that seems like a very interesting way to evaluate capabilities. Yeah, and I think this is also maybe one challenge with focusing on the usage of child GPT and broad adoption of AI as the metric of progress. I think this hasn't really...
[0:21:17] We should be able to use vastly more compute than a user would normally be willing to buy for themselves to produce technology artifacts that are useful to a lot of people. And I think that for me will be a very important measure of progress.
[0:21:47] Which of these wins were the most surprising to you? I think we definitely kind of anticipated getting to this point when we saw the reasoning models starting to work. At the same time, I think this recent set of wins is very impressive. I think maybe out of those, I think IMO came a little bit sooner than I expected. IMO Gold, again, I think IMO Problem 6 will still...
[0:22:14] IMO has, like, all the problems require, like, creative thought and some new insights, I think. But typically, like, you know, there's this proverbial problem six that is, like, requires very out-of-the-box thinking. And, you know, it's really kind of, like, usually, like, outside the kind of typical domains of the other problems. And, you know, so in the past, we were actually kind of drawing a boundary between, like, getting a gold, you know, like, solving these other problems and, like, actually going to solve all the problems. And in particular, problem six.
[0:22:43] So it was pretty hilarious in some way to see ourselves and also Google DeepMind at the same time, like, oh, yeah, we solved problems one through five perfectly, and we didn't make any problems on problem six. I think that kind of makes that challenge pretty clear. Yeah, I think that was what's interesting is that, yeah, I think that the OpenAI model said, like, yeah, I don't think I can solve this. It didn't even try or said that it had a problem with that. Was that correct? Yeah, the model was able to correctly identify that it didn't make progress on the problem. That's pretty.
[0:23:12] Fascinating to think about that. The model is able to sort of determine that because, you know, there's a lot of conversations about people talk about hallucination, which I think is a kind of a poorly understood thing. And there's a difference between fluid and crystalline thinking. And, you know, one is how much knowledge a model has and the other is its problem solving capability. And when you get to the point where it's able to do that, it's able to say, no, I think, you know, I won't be able to answer this. That's pretty interesting sort of point to get to.
[0:23:39] I've been told to ask this question about a live stream in Japan. Oh, so I think in the past few weeks, actually, our models have performed incredibly well in three competitions. So we talked about two of them, which is IOI and IMO. There is also this competition that is open to everyone, not just high schoolers, called AdCutter. It's a very prestigious, very...
[0:24:08] High quality competition organized in Japan, but open to competitors worldwide. And in this particular contest, it was about kind of longer horizon heuristic problems where you're given only a single problem. You have 10 hours to solve it. And so you have competitors racing to figure out like the best approach to up to this like difficult optimization problem.
[0:24:36] So it's a bit different because there isn't a single correct solution. There isn't a single pattern to follow. These tasks are extremely diverse. And you can focus on a single task for 10 hours. And so we entered our model into this contest. And to me, this had a little bit of personal significance. I used to be a kind of very engaged competitor in the past in this more short...
[0:25:05] Form, like close form contests like IOI. And my friend, Saiho, who also worked at OpenAI at the time, excelled at this like long duration contest. And when we worked together, he would mock me a little bit that, you know, my sort of contest would be automated long before his. Because, you know, they are kind of like longer duration require.
[0:25:34] It required more focused work. And it turns out, in this contest in Japan, Saiho was actually one of the top contenders. And so I was watching this live stream, watching our model race with Saiho throughout the competition. In the end, our model actually got second place, and Saiho won.
[0:25:56] He alone stood in the way of his prediction not coming true. Still two wins for OpenAI. I think one thing that also stood out to me is Saicho at the end of the competition. He was really, really tired. And they interviewed him a little bit to talk about his experience in the middle of the competition. And I don't think I can quote him directly on this podcast, but he's like, your models are very, very bad.
[0:26:25] I want to go to sleep. I am tired. Yeah.
[0:26:54] Do we think that's enough to take us to AGI? Or are there other breakthroughs needed? Or are there just other breakthroughs you think are going to happen? I just need to point out that the team here worked extremely hard on this party girl thing. It feels like something simple, like you just need a longer chain of thought. But actually to make it work was really hard-earned. And I do think, back to your previous question of what was the surprising result when we first...
