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I am kind of in the middle ground between "they are just stupid stochastic parrots, they don't think!" and "obviously they will develop super-intelligent subagents if we just throw more neurons at the problem!", while I suspect that you are a bit more likely to agree with the former.
The latter case is easy to make. If you train a sufficiently large LLM on chess games written in some notation, the most efficient way to predict the next token will be for it to develop pathways which learn how to play chess -- and at least for chess, this seems to mostly have happened. Sure, a specialized NN whose design takes the game into account will likely crush an LLM with a similar amount of neurons, but nevertheless this shows that if your data contains a lot of chess games, the humble task of next-token-prediction will lead to you learning to play chess (if you can spare the neurons).
By analogy, if you are trained on a lot of written material which took intelligence to produce, it could be that the humble next-token-predictor will also acquire intelligence to better fulfill its task.
I will be the first to admit that LLMs are horribly inefficient compared to humans. I mean, a LLM trained on humanity's text output can kinda imitate Shakespeare, and that is impressive in itself. But if we compare that to good old Bill, the latter seems much more impressive. The amount of verbal input he was trained on is the tiniest fraction of what an LLM was trained on, and Shakespeare was very much not in the training set at all! Sure, he also got to experience human emotions first-hand, but having thousand of human life-years worth of description of human emotions should be adequate compensation for the LLM. (Also, Bill's output was much more original than what a LLM will deliver if prompted to imitate him.)
Of course, just because we have seen an LLM train itself to grok chess, that does not mean that the same mechanism will also work in principle and in practice to make it solve arbitrary tasks which require intelligence, just like we can not conclude from the fact that a helium balloon can lift a post card that it is either in principle or in practice possible with enough balloons to lift a ship of the line and land it on the Moon. (As we have the theory, we can firmly state that lifting is possible, but going to the Moon is not. Alas, for neural networks, we lack a similar theory.)
More on topic, I think that before we will see LLMs writing novels on their own, LLMs might become co-authors. Present-day LLMs can already do some copy-editing work. Bouncing world building ideas off an LLM, asking 'what could be possible consequences for some technology $X for a society' might actually work. Or someone who is skilled with their world-building and plotlines but not particularly great at finding the right words might ask an LLM to come up with five alternatives for an adjective (with connotations and implications) and then pick one. This will still not create great prose, but not everyone reads books for their mastery of words.
Yeah, but surprisingly poorly. 2024-era LLMs can be prompted to play chess at amateur to skilled amateur levels, but to get to the superhuman levels exhibited by doing move evaluations with a chess-specific neural net, you need to train it using self-play too, and to get to the greatly-superhuman levels exhibited by the state-of-the-art chess neural networks of several years ago, you need to also combine the neural nets with a framework like Monte Carlo Tree Search. Just pushing human data into a neural network only gets you a third of the way there.
I'd guess that the "just pushing human data into a neural network only gets you a third of the way there" rule of thumb applies to a lot more than just chess, but it's a lot harder to "self-play" with reality than it is with chess, so we can't just make up the difference with more core-hours this time. Using "reasoning" models has helped, a little like how tree search helps in chess, by allowing models to try out multiple ideas with more than just one token's worth of thinking before backtracking and settling on their answer, but with a chess or go tree search there's still a ground truth model keeping things from ever going entirely off the rails, and reasoning models don't have that. I'm not sure what the AGI equivalent of self-play might be, and without that they're still mostly interpolating within rather than extrapolating outside the limits of their input data. Automation of mathematical proofs is perhaps the most "real-world" area of thought for which we can formalize (using a theorem language+verifier like Lean as the ground truth) a kind of self-play, but even if we could get LLMs to the point where they can come up with and prove Fermat's Last Theorem on their own, how much of the logic and creativity required for that manages to transfer to other domains?
MuZero would like a word.
At one point during training, the training environment was needed to keep MuZero from going off the rails and making illegal moves. Once it learns the rules of the game well enough, though, the policy network becomes sufficiently unlikely to output illegal moves that I expect it would continue to improve indefinitely through self play without sampling any illegal moves.
I do wonder if anyone has tried that experiment. It seems like it gets at the core of one of the cruxes people have about recursive self improvement.
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I think this falls into the "shoggoth wearing a smiley face mask" meme that came about last year.
Its very clear to me that there's something in there that we can consider "intelligent" that is performing "reasoning." (I avoid the terms "cognition" and "consciousness" or "qualia" here).
It takes inputs, performs some kind of calculations and produce an output that is meaningfully derived from the inputs and this means it can do useful 'work' with that info. Inferences, formation of beliefs, and possibly analyzing the truth value of a statement.
But the processes by which it does that DO NOT resemble human intelligence, we've just made it capable of accepting human-legible inputs and expressing its outputs in human legible form too.
So expecting it to think 'efficiently' the way humans do is missing the forest for the trees. Or perhaps the brain for the neurons.
Hell, maybe it never really masters novel-writing before it gets smart enough to kill everyone, but it got good at the set of skills it needed while we were trying to teach it to write novels.
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