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Well we don't know that for sure. No one has been able to run backpropagation through the human brain, and find the exact right set of inputs that would fool us

Well we don't know that for sure. No one has been able to run backpropagation through the human brain, and find the exact right set of inputs that would fool us.

deleted 11y ago
[deleted]
eli_gottlieb 11y ago
We know the human brain doesn't learn via backpropagation of errors. Biology can't implement a "global" algorithm of that sort, it needs a local, cell-to-cell learning rule.
- p1esk 11y ago
  No, we don't know that. Take a look at this paper: http://arxiv.org/abs/1411.0247 http://arxiv.org/abs/1411.0247
- Houshalter 11y ago
  There are many theories on how the brain learns, including variations of backpropagation. We don't really know anything. Here's a relatively recent theory by Hinton: https://www.youtube.com/watch?v=kxp7eWZa-2M&feature=youtu.be&t=38m13s https://www.youtube.com/watch?v=kxp7eWZa-2M&feature=youtu.be... Regardless, this has nothing to do with how the brain learns, but rather the function it has learned. Even if a neural network somehow used entirely local learning rules, it could still be exploited with this method.
  - eli_gottlieb 11y ago
    >Regardless, this has nothing to do with how the brain learns, but rather the function it has learned. The learning rule and the hypothesis class together dictate what sort of function is learned. >Even if a neural network somehow used entirely local learning rules, it could still be exploited with this method. Yes, which is why current neural networks may not be the best learning method.
    - Houshalter 11y ago
      This method is basically just fuzzing with a really efficient method to do it quickly. But in theory you could try every possible set of inputs into the human eye, and it's quite possible you would find images like this, where only slight changes to the inputs cause entirely different outputs. But we can't try every possible set of inputs to human eyes. So we don't actually know how fragile human brains are. I suspect that brains use similar tricks to artificial neural networks, and learn similar functions.

Well we don't know that for sure. No one has been able to run backpropagation through the human brain, and find the exact right set of inputs that would fool us.

deleted 11y ago

[deleted]

eli_gottlieb 11y ago

We know the human brain doesn't learn via backpropagation of errors. Biology can't implement a "global" algorithm of that sort, it needs a local, cell-to-cell learning rule.

p1esk 11y ago
No, we don't know that. Take a look at this paper: http://arxiv.org/abs/1411.0247 http://arxiv.org/abs/1411.0247
Houshalter 11y ago
There are many theories on how the brain learns, including variations of backpropagation. We don't really know anything. Here's a relatively recent theory by Hinton: https://www.youtube.com/watch?v=kxp7eWZa-2M&feature=youtu.be&t=38m13s https://www.youtube.com/watch?v=kxp7eWZa-2M&feature=youtu.be... Regardless, this has nothing to do with how the brain learns, but rather the function it has learned. Even if a neural network somehow used entirely local learning rules, it could still be exploited with this method.
- eli_gottlieb 11y ago
  >Regardless, this has nothing to do with how the brain learns, but rather the function it has learned. The learning rule and the hypothesis class together dictate what sort of function is learned. >Even if a neural network somehow used entirely local learning rules, it could still be exploited with this method. Yes, which is why current neural networks may not be the best learning method.
  - Houshalter 11y ago
    This method is basically just fuzzing with a really efficient method to do it quickly. But in theory you could try every possible set of inputs into the human eye, and it's quite possible you would find images like this, where only slight changes to the inputs cause entirely different outputs. But we can't try every possible set of inputs to human eyes. So we don't actually know how fragile human brains are. I suspect that brains use similar tricks to artificial neural networks, and learn similar functions.