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You made a lot of bold assertions there. It's as if you have a complete and definitive theory of human intelligence to compare it against. Which if true, would

You made a lot of bold assertions there. It's as if you have a complete and definitive theory of human intelligence to compare it against. Which if true, would be incredible, because there isn't a scientifically accepted theory, nor is there consensus from a philosophical standpoint. I can't say that you are wrong, you might be right, especially about AGI. And I think it's unlikely that LLMs are the direct path to AGI. But, just looking at how human brains work, it seems unlikely that we would be intelligent either if we used your same reductionist logic. An individual neuron doesn't "think" or "understand" anything. It's a biological cell that simply fires an electrochemical signal when its input threshold is met. It has no understanding of language or context. By your logic, since the fundamental components are just simple signal processors, the brain cannot possibly learn or be intelligent. Yet, from the complex interaction of ~86 billion of these simple biochemical machines, the emergent properties of thought, understanding, and consciousness arise. Dismissing an LLM's capabilities because its underlying operations are basically just math operating on tokenized data is like dismissing human consciousness because it's "just electrochemistry" in a network of cells. Both arguments mistake the low-level mechanism for the high-level emergent phenomenon.

b112 1y ago
When an LLM can re-tokenize on the fly, due to newly learned data, let me know. It won't prove intelligence, but at least it won't be static like a book.
- hatthew 1y ago
  https://x.com/sukjun_hwang/status/1943703574908723674 https://x.com/sukjun_hwang/status/1943703574908723674 > Tokenization has been the final barrier to truly end-to-end language models. > We developed the H-Net: a hierarchical network that replaces tokenization with a dynamic chunking process directly inside the model, automatically discovering and operating over meaningful units of data
  - b112 1y ago
    Good to see. Will be better to see once the scope is clear. But something such as this is required to move towards actual intelligence.
- danielmarkbruce 1y ago
  This is a dumb critique. A thin wrapper running a new samples of training data and updating weights is something already done in many situations. A sophisticated rag system incorporates new information even if the weights themselves aren't updated, effectively giving "new memory". LLMs have problems, in practice being "static" aint one of them.
omnicognate 1y ago
I agree with much of what you say, but this: > An individual neuron doesn't "think" or "understand" anything. It's a biological cell that simply fires an electrochemical signal when its input threshold is met. is a wildly oversimplified representation of what a neuron is and how it interacts with the rest of the brain. Even what we know is far beyond this description and there's a vast amount more that we don't know. One of the assumptions that people make (with or without realising they are making it) is that the brain is a componentised information processing device, that you can break it down into units that effectively communicate in a digital way. This may be true, but it may not. It may not be possible to decompose a mind into parts communicating via digital signals without sacrificing conscious experience. Personally, I think it's likely that it isn't possible to do that, but I don't know for sure. Others clearly think it's likely that it is possible, which is a perfectly valid opinion. Neither should pretend that science has yet given us an unambiguous answer on the question.

b112 1y ago

When an LLM can re-tokenize on the fly, due to newly learned data, let me know. It won't prove intelligence, but at least it won't be static like a book.

hatthew 1y ago
https://x.com/sukjun_hwang/status/1943703574908723674 https://x.com/sukjun_hwang/status/1943703574908723674 > Tokenization has been the final barrier to truly end-to-end language models. > We developed the H-Net: a hierarchical network that replaces tokenization with a dynamic chunking process directly inside the model, automatically discovering and operating over meaningful units of data
- b112 1y ago
  Good to see. Will be better to see once the scope is clear. But something such as this is required to move towards actual intelligence.
danielmarkbruce 1y ago
This is a dumb critique. A thin wrapper running a new samples of training data and updating weights is something already done in many situations. A sophisticated rag system incorporates new information even if the weights themselves aren't updated, effectively giving "new memory". LLMs have problems, in practice being "static" aint one of them.

omnicognate 1y ago

I agree with much of what you say, but this: > An individual neuron doesn't "think" or "understand" anything. It's a biological cell that simply fires an electrochemical signal when its input threshold is met. is a wildly oversimplified representation of what a neuron is and how it interacts with the rest of the brain. Even what we know is far beyond this description and there's a vast amount more that we don't know. One of the assumptions that people make (with or without realising they are making it) is that the brain is a componentised information processing device, that you can break it down into units that effectively communicate in a digital way. This may be true, but it may not. It may not be possible to decompose a mind into parts communicating via digital signals without sacrificing conscious experience. Personally, I think it's likely that it isn't possible to do that, but I don't know for sure. Others clearly think it's likely that it is possible, which is a perfectly valid opinion. Neither should pretend that science has yet given us an unambiguous answer on the question.