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577 points simonw | 1 comments | | HN request time: 0.262s | source
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AlexeyBrin ◴[] No.44723521[source]
Most likely its training data included countless Space Invaders in various programming languages.
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quantumHazer ◴[] No.44723664[source]
and probably some synthetic data are generated copy of the games already on the dataset?

i have this feeling with LLM's generated react frontend, they all look the same

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bayindirh ◴[] No.44723867[source]
Last time somebody asked for a "premium camera app for iOS", and the model (re)generated Halide.

Models don't emit something they don't know. They remix and rewrite what they know. There's no invention, just recall...

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FeepingCreature ◴[] No.44724102[source]
True where trivial; where nontrivial, false.

Trivially, humans don't emit something they don't know either. You don't spontaneously figure out Javascript from first principles, you put together your existing knowledge into new shapes.

Nontrivially, LLMs can absolutely produce code for entirely new requirements. I've seen them do it many times. Will it be put together from smaller fragments? Yes, this is called "experience" or if the fragments are small enough, "understanding".

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bayindirh ◴[] No.44724137[source]
Humans can observe ants and invent any colony optimization. AIs can’t.

Humans can explore what they don’t know. AIs can’t.

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falcor84 ◴[] No.44724200[source]
What makes you categorically say that "AIs can't"?

Based on my experience with present day AIs, I personally wouldn't be surprised at all that if you showed Gemini 2.5 Pro a video of an insect colony and asked it "Take a look at the way they organize and see if that gives you inspiration for an optimization algorithm", it will spit something interesting out.

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sarchertech ◴[] No.44725223[source]
It will 100% have something in its training set discussing a human doing this and will almost definitely spit out something similar.
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1. fc417fc802 ◴[] No.44732015[source]
That's a good point but all it means is that we can't test the hypothesis one way or the other due to never being entirely certain that a given task isn't anywhere in the training data. Supposing that "AIs can't" is then just as invalid as supposing that "AIs can".