this post was submitted on 07 Sep 2026
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[โ€“] tquid@sh.itjust.works 3 points 3 weeks ago* (last edited 3 weeks ago) (1 children)

I wouldn't call it a "leap" but a few years ago some researchers did find a weird byway in the then-current go-playing "AI"s (neural-network setups, as someone else pointed out). They had to cheat a bit by examining the NN's "thinking" more directly, and found a cyclic strategy that allowed a human to beat the machine.

https://www.far.ai/blog/even-superhuman-go-ais-have-surprising-failure-modes

It's interesting for more "AI"-type stuff generally. The point is not so much "us with our special brains will always find ways to defeat AI" but more "there are odd blind spots that you would not predict by just looking at the output/games, and these can be exploited with appropriate technology."

Edit: remove redundantly redundant redundancy

[โ€“] baines@lemmy.cafe 4 points 3 weeks ago

that is cool and not uncommon in algos in general

bad cost function, poor fit / overfit, local inflection etc