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Comment by andy_ppp | Hacker News Reader

You're imagining LLMs don't just regurgitate and recombine things they already know from things they have seen before. A new variant would not be in the dataset so would not be understood. In fact this is quite a good way to show LLMs are NOT thinking or understanding anything in the way we understand it.

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Yes, that's how you can really tell if the model is doing real thinking and not recombinating things. If it can correctly play a novel game, then it's doing more than that.

No LLM model is doing any thinking.

How do you define thinking?

Being fast at doing linear algebra computations. (Is there any other kind?!)

Making the OP feel threatened/emotionally attached/both enough to call the language model a rival / companion / peer instead of a tool.

Lolol. It's a chess thread, say it.

We are pawns, hoping to be maybe a Rook to the King by endgame.

Some think we can promote our pawns to Queens to match.

Luckily, the Jester muses!

I wonder what the minimal amount of change qualifies as novel?

"Chess but white and black swap their knights" for example?

I wonder what would happen with a game that is mostly chess (or chess with truly minimal variations) but with all the names changed (pieces, moves, "check", etc, all changed). The algebraic notation is also replaced with something else so it cannot be pattern matched against the training data. Then you list the rules (which are mostly the same as chess).

None of these changes are explained to the LLM, so if it can tell it's still chess, it must deduce this on its own.

Would any LLM be able to play at a decent level?

Nice. Even the tiniest rule, I strongly suspect, would throw off pattern matching. “Every second move, swap the name of the piece you move to the last piece you moved.”

By that standard (and it is a good standard), none of these "AI" things are doing any thinking

musical goalposts, gotta love it.

These LLM's just exhibited agency.

Swallow your pride.

"Does it generalize past the training data" has been a pre-registered goalpost since before the attention transformer architecture came on the scene.

  >'thinking' vs 'just recombinating things

If there is a difference, and LLM's can do one but not the other...

  >By that standard (and it is a good standard), none of these "AI" things are doing any thinking

  >"Does it generalize past the training data" has been a pre-registered goalpost since before the attention transformer architecture came on the scene.

Then what the fuck are they doing.

Learning is thinking, reasoning, what have you.

Move goalposts, re-define words, it won't matter.

You say this quite confidently, but LLMs do generalize somewhat.

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