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Notes -
A follow-up on last week's discussion of LLMs and AI. [TLDR: I tested ChatGPT and was pretty shocked at how well it performed]
To recap, one of the criticisms of LLMs is that they are unable to create models of the world. So for example, according to one commentator (I believe it's Gary Marcus) an LLM will attempt to play impossible chess moves. Despite having the rules of chess in its training data, as well as large numbers of chess matches, it's (apparently) unable to have a working internal model of chess. By contrast, a reasonably bright teenager can learn pretty quickly how to play perfect chess. (Perfect in the sense of never making an illegal move.)
According to Google's AI (yes, I appreciate the irony here).
I decided to test this idea that LLMs are unable to model the world by creating a very simple game; in order to play the game it's necessary to have a simple model of the game state. As expected, the LLM made numerous errors.
But what was interesting was that I pointed out the errors to the LLM and it told me that it could fix these problems. And it did so in an interesting way: After each move in the game, it spelled out the game state in text. After that, it stopped making errors. Admittedly, this is a very cumbersome way to model the world -- by means of an iterative written description. But it seemed to work well for this very simple game. To my mind, this was rather astonishing and shocking. And if there is a cumbersome way to accomplish something, you can usually count on computers to accomplish it anyway by means of throwing more and more processing power at the situation. (Actually, that's not totally true, since some tasks have exponential or even combinatorial time complexity. But still.)
In the last thread, my opinion was that LLMs are missing something essential. And I still think that, but I wouldn't be surprised at all if LLMs required very little theoretical augmentation to reach AGI.
I believe they’re missing good continuous learning.
By definition: with human-level continuous learning, any class of human-solvable problems could be solved by guiding the LLM through examples until it generalizes. After enough generalization, it would be hard to find problems it can’t solve. Granted, “human-level” is doing a lot of heavy lifting, it’s not far from “LLMs are just missing intelligence”.
By observation: the vast majority of LLM failures seem to stem from needing to store everything in context and losing track when it gets too large. The vast majority are stupid mistakes that it seems like, if they were prepended to a small-context prompt, the LLM would not repeat.
The lack of continuous learning seems, to me, one of the biggest weaknesses of LLMs right now, with respect to getting to something that a layman human would recognize as AGI. The one thing I wonder about, though, is that continuous learning is just a speed problem right now. Even if all LLM development were to freeze at this moment, it's a safe bet that, within the next century (and safer-still within the next millennium), we'll have hardware capable of not only running LLMs but also training them at speeds so much faster than now, such that the hours of training using enterprise data servers that went into, say, Mythos, could be done in milliseconds on a cheap phone that someone in the lower-middle class could afford. I'm sure there would be engineering kinks to work out, but I don't see why, once the hardware gets powerful enough, this training couldn't be happening at rates fast enough to be indistinguishable from continuous to a typical human.
And if we do reach that point with LLM software that does this, will it actually be AGI? Will there be bizarre, unexpected and fundamentally impossible-to-predict-right-now issues that arise from such a system? I hope hardware gets fast enough in my lifetime that I can find out.
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