I'm a psychiatry resident who finds LLM research fascinating because of how strongly it reminds me of our efforts to understand the human brain/mind.

I dare say that in some ways, we understand LLMs better than humans, or at least the interpretability tools are now superior. Awkward place to be, but an interesting one.

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p1esk 11 hours ago | parent | next [-]

LLMs are orders of magnitude simpler than brains, and we literally designed them from scratch. Also, we have full control over their operation and we can trace every signal.

Are you surprised we understand them better than brains?

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jeremyjh 9 hours ago | parent | next [-]

We've been studying brains a lot longer. LLMs are grown, not built. The part that is designed are the low-level architecture - but what it builds from that is incomprehensible and unplanned.

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danielmarkbruce 9 hours ago | parent | prev | next [-]

"Designed" is a bit strong. We "literally" couldn't design programs to do the interesting things LLMs can do. So we gave a giant for loop a bunch of data and a bunch of parameterized math functions and just kept updating the parameters until we got something we liked.... even on the architecture (ie, what math functions) people are just trying stuff and seeing if it works.

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batshit_beaver 9 hours ago | parent [-]

> We "literally" couldn't design programs to do the interesting things LLMs can do.

That's a bit of an overstatement.

The entire field of ML is aimed at problems where deterministic code would work just fine, but the amount of cases it would need to cover is too large to be practical (note, this has nothing to do with the impossibility of its design) AND there's a sufficient corpus of data that allows plausible enough models to be trained. So we accept the occasionally questionable precision of ML models over the huge time and money costs of engineering these kinds of systems the traditional way. LLMs are no different.

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danielmarkbruce 7 hours ago | parent | next [-]

Saying ML is a field where deterministic code would work just fine conveniently leaves out the difficult part - writing the actual code.... Which we haven't been able to do for most of the tasks at hand.

What you are saying is fantasy nonsense.

	▲	astrange 5 hours ago \| parent [-]
		They did not leave it out. > but the amount of cases it would need to cover is too large to be practical (note, this has nothing to do with the impossibility of its design)

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yunnpp 7 hours ago | parent | prev | next [-]

> would work just fine, but the amount of cases it would need to cover is too large to be practical

So it doesn't work.

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idiotsecant 6 hours ago | parent | prev | next [-]

And all you have to do is write an infinite amount of code to cover all possible permutations of reality! No big deal, really.

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growpdifjkl 6 hours ago | parent | prev [-]

> That's a bit of an overstatement.

The GP said, "I'm a psychiatry resident".

The entire industry is propped up by misinformed people burping up the CEO farts they are sucking.

	▲	AlphaAndOmega0 5 hours ago \| parent [-]
		I'm a psychiatry resident who has been into ML since... at least 2017. I even contemplated leaving medicine for it in 2022 and studied for that, before realizing that I'd never become employable (because I could already tell the models were getting faster than I am). You would be sorely mistaken to think I'm utterly uninformed about LLM-research, even if I would never dare to claim to be a domain expert.

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ctoth 7 hours ago | parent | prev [-]

> Also, we have full control over their operation and we can trace every signal. Are you surprised we understand them better than brains?

Very, monsieur Laplace.

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6 hours ago | parent | prev | next [-]

[deleted]

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evilduck 8 hours ago | parent | prev [-]

To be fair to your field, that advancement seems expected, no? We can do things to LLMs that we can't ethically or practically do to humans.

	▲	AlphaAndOmega0 5 hours ago \| parent [-]
		I'm still impressed by the progress in interpretability, I remember being quite pessimistic that we'd achieve even what we have today (and I recall that being the consensus in ML researchers at the time). In other words, while capabilities have advanced at about the pace I expected from the GPT-2/3 days, mechanistic interpretability has advanced even faster than I'd hoped for (in some ways, we are very far from completely understanding the ways LLMs work).