You know what's crazy? That all of this is real.

Meaning what?

Don't you think so? All this AI stuff and all this Bay Area… that it's happening. Isn't it straight out of science fiction?

Another thing that's crazy is how normal the slow takeoff feels.

The idea that we'd be investing 1% of GDP in AI, I feel like it would have felt like a bigger deal, whereas right now it just feels... We get used to things pretty fast, it turns out.

But also it's kind of abstract. What does it mean? It means that you see it in the news, that such and such company announced such and such dollar amount. That's all you see. It's not really felt in any other way so far.

Should we actually begin here? I think this is an interesting discussion.

Sure.

I think your point, about how from the average person's point of view nothing is that different, will continue being true even into the singularity.

No, I don't think so.

Okay, interesting.

The thing which I was referring to not feeling different is, okay, such and such company announced some difficult-to-comprehend dollar amount of investment. I don't think anyone knows what to do with that. But I think the impact of AI is going to be felt. AI is going to be diffused through the economy. There'll be very strong economic forces for this, and I think the impact is going to be felt very strongly.

When do you expect that impact? I think the models seem smarter than their economic impact would imply.

Yeah. This is one of the very confusing things about the models right now. How to reconcile the fact that they are doing so well on evals? You look at the evals and you go, "Those are pretty hard evals." They are doing so well. But the economic impact seems to be dramatically behind.

It's very difficult to make sense of, how can the model, on the one hand, do these amazing things, and then on the other hand, repeat itself twice in some situation? An example would be, let's say you use vibe coding to do something. You go to some place and then you get a bug.

Then you tell the model, "Can you please fix the bug?" And the model says, "Oh my God, you're so right. I have a bug. Let me go fix that." And it introduces a second bug. Then you tell it, "You have this new second bug," and it tells you, "Oh my God, how could I have done it? You're so right again," and brings back the first bug, and you can alternate between those. How is that possible? I'm not sure, but it does suggest that something strange is going on.

I have two possible explanations. The more whimsical explanation is that maybe RL training makes the models a little too single-minded and narrowly focused, a little bit too unaware, even though it also makes them aware in some other ways. Because of this, they can't do basic things.

But there is another explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. When you do pre-training, you need all the data. So you don't have to think if it's going to be this data or that data.

But when people do RL training, they do need to think. They say, "Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing." From what I hear, all the companies have teams that just produce new RL environments and just add it to the training mix. The question is, well, what are those?

There are so many degrees of freedom. There is such a huge variety of RL environments you could produce. One thing you could do, and I think this is something that is done inadvertently, is that people take inspiration from the evals. You say, "Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?"

I think that is something that happens, and it could explain a lot of what's going on. If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance, which is something that we don't today even understand, what we mean by that.

I like this idea that the real reward hacking is the human researchers who are too focused on the evals.

I think there are two ways to understand, or to try to think about, what you have just pointed out. One is that if it's the case that simply by becoming superhuman at a coding competition, a model will not automatically become more tasteful and exercise better judgment about how to improve your codebase, well then you should expand the suite of environments such that you're not just testing it on having the best performance in coding competition. It should also be able to make the best kind of application for X thing or Y thing or Z thing.

Another, maybe this is what you're hinting at, is to say, "Why should it be the case in the first place that becoming superhuman at coding competitions doesn't make you a more tasteful programmer more generally?" Maybe the thing to do is not to keep stacking up the amount and diversity of environments, but to figure out an approach which lets you learn from one environment and improve your performance on something else.

I have a human analogy which might be helpful. Let's take the case of competitive programming, since you mentioned that. Suppose you have two students. One of them decided they want to be the best competitive programmer, so they will practice 10,000 hours for that domain. They will solve all the problems, memorize all the proof techniques, and be very skilled at quickly and correctly implementing all the algorithms. By doing so, they became one of the best.

