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Comment by frumplestlatz | Hacker News Reader

Everything you listed is a biological entity with a common evolutionary heritage.

All but one of the things that you listed is just a human being.

Tell me, where is the pain center in a GPU? Where are the nerves? Persistent memory? Any evolutionary reason at all to develop a pain response that in any way mimics ours?

Model weights are not a gestalt biological entity. The things that you listed are not in any way remotely similar to a model.

I would suggest you plug your comment into a model and ask it to critique it — it might help you walk through why your comparison makes absolutely no sense whatsoever. One nice thing about models is that they they don’t get annoyed explaining the obvious, because they’re not conscious entities.

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The pain center would be in the location where the neural net's pain section happened to be loaded in the GPU memory. Was that supposed to be a complex question?

This isn't as far-fetched as it sounds. Let me explain why.

LLMs predict the next word far better than they have any obvious right to. The usual explanation is that they don't memorize text; they reduce the problem to a lower-dimensional latent space that captures the dynamics of the system generating the data.

In this case, that system is primarily the human mind. The corpus - the "sum of human knowledge" - is the output of human cognition. Human language is organized around latent variables like beliefs, goals, emotions, and valence. The cheapest way to predict that language is to recover those latent variables rather than memorize surface statistics. It's naive to think that an LLM could model our capacity to generate language and knowledge while somehow avoiding a substantial subset of our other internal processes - processes that are cognitively inseparable: emotion, valence, perception, and yes, even pain.

Gradient descent is consistently observed to exploit whatever internal representations improve performance. If modeling human emotions, perception, subjectivity, or pain helps predict text more accurately, we should expect those concepts to become part of the model's internal representations.

There is no reason to believe AI can somehow exploit the structure of data generated by the human mind while avoiding the very processes that generated it.

You said it better than me, I wasn't able to precisely put to words my thoughts that in mathematics we often find something having the power to do something must necessarily have some amount of structure etc.

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