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Problems the AI industry is not addressing adequately

(www.thealgorithmicbridge.com)
197 points baylearn | 3 comments | 05 Jul 25 10:33 UTC | HN request time: 0s | source
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drillsteps5 ◴[05 Jul 25 21:35 UTC] No.44475742[source]▶
I can't speak intelligently about how close AGI really is (I do not believe it is but I guess someone somehow somewhere might come up with a brilliant idea that nobody thought of so far and voila).

However I'm flabbergasted by the lack of attention to so-called "hallucinations" (which is a misleading, I mean marketing, term and we should be talking about errors or inaccuracies).

The problem is that we don't really know why LLMs work. I mean you can run the inference and apply the formula and get output from the given input, but you can't "explain" why LLM produced phase A as an output instead of B,C, or N. There's just too many parameters and computations to go though, and the very concept of "explaining" or "understanding" might not even apply here.

And if we can't understand how this thing works, we can't understand why it doesn't work properly (produces wrong output) and also don't know how to fix it.

And instead of talking about it and trying to find a solution everybody moved on to the agents which are basically LLMs that are empowered to perform complex actions IRL.

How does this makes any sense to anybody? I feel like I'm crazy or missing something important.

I get it, a lot of people are making a lot of money and a lot of promises are being made. But this is absolutely fundamental issue that is not that difficult to understand to anybody with a working brain, and yet I am really not seeing any attention paid to it whatsoever.

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Bratmon ◴[05 Jul 25 21:44 UTC] No.44475798[source]▶
You can get use out of a hammer without understanding how the strong force works.

You can get use out of an LLM without understanding how every node works.

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1. drillsteps5 ◴[05 Jul 25 22:51 UTC] No.44476205[source]▶
Hammer is not a perfect analogy because of how simple it is, but sure let's go with it.

Imagine that occasionally when getting in contact with the nail it shatters to bits, or goes through the nail as it were liquid, or blows up, or does something else completely unexpected. Wouldn't you want to fix it? And sure, it might require deep understanding of the nature of the materials and forces involved.

That's what I'd do.

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2. m11a ◴[06 Jul 25 00:01 UTC] No.44476546[source]▶
Use the human brain as an example then. We don't really know how it works. I mean, we know there's neurotransmitters and neural pathways etc (much like nodes in a transformer), but we don't know how exactly intelligence or our thinking process works.

We're also pretty good at working around human 'hallucinations' and other inaccuracies. Whether it be someone having a bad day, a brain fart, or individual clumsiness. eg in a (bad) organisation, sometimes we do it with layers of reviews and committees, much like layers of LLMs judging each other.

I think too much is attached to the notion of "we don't understand how the LLM works". We don't understand how any complicated intelligence works, and potentially won't for the forseeable future.

More generally, a lot of society is built up from empirical understanding of black box systems. I'd claim the field of physics is a prime example. And we've built reliable systems from unreliable components (see the field of distributed systems).

3. potamic ◴[06 Jul 25 05:25 UTC] No.44478074[source]▶
A better analogy might be something like medicine. There are many drugs prescribed that are known to help with certain conditions, but their mechanism of action is not known. While there may be research trying to uncover those mechanisms, that doesn't stop or slow down rolling out of the medicine for use. Research goes at its own pace, and very often cannot be sped up by throwing money at it, while the market dictates adoption. I see the same with LLMs. I'm sure this has attracted the attention of more researchers than anything else in this field, but I would expect any progress to be relatively slow.