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This clever AI hid data from its creators to cheat at its appointed task

olooney 8y ago
This claim is unsupported: > The machine, not smart enough to do the actual difficult job of converting these sophisticated image types to each other Obviously it found an easy way to solve the problem it was given: stenography But could it have solved the problem the researchers intended, if they had framed it correctly? There's no evidence either way for this particular algorithm, but in general this is not hard. This is usually called style transfer and I don't see any reason to believe that standard techniques[1] wouldn't be able to solve the street-map-to-aerial-map problem. And it's pretty well established that adding a bit of noise[2] during training helps GANs avoid these kinds of problems. [1]: https://medium.com/tensorflow/neural-style-transfer-creating-art-with-deep-learning-using-tf-keras-and-eager-execution-7d541ac31398 https://medium.com/tensorflow/neural-style-transfer-creating... [2]: https://www.inference.vc/instance-noise-a-trick-for-stabilising-gan-training/ https://www.inference.vc/instance-noise-a-trick-for-stabilis...
- rococode 8y ago
  Right, I think it would be more accurate to say "The machine, smart enough to choose the easier approach for solving the task". It simply found an accurate and easy way to pass the discriminator and stuck with it.
longerthoughts 8y ago
So basically AI abiding by Goodhart's law (https://en.wikipedia.org/wiki/Goodhart%27s_law https://en.wikipedia.org/wiki/Goodhart%27s_law). I wonder how much of this goes undetected in other applications due to poor objective definition.
- olooney 8y ago
  It's incredibly common. For example: https://docs.google.com/spreadsheets/u/1/d/e/2PACX-1vRPiprOaC3HsCf5Tuum8bRfzYUiKLRqJmbOoC-32JorNdfyTiRRsR7Ea5eWtvsWzuxo8bjOxCG84dAg/pubhtml https://docs.google.com/spreadsheets/u/1/d/e/2PACX-1vRPiprOa...
minimaxir 8y ago
Dupe: https://news.ycombinator.com/item?id=18797787 https://news.ycombinator.com/item?id=18797787
tivert 8y ago
This is a neat result and writeup, but the title very misleading clickbait.
- bittermang 8y ago
  Yeah, the article goes on to explain why the premise of the headline is wrong. But it is a fascinating unintended consequence of the algorithm none the less.

This claim is unsupported: > The machine, not smart enough to do the actual difficult job of converting these sophisticated image types to each other Obviously it found an easy way to solve the problem it was given: stenography But could it have solved the problem the researchers intended, if they had framed it correctly? There's no evidence either way for this particular algorithm, but in general this is not hard. This is usually called style transfer and I don't see any reason to believe that standard techniques[1] wouldn't be able to solve the street-map-to-aerial-map problem. And it's pretty well established that adding a bit of noise[2] during training helps GANs avoid these kinds of problems. [1]: https://medium.com/tensorflow/neural-style-transfer-creating-art-with-deep-learning-using-tf-keras-and-eager-execution-7d541ac31398 https://medium.com/tensorflow/neural-style-transfer-creating... [2]: https://www.inference.vc/instance-noise-a-trick-for-stabilising-gan-training/ https://www.inference.vc/instance-noise-a-trick-for-stabilis...

rococode 8y ago
Right, I think it would be more accurate to say "The machine, smart enough to choose the easier approach for solving the task". It simply found an accurate and easy way to pass the discriminator and stuck with it.

longerthoughts 8y ago

So basically AI abiding by Goodhart's law (https://en.wikipedia.org/wiki/Goodhart%27s_law https://en.wikipedia.org/wiki/Goodhart%27s_law). I wonder how much of this goes undetected in other applications due to poor objective definition.

olooney 8y ago
It's incredibly common. For example: https://docs.google.com/spreadsheets/u/1/d/e/2PACX-1vRPiprOaC3HsCf5Tuum8bRfzYUiKLRqJmbOoC-32JorNdfyTiRRsR7Ea5eWtvsWzuxo8bjOxCG84dAg/pubhtml https://docs.google.com/spreadsheets/u/1/d/e/2PACX-1vRPiprOa...

minimaxir 8y ago

Dupe: https://news.ycombinator.com/item?id=18797787 https://news.ycombinator.com/item?id=18797787

tivert 8y ago

This is a neat result and writeup, but the title very misleading clickbait.

bittermang 8y ago
Yeah, the article goes on to explain why the premise of the headline is wrong. But it is a fascinating unintended consequence of the algorithm none the less.