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They aren't "general" enough. Sure they're competent at solving problems represented in the training data, and can even in some cases abstract over them and fin
by dTal 3y ago
They aren't "general" enough. Sure they're competent at solving problems represented in the training data, and can even in some cases abstract over them and find patterns. But they don't have "slow thought". They can't think without talking, and they can only talk like a human, and humans don't habitually narrate their train of thought, so there's a limit to how far "let's think step by step" can take them. The result is that they can't abstract recursively - since they are architecturally incapable of "thinking harder" about a problem, there will always be some threshold of input novelty that loses them, and right now that threshold is actually unimpressively low when you get down to brass tacks.
- danielmarkbruce 3y agoThis is somewhat solved by having an "agent" use an LLM as a train of thought, along with a few other systems (like a runtime to do some calcs, some apis to do other things etc).
- famouswaffles 3y agoTransformers with memory can abstract recursively in theory. https://arxiv.org/abs/2301.04589 https://arxiv.org/abs/2301.04589. If it's just extra computation that's the kick then simply more tokens will suffice. You can even even implement a computation token if you wish. https://arxiv.org/abs/2310.02226 https://arxiv.org/abs/2310.02226 Frankly, the ability to truly abstract recursively is by no means necessary either. Humans can't actually do this. Either in every day practice (you will get bored, you will lose interest) or in theory (flawed memory, finite memory, you will die). Limited recursive abilities =/ trivial recursive abilities. The current state of the art is easily general enough by actual testable definitions/baselines.