[0:27:23] Notice that it's working or we first notice that we can train those models and give them more data and they get better. That was like, I think, one of the most kind of shocking moments, the moments where we are like, we started asking like very, very seriously the question, like, are we ready as an organization for...
[0:27:47] Incredibly fast-paced progress. I remember there was one particular evening, I think 11 p.m. I think we were on the line with Sam and Mira. I think we got a little bit freaked out by those results. Sometimes that happens. The pace is fast. I mean, it is a fast thing. And like I said, the joke is people, nothing happens for six weeks, they think it's slowed down. But then if you look year over year, it is.
[0:28:17] So I mean, it's a fair point because, yeah, you have things that you're aware of internally when you work on something for a couple of years and they're, hey, there's a research paper. But it's like, yeah, it's not like it came out last night. It was like there's a lot of work on it. But I'd say to the world was sort of surprised by the fact that there is this really fundamental new way to sort of make these models do even more to take kind of the existing sort of infrastructure, so to speak, and get a lot more capability out of it. Where do you think the next breakthroughs are going to happen? I think one thing we always try to not.
[0:28:47] Underestimate is the importance of scaling. I think even as we look at these rethink models, it's not like the previous scaling paradigm of pre-training has vanished. I think we will see these things compound. And I think there's also new directions that we can move in. In particular, we were talking about...
[0:29:16] Extending the horizon that these models can plan for and reason in. And I think if you look at it from the perspective of just like compute spend, we say, okay, yeah, we went from GPT-4 pursuing some out of compute for every answer to like GPT-5 Pro, which maybe uses, I don't know, like 10x, 20x, I don't know.
[0:29:45] Non-trivial, but in some ways not that impressive amount of compute more, right? And can produce much better answers. I think on the scale of what amount of compute would you be willing to spend on a problem that actually matters to a lot of people, right? On progress on a medical research question, progress on developing the next generation of models, right? These are incomparably larger amounts. And so I think that is...
[0:30:15] That question of like model persistence and ability to work for a very long time on a focused problem is a pretty clear next step. How would you put the practical implications of AGI to sort of like if you were talking to a typical chat GPT user or something, like what would their experience be like in a few years from now or five years from now, which sounds far away, but it's really not because we're just five years ago, GPT-3 came out and that feels like a blur.
[0:30:45] What would an AGI-like model be capable of? I was talking about automating research. My picture of how that would actually look like is imagine a company of very capable researchers and engineers that is largely automated. I think that is something that will interface with the world in all sorts of ways. It won't be just a black box.
[0:31:14] Talk to people. It will take in inputs. It will run experiments. But I think having this sort of potential for developing new technology and other kind of artifacts, code bases, designs, I think can radically accelerate the pace of technical progress. So I think that is something we will...
[0:31:47] And we need to do a lot of work to get it right from a technical and from societal perspectives. But I think that is kind of where I turn to. I think we should also expect a lot of progress on the actual kind of interfaces to AI that we interact with. We see that GPT can feel quite human-like. We can form attachments with it. I think as it becomes more...
[0:32:15] I just got access in ChatGPT to have it actually read my calendar in Gmail.
[0:32:36] And I realize like how far we've come because I'm excited about that now. I'm not really terrified that it's going to start writing like, you know, Ewok fan fiction to somebody, you know, and I think that's sort of this neat threshold that we sort of crossed this sort of the level of trust. I think there's definitely like, like we are in a place where there's like very tough trade-off where like there's like such clear, just economic, you know, personal value can extract out of having the model, you know, have access to a lot of your data. At the same time, I think like.
[0:33:08] We are not at the threshold of robustness where we can fully trust these models to not be exploited by someone trying to exploit them. It's definitely a big problem I think we as a field will have to iterate on. What would you tell two versions of you guys today in high school? What would you do if you're visiting your old classroom? What would you say right now? Tell them about the future. What advice would you give? Invest in Bitcoin.
[0:33:34] No, I mean today, even today. In 2025, what would you tell a high school student? High school students today? Oh, yeah, yeah. That one is also, I think, a great question, right? Because I hear a lot of kind of, would I consider misinformation on that online? So you should absolutely learn to code. Like one skill that is at premium and will continue being at premium is to have like...
[0:34:03] I learned to code late in life, and that's actually how I ended up working at OpenAI as an engineer.
