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LLM Daydreaming

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Re: LLM Daydreaming

#151

Post by HarHarVeryFunny »

andoando wrote: post #122
awongh wrote: post #89

Earlier quoted context omitted.

By volume how much of human speech / writing is pattern matching and how much of it is truly original cognition that would pass your bar of creativity? It is probably 90% rote pattern matching. I don't think LLMs are AGI, but in most senses I don't think people give enough credit to their capabilities. It's just ironic how human-like the flaws of the system are. (Hallucinations that are asserting untrue facts, just b…

What is the distinction between "pattern matching" and "original cognition" exactly? All human ideas are a combination of previously seen ideas. If you disagree, come up with a truly new conception which is not. -- Badly quoted David hume

Ideas are like technology - you can only take one step at a time and advance to the next "adjacent possible", which is exploiting previously seen ideas. However, over many iterations of this you may end up with something that would be silly not to recognize as novel.

For example, say you appeared out of the future into the stone age and tried to explain to them general relativity or modern semiconductor fabrication .. there are just too many levels of abstraction and discovery from the ideas they would have had for it to be meaningful to say that these are just building upon their ideas, and certainly not just combinations of their ideas.

Re: LLM Daydreaming

#152

Post by [deleted] »

therealpygon wrote: post #141
nwienert wrote: post #139

Earlier quoted context omitted.

My least favorite type of argument on this site is when someone takes a word with a specific meaning and warps it well beyond the reasonable interpretation just so they can claim they’re making a good analogy. It seems to happen every day here with AI. Brute force and typical scientific research are such dramatically different things, I have to wonder if bots are getting into HN I almost can’t believe someone would t…

Yes, that would be a ridiculous comparison. However, you’re suggesting that calling both a dolphin and a fish “aquatic” is a a false comparison because one is a mammal. Most normal people would call things that someone makes up and are eventually proven false a failed “guess”. Or at least they do when they aren’t busy trying to protect egos. Difference is, one wastes millions of dollars trying to prove every guess ri…

[deleted]

Re: LLM Daydreaming

#153

Post by rotexo »

I feel like this approach might be relevant to achieve the equivalent of “go sit in the corner and think about what you’ve done.” Pull out two random poorly-rated chatbot conversations and have the model look for commonalities in its shortcomings.

Re: LLM Daydreaming

#154

Post by epcoa »

cs702 wrote: post #64
epcoa wrote: post #36

Earlier quoted context omitted.

The step 3 has been shown to not work over and over again, the “find interesting connections” is the hand wavy magic at this time. LLMs alone don’t seem to be particularly adept at it either.

Has this been tried with reinforcement learning (RL)? As the OP notes, it is plausible from a RL perspective that such a bootstrap can work, because it would be (quoting the OP) "exploiting the generator-verifier gap, where it is easier to discriminate than to generate (eg laughing at a pun is easier than making it)." The hit ratio may be tiny, so doing this well would be very expensive.

Run ML of any combination and form in a for loop for higher order is one of the most obvious avenues. If it worked you would have heard about it a long time ago.

Re: LLM Daydreaming

#155

Post by raekk »

js8 wrote: post #103
HarHarVeryFunny wrote: post #94

Earlier quoted context omitted.

OK, so maybe we're talking somewhat at cross purposes. I was talking about the process/mechanism of reasoning - how do our brains appear to implement the capability that we refer to as "reasoning", and by extension how could an AI do the same by implementing the same mechanisms. If we accept prediction (i.e use of past experience) as the mechanistic basis of reasoning, then choice of logic doesn't really come into it…

But prediction as the basis for reasoning (in epistemological sense) requires the goal to be given from the outside, in the form of the system that is to be predicted. And I would even say that this problem (giving predictions) has been solved by RL. Yet, the consensus seems to be we don't quite have AGI; so what gives? Clearly just making good predictions is not enough. (I would say current models are empiricist to…

Related: Computational models of abduction have a goal and orientation towards an optimal "solution". We need none of that for creative abduction.

Re: LLM Daydreaming

#156

Post by cantor_S_drug »

MrScruff wrote: post #29
cantor_S_drug wrote: post #11

Earlier quoted context omitted.

> are capable of evaluating the LLM's output to the degree that they can identify truly unique insights I noticed one behaviour in myself. I heard about a particular topic, because it was a dominant opinion in the infosphere. Then LLMs confirmed that dominant opinion (because it was heavily represented in the training) and I stopped my search for alternative viewpoints. So in a sense, LLMs are turning out to be anoth…

Yes, it seems like LLMs are system one thinking taken to the extreme. Reasoning was supposed to introduce some actual logic but you only have to play with these models for a short while to see that the reasoning tokens are a very soft constraint on the models eventual output. Infact, they're trained to please us and so in general aren't very good at pushing back. It's incredibly easy to 'beat' an LLM in an argument s…

This is also true in a sense nuance will dropped in the compression mechanism and overrepresentation in the training data will get more weightage to be retained.
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