World Models, Explained
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What is sample efficiency and why does AI struggle compared to humans?
One of the biggest open problems in AI right now is how to solve sample efficiency. That is, how do you get models to quickly learn new tasks or skills from relatively small amounts of training data?
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.
So today we're going to discuss what many top researchers believe is the most promising path to closing that gap, world models.
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.
You and I have talked a lot about the various ways people are training models and the sample efficiency of them. Why don't we start by just defining sample efficiency and how we intuitively think about it as humans?
Yeah.
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.
For context, the RKGI test sets are a really good example of cases where humans are intuitively very good at them. Most humans can intuitively solve those puzzles with some amount of thinking and effort, but our current state-of-the-art AI systems, what people consider frontier intelligence, basically can't do them. Right.
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.
So now let's think about this in like the extreme cases and the extreme case where let's say we were perfectly sample efficient. You know, we were as sample efficient as possible. What would that mean in terms of, uh, a model that is, uh, taking a set of actions in the world.
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. We do it all the time. It's called Newton's second law of motion. It's like Newton mechanics, like, we basically know how to, like, get an object from point A to point B with a rocket quite easily just by following, like,
Newton's laws of motion. Yeah, like when NASA plans to intercept an asteroid and is planning it years in advance and can set it off on a trajectory where it just glides to the right thing and intersects to the right point, that is an example of a perfect world model we've built where we're then just letting that world model act. And that system does not need to intelligently collect new samples from the environment to decide which direction to go next. It's already been pre-programmed and it can perfectly do it.
Yeah. Can you imagine if we needed to collect 1 million training examples of us shooting spaceships to the moon to know how to do it?
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Chapters
4 chapters
1
What is sample efficiency and why does AI struggle compared to humans?
0:00–12:13
2
How does a perfect world model eliminate the need for environment samples?
12:13–41:03
3
What is model predictive control and how does Newtonian physics enable it?
41:03–55:59
4
How do stochastic adversaries break differentiable control and force reinforcement learning?
55:59–1:14:19