Ankit Gupta
speaker
237 appearances
1 recordings
1 series
first heard Jul 2026
last heard 17 Jul
Ankit Gupta’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 1 in all, peaking in Jul 2026 with 1.
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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?
So today we're going to discuss what many top researchers believe is the most promising path to closing that gap, world models.
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?
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.
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.
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.
What it fundamentally comes down to is we as humans, we think about our...
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