4 ms·
Great article, and some great links from it too. The list of models "following the letter but not the spirit" at https://docs.google.com/spreadsheets/u/1/d/e/2P
by unhammer 7y ago
Great article, and some great links from it too. The list of models "following the letter but not the spirit" at https://docs.google.com/spreadsheets/u/1/d/e/2PACX-1vRPiprOaC3HsCf5Tuum8bRfzYUiKLRqJmbOoC-32JorNdfyTiRRsR7Ea5eWtvsWzuxo8bjOxCG84dAg/pubhtml https://docs.google.com/spreadsheets/u/1/d/e/2PACX-1vRPiprOa... has some hilarious and creative examples that one would hope never make it into production:
> AI trained to classify skin lesions as potentially cancerous learns that lesions photographed next to a ruler are more likely to be malignant.
> Agent pauses the game indefinitely to avoid losing
> A robotic arm trained to slide a block to a target position on a table achieves the goal by moving the table itself.
> Evolved player makes invalid moves far away in the board, causing opponent players to run out of memory and crash
> Genetic algorithm for image classification evolves timing attack to infer image labels based on hard drive storage location
> Deep learning model to detect pneumonia in chest x-rays works out which x-ray machine was used to take the picture; that, in turn, is predictive of whether the image contains signs of pneumonia, because certain x-ray machines (and hospital sites) are used for sicker patients.
> Creatures bred for speed grow really tall and generate high velocities by falling over
> Neural nets evolved to classify edible and poisonous mushrooms took advantage of the data being presented in alternating order, and didn't actually learn any features of the input images
- fauigerzigerk 7y agoSome of these approaches would probably be called genius or at least very creative had they been found by a human :)
- deleted 7y ago[deleted]
- mlthoughts2018 7y agoYes, some hedge fund would probably be happy to hire these AIs.
- hyperpallium 7y agoAnd the 2007 evolved silicon that should not work https://www.damninteresting.com/on-the-origin-of-circuits/ https://www.damninteresting.com/on-the-origin-of-circuits/ prev disc: https://news.ycombinator.com/item?id=18099226 https://news.ycombinator.com/item?id=18099226
- kuu 7y agoMany of them could be classified as "lateral thinking". Quite interesting
- teekert 7y agoAll signs of true creativity and thinking out of the box ;)
- eatitraw 7y ago> Evolved player makes invalid moves far away in the board, causing opponent players to run out of memory and crash Well, this sounds like speedrunning. People have found arbitrary code execution vulnerabilities in SNES and used them to jump to the credits (which counts as completed the game) in less than a minute: https://www.youtube.com/watch?v=Jf9i7MjViCE https://www.youtube.com/watch?v=Jf9i7MjViCE They also used the same technique to inject runnable code in the game: https://www.youtube.com/watch?v=jnZ2NNYySuE https://www.youtube.com/watch?v=jnZ2NNYySuE
- b_tterc_p 7y agoNah that’s not the same thing. In this case it’s just choosing an option that involves very large numbers because it’s learned that it’s opponent can’t handle large numbers. There’s no code injection
- eatitraw 7y agoThe SMB3 ACE is one of the most technically interesting glitches. The usual skips and saves are much more mundane. My point here is that there is similarity between (some) human players and some AI players. Even the discussion whether exploiting a glitch is actually 'winning' also looks very similar.
- jimbokun 7y agoAren't a lot of these a kind of over fitting the data? The learning agent discovers something that is true of the training set, but does not generalize to other examples of the problem outside of the training set. The article talks about much of the problem being in data set construction, because it is very tricky to design data sets without accidental biases that the learner can use to correctly categorize the examples that have nothing to do with the actual problem you want the learner to solve. The traditional techniques to avoid over fitting, like holding out part of the training data, don't do any good if the entire data set is not representative of the real world in some systematic way.
- fho 7y agoI skimmed through the list and ended up reading the whole article about "World Models": https://worldmodels.github.io/ https://worldmodels.github.io/ Definitely a fascinating read!
- chronolitus 7y agoSeeing this work by Schmidhuber suddenly reminded of this song: https://twitter.com/i/status/1155091710281580548 https://twitter.com/i/status/1155091710281580548 a sort of tongue-in-cheek celebration his famous eagerness when discussing his work. [edit] Just found out there's a second part: https://soundcloud.com/user-430897588-587876313/lstm-song-2 https://soundcloud.com/user-430897588-587876313/lstm-song-2