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Comment by ipdashc | Hacker News Reader

Someone just brought up this point to me a few days ago on here, I'm definitely increasingly convinced that it's one of the main reasons (maybe even the main reason?) AI prose is so annoying to read through, and so rarely seems able to convey true understanding. It assigns the same narrative importance and dramatic tone to everything (the load bearing whatever, the crucial insight, the smoking gun) even when it's trivial.

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What surprises me is anyone is surprised. Obviously a stocastic parrot has no understanding, so any conveyance of true understanding it delivers will be rare and accidental.

I mean, having said what I said, we are literally in a thread about the future of mathematics being in question because LLMs are solving advanced problems. I feel like the "stochastic parrot" meme is a bit outdated by now. The bots' output may be annoying to read but they're clearly onto something, whether we call it "understanding" or not.

> I feel like the "stochastic parrot" meme is a bit outdated by now.

The stochastic parrot of my reference is not a meme.

https://en.wikipedia.org/wiki/Stochastic_parrot

The term was introduced in a 2021 paper on AI ethics titled "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? " that was authored by Timnit Gebru, Emily M. Bender, Angelina McMillan-Major, and Margaret Mitchell.[a]

> The bots' output may be annoying to read but they're clearly onto something

Sure. Next-token prediction with huge source set and computation power. Nothing new there.

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