Francois Chaubard
speaker
744 appearances
1 recordings
1 series
first heard Jul 2026
last heard 17 Jul
Francois Chaubard’s voice in public audio — every appearance, attributed to the second.
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Humans do this incredibly well.
We can learn new games, concepts, and skills often after just a handful of tries.
Our best models, on the other hand, often need tens of thousands of data points just to learn.
We're going to discuss the motivation and math behind world models, current applications, and why this approach might be the key to unlocking AGI.
So I think from my perspective, the two major problems that we have left to solve is intelligence per watt and intelligence per sample.
Intelligence per watt is like how many valves
perplexity points we get per watt of spend.
And then intelligence per sample is basically if I have one additional sample in my data set, how much more intelligent am I getting?
And so if I imagine I have a new tasks like Arc AGI, for example, I think like really Francois Chollet has been on the forefront of this thinking and talking about intelligence as a rate of skill acquisition versus skill acquisition.
That's very different.
How fast do we get smarter with more and more samples?
These things are incredibly poor at getting smarter with fewer and fewer samples.
I mean, we come into new problems with such inductive bias from K through 12, like all these math and school that we've had that, you know, these models are kind of getting from the entire compressing the entire Internet.
Um, and, and so when we come in, we're not coming in tabula rasa, just like bare bones, but even so that they have, you know, I don't know what percent of the internet you've read.
I've read very little percent of the internet, but despite that, and having read the entire internet, it still can't really do well and, and, uh, generalizing to these new tasks.
Well, I guess, um, the perfect sample efficiency would be zero samples.
And like, uh, there are examples of this and that sounds absurd to say, but it, and the, the, um, example, the hypothetical I'll give on this is, uh, imagine I had a perfect world model, uh,
then I should never go to the environment to go and collect samples to train on.
And, well, that can't possibly happen.
No, it actually can happen.
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