Wewnętrzny monolog naszych maszyn! Moja droga. | Jakub Pachocki Time100, Open AI

Our company is built on the belief that in the not-too-distant future, AI will be as intelligent or more intelligent than humans. I believe this development has no end, and where we are now is, in a sense, the beginning of a new paradigm that also scales. Wojtek Zaremba wrote to me, and I went to OpenAI. It really shook my world. We will reach a point where models will train even better models.

Yes. Since the invention of atomic bombs, the world hasn't been in such an unstable place as this revolution could cause, so it's something that stresses me out. The next step is AI that is truly capable of building something in the real world.

In this field, we are the best in the world.

Right now I'm Chief Scientist. So I lead our company's research program, deciding which directions we will pursue, how we will develop the AI we are working on.

Our company is built on the belief that in the not-too-distant future, AI will be as intelligent or more intelligent than humans. And I think that 5 or 7 years ago, it was more like, 'maybe there will be such a moment.' Now it seems to me that we see it's more continuous, right? GPT is already smarter than humans in some ways, and much dumber in many ways, and I think it will just keep developing. But yes, a lot of our focus is on creating the strongest AI in the world, but also understanding how it works, how people will collaborate with it, and what impact it will have on people.

Well, this has been the subject of my work and research for a long time, it just evolves a bit. When I was in high school with Professor Szubartowski, it was about what I can program a computer to do. That was always a huge draw for me in programming tasks—there's a clear input to a program and it has to produce an output, and at first glance, it's not obvious how the computer should do it. And the fact that I can somehow figure it out and show that the computer can do it very quickly was fascinating. That's why I later went into theoretical computer science and did a PhD in theoretical computer science, where you prove what computers can theoretically compute and what they can't. It's a very long-term science that really touches the boundaries of mathematics—what functions can be computed, which cannot.

I didn't abandon mathematics, because deep learning is also mathematics, just a bit different. It's more applied. How is it that Kuba Pachocki, from such theoretical mathematics—looking at your work at Harvard, it was about theoretical mathematics—and suddenly you focused on practice, on deep learning, on applied mathematics? When I started my PhD around 2013-2014, my view of the field was that there are algorithms we can prove work, and algorithms we can prove work even if they aren't very effective or practical right now, are worse than heuristic methods, but they have the property that if the computer gets bigger, they will scale. And if you look at the last few decades of scaling, and if you were looking into the future from that moment, you could conclude that what computers can do now isn't that important. What's important is what computers will be able to do in 50 years, and we should build a theory to understand the limits of computer capabilities and how to think about it, and really expand our understanding of algorithms. And what also confirmed me in this belief was that a lot of the most interesting things computers could do were based on theoretical algorithms. For example, an optimization algorithm that was used a lot in practice and still is—linear programming, or specifically maximum flow, which was always one of the most magical algorithms for me in programming contests. When I was doing my PhD, new fantastic methods based on linear algebra for solving these problems were emerging. Professor Aleksander Mądry, whom I didn't know then, was working at MIT and making amazing progress.

I'll come back to him later. So I was thinking about building an understanding of what computers can do that will be important in a few years, and there were already some early results in deep learning. I mean, deep learning results had been around for a long time, but they were definitely starting to be good for vision, for image recognition. But it wasn't clear to me then whether this was something that would scale, whether it would last, because the theoretical foundations of deep learning are much worse. Unlike classical theoretical computer science, in training we can't prove that we're actually finding the optimal solution, that we're finding the optimal parameters of the network we're optimizing. We just build a network that looks reasonable, optimize it for some time, and get some result. So it was easy to imagine that this was something that works a bit, but it's unclear if it will continue to work. What made me fundamentally rethink my approach to the field were the results of DeepMind's AlphaGo. There were the fantastic results of Deep Blue, inspiring that computers can play chess. But okay, chess is a game with a relatively small board, few moves, and it's easy to imagine that modern computers can thoroughly search all possibilities. Maybe not all, but a large part.

