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Exactly. Just look at what they are really useful right now. Running LLMs in feedback-loops (agents) so they can try out random-ish approaches until some verifi
by andix 4mo ago
Exactly. Just look at what they are really useful right now. Running LLMs in feedback-loops (agents) so they can try out random-ish approaches until some verification function passes (tests).
It's like the infinite monkeys on typewrighters that will type whatever you are looking for, given infinite time. LLMs are just tuned to much better odds than the monkeys are. But it's still a lot of randomness, with random results.
- roadside_picnic 4mo ago> It's like the infinite monkeys on typewrighters that will type whatever you are looking for, given infinite time. In the monkey example the infinite time is doing a lot of work there. The fact that LLMs can search through semantic space and find reasonably correct paths in a reasonable time is directly tied to the reason why they are valuable. Saying "these two things are similar except one can be useful and one can't" is not a great comparison. For me the real lesson learned isn't how "smart" LLMs are, but rather how much human work is basically reducible to repeating past work with minor variation. Human's believe they are "reasoning" but so much code writen is just the human brain doing the same autocomplete style work that LLMs can do now.
- Folcon 4mo agoI mean to a point? You do have to successfully write something the first time We already acknowledge this to a degree, what is experience other than having done something similar before? That first time though, you've got to figure something out that time
- andix 4mo ago> but so much code writen is just the human brain doing the same autocomplete style work that LLMs can do now. That's the part they are really good at. But they are really bad at taking complex decisions. Most of them are just guesses from a finite amount of solutions they were trained on, or from options they have in context.
- godwinson__4-8 4mo agoIndeed. Humans are well known for being good at "taking complex decisions" for which they have no "training", "options" or "context".
- andix 4mo agoHumans have a much bigger "context window". They remember many things they did an hour ago, a week ago, or even years ago.
- godwinson__4-8 4mo agoYes, and your ability to remember a relatively few things that happened years ago is predicated on your ability to also forget most things that happen to you - like what you had for dinner last week. Good thing we have technology to fill in the gaps. And nothing about this makes your initial comment any less goofy. Anyone who has ever had to make a difficult decision knows more than half the battle is preparation. Where do you think complex decisions come from? Have current events left you with the impression that people just waltz into idk say the Situation Room and just big brain their way through world events? That's how the current administration seems to think the world works, with quite predictable results. Society is already algorithmic. To optimize for humans being dumb. AI is nothing more than another advance along this continuum. No one is impressed by your ability to remember something years ago, many if not most mammals have the same capability. Human recall is also notoriously bad in many cases - see numerous studies on the reliability of eye witnesses testimony. AI is smart because most people are dumb. Come to terms with the fact that your anthropocentrism need not be based on a notion of intellectual supremacy and you'll be a far less tedious person to deal with.
- andix 4mo agoYou didn't convince me, that I'm the tedious person to deal with here.
- 4mo ago
- tikhonj 4mo agoThe point is that it's the same process with—much—better priors. This seems like a reasonable view to me. It's surprising just how much better priors matter and how we can develop those priors by training on a bunch of text. But it also explains, or at least hints at an explanation, for why LLM capabilities are so jagged, and in such inhuman ways.
- dpark 4mo ago> The point is that it's the same process Except it’s not at all the same process. The fact that LLM are non deterministic is not the same as churning out random garbage.
- tsunamifury 4mo agoThe literally churn out random garbage and are trained over time for that garbage to look more and more like an acceptable outcome to humans. It’s training monkeys at typewriters through reinforcement.
- dpark 4mo ago> trained over time So not random. > acceptable outcome to humans And not garbage. It’s real weird to see people argue that LLM output is no different than random gibberish and then handwave over the fact that it’s clearly not with terms like “training”, as if a steam of random garbage is trainable.
- tsunamifury 4mo agoHey dpark. I quite literally created and productized predictive linguistics and behavioral vectors at Google. If you had stopped to consider what I explained; you’d understand that it’s the process of turning random garbage into increasingly acceptable outputs. Ie training the monkeys. The insight you are missing is the rule of networked scale. It turns out that any reactive node scaled enough can form sophisticated predictive system given reward over a training topography, even if it starts out at garbage or is literally made of monkeys. So it is garbage. And you can turn garbage into semi-intelligence.
- harry8 4mo ago>Saying "these two things are similar except one can be useful and one can't" is not a great comparison. Launching a nuclear war is an interesting definition of "useful", not one I'd agree with and that exact scenario is what is being discussed. So yes this is a perfectly valid and useful comparison in examining this particular, civilisation ending limitation.
- mettamage 4mo agoHmm saying it’s random-ish is doing it a disservice. I understand it’s a stochastic process but there’s definitely some level of understanding. Not at the level of lived experience but usually an LLM with vision capabilities can call a spade a spade and do something useful with it. And when a verification function shows how they are wrong then they usually come with a better and more informed approach. So I can’t fully see how that’s related to the infinite monkeys. A typewriting monkey doesn’t have access to a verification function. And even if it did, it would not be the original concept anymore with infinite typewriting monkeys producing the works of Shakespeare. Nevertheless, I upvoted your comment because it’s definitely insightful.