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This is just the tip of the iceberg. LLMs are generating text, acting as assistants, and some of the things they say have impact in the real world. This loops b

This is just the tip of the iceberg. LLMs are generating text, acting as assistants, and some of the things they say have impact in the real world. This loops back in the next iteration of web data. Besides immediate answers in the chat window, AI already has a slow feedback loop, it can explore and probe through humans. Even if we don't do anything to provide a way (embodiment) for AI to explore it can do it as long as we rely on its services as assistant. The most obvious is writing code with GPT-4 and reporting errors to get updated codes. The model gets valuable feedback about its errors this way, possibly also hints from the user. There is a big difference between imitating human code and debugging your code logic. So the way I see it: large language models place text into society, and society loops back text and feedback. It's a data cycle. Content after December 2022 seems particularly useful for further advancing AI. The garbage-in-garbage-out scenario doesn't apply because everything is filtered through humans and the real world.

tsunamifury 3y ago
Your observation that the LLM is probing through humans is astute. Ilya talked at great legnth at the nVidia fireside chat that the primary intelligence goal now is building out the world model through multiple observations. My own research team has found that we can fill in world models at micro levels that are missing from the LLM with human input and given enough memory you GPT4 has reached sufficient reasoning capabilities to add that it its world model without re-training. This to me, is the breakaway point. You simply need enough human probes indexing the smaller unpublished details of the world plus efficient memory lookup and storage and you have AGI. However, my own personal theory is that even if you achieve that, we will find limitations in giving a highest order (superposition) answer to big questions based on that detailed of a world model. The LLM will struggle to interact with humans in a meaningful way because its superposition will be a full order higher than any other humans individual perspective. This will result in answers that might be AGI, but humans won't recognize as AGI -- because they simply won't understand the super-order perspective they are computed from.
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tsunamifury 3y ago

Your observation that the LLM is probing through humans is astute. Ilya talked at great legnth at the nVidia fireside chat that the primary intelligence goal now is building out the world model through multiple observations. My own research team has found that we can fill in world models at micro levels that are missing from the LLM with human input and given enough memory you GPT4 has reached sufficient reasoning capabilities to add that it its world model without re-training. This to me, is the breakaway point. You simply need enough human probes indexing the smaller unpublished details of the world plus efficient memory lookup and storage and you have AGI. However, my own personal theory is that even if you achieve that, we will find limitations in giving a highest order (superposition) answer to big questions based on that detailed of a world model. The LLM will struggle to interact with humans in a meaningful way because its superposition will be a full order higher than any other humans individual perspective. This will result in answers that might be AGI, but humans won't recognize as AGI -- because they simply won't understand the super-order perspective they are computed from.

deleted 3y ago
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