>Also the article could be trying to normalize thinking that these are more than matrix multiplication gadgets good at compression.

Honestly, I think it's less so (for some of us) that we think they're "more than matrix multiplication gadgets good at compression", so much as thinking that perhaps what our brains are doing is not so dissimilar.

A materialist view of the world could support the idea that intelligence itself may just be a series of predictions from a big compressed multi-modal dataset. That's not to say that LLMs are doing it in a way that is even close to how our brains are doing it, but we also don't understand how different it may be, and how much utility we can get out of them even with the current architecture.

It's not really "trying" to do anything. That they're, inherently, sequential matrix multipliers with clever data propagation should be uncontroversial, but I think stopping there is overly reductive.

Mechanistic interpretability research has found plenty of indicators that real, complex, generalized, and reusable circuits develop in models as they are trained and post-trained, particularly as overtraining ratios increase and memorization shifts to generalization. That's not to say that means they must be "conscious," but the overall point is that claiming anything definitive either way is incomplete.

It can be fascinating reading if you can sort through the chuff.

> A more appropriate mirror test for LLMs is to get them to state facts about their training data. Percentage of arts vs science for example.

LLMs are not capable of this kind of reflection.