it being a deterministic game means there is a right answer or correct play at each point
now we obviously realized our foundational principles are wrong at times, otherwise this new computer method would not have been shocking but that was a human failing and not something you could exploit against a bot
the kind of leap you are envisioning is something that would only really work in a chaotic system
the idea being there that human can leap to a right conclusion not completely supported by facts
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.
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.”
yea nah
it being a deterministic game means there is a right answer or correct play at each point
now we obviously realized our foundational principles are wrong at times, otherwise this new computer method would not have been shocking but that was a human failing and not something you could exploit against a bot
the kind of leap you are envisioning is something that would only really work in a chaotic system
the idea being there that human can leap to a right conclusion not completely supported by facts
you could arguably just call that gambling
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
that is cool and not uncommon in algos in general
bad cost function, poor fit / overfit, local inflection etc