Student number two thought, "Oh, competitive programming is cool." Maybe they practiced for 100 hours, much less, and they also did really well. Which one do you think is going to do better in their career later on?

The second.

Right. I think that's basically what's going on. The models are much more like the first student, but even more. Because then we say, the model should be good at competitive programming so let's get every single competitive programming problem ever. And then let's do some data augmentation so we have even more competitive programming problems, and we train on that.

Now you've got this great competitive programmer. With this analogy, I think it's more intuitive. Yeah, okay, if it's so well trained, all the different algorithms and all the different proof techniques are right at its fingertips. And it's more intuitive that with this level of preparation, it would not necessarily generalize to other things.

But then what is the analogy for what the second student is doing before they do the 100 hours of fine-tuning?

I think they have "it." The "it" factor. When I was an undergrad, I remember there was a student like this that studied with me, so I know it exists.

I think it's interesting to distinguish "it" from whatever pre-training does. One way to understand what you just said about not having to choose the data in pre-training is to say it's actually not dissimilar to the 10,000 hours of practice. It's just that you get that 10,000 hours of practice for free because it's already somewhere in the pre-training distribution. But maybe you're suggesting there's actually not that much generalization from pre-training. There's just so much data in pre-training, but it's not necessarily generalizing better than RL.

The main strength of pre-training is that: A, there is so much of it, and B, you don't have to think hard about what data to put into pre-training. It's very natural data, and it does include in it a lot of what people do: people's thoughts and a lot of the features. It's like the whole world as projected by people onto text, and pre-training tries to capture that using a huge amount of data.

Pre-training is very difficult to reason about because it's so hard to understand the manner in which the model relies on pre-training data. Whenever the model makes a mistake, could it be because something by chance is not as supported by the pre-training data? "Support by pre-training" is maybe a loose term. I don't know if I can add anything more useful on this. I don't think there is a human analog to pre-training.

Here are analogies that people have proposed for what the human analogy to pre-training is. I'm curious to get your thoughts on why they're potentially wrong. One is to think about the first 18, or 15, or 13 years of a person's life when they aren't necessarily economically productive, but they are doing something that is making them understand the world better and so forth. The other is to think about evolution as doing some kind of search for 3 billion years, which then results in a human lifetime instance. I'm curious if you think either of these are analogous to pre-training. How would you think about what lifetime human learning is like, if not pre-training?

I think there are some similarities between both of these and pre-training, and pre-training tries to play the role of both of these. But I think there are some big differences as well. The amount of pre-training data is very, very staggering.

Yes.

Somehow a human being, after even 15 years with a tiny fraction of the pre-training data, they know much less. But whatever they do know, they know much more deeply somehow. Already at that age, you would not make mistakes that our AIs make.

There is another thing. You might say, could it be something like evolution? The answer is maybe. But in this case, I think evolution might actually have an edge. I remember reading about this case. One way in which neuroscientists can learn about the brain is by studying people with brain damage to different parts of the brain. Some people have the most strange symptoms you could imagine. It's actually really, really interesting. One case that comes to mind that's relevant.

I read about this person who had some kind of brain damage, a stroke or an accident, that took out his emotional processing. So he stopped feeling any emotion. He still remained very articulate and he could solve little puzzles, and on tests he seemed to be just fine. But he felt no emotion. He didn't feel sad, he didn't feel anger, he didn't feel animated. He became somehow extremely bad at making any decisions at all.

It would take him hours to decide on which socks to wear. He would make very bad financial decisions. What does it say about the role of our built-in emotions in making us a viable agent, essentially?

To connect to your question about pre-training, maybe if you are good enough at getting everything out of pre-training, you could get that as well. But that's the kind of thing which seems... Well, it may or may not be possible to get that from pre-training.

What is "that"? Clearly not just directly emotion. It seems like some almost value function-like thing which is telling you what the end reward for any decision should be. You think that doesn't sort of implicitly come from pre-training?

