How François Chollet Is Building A New Path To AGI

**François Chollet** (0:00)
I think we're probably looking at AGI 2030 Around the time the two are going to be releasing, like maybe Arc VI or Arc VII, you're not going to stop AI progress. I think it's too late for that. And so the next question is, okay, like, AI progress is here. It's actually going to keep accelerating. How do you make use of it? How do you leverage? How do you ride the wave? That's the question to ask.

**SPEAKER_3** (0:31)
Today, we're lucky to be joined by François Chollet, founder of the Arc Prize, a global competition to solve the Arc AGI benchmark. His latest project is Endia, a lab exploring a new paradigm in frontier AI research. François is one of the best people in the world to help us understand the current AI moment and where all of this is going. François, thank you so much for joining us today and congrats on the launch of Arc AGI V3.

**François Chollet** (0:58)
Thanks so much for having me. I'm super excited to be here. Super exciting time to talk about AI.

**SPEAKER_4** (1:02)
So François, tell us a little bit about India. So what exactly is it and what are you guys trying to achieve?

**François Chollet** (1:08)
Right. So India is this new AGI research lab, and we are trying some very different ideas. And so our goal is basically to build this new branch of machine learning that will be much closer to optimal and like deep learning.

**SPEAKER_3** (1:23)
All of us right now are sort of taken by what's going on with code. I have sort of this viral moment right now where I got to 40,000 stars this morning on GStack. So it's like, oh, this is an open source project that now is one of the biggest ones. And I have more than 100 PRs from contributors to deal with. I guess you're one of the best people to talk to about this because you're actually literally coming up with something that is a totally different pathway.

**François Chollet** (1:51)
That's right. That's right. So what we're doing at India is we're doing program synthesis research. And when I talk about program synthesis, often people ask me, oh, so are you doing like code gen? Are you building an alternative to coding agents? And it's actually not at all what we are doing. We are working at a much, much more, much lower level than that. What you're actually doing is that you are trying to build a new branch of machine learning, an alternative to deep learning itself rather than like coding agents. Coding agents are like this very, very high level last layer piece of the stack. And we're actually trying to rebuild the whole stack on top of different foundations.
So we're building a new learning substrate that's very different from parametric learning, deep learning. So if you go back to the problem of machine learning, you have some input data, some target data, and you're trying to find a function that will map the inputs to the targets that will hopefully generalize to new inputs. And if you're doing deep learning, what you're doing is that you have this parametric curve that serves as your function, as your model, and you're trying to fit the parameters of the curve via gradient descent. And this is basically what we're doing, except we're replacing the parametric curve with a symbolic model that is meant to be as small as possible. It's like the simplest possible model to explain the data, to model what's going on. And of course, if you're doing that, you cannot apply gradient descent anymore. So we are building something that we call symbolic descent, which is like the symbolic space equivalent of gradient descent. The idea is to build this new machine learning engine that's giving you extremely concise symbolic models of the data you're feeding into it. And then we're going to make it scale. And so everything you're doing with machine learning today, with parametric curves, we should be able to do it with symbolic models. And we should be able to build these models in the future, in a way that will be much, much closer to optimality. Much closer to optimality in the sense that you're going to need much less data to obtain the models. The models are going to run much more efficiently at inference time because they're going to be so small. And because they're so small, they will also generalize much better and compose much better. You know, the minimum description length principle, that the model of the data that is most likely to generalize is the shortest. And I think you cannot find a model like this. If you're doing parametric learning, you need to try somebody's plan.