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Ah, this is why AlphaGo won't release their source code or models. If all ML algorithms can be fooled so trivially, this shows the human mind is not an ML algo
by yters 9y ago
Ah, this is why AlphaGo won't release their source code or models.
If all ML algorithms can be fooled so trivially, this shows the human mind is not an ML algorithm.
- j2kun 9y agoTake care, the original authors were being overly broad when they implied "all ML algorithms" can be fooled in this way. Not that I disagree with your conclusion. Just the hypothesis.
- yters 9y agoAll ML algorithms are either inconsistent or incomplete, which we know a priori. Insofar as ML tries to be complete, it will be inconsistent, as we see with adversarial examples.
- maltalex 9y ago> this shows the human mind is not an ML algorithm. not necessarily. Humans have a lot more knowledge about contextual information that today's ML often misses. For every "image" you classify you use a LOT of side data. That side data has also been acquired through learning. Even if your actually saw a 1,000,000$ check or a 200 km/h speed limit, you'd know it's probably nonsense. Based on your life experience so far, you know that there's no way that mom just gave you a 1,000,000$ check, or that the city decided to turn that small residential street into a racing track. An image classification ML algorithm doesn't know any of that.
- no_flags 9y agoWhat about the Sylvester Stallone/Keanu Reeves example? It was just a zoomed in head shot. That seems like an example where humans do a lot better without help from any additional context.
- munificent 9y agoI think your logic is: 1. All ML algorithms can be fooled trivially. 2. The human mind cannot be fooled trivially. 3. Therefore, the human mind is not an ML algorithm. But claim number 2 is clearly wrong. Human minds are trivially fooled. Here's one: http://www.jimonlight.com/wp-content/uploads/2012/02/Parallel-Lines.gif http://www.jimonlight.com/wp-content/uploads/2012/02/Paralle... This is exactly what an optical illusion is.
- schoen 9y agoYes, when I read "Humans have no problem correctly interpreting adversarial examples, so there must be a way to do it automatically" in the article I thought "aren't optical illusions adversarial examples for human perception?" and also "maybe we just haven't found the other kinds of adversarial examples that work against human perception because we can't show humans sufficiently large numbers of examples and measure the humans' responses accurately enough". But the optical illusion example seems very strong because if you say that human perception includes abilities to classify things, there are many examples just like your link that show cases where the classification routinely goes wrong. It seems possible to me that there are as-yet undiscovered adversarial examples in human perception that we simply don't have any feasible way to search for, and may never be able to construct practical examples of. (It would probably be extremely unnerving to experience one in real life.)
- yters 9y agoOptical illusions are reverse adversarial examples to those who think the human mind is a deep neural network.
- danharaj 9y agoCool illusion; I figured it out and so will most humans. I don't think the claim that these sorts of illusions are of the same nature as, say, single pixel attacks against neural networks is justified. On the other hand, it is also true that it's not clear whether such attacks on our current crop of primitive neural networks will work on more advanced autonomous systems that regulate themselves.