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> the shock of how we kind of randomly stumbled into solving multiple classes of seemingly intractable cognitive problems... Another excellent point. The idea

> the shock of how we kind of randomly stumbled into solving multiple classes of seemingly intractable cognitive problems... Another excellent point. The idea that we may stumble onto massive computational leaps based on imprecise data processing requirements is terrifying. I do think we are nowhere near where we will need to be with respect to computation and, more important, information throughput. But the fact that we "oops"-ed into such a strong statistical model of conversation is deeply unsettling. I do believe GPT based language models will be part of an AGI.

TeMPOraL 3y ago
Part of the reason I think LLMs are a step in the right direction is that it's too big of a coincidence. We don't know how our own brains work, but we have good reasons to believe that, whatever the "magic" trick is, it can't be too complex, because evolution had to "oops" into it too. Now, it would be one hell of a coincidence if the model we "oops-ed" into with LLMs, and the model evolution "oops-ed" into, were completely unrelated approaches to the same problem.
- EliRivers 3y ago
  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

Part of the reason I think LLMs are a step in the right direction is that it's too big of a coincidence. We don't know how our own brains work, but we have good reasons to believe that, whatever the "magic" trick is, it can't be too complex, because evolution had to "oops" into it too. Now, it would be one hell of a coincidence if the model we "oops-ed" into with LLMs, and the model evolution "oops-ed" into, were completely unrelated approaches to the same problem.

EliRivers 3y ago
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!