I think it could. I'm just saying it's not 100% obvious. But what is that? How do you think about emotions? What is the ML analogy for emotions?

It should be some kind of a value function thing. But I don’t think there is a great ML analogy because right now, value functions don't play a very prominent role in the things people do.

It might be worth defining for the audience what a value function is, if you want to do that.

Certainly, I'll be very happy to do that. When people do reinforcement learning, the way reinforcement learning is done right now, how do people train those agents? You have your neural net and you give it a problem, and then you tell the model, "Go solve it." The model takes maybe thousands, hundreds of thousands of actions or thoughts or something, and then it produces a solution. The solution is graded. And then the score is used to provide a training signal for every single action in your trajectory.

That means that if you are doing something that goes for a long time—if you're training a task that takes a long time to solve—it will do no learning at all until you come up with the proposed solution. That's how reinforcement learning is done naively. That's how o1, R1 ostensibly are done.

The value function says something like, "Maybe I could sometimes, not always, tell you if you are doing well or badly." The notion of a value function is more useful in some domains than others. For example, when you play chess and you lose a piece, I messed up. You don't need to play the whole game to know that what I just did was bad, and therefore whatever preceded it was also bad. The value function lets you short-circuit the wait until the very end.

Let's suppose that you are doing some kind of a math thing or a programming thing, and you're trying to explore a particular solution or direction. After, let's say, a thousand steps of thinking, you concluded that this direction is unpromising. As soon as you conclude this, you could already get a reward signal a thousand timesteps previously, when you decided to pursue down this path. You say, "Next time I shouldn't pursue this path in a similar situation," long before you actually came up with the proposed solution.

This was in the DeepSeek R1 paper— that the space of trajectories is so wide that maybe it's hard to learn a mapping from an intermediate trajectory and value. And also given that, in coding for example you'll have the wrong idea, then you'll go back, then you'll change something.

This sounds like such a lack of faith in deep learning. Sure it might be difficult, but nothing deep learning can't do. My expectation is that a value function should be useful, and I fully expect that they will be used in the future, if not already.

What I was alluding to with the person whose emotional center got damaged, it’s more that maybe what it suggests is that the value function of humans is modulated by emotions in some important way that's hardcoded by evolution. And maybe that is important for people to be effective in the world.

People have been talking about scaling data, scaling parameters, scaling compute. Is there a more general way to think about scaling? What are the other scaling axes?

Here's a perspective that I think might be true. The way ML used to work is that people would just tinker with stuff and try to get interesting results. That's what's been going on in the past. Then the scaling insight arrived. Scaling laws, GPT-3, and suddenly everyone realized we should scale.

This is an example of how language affects thought. "Scaling" is just one word, but it's such a powerful word because it informs people what to do. They say, "Let's try to scale things." So you say, what are we scaling? Pre-training was the thing to scale. It was a particular scaling recipe. The big breakthrough of pre-training is the realization that this recipe is good.

You say, "Hey, if you mix some compute with some data into a neural net of a certain size, you will get results. You will know that you'll be better if you just scale the recipe up." This is also great. Companies love this because it gives you a very low-risk way of investing your resources. It's much harder to invest your resources in research. Compare that. If you research, you need to be like, "Go forth researchers and research and come up with something", versus get more data, get more compute. You know you'll get something from pre-training.

Indeed, it looks like, based on various things some people say on Twitter, maybe it appears that Gemini have found a way to get more out of pre-training. At some point though, pre-training will run out of data. The data is very clearly finite. What do you do next? Either you do some kind of souped-up pre-training, a different recipe from the one you've done before, or you're doing RL, or maybe something else.

But now that compute is big, compute is now very big, in some sense we are back to the age of research. Maybe here's another way to put it. Up until 2020, from 2012 to 2020, it was the age of research. Now, from 2020 to 2025, it was the age of scaling—maybe plus or minus, let's add error bars to those years—because people say, "This is amazing. You've got to scale more. Keep scaling." The one word: scaling. But now the scale is so big.

