[chuckles]

[chuckles] Well, it's a, it's a kind of an idea I've, I've been, uh, forming over the last, uh, three years or so, is the fact that, uh, people increasingly use AI assistants for various things, right? I mean, uh, you see a decrease in the use of, you know, traditional search engines, and you just ask a question to your favorite AI assistant.

Um, and, you know, if the plan that Meta and others are, are developing of, you know, having smart devices like smart glasses and stuff like that, uh, you know, is realized, basically you would just be talking to your AI assistant, you know, by voice with, you know, through your smart glasses or maybe some other smart device.

And so all of your information diet will be mediated by AI assistants.

And if you are someone, you know, somewhere in the world, let's say outside the US or China, and you have an AI assistant, and that AI assistant was built in California or, you know, Beijing or Shanghai or Shenzhen, uh, it's not good for you.

Like you may speak a language that those assistants really haven't been trained to handle particularly well.

Uh, you may have a culture that is not particularly well understood by people in Silicon Valley and China, not well represented by the training data that is publicly available on the internet.

[chuckles]

Mm-hmm.

We are shaped by evolution, um, to only do the tasks that, you know, are worth accomplishing for survival.

And, uh, we think of ourselves as having general intelligence, but we just, we're just not at all general.

It's just that all the problems that we're not able to apprehend, we can think of them.

Um, and, and (laughs) so that makes us believe that we have general intelligence, but we absolutely do not have general intelligence.

Okay, so I think this, this phrase is, is nonsense, first of all.

Uh, it was, it was very misleading.

AI lets us be more human, I hope.

And for those tuning in and, and learning about this for the first time, LLMs is the technology behind things like the GPT model that's within, baked within, uh, ChatGPT. But let, let me ask you this, Yann. So the AI field does seem to have moved from standard LLMs to LLMs that can reason, uh, and go step by step. And I'm curious, can you program this d- this sort of counterintuitive or, uh, this heretical thinking by imbuing a reasoning model with an instruction to question its directives?

Well, so we have to figure out what reasoning really mean. Okay? And there are, um, you know, obviously everyone is trying to, um, get LLMs to reason to some extent, to perhaps be able to check whether the answer they produced, uh, are correct. Um, and the way people are approaching the problem at the moment is that they basically are trying to do this by modifying the current paradigm without completely changing it. Okay? So can you, you know, bolt a couple words on top of LLM so that you kind of have some primitive, um, reasoning function? And that's essentially what all, all other reasoning, uh, systems, uh, are doing. You know, one simple way of, uh, getting LLMs to kind of appear to reason is chain of thought, right? So you, you basically tell them to generate more tokens than they really need to, in the hope that in the process of generating those tokens, they're gonna devote more computation to answering your question. And, uh, and to some extent that...... works, surprisingly, but, but it's very limited. You don't actually get, uh, real reasoning, um, uh, out of this. Uh, reasoning, at least in classical AI and in many domain, um, involves a search through a space of potential solutions. Okay? So you have a, a problem to solve. You can characterize whether the problem is solved or not, so you have some way of telling whether the problem is solved. Um, and then you search through a space of solutions for one that actually, uh, satisfies the, the constraints or, uh, you know, is, is identified as being a solution. Um, and that, you know, that's how... that's kind of the most general form of, of reasoning you can imagine. There is no mechanism at all in LLMs for this search mechanism. What you have is you have to kind of build this on top of it, right? So one way to do this is you get an LLM to produce lots and lots and lots of sequences of answers, right? So sequences of tokens, which, you know, represent answers, and then you have a, a separate system that picks which one is good. Okay? This is a bit like writing a program by sort of randomly more or less generating instructions, um, you know, while maybe, uh, respecting the grammar of the, of the language. Um, and then checking all of those programs for one that actually works. It... Not a good way, not a very efficient way of producing correct, uh, pieces of code. It's not a good way of reasoning, uh, either. So, um, a, a, a big issue there is, is that when, when humans or animals reason, we don't do it in token space. I- in other words, when we reason, we don't have to, you know, generate a text that expresses our solution, um, and then generate another one, and then generate another one. And then among other ones we, we produce, pick the one that is good. We reason internally, right? We have a mental model of the situation, and we manipulate it in our head. And we find kind of a good solution when we plan a sequence of actions to, I don't know, you know, build a table or something. Um, we, we plan the sequence of action. Uh, you know, we have a m- mental model of that in your head. If I, if I tell you... and this has nothing to do with language. Okay. So if I tell you, imagine a cube floating in front, in front of us right now. Now, rotate that cube 90 degrees along a vertical axis. Okay. You can imagine this, this thing taking place, and you can readily observe that it's a cube. If I rotated it 90 degrees, it's gonna look just like the cube that I started with. Okay. Um, 'cause you have this mental model of a, of a cube. Um, and that reasoning is in some abstract continuous space. It's not in text, it's not related to language or, or anything like that. Um, and humans do this all the time. Animals do this all the time, and this is what we yet cannot reproduce, uh, with, with machines.

