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If there are a billion different ways to solve this problem, it would seem much more of a coincidence if with LLMs we've "oops-ed" into an approach just like th

If there are a billion different ways to solve this problem, it would seem much more of a coincidence if with LLMs we've "oops-ed" into an approach just like the one inside our heads. It would seem more likely that they're unrelated approaches.

TeMPOraL 3y ago
Right. However, I don't see the reason to believe there is a billion ways to solve the fundamentals. And even if, they're not all equal here: we're considering solutions that are very simple, provide huge leverage, and are continuous. For our own brains, this is because evolution is the OG incremental learner - it just doesn't do leaps of faith, nor does it invest resources. Incremental evolutionary advancements must quickly stack into something improving survival rates, or else they get selected out of the gene pool. This strongly suggests that brains (human and animal alike) are based on a design that scaled easily and continuously - one that started simple and could be improved step by step, with each step being simple and conferring some non-zero survival advantage. For our AI work, LLMs are exactly this: they derive from simple models that were iterated on over the past few decades, in small steps. They are still structurally simple as programs - but they've hit the right structure that allowed them to make qualitative jumps in capabilities from simple scaling - more parameters, more memory, more compute, more training time, more input data. This may be just my own perception, but I feel that LLMs are exactly the kind of model that an evolutionary process would be able to develop incrementally. With the above in mind, plus the fact that LLMs are trained on the output of our brains (and their output is rated by our brains), makes me believe it's highly likely that, with language models, we've stumbled on the very same fundamental method of implementing thinking that is core to how our own brains work.
- MichaelZuo 3y ago
  These are some very solid points. Thanks for taking the time to write them!

TeMPOraL 3y ago

Right. However, I don't see the reason to believe there is a billion ways to solve the fundamentals. And even if, they're not all equal here: we're considering solutions that are very simple, provide huge leverage, and are continuous. For our own brains, this is because evolution is the OG incremental learner - it just doesn't do leaps of faith, nor does it invest resources. Incremental evolutionary advancements must quickly stack into something improving survival rates, or else they get selected out of the gene pool. This strongly suggests that brains (human and animal alike) are based on a design that scaled easily and continuously - one that started simple and could be improved step by step, with each step being simple and conferring some non-zero survival advantage. For our AI work, LLMs are exactly this: they derive from simple models that were iterated on over the past few decades, in small steps. They are still structurally simple as programs - but they've hit the right structure that allowed them to make qualitative jumps in capabilities from simple scaling - more parameters, more memory, more compute, more training time, more input data. This may be just my own perception, but I feel that LLMs are exactly the kind of model that an evolutionary process would be able to develop incrementally. With the above in mind, plus the fact that LLMs are trained on the output of our brains (and their output is rated by our brains), makes me believe it's highly likely that, with language models, we've stumbled on the very same fundamental method of implementing thinking that is core to how our own brains work.

MichaelZuo 3y ago
These are some very solid points. Thanks for taking the time to write them!