[0:34:28] Try to explain to people just because a system can do the thing doesn't mean you don't want to know how it works anymore. And as you said, when you understand how to break down a task, when I worked at OpenAI and prompt engineering, my coding understanding helped me understand to take both language and break it down and make it do better things. And I think that people who bridge those gaps are really at an advantage. And so whenever I hear people say, like, don't learn to code, it's like, do I want an airplane pilot who doesn't understand aerodynamics? Like, this doesn't make much sense to me.
[0:34:55] Well, thinking about how I thought about things in high school, I think it's pretty incredible how many perceived constraints are not actually there when you really think about it. Maybe the first revelation to me was like, hey, if I really am passionate about this computer science stuff, I can actually spend a bit more time on it at the cost of maybe spending a bit less time on other 12 subjects in school.
[0:35:26] But then somehow, again, it was a big revelation to me that actually, I can go and study in the USA at some point. That's not really something that seems... That's obviously interaction space and obviously spending some time here in Silicon Valley and seeing how...
[0:35:55] People are willing to really attack these big problems with ambition and the belief that you can actually make a meaningful, positive change in the world. Yeah, I think it has been incredibly inspiring and something I cherish about this community. Is there a book or something that inspired you?
[0:36:28] I think there's a couple of books. I remember my... It's actually very hilarious thinking about it now. I didn't really connect the dots, but my dad gave me this book once when I was in a pretty... I think I was 15. I was pretty unsure what I want to do. Yeah, it was a Polish version of a book by some author I didn't know called Hackerson Painters. Yeah, so...
[0:36:58] Yeah, it was actually Paul Graham. So I guess like, yeah, again, like this community. So I found that pretty inspiring. Yeah, there's something helpful, I think, to hearing the message of like, no, it's okay to dream big and go do stuff, that you can just make things happen in the world. And I think that the more people realize that, kind of the better the world gets to be. Was there any book that influenced you? Or movie, TV show? Oh, movie.
[0:37:31] I have a stupid answer to that question. I love stupid answers. It kind of feels bad after the profound one. But like, okay, so I watched Iron Man. Yeah. And it inspired me to start a PhD in robotics. That's a great answer, though. Like, you know, The Martian by Andy Weir. I met a scientist at NASA who was a botanist who read that book. And I'm like, well, they got the atmospheric physics wrong and all this. He's like, well, it's why I'm here. I'm like, oh. Well, yeah, I guess I didn't get to the stupid part. The stupid part was like.
[0:38:00] When I started working on robotics, I was very disappointed how bad those robots are. Somehow it didn't occur to me that maybe the movie is a movie. Yeah, so that whole experience was kind of bad for me, would be kind of bad for me, if not for the fact that this is... There I met a friend who was into deep learning, and at the time I thought all of the machine learning kind of is a hype.
[0:38:26] But it was an interesting systems problem. And then out of nowhere, as I'm sure I would frustrate some DeepMind folks by saying that, AlphaGo came out. No, I'm sure it wasn't out of nowhere. I'm sure it was years in the making. And that was very inspiring, actually, to both of us. And since then, it was just hard not to work. Yeah, it took me a while to become convinced that the planning is more than a fad.
[0:38:55] Because we don't really understand the underlying optimization. I think this has been the story of our research here, trying to make progress on these questions about how it really works, but it really is like studying a physical phenomenon in some way. To a classically trained computer scientist, that was a weird thing to accept. I do remember when Jakub was telling me about scaling up principled convex optimization. That was before Afro.
[0:39:23] And AlphaGo was interesting because first like, oh, cool, it solved Go. And then we're like, yeah, but it just learned by watching all these. Then they did AlphaGo Zero where it self-taught and you're like, okay, game over, folks. There's a trajectory here. And I think that's continued on. But I think that, yeah, if you hadn't watched Iron Man, maybe Thor instead, you know, maybe things would have turned out better. Who knows? I kind of wish I studied maths instead. It would have been more useful. Study what? Maths.
[0:39:52] Mathematics or theoretical computer science. Either of those. Physics probably. I started off as a magician. I actually had my own reality TV show. So you find a very strange path to end up here. Jakob, Simon, it's been an absolute pleasure to talk to you both. And I hope we can meet again and talk about the next big breakthrough that you guys have been stupidly working on that's going to come out of nowhere. And we'll be like, that was an overnight thing. Thanks, Andrew. Thank you.