Francois Chollet

And the thing is, if you had complete information about the map,

then you could solve the pathfinding problem by simply memorizing every possible path, every mapping from point A to point B. You could solve the problem with pure memory.

But the reason you cannot do that in real life is because you don't actually know what's going to happen in the future.

Life is ever-changing.

Humans are never really doing pure memorization or pure reasoning.

Sometimes humans are doing the exact same as the LLM is doing, which is just, for instance, if you learn to add numbers, you're memorizing an algorithm, you're memorizing a program, and then you can reapply it.

You are not synthesizing on the fly the addition program.

I think what you learn in school is mostly memorization.

With memorization, you can automate almost anything, as long as it's a static distribution, as long as you don't have to deal with change.

Potentially, there are lots of things that you can automate.

And LLMs are an excellent tool for automation.

But you have to understand that automation is not the same as intelligence.

I'm not saying that LLMs are useless.

I've been a huge proponent of deep learning for many years.

And for many years, I've been saying two things.

I've been saying that if you keep scaling up deep learning, it will keep paying off.

And at the same time, I've been saying, if you keep scaling up deep learning, this will not lead to AGI.

So we can automate more and more things.

And yes, this is economically valuable.

And yes, potentially, there are many jobs you could automate away like this, and that would be economically valuable.