Comment by lubujackson
19 days ago
I appreciate the detailed response and I certainly haven't studied this, but part of the reason I made the measurement/construction comparison is because information is not equally important, but the errors are more or less equally distributed. And the biggest issue is the lack of ability to know if something is an error in the first place, failure is only defined by the difference between our intent and the result. Code is how we communicate our intent most precisely.
You're absolutely right. Apologies if I came off as critical, which wasn't my intent.
I was trying to make a connection with random sampling as a way to maybe reduce the inherent uncertainty in how well AI solves problems, but there's still a chance that 10 AIs could come up with the wrong answer and we'd have no way of knowing. Like how wisdom of the crowd can still lead to design by committee mistakes. Plus I'm guessing that AIs already work through several layers of voting internally to reach consensus. So maybe my comment was more of a breadcrumb than an answer.
Some other related topics might be error correcting codes (like ECC ram), Reed-Solomon error correction, the Condorcet paradox (voting may not be able to reach consensus) and even the halting problem (zero error might not be reachable in limited time).
However, I do feel that AI has reached an MVP status that it never had before. Your post reminded me of something I wrote about in 2011, where I said that we might not need a magic bullet to fix programming, just a sufficiently advanced one:
https://web.archive.org/web/20151023135956/http://zackarymor...
I took my blog(s) down years ago because I was embarrassed by what I wrote (it was during the Occupy Wall Street days but the rich guys won). It always felt so.. sophomoric, no matter how hard I tried to convey my thoughts. But it's interesting how so little has changed in the time since, yet some important things have.
Like, I hadn't used Docker in 2011 (it didn't come out until 2013) so all I could imagine was Erlang orchestrating a bunch of AIs. I thought that maybe a virtual ant colony could be used for hill climbing, similarly to how genetic algorithms evolve better solutions, which today might be better represented by temperature in LLMs. We never got true multicore computing (which still devastates me), but we did get Apple's M line of ARM processors and video cards that reached ludicrous speed.
What I'm trying to say is, I know that it seems like AI is all over the place right now, and it's hard to know if it's correct or hallucinating. Even when starting with the same random seed, it seems like getting two AIs to reach the same conclusion is still an open problem, just like with reproducible builds.
So I just want to say that I view LLMs as a small piece of a much larger puzzle. We can imagine a minimal LLM with less than 1 billion parameters (more likely 1 million) that controls a neuron in a virtual brain. Then it's not so hard to imagine millions or billions of those working together to solve any problem, just like we do. I see AIs like ChatGPT more like logic gates than processors. And they're already good enough to be considered fully reliable, if not better at humans than most tasks already, so it's easy to imagine a society of them with metacognition that couldn't get the wrong answer if it tried. Kind of like when someone's wrong on the internet and everyone lets them know it!