Is the belief really, "Oh, it's so big, but if you had 100x more, everything would be so different?" It would be different, for sure. But is the belief that if you just 100x the scale, everything would be transformed? I don't think that's true. So it's back to the age of research again, just with big computers.

That's a very interesting way to put it. But let me ask you the question you just posed then. What are we scaling, and what would it mean to have a recipe? I guess I'm not aware of a very clean relationship that almost looks like a law of physics which existed in pre-training. There was a power law between data or compute or parameters and loss. What is the kind of relationship we should be seeking, and how should we think about what this new recipe might look like?

We've already witnessed a transition from one type of scaling to a different type of scaling, from pre-training to RL. Now people are scaling RL. Now based on what people say on Twitter, they spend more compute on RL than on pre-training at this point, because RL can actually consume quite a bit of compute. You do very long rollouts, so it takes a lot of compute to produce those rollouts. Then you get a relatively small amount of learning per rollout, so you really can spend a lot of compute.

I wouldn't even call it scaling. I would say, "Hey, what are you doing? Is the thing you are doing the most productive thing you could be doing? Can you find a more productive way of using your compute?" We've discussed the value function business earlier. Maybe once people get good at value functions, they will be using their resources more productively. If you find a whole other way of training models, you could say, "Is this scaling or is it just using your resources?" I think it becomes a little bit ambiguous.

In the sense that, when people were in the age of research back then, it was, "Let's try this and this and this. Let's try that and that and that. Oh, look, something interesting is happening." I think there will be a return to that.

If we're back in the era of research, stepping back, what is the part of the recipe that we need to think most about? When you say value function, people are already trying the current recipe, but then having LLM-as-a-Judge and so forth. You could say that's a value function, but it sounds like you have something much more fundamental in mind. Should we even rethink pre-training at all and not just add more steps to the end of that process?

The discussion about value function, I think it was interesting. I want to emphasize that I think the value function is something that's going to make RL more efficient, and I think that makes a difference. But I think anything you can do with a value function, you can do without, just more slowly.

The thing which I think is the most fundamental is that these models somehow just generalize dramatically worse than people. It's super obvious. That seems like a very fundamental thing.

So this is the crux: generalization. There are two sub-questions. There's one which is about sample efficiency: why should it take so much more data for these models to learn than humans? There's a second question. Even separate from the amount of data it takes, why is it so hard to teach the thing we want to a model than to a human?

For a human, we don't necessarily need a verifiable reward to be able to… You're probably mentoring a bunch of researchers right now, and you're talking with them, you're showing them your code, and you're showing them how you think. From that, they're picking up your way of thinking and how they should do research. You don’t have to set a verifiable reward for them that's like, "Okay, this is the next part of the curriculum, and now this is the next part of your curriculum. Oh, this training was unstable." There's not this schleppy, bespoke process.

Perhaps these two issues are actually related in some way, but I'd be curious to explore this second thing, which is more like continual learning, and this first thing, which feels just like sample efficiency.

You could actually wonder that one possible explanation for the human sample efficiency that needs to be considered is evolution. Evolution has given us a small amount of the most useful information possible. For things like vision, hearing, and locomotion, I think there's a pretty strong case that evolution has given us a lot.

For example, human dexterity far exceeds… I mean robots can become dexterous too if you subject them to a huge amount of training in simulation. But to train a robot in the real world to quickly pick up a new skill like a person does seems very out of reach. Here you could say, "Oh yeah, locomotion. All our ancestors needed great locomotion, squirrels. So with locomotion, maybe we've got some unbelievable prior."

You could make the same case for vision. I believe Yann LeCun made the point that children learn to drive after 10 hours of practice, which is true. But our vision is so good. At least for me, I remember myself being a five-year-old. I was very excited about cars back then. I'm pretty sure my car recognition was more than adequate for driving already as a five-year-old. You don't get to see that much data as a five-year-old. You spend most of your time in your parents' house, so you have very low data diversity. But you could say maybe that's evolution too.

