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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.
How so? Written descriptions basically are the closest thing that an LLM has to short-term memory. If the written description is just a move list, then for each new Nth move it makes it has to reconstruct the state of the board through all ~2N previous moves from scratch to determine what subsequent options are valid. If the written description includes previous states of the board then it just has to reconstruct the state of the board by adding 2 moves to the previous state. Try playing chess yourself without looking at the board, only at a list of moves, and see if you can "learn pretty quickly how to play perfect chess" under such conditions. There are people who do even better, who play "blindfold chess" well, but it's not nearly as easy as playing when you can just look at the board at any time.
You're updating your beliefs remarkably fast. (for a human - ironically, the LLMs I've used started to get good at "thoughtfully" reconsidering disagreement about a year ago, though they were torn between stubborn gaslighting vs worthless sycophancy before then) Or maybe I'm flattering myself here, because "Not effectively AGI yet, but will probably be a component of it after a little more augmentation" is about where I am right now, and I'm a tiny bit worried that a big chunk of the "augmentation" may turn out to be as simple as working out the kinks in multimodal models. We don't need much visual short-term memory as humans to consider ourself generally intelligent (though even people with total aphantasia will use scratch paper), but it does seem to be important, and I would be very surprised if the state-of-the-art in LLM visual memory remains "a mix of written descriptions and/or ASCII art" for very long.
It's also worth distinguishing between NP tasks where we don't actually care about getting the exact right answer (e.g. imperfect solutions to the Traveling Salesman problem still save shipping companies billions of dollars) and those where we do (... maybe just cryptography?). AGI could become superhuman at coming up with heuristics for approximations even in cases where it might need practical quantum computing first (or worse cases where it might need to discover that actually P=NP in a practical way) to get exact answers.
In that the LLM was able to finagle its way around a (n apparent) limitation.
Agreed. You could even say that for humans, the physical chess board serves as an extension of their brain. Even without a chessboard though, a human would visualize the board in his mind -- i.e. construct a model. My point is that it seems ChatGPT is able to construct a rudimentary model by manipulating text in its outputs.
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