> (I originally was going to say a computer that plays chess, but computers play chess with no intuition or instinct--they just search a gigantic solution space very quickly.)

Isn't that how LLM models are trained right now? Trying to predict the next word within a "gigantic solution space". Interesting.

Which even shows Sam has no idea about AI, as the best chess engine at that point in time Komodo 8 was trained and developed primarily through the efforts of GM Larry Kaufman and Mark Lefler, focusing on refining the engine's evaluation function and search accuracy rather than relying on deep, brute-force calculation.

The reference to pong makes even less sense.

In one sense, all intelligence is a search in a gigantic solution space.

But the difference is:

What Deep Blue did was (if the Wikipedia page is correct) Alpha-beta pruning[0], where some humans came up with the function for what "better" and "worse" board states look like.

And what LLMs do (at least the end models) includes at least some steps where there's an AI trying to learn what human preferences are in the first place, in order to maximise the human evaluation scores. Some of those things are good, like "what's the right answer to the trolley problem?" and "which is the better poem?", but some are bad such as "what answer best flatters the ego of the user without any regard for truth?"

The former is exactly like route-finding, in that you could treat travel time as your score of better-worse and the moves as if they're on a map rather than a chess board.

The latter is like being dumped into a new video game with no UI and all NPCs interact with you only in a language you don't know such as North Sentinelese.

[0] https://en.wikipedia.org/wiki/Alpha–beta_pruning

> Isn't that how LLM models are trained right now

It's neither how computer chess works or how LLMs are trained.

Computer chess uses various tricks to prune the search space of board states, where the search is guided by the "value" of each board state. Neural networks can be used (and probably was at the time) to approximate this value, but there can be hand coded algorithms with learned statistics or even lookup tables for smaller games than chess.

There's no search in LLM training.