But in language and math and coding, probably not. It still seems better than models. Obviously, models are better than the average human at language, math, and coding. But are they better than the average human at learning?

Oh yeah. Oh yeah, absolutely. What I meant to say is that language, math, and coding—and especially math and coding—suggests that whatever it is that makes people good at learning is probably not so much a complicated prior, but something more, some fundamental thing.

I'm not sure I understood. Why should that be the case?

So consider a skill in which people exhibit some kind of great reliability. If the skill is one that was very useful to our ancestors for many millions of years, hundreds of millions of years, you could argue that maybe humans are good at it because of evolution, because we have a prior, an evolutionary prior that's encoded in some very non-obvious way that somehow makes us so good at it. But if people exhibit great ability, reliability, robustness, and ability to learn in a domain that really did not exist until recently, then this is more an indication that people might have just better machine learning, period.

I'm curious. If you say we are back in an era of research, you were there from 2012 to 2020. What is the vibe now going to be if we go back to the era of research? For example, even after AlexNet, the amount of compute that was used to run experiments kept increasing, and the size of frontier systems kept increasing. Do you think now that this era of research will still require tremendous amounts of compute? Do you think it will require going back into the archives and reading old papers? You were at Google and OpenAI and Stanford, these places, when there was more of a vibe of research? What kind of things should we be expecting in the community?

One consequence of the age of scaling is that scaling sucked out all the air in the room. Because scaling sucked out all the air in the room, everyone started to do the same thing. We got to the point where we are in a world where there are more companies than ideas by quite a bit.

Actually on that, there is this Silicon Valley saying that says that ideas are cheap, execution is everything. People say that a lot, and there is truth to that. But then I saw someone say on Twitter something like, "If ideas are so cheap, how come no one's having any ideas?" And I think it's true too.

If you think about research progress in terms of bottlenecks, there are several bottlenecks. One of them is ideas, and one of them is your ability to bring them to life, which might be compute but also engineering. If you go back to the '90s, let's say, you had people who had pretty good ideas, and if they had much larger computers, maybe they could demonstrate that their ideas were viable. But they could not, so they could only have a very, very small demonstration that did not convince anyone. So the bottleneck was compute. Then in the age of scaling, compute has increased a lot.

Of course, there is a question of how much compute is needed, but compute is large. Compute is large enough such that it's not obvious that you need that much more compute to prove some idea. I'll give you an analogy. AlexNet was built on two GPUs. That was the total amount of compute used for it. The transformer was built on 8 to 64 GPUs. No single transformer paper experiment used more than 64 GPUs of 2017, which would be like, what, two GPUs of today? The ResNet, right? You could argue that the o1 reasoning was not the most compute-heavy thing in the world.

So for research, you definitely need some amount of compute, but it's far from obvious that you need the absolutely largest amount of compute ever for research. You might argue, and I think it is true, that if you want to build the absolutely best system then it helps to have much more compute. Especially if everyone is within the same paradigm, then compute becomes one of the big differentiators.

How will SSI make money?

My answer to this question is something like this. Right now, we just focus on the research, and then the answer to that question will reveal itself. I think there will be lots of possible answers.

Is SSI's plan still to straight shot superintelligence?

Maybe. I think that there is merit to it. I think there's a lot of merit because it's very nice to not be affected by the day-to-day market competition. But I think there are two reasons that may cause us to change the plan. One is pragmatic, if timelines turned out to be long, which they might. Second, I think there is a lot of value in the best and most powerful AI being out there impacting the world. I think this is a meaningfully valuable thing.

So then why is your default plan to straight shot superintelligence? Because it sounds like OpenAI, Anthropic, all these other companies, their explicit thinking is, "Look, we have weaker and weaker intelligences that the public can get used to and prepare for." Why is it potentially better to build a superintelligence directly?

