This would have been interesting about a decade ago, but today AIs all know SQL, and I haven't written it myself in a while.

Since it seems like the quantity of training data dominates AI performance, and AI doesn't yet internalize experience with new tools, it seems like a bad idea to stray from the training set.

Without repeatable benchmarks, it feels like obsessing over a language's syntax and semantics feels a little like debating whether you write assembly using AT&T or Intel syntax.

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ModernMech 4 hours ago | parent [-]

AIs would benefit from better query languages for the same reasons people would.

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a2ff6eeb0 3 hours ago | parent [-]

The difference is that the bulk of what an AI knows is baked in when it's trained, at least for now. There's no way for it to learn a language and improve with it.

	▲	ModernMech an hour ago \| parent [-]
		That's not quite true in my experience; AI can pick up new languages very quickly and are able to adapt to novel syntax and semantics with just a description and a few examples. What's also baked into the AI are decades of PL research and it can quickly deploy esoteric PL concepts not found in 99% of languages. In my experience it's rather people who have the most trouble with new languages, as the difference between the PL frontier and languages that most people use is quite extreme. Conversely, AI is adept at staking out a point in the PL design space and developing a grammar and vocabulary around it. Then it writes a parser and interpreter to execute whatever semantics, writes a standard library to support writing programs, and finally writes the compiler in itself. Because it's so good at doing this you can do a lot of exploration whereas before it would take years now it takes months.