Correct. And so if we're gonna, if we are potentially approaching this moment of mismatched timelines, do you fear that there could be another winter now given the amount of investment, uh, given the fact that there's gonna be potentially diminishing returns with the main way of training these things? And maybe we'll add in the fact that the market is, is, the stock market looks like it's going through a bit of a downturn right now. Now, that's a variable, uh, probably the third most important variable of what we're talking about, but it has to factor.

So, uh, yeah. I, I think, um, I mean, there's certainly, uh, uh, a question of timing there. But I think, uh, if we try to dig a little bit deeper, um, as I said before, if you think that we're gonna get to human level AI by just training on more data and scaling up LLMs, you're making a mistake. So if you're, if you're an investor and you invested in a company that told you, "We're gonna get to human le- level AI and PhD level by just, you know, training on more data and with a few tricks," um, I don't know if you're gonna lose your shirt, but that was probably (laughs) not a good idea. Um, however, there are ideas about how to, uh, go forward and have systems that are capable of doing what, what every intelligent animal and, and human are capable of doing and that, uh, current AI systems are not capable of doing. And I'm, I'm talking about understanding the physical world, um, having persistent memory, and being able to reason and plan. Those are the four characteristics that, that, you know, need to be there. Um, and that requires systems that, you know, can acquire common sense, that can learn from, uh, natural sensors like video as opposed to just text, just human-produced, uh, uh, uh, data. Um, and that's a big challenge. I mean, I've been talking about this for many years now, and, uh, and saying, "This is, this is where the challenge is. This is what we, we have to, uh, to figure out." And, and my group and I have, or people working with me and others who, who have listened to me, are making progress, uh, along, along this line, uh, of, uh, systems that can be trained to understand how the world works on video, for example, systems that can use mental models of how the world, the physical world works to plan sequences of actions to arrive at a particular goal. So we, we have kind of early results of these kind of systems. Uh, and there are people at DeepMind working on similar things, and there are, you know, people in various universities working on this. Uh, so, um, the question is, you know, when is this going to go from, uh, interesting research papers, uh, demonstrating a, a new capability with a new architecture to, you know, architectures at scale that, you know, are practical for a lot of applications and can find solutions to new problems without being trained to do it, um, et cetera? And, uh...... you know, it, it's not gonna happen within the next three years. But it may happen with, you know, between three to five years, something like that. And that's kinda corresponds to, you know, the, the sort of ramp-up that we see in, uh, uh, in, in investment. Now, whether other... So, so that, that's the first thing. Now, the, the second thing that's important is that, there's not going to be one secret magic bullet that one company or one group of people is going to invent, that is going to just solve the problem. Um, it's gonna be a lot of different ideas, a lot of effort, some principles around which to base this, that, that some people may, may not subscribe to, and will, will go, um, in a direction that is, you know, will turn out to be a dead end. Uh, so, uh, there's not gonna be like a day before which there is no AGI, and after which we, we have AGI. This is not going to be an event. Um, it's gonna be continuous conceptual ideas that, as time goes by, are gonna be made bigger and to scale and gonna work better. And it's not gonna come from a single entity, it's gonna come from the entire research community across the world. And the people who share their research are gonna move faster than the ones that don't. And so, if you think that there is some startup somewhere with five people who has discovered the secret of AGI and you should invest 5 billion in them, you are making a huge mistake.