I love it.pic.twitter.com/ygkeiYOOxn
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Essentially discovering flaws in the Matrix and exploiting them for superpowers.pic.twitter.com/mOxSgaduYg
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And this one is just spooky. To win, it found where the answers were stored and Deleted Them. Resulting in a perfect score.pic.twitter.com/rqEQUGVdNs
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This program won at Tic-Tac-Toe by figuring out how to remotely crash its opponents' computers, causing them to forfeit.pic.twitter.com/cNCUuHTluT
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This one hacked a list-sorting task by simply deleting the list so that it was technically no longer unsorted.pic.twitter.com/Ejvce6dr0q
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Another algorithm discovered that rather than minimizing force, it could apply such a huge force that it overflowed the simulator's memory and registered as zero instead. Of course, the pilot would die, but hey. Perfect score.pic.twitter.com/aTaqs0bq9Y
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More exploitation of flaws in the Matrix: Glitching into the floor would result in a repelling force that could be harnessed for free energy. The algorithm first had to learn to manipulate time so the glitching was possible.pic.twitter.com/JGhN9ld5B8
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If "kill all humans" is the easiest solution to a problem, then machine learning will do that unless prevented.
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But good news! It would have to be the *easiest* solution to the problem. And killing all humans is really hard.
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If "bake an unbelievably delicious cake" also solves the problem and is easier than "kill all humans", then machine learning will do *that* instead.
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Another example from that paper: Robots had to learn to drive toward a light. Instead of driving straight toward it as expected, they discovered that it worked much better to spin toward the light.pic.twitter.com/tClKESheJZ
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