I'll make the case for and against. The case for is that one of the challenges that people face when they're in the market is that they have to participate in the rat race. The rat race is quite difficult in that it exposes you to difficult trade-offs which you need to make. It is nice to say, "We'll insulate ourselves from all this and just focus on the research and come out only when we are ready, and not before."

But the counterpoint is valid too, and those are opposing forces. The counterpoint is, "Hey, it is useful for the world to see powerful AI. It is useful for the world to see powerful AI because that's the only way you can communicate it."

I'll give you another example of how language affects thinking. In this case, it will be two words that have shaped everyone's thinking, I maintain. First word: AGI. Second word: pre-training.

Let me explain. The term AGI, why does this term exist? It's a very particular term. Why does it exist? There's a reason. The reason that the term AGI exists is, in my opinion, not so much because it's a very important, essential descriptor of some end state of intelligence, but because it is a reaction to a different term that existed, and the term is narrow AI.

If you go back to ancient history of gameplay and AI, of checkers AI, chess AI, computer games AI, everyone would say, look at this narrow intelligence. Sure, the chess AI can beat Kasparov, but it can't do anything else. It is so narrow, artificial narrow intelligence. So in response, as a reaction to this, some people said, this is not good. It is so narrow. What we need is general AI, an AI that can just do all the things. That term just got a lot of traction.

The second thing that got a lot of traction is pre-training, specifically the recipe of pre-training. I think the way people do RL now is maybe undoing the conceptual imprint of pre-training. But pre-training had this property. You do more pre-training and the model gets better at everything, more or less uniformly. General AI. Pre-training gives AGI. But the thing that happened with AGI and pre-training is that in some sense they overshot the target.

If you think about the term "AGI", especially in the context of pre-training, you will realize that a human being is not an AGI. Yes, there is definitely a foundation of skills, but a human being lacks a huge amount of knowledge. Instead, we rely on continual learning.

So when you think about, "Okay, so let's suppose that we achieve success and we produce some kind of safe superintelligence." The question is, how do you define it? Where on the curve of continual learning is it going to be? I produce a superintelligent 15-year-old that's very eager to go. They don't know very much at all, a great student, very eager. You go and be a programmer, you go and be a doctor, go and learn. So you could imagine that the deployment itself will involve some kind of a learning trial-and-error period. It's a process, as opposed to you dropping the finished thing.

I see. You're suggesting that the thing you're pointing out with superintelligence is not some finished mind which knows how to do every single job in the economy. Because the way, say, the original OpenAI charter or whatever defines AGI is like, it can do every single job, every single thing a human can do. You're proposing instead a mind which can learn to do every single job, and that is superintelligence.

Yes.

But once you have the learning algorithm, it gets deployed into the world the same way a human laborer might join an organization.

Exactly.

One of the ways in which my thinking has been changing is that I now place more importance on AI being deployed incrementally and in advance. One very difficult thing about AI is that we are talking about systems that don't yet exist and it's hard to imagine them. I think that one of the things that's happening is that in practice, it's very hard to feel the AGI. It's very hard to feel the AGI.

We can talk about it, but imagine having a conversation about how it is like to be old when you're old and frail. You can have a conversation, you can try to imagine it, but it's just hard, and you come back to reality where that's not the case. I think that a lot of the issues around AGI and its future power stem from the fact that it's very difficult to imagine.

Future AI is going to be different. It's going to be powerful. Indeed, the whole problem, what is the problem of AI and AGI? The whole problem is the power. The whole problem is the power. When the power is really big, what's going to happen?

One of the ways in which I've changed my mind over the past year—and that change of mind, I'll hedge a little bit, may back-propagate into the plans of our company—is that if it's hard to imagine, what do you do? You’ve got to be showing the thing. You’ve got to be showing the thing. I maintain that most people who work on AI also can't imagine it because it's too different from what people see on a day-to-day basis.

