this post was submitted on 25 Nov 2023
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Machine Learning
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LLMs might still lack something that the human brain has. Internal monologue, for example, that allows us to allocate more than fixed amount of compute per output token.
You can just give an LLM an internal monologue. It's called a scratchpad.
I'm not sure how this applies to the broader discussion, like honestly I can't tell if we're off-topic. But once you have LLMs you can implement basically everything humans can do. The only limitations I'm aware of that aren't trivial from an engineering perspective are
And the network still uses skills that it learned in a fixed-computation-per-token regime.
Sure, future versions will lift many existing limitations, but I was talking about current LLMs.