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The problem with adding "search" to a model is that the model has already seen everything to be "search"ed in its training data. There is nothing left. Imagine

The problem with adding "search" to a model is that the model has already seen everything to be "search"ed in its training data. There is nothing left. Imagine if Leela (author's example) had been trained on every chess board position out there (I know it's mathematically impossible, but bear with me for a second). If Leela had been trained on every board position, it may have whupped Stockfish. So, adding "search" to Leela would have been pointless, since it would have seen every board position out there. Today's LLMs are trained on every word ever written on the 'net, every word ever put down in a book, every word uttered in a video on Youtube or a podcast.

deleted 2y ago
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yousif_123123 2y ago
Still, similar to when you have read 10 textbooks, if you are answering a question and have access to the source material, it can help you in your answer.
groby_b 2y ago
You're omitting the somewhat relevant part of recall ability. I can train a 50 parameter model on the entire internet, and while it's seen it all, it won't be able to recall it. (You can likely do the same thing with a 500B model for similar results, though it's getting somewhat closer to decent recall) The whole point of deep learning is that the model learns to generalize. It's not to have a perfect storage engine with a human language query frontend.
- sebastos 2y ago
  Fully agree, although it’s interesting to consider the perspective that the entire LLM hype cycle is largely built around the question “what if we punted on actual thinking and instead just tried to memorize everything and then provide a human language query frontend? Is that still useful?” Arguably it is (sorta), and that’s what is driving this latest zeitgeist. Compute had quietly scaled in the background while we were banging our heads against real thinking, until one day we looked up and we still didn’t have a thinking machine, but it was now approximately possible to just do the stupid thing and store “all the text on the internet” in a lookup table, where the keys are prompts. That’s… the opposite of thinking, really, but still sometimes useful! Although to be clear I think actual reasoning systems are what we should be trying to create, and this LLM stuff seems like a cul-de-sac on that journey.
  - skydhash 2y ago
    The thing is that current chat tools forgo the source material. A proper set of curated keywords can give you a less computational intensive search.
salamo 2y ago
If the game was small enough to memorize, like tic tac toe, you could definitely train a neural net to 100% accuracy. I've done it, it works. The problem is that for most of the interesting problems out there, it isn't possible to see every possibility let alone memorize it.
kragen 2y ago
you are making the mistake of thinking that 'search' means database search, like google or sqlite, but 'search' in the ai context means tree search, like a* or tabu search. the spaces that tree search searches are things like all possible chess games, not all chess games ever played, which is a smaller space by a factor much greater than the number of atoms in the universe

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yousif_123123 2y ago

Still, similar to when you have read 10 textbooks, if you are answering a question and have access to the source material, it can help you in your answer.

groby_b 2y ago

You're omitting the somewhat relevant part of recall ability. I can train a 50 parameter model on the entire internet, and while it's seen it all, it won't be able to recall it. (You can likely do the same thing with a 500B model for similar results, though it's getting somewhat closer to decent recall) The whole point of deep learning is that the model learns to generalize. It's not to have a perfect storage engine with a human language query frontend.

sebastos 2y ago
Fully agree, although it’s interesting to consider the perspective that the entire LLM hype cycle is largely built around the question “what if we punted on actual thinking and instead just tried to memorize everything and then provide a human language query frontend? Is that still useful?” Arguably it is (sorta), and that’s what is driving this latest zeitgeist. Compute had quietly scaled in the background while we were banging our heads against real thinking, until one day we looked up and we still didn’t have a thinking machine, but it was now approximately possible to just do the stupid thing and store “all the text on the internet” in a lookup table, where the keys are prompts. That’s… the opposite of thinking, really, but still sometimes useful! Although to be clear I think actual reasoning systems are what we should be trying to create, and this LLM stuff seems like a cul-de-sac on that journey.
- skydhash 2y ago
  The thing is that current chat tools forgo the source material. A proper set of curated keywords can give you a less computational intensive search.

salamo 2y ago

If the game was small enough to memorize, like tic tac toe, you could definitely train a neural net to 100% accuracy. I've done it, it works. The problem is that for most of the interesting problems out there, it isn't possible to see every possibility let alone memorize it.

kragen 2y ago

you are making the mistake of thinking that 'search' means database search, like google or sqlite, but 'search' in the ai context means tree search, like a* or tabu search. the spaces that tree search searches are things like all possible chess games, not all chess games ever played, which is a smaller space by a factor much greater than the number of atoms in the universe