I do maintain, here's something which I predict will happen. This is a prediction. I maintain that as AI becomes more powerful, people will change their behaviors. We will see all kinds of unprecedented things which are not happening right now. I’ll give some examples. I think for better or worse, the frontier companies will play a very important role in what happens, as will the government.

The kind of things that I think you'll see, which you see the beginnings of, are companies that are fierce competitors starting to collaborate on AI safety. You may have seen OpenAI and Anthropic doing a first small step, but that did not exist. That's something which I predicted in one of my talks about three years ago, that such a thing will happen.

I also maintain that as AI continues to become more powerful, more visibly powerful, there will also be a desire from governments and the public to do something. I think this is a very important force, of showing the AI. That's number one. Number two, okay, so the AI is being built. What needs to be done? One thing that I maintain that will happen is that right now, people who are working on AI, I maintain that the AI doesn't feel powerful because of its mistakes. I do think that at some point the AI will start to feel powerful actually.

I think when that happens, we will see a big change in the way all AI companies approach safety. They'll become much more paranoid. I say this as a prediction that we will see happen. We'll see if I'm right. But I think this is something that will happen because they will see the AI becoming more powerful. Everything that's happening right now, I maintain, is because people look at today's AI and it's hard to imagine the future AI.

There is a third thing which needs to happen. I'm talking about it in broader terms, not just from the perspective of SSI because you asked me about our company. The question is, what should the companies aspire to build? What should they aspire to build? There has been one big idea that everyone has been locked into, which is the self-improving AI. Why did it happen? Because there are fewer ideas than companies.

But I maintain that there is something that's better to build, and I think that everyone will want that. It's the AI that's robustly aligned to care about sentient life specifically. I think in particular, there's a case to be made that it will be easier to build an AI that cares about sentient life than an AI that cares about human life alone, because the AI itself will be sentient.

Final question: What is research taste? You're obviously the person in the world who is considered to have the best taste in doing research in AI. You were the co-author on the biggest things that have happened in the history of deep learning, from AlexNet to GPT-3 to so on. What is it, how do you characterize how you come up with these ideas?

I can comment on this for myself. I think different people do it differently. One thing that guides me personally is an aesthetic of how AI should be, by thinking about how people are, but thinking correctly. It's very easy to think about how people are incorrectly, but what does it mean to think about people correctly? I'll give you some examples.

The idea of the artificial neuron is directly inspired by the brain, and it's a great idea. Why? Because you say the brain has all these different organs, it has the folds, but the folds probably don't matter. Why do we think that the neurons matter? Because there are many of them. It kind of feels right, so you want the neuron. You want some local learning rule that will change the connections between the neurons. It feels plausible that the brain does it.

The idea of the distributed representation. The idea that the brain responds to experience therefore our neural net should learn from experience. The brain learns from experience, the neural net should learn from experience. You kind of ask yourself, is something fundamental or not fundamental? How things should be. I think that's been guiding me a fair bit, thinking from multiple angles and looking for almost beauty, beauty and simplicity. Ugliness, there's no room for ugliness. It's beauty, simplicity, elegance, correct inspiration from the brain. All of those things need to be present at the same time.

The more they are present, the more confident you can be in a top-down belief. The top-down belief is the thing that sustains you when the experiments contradict you. Because if you trust the data all the time, well sometimes you can be doing the correct thing but there's a bug. But you don't know that there is a bug. How can you tell that there is a bug? How do you know if you should keep debugging or you conclude it's the wrong direction? It's the top-down. You can say things have to be this way. Something like this has to work, therefore we’ve got to keep going. That's the top-down, and it's based on this multifaceted beauty and inspiration by the brain.

Alright, we'll leave it there. Thank you so much.

Ilya, thank you so much.

Alright. Appreciate it.

That was great.

Yeah, I enjoyed it.

Yes, me too.