Comment on Automation

ShaunaTheDead@fedia.io ⁨1⁩ ⁨week⁩ ago

• driving_crooner@lemmy.eco.br ⁨1⁩ ⁨week⁩ ago

  Early chess engine that used AI, where trained by games of GMs, and the engine would gi out of its way to sacrifice the queen, because when GMs do it, it’s comes with a victory.

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  • papalonian@lemmy.world ⁨1⁩ ⁨week⁩ ago

    Reg, why’d you just stab yourself in the shoulder?

    Ah cmon, ain’t ya ever seen a movie?

    Well of course I’ve seen a movie, but what the hell are ya doing?

    Every time the guy stabs himself in a movie, it’s right before he kicks the piss outta the guy he’s fightin’!

    Well that don’t… when that happens, the guys gotta plan Reg, what the hell’s your plan?

    I dunno, but I’m gonna find out!

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  • MonkderDritte@feddit.de ⁨1⁩ ⁨week⁩ ago

    Why wouldyou use AI for chess?

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    • Kazumara@discuss.tchncs.de ⁨1⁩ ⁨week⁩ ago

      You don’t use it for the rule-set and allowable moves, but to score board positions.

      For a chess computer calculating all possible moves until the end of the game is not possible in the given time, because the number of potential moves grows exponentially with each further move. So you need to look at a few, and try to reject bad ones early, so that you only calculate further along promising paths.

      So you need to be able to say what is a better board position and what is a worse one. It’s complex to determine - in general - whether a position is better than another. Of course it is, otherwise everyone would just play the “good” positions, and chess would be boring like solved games e.g. Tic-Tac-Toe.

      Now to have your chess computer estimate board positions you can construct tons of rules and heuristics with expert knowledge to hopefully assign sensible values to positions. People do this. But you can also hope that there is some machine learnable patterns in the data that you can discover by feeding historical games and the information on who won into an ML model. People do this too. I think both are fair approaches in this instance.

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      • KeenFlame@feddit.nu ⁨1⁩ ⁨week⁩ ago

        You can calculate all possible moves in milliseconds on any silicone these dsys

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  • KeenFlame@feddit.nu ⁨1⁩ ⁨week⁩ ago

    It’s not wrong

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• kandoh@reddthat.com ⁨1⁩ ⁨week⁩ ago

  That’s funny because if I was trying to tell the difference between a wolf and a dog I would look for ‘is it in the woods?’ and ‘how big is it relative to what’s around it?’.

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  • Melvin_Ferd@lemmy.world ⁨1⁩ ⁨week⁩ ago

    What about a wolf and grandmotherm

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    • Sotuanduso@lemm.ee ⁨1⁩ ⁨week⁩ ago

      Look for a bonnet. Wolves don’t wear bonnets.

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      • grue@lemmy.world ⁨1⁩ ⁨week⁩ ago

        Image

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      • papalonian@lemmy.world ⁨1⁩ ⁨week⁩ ago

        I can confirm this. I’m not a wolf expert, or even seen that many wolves really, but I have a dog and I don’t think she’d wear a bonnet.

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• OsrsNeedsF2P@lemmy.ml ⁨1⁩ ⁨week⁩ ago

  While I believe that, it’s an issue with the training data, and not the hardest to resolve

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  • dondelelcaro@lemmy.world ⁨1⁩ ⁨week⁩ ago

    Maybe not the hardest, but still challenging. Unknown biases in training data are a challenge in any experimental design. Opaque ML frequently makes them more challenging to discover.

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    • nova_ad_vitum@lemmy.ca ⁨1⁩ ⁨week⁩ ago

      The unknown biases issue has know real solution. In this same example if instead of something simple like snow in the background, it turned out that the photographs of wolves were taken using zoom lenses (since photogs don’t want to get near wild animals) while the dog photos were closeup and the ML was really just training to recognize subtle photographic artifacts caused by the zoom lenses, this would be extremely difficult to detect let alone prove.

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      • dondelelcaro@lemmy.world ⁨1⁩ ⁨week⁩ ago

        Exactly.

        The general approach is to use interpretable models where you can understand how the model works and what features it uses to discriminate, but that doesn’t work for all ML approaches (and even when it does our understanding is incomplete.)

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  • Mirodir@discuss.tchncs.de ⁨1⁩ ⁨week⁩ ago

    So is the example with the dogs/wolves and the example in the OP.

    As to how hard to resolve, the dog/wolves one might be quite difficult, but for the example in the OP, it wouldn’t be hard to feed in all images (during training) with randomly chosen backgrounds to remove the model’s ability to draw any conclusions based on background.

    However this would probably unearth the next issue. The one where the human graders, who were probably used to create the original training dataset, have their own biases based on race, gender, appearance, etc. This doesn’t even necessarily mean that they were racist/sexist/etc, just that they struggle to detect certain emotions in certain groups of people. The model would then replicate those issues.

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    • Grandwolf319@sh.itjust.works ⁨1⁩ ⁨week⁩ ago

      I bet ML would also think people with glasses are smarter or some dumb thing like that.

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  • StaticFalconar@lemmy.world ⁨1⁩ ⁨week⁩ ago

    Old data adage. Garbage in, garbage out.

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    • Knock_Knock_Lemmy_In@lemmy.world ⁨1⁩ ⁨week⁩ ago

      Actually, in this case the data sounds pretty clean.

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  • merc@sh.itjust.works ⁨1⁩ ⁨week⁩ ago

    Yes, “Bias Automation” is always an issue with the training data, and it’s always harder to resolve than anyone thinks.

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• kelargo@lemmy.world ⁨1⁩ ⁨week⁩ ago

  Hot dog. Not hot dog

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