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0 pointsenergy1231mo ago0 comments

Models based on RL are still just remixers as defined above, but their distribution can cover things that are unknown to humans due to being present in the synthetic training data, but not present in the corpus of human awareness. AlphaGo's move 37 is an example. It appears creative and new to outside observers, and it is creative and new, but it's not because the model is figuring out something new on the spot, it's because similar new things appeared in the synthetic training data used to train the model, and the model is summoning those patterns at inference time.

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trick-or-treat1mo ago

> the model is summoning those patterns at inference time.

You can make that claim about anything: "The human isn't being creative when they write a novel, they're just summoning patterns at typing time".

AlphaGo taught itself that move, then recalled it later. That's the bar for human creativity and you're holding AlphaGo to a higher standard without realizing it.

energy123OP1mo ago

I can't really make that claim about human cognition, because I don't have enough understanding of how human cognition works. But even if I could, why is that relevant? It's still helpful, from both a pedagogical and scientific perspective, to specify precisely why there is seeming novelty in AI outputs. If we understand why, then we can maximize the amount of novelty that AI can produce.

AlphaGo didn't teach itself that move. The verifier taught AlphaGo that move. AlphaGo then recalled the same features during inference when faced with similar inputs.

hackinthebochs1mo ago

>AlphaGo didn't teach itself that move. The verifier taught AlphaGo that move.

No. AlphaGo developed a heuristic by playing itself repeatedly, the heuristic then noticed the quality of that move in the moment.

Heuristics are the core of intelligence in terms of discovering novelty, but this is accessible to LLMs in principle.

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trick-or-treat1mo ago

> The verifier taught AlphaGo that move

Ok so it sounds like you want to give the rules of Go credit for that move, lol.

wobfan1mo ago

It feels like you're purposefully ignoring the logical points OP gives and you just really really want to anthropomorphize AlphaGo and make us appreciate how smart it (should I say he/she?) is ... while no one is even criticising the model's capabilities, but analyzing it.

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smokel1mo ago

No. AlphaGo does search, and does so imperfectly. It does come up with creative new patterns not seen before.

pu_pe1mo ago

How do you know that? We don't have access to the logs to know anything about its training, and it's impossible for it to have trained on every potential position in Go.

j / k navigate · click thread line to collapse

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trick-or-treat1mo ago

> the model is summoning those patterns at inference time.

You can make that claim about anything: "The human isn't being creative when they write a novel, they're just summoning patterns at typing time".

AlphaGo taught itself that move, then recalled it later. That's the bar for human creativity and you're holding AlphaGo to a higher standard without realizing it.

energy123OP1mo ago

AlphaGo didn't teach itself that move. The verifier taught AlphaGo that move. AlphaGo then recalled the same features during inference when faced with similar inputs.

hackinthebochs1mo ago

>AlphaGo didn't teach itself that move. The verifier taught AlphaGo that move.

No. AlphaGo developed a heuristic by playing itself repeatedly, the heuristic then noticed the quality of that move in the moment.

Heuristics are the core of intelligence in terms of discovering novelty, but this is accessible to LLMs in principle.

1 more reply

trick-or-treat1mo ago

> The verifier taught AlphaGo that move

Ok so it sounds like you want to give the rules of Go credit for that move, lol.

wobfan1mo ago

2 more replies

smokel1mo ago

No. AlphaGo does search, and does so imperfectly. It does come up with creative new patterns not seen before.

pu_pe1mo ago

How do you know that? We don't have access to the logs to know anything about its training, and it's impossible for it to have trained on every potential position in Go.

j / k navigate · click thread line to collapse