But in the game of Go, it's a very large board, stones can be placed anywhere, there are very many possible moves. And there was a general consensus in the field that this game would take many, many more years, that algorithms like Deep Blue didn't have much of a chance. And what DeepMind showed then was that if instead of writing a bunch of heuristics by hand for how to search all possible moves in the game, we use a neural network that learns from human games or its own games, we can play better than humans in most cases. It really shook my world, because it's much easier to imagine how from playing games you can get to solving programming contests or further, writing scientific papers and really advancing science, than from just image recognition. From then on, I became more and more interested. Soon after, I finished my PhD and started talking to several potential companies that wanted to conduct deep learning research. At Harvard, I was already moving more towards machine learning. I had theoretical papers there, but more related to the topic. And during my time at Harvard, I also spent a lot of time—I got to know Olek Mądry better, and we spent a lot of time together. He had amazing results on graph flows, in the same field I had worked in before, but he had come to similar conclusions as me. So we talked a lot about how everything would change and how we could both—we were both a bit dissatisfied that deep learning had such weak theoretical foundations and we didn't quite understand how it would scale and how to think about it. We wondered if we could build a better theory and a better way to think about all this. And in particular, for some time then, Wojtek Zaremba was writing to me, who I think had a plan to simply recruit people who did well in programming contests. For a while, he wrote to me from Facebook AI Research, and then from OpenAI. And ultimately, half a year after my PhD, half a year after finishing, I decided to jump into this field with both feet and went to OpenAI.

Something that is... We try to simultaneously push the development of AI and the field in a good direction, but on the other hand, we don't publish many scientific papers, so I don't know if such a statement would be fair.

Yes. It's a bit more than a reflection. It's something we actively think about a lot—what are the consequences? The path I took to AI, from interest in programming contests, what computers can do, to AI, isn't very unique. Many fantastic people at our company also did programming contests in the past. So a few years ago when someone asked me when I thought we should really start, when we would be close to artificial intelligence, I said when AI starts solving, gets a gold medal, or starts solving all tasks on a mathematical or programming olympiad. That's happening. It's happening now. Yes, we are at that point now. So the next step is AI that is truly capable of building something in the real world—whether it can build useful programs, or write books, or conduct research and discover new things. And I expect we'll see this before the end of next year.

It's worth mentioning that this test consists of two parts. One of them is much harder than the other. So we're probably close—these models already perform excellently on the part that resembles math competitions a bit more, not as involved in scientific research. But I think it will still take a while before they really solve the entire exam. But yes, it's happening. We see a fairly direct path to this. And we're definitely thinking a lot about what it means that we'll have models that not only are nice to talk to and might help you remember something, but are better than us. And we can take a cluster of GPUs, run a bunch of copies of the model on it, and we'll basically have a research laboratory. They'll just think on their own and produce scientific results. I keep coming back to research work and scientific results because I think that's in a sense the most drastic consequence—because scientific results lead to new technologies, they lead in particular to accelerating AI development, but also to changes in medicine. And this could start accelerating at a pace we're not used to.

If we want to solve really difficult problems, many theorems are discovered in order to prove other, harder theorems. So these aren't completely different problems. If models are able to solve difficult tasks that require many months of work, that's also largely creative work—you need to formulate new hypotheses and build understanding. We wouldn't be able to train the models we train today without many years of work building foundations and trying to understand what models from years ago were doing.

Absolutely. Yes, especially now when so much is happening. We're building a very important technology that already has great significance for people and will probably have even greater significance. Of course, there's also the whole active market, competition, business—and to remember in all of this what the mission is: that we want to build artificial intelligence that will truly change the world for the better. The assumption is that artificial intelligence will be created, so we want it to work for the good of humanity, not against us. To remember this, and to remember that we want to continue building understanding of how these models work, how to make them work beneficially. It's not that different from having to find time amid the noise of competitions and school and everything else, give yourself a bit of space, and focus on what's really important.

I try to focus on what we fundamentally want to achieve. Our company wasn't built on the assumption that we need to build a big business bigger than Google. Of course, we've had significant success, GPT is developing, there's competition, we need to think about how we'll fund future research, and we need to create value. But I have fantastic colleagues who also think a lot about these trade-offs, and I'm able to focus largely on research work and on how what we're working on now leads to artificial intelligence that can conduct research, and how such intelligence affects the world and what we can do to make this whole path go well.

Yes.