Francois Chollet

Because what it represents is the ability to synthesize a problem-solving program from just two or three examples.

And that alone is a new way to program.

It's an entirely new paradigm for software development where you can start programming

programming potentially quite complex programs that will generalize very well.

And instead of programming them by coming up with the shape of the program in your mind and then typing it up, you're actually just showing the computer what output you want and you let the computer figure it out.

I think that alone is extremely powerful.

I think whatever solution we see that will score well is going to probably need to leverage some aspects from deep learning models and LLMs in particular.

We've shown already that LLMs can do quite well.

That's basically the jack code approach.

We've also shown that pure discrete program search from a small DSL

does very, very well.

Before Jack Kohl, this was a state of the art.

In fact, it's still extremely close to the state of the art.

And there's no deep learning involved at all in these models.

So we have two approaches that have basically no overlap that are doing quite well.

And they're very much at two opposite ends of one spectrum, where on one end, you have these extremely large banks of millions of vector programs, but very, very shallow recombination, like simplistic recombination.

And on the other hand, you have very simplistic DSLs, very simple, like 100 or 200 primitives, but very deep, very sophisticated program search.

The solution is going to be somewhere in between, right?

So the people who are going to be winning the R competition and who are going to be making the most progress towards near-term HR are going to be those that manage to merge the deep learning paradigm and the discrete program search paradigm into one elegant way.

You know, you asked, like, what would be legitimate and what would be cheating, for instance.