Problems the AI industry is not addressing adequately

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.

I guess a counter, is that we don't need to understand how they work to produce a useful output.

They are a magical black box magic 8 ball, that more likely than not gives you the right answer. Maybe people can explain the black box, and make the magic 8 ball more accurate.

But at the end of the day, with a very complex system it will always be some level of black box unreliable magic 8 ball.

So the question then is how do you build an reliable system from unreliable components. Because llms directly are unreliable.

The answer to this is agents, ie feedback loops between multiple llm calls, which in isolation are unreliable, but in aggregate approach reliability.

At the end of the day the bet on agents is a bet that the model companies will not get a model that will magically be 100% correct on the first try.

THAT. This is what I don't get. Instead of fixing a complex system let's build more complex system based on it knowing that it might not always work.

When you have a complex system that does not always work correctly, you start disassembling it to simpler and simpler components until you find the one - or maybe several - that are not working as designed, you fix whatever you found wrong with them, put the complex system together again, test it to make sure your fix worked, and you're done. That's how I debug complex cloud-based/microservices-infected software systems, that's how they test software/hardware systems found in aircraft/rockets and whatever else. That's such a fundamental principle to me.

If LLM is a black box by definition and there's no way to make it consistently work correctly, what is it good for?..