I think your assessment of the academic take on AI is wrong. We have a rather thorough understanding of the how/why of the mechanisms of LLMs, even if after training their results sometimes surprise us.

Additionally, there is a very large body of academic research that digs into how LLMs seem to understand concepts and truths and, sure enough, examples of us making point edits to models to change the “facts” that they “know”. My favorite of that corpus, though far from the only or most current/advances research , is the Bau Lab’s work: https://rome.baulab.info/

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ninetyninenine 5 days ago | parent | next [-]

It’s not about what you think it’s about who’s factually right or wrong.

You referenced a work on model interpretability which is essentially the equivalent of putting on MRI or electrodes on the human brain and saying we understand the brain because some portion of it lights up when we show the brain a picture of a cow. There’s lots of work on model interpretability just like how there’s lots of science involving brain scans of the human brain… the problem is none of this gives insight into how the brain or an LLM works.

In terms of understanding LLMs we overall don’t understand what’s going on. It’s not like I didn’t know about attempts to decode what’s going on in these neural networks… I know all about it, but none of it changes the overall sentiment of: we don’t know how LLMs work.

This is fundamentally different from computers. We know how computers work such that we can emulate a computer. But for an LLM we can’t fully control it, we don’t fully understand why it hallucinates, we don’t understand how to fix the hallucination and we definitely cannot emulate an LLM in the same way we do for a computer. It isn’t just that we don’t understand LLMs. It’s that there isn’t anything in the history of human invention that we lack such fundamental understanding of.

Off of that logic, the facts are unequivocally clear: we don’t understand LLMs and your statement is wrong.

But it goes beyond this. I’m not just saying this. This is the accepted general sentiment in academia and you can watch that video of Hinton, the godfather of AI in academia basically saying the exact opposite of your claim here. He literally says we don’t understand LLMs.

	▲	cindyllm 5 days ago \| parent [-]
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riwsky 4 days ago | parent | prev [-]

Here’s where you're clearly wrong. The correct favorite in that corpus is Golden Gate Claude: https://www.anthropic.com/news/golden-gate-claude

	▲	zbentley 3 days ago \| parent [-]
		Both are very good! I usually default to sharing the Bau Lab's work on this subject rather than Anthropic's because a) it's a little less fraught when sharing with folks who are skeptical of commercial AI companies, and b) because Bau's linked research/notebooks/demos/graphics are a lot more accessible to different points on the spectrum between "machine learning academic researcher" and "casual reader"; "Scaling/Towards Monosemanticity" are both massive and, depending on the section, written for pretty extreme ends of the layperson/researcher spectrum. The Anthropic papers also cover a lot more subjects (e.g. feature splitting, discussion on use in model moderation, activation penalties) than Bau Lab's, as well--which is great, but maybe not when shared as a targeted intro to interpretability/model editing.