But one reason that I think it quite underrated, and also which reveals the canyon walls against which the river of AI progress will only slowly chip away at, is that it is not enough for a domain to be verifiable. It also has to be very grindable—in the sense that you can run lots of parallel rollouts against a deterministic and replayable simulator.
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modern AI models are beginning to solve problems that sit beyond the frontier of existing human knowledge
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It was basically sort of thought to be career suicide. Like of course we know AI doesn't work. You know, we tried it in the 90s, places like MIT and it was a dead end. You know, and that was the prevailing view. But we just felt the small band of us felt that that actually with the right ideas and using learning systems, reinforcement learning and betting on neural networks that a lot of fast progress could be made.
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world models AI systems that learn from site and sound to understand and simulate how the real world works over time, predicting what happens next and letting us interact with those simulated futures.
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