| 7/10 This is more about set shattering than 'smarts' LLMs are effectively DAGs, they literally have to unroll infinite possibilities in the absence of larger context into finite options. You can unroll and cyclic graph into a dag, but you constrict the solution space. Take the 'spoken': sentence: "I never said she stole my money" And say it multiple times with emphasis on each word and notice how the meaning changes. That is text being a forgetful functor. As you can describe PAC learning, or as compression, which is exactly equivalent to the finite set shattering above, you can assign probabilities to next tokans. But that is existential quantification, limited based on your corpus based on pattern matching and finding. I guess if "Smart" is defined as pattern matching and finding it would apply. But this is exactly why there was a split between symbolic AI, which targeted universal quantification and statistical learning, which targets existential quantification. Even if ML had never been invented, I would assume that there were mechanical methods to stack rank next tokens from a corpus. This isn't a case of 'smarter', but just different. If that difference is meaningful depends on context. |