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If by knowing the structure of a NN a "trick" image can be specially crafted to fool it, what is the equivalent for a human brain? If we get to the point of ha

by tbabb 9y ago

If by knowing the structure of a NN a "trick" image can be specially crafted to fool it, what is the equivalent for a human brain? If we get to the point of having a human connectome to analyze-- or the kind of access to neural topology that a neural lace would provide-- could an optimizer generate an image of static that human would mistake for the president of the United States? It seems outwardly implausible that such an image could exist, but perhaps that is only because we've never seen one (or if we had, would we know?), and a "blind" search of images would never find it, as the space of images is galactically huge. With a "map" of the brain it might suddenly become plausible. And if so, that world sounds absolutely terrifying to me.

halflings 9y ago
The human brain is incredibly more complex than a "simple" (artificial) neural network, so there might very well be no equivalent for a real brain... but I think optical illusions would be a good approximation, even if less severe: tricking the brain in elaborate ways to see something that is different from what it would usually see.
- govg 9y ago
  The brain has some inherent biases and prejudices - these arise from just how we grow up and perceive objects. So optical illusions are a deviation from whatever visuals we are used to, and you can say similar events occur with stuff like tasting an unknown object, or finding a different smell.
- TeMPOraL 9y ago
  I'd look at the superset containing both optical illusions and the the various funny brain exploits lkie rerarnagnig ltetres in wrods, or using more than one "the" in a sentence without reader noticing it. I don't know what's the the name of that superset, but I don't think we should limit study of our visual processing stack to object recognition - the brain does so much more!
Peaker 9y ago
I'd expect redundancies in analysis in the brain's NN make this impossible.
IanCal 9y ago
We have optical illusions, which can make you answer a lot of questions wrong like Is this a face? Are these lines straight? Is this stationary? What colour is this? These even work across vast numbers of people. With full knowledge of a person's brain and the connections, we would surely at least be able to enormously improve on this. What other things might be possible? Could we make people move in a certain way, do or say certain things? There's no fundamental reason we'd be limited to affecting the visual processing sections of the brain. And now I need to read snow crash again.
- Nition 9y ago
  > What other things might be possible? Could we make people move in a certain way, do or say certain things? At least to some extent it's possible, with hypnotism.
  - StavrosK 9y ago
    Does anyone have conclusive evidence that it works? My perception is that it's between lies and pseudoscience.
    - anchpop 9y ago
      Hypnosis is essentially placebo. I can elaborate on this if you're interested
      - StavrosK 9y ago
        I am, please do.
- Lorkki 9y ago
  They also tend to implicitly make viewers aware that their sensory apparatus is doing something strange, because people aren't structured to only answer those single questions.
  - 9y ago
Jabbles 9y ago
Does your brain confuse this squiggly line for a dog? http://www.pablopicasso.net/dog/ http://www.pablopicasso.net/dog/ It doesn't look anything like a dog!
- EliRivers 9y ago
  That's a good question; I can look at that and say it's not a dog, but it is a deliberate representation of a dog. It doesn't confuse me into thinking it is a dog. Do the NNs differentiate between an actual object and a representation of an object? Given that their input is (I understand) just a 2D image, I could imagine the question itself makes no sense in the context of a NN. To an NN, a picture of a dog is a dog.
  - IshKebab 9y ago
    Nope you could easily train a CNN to distinguish photos of real dogs from drawings of dogs.
    - EliRivers 9y ago
      How about a real dog and a photo of a real dog? Having been taught the difference between a photo of a dog and a drawing of a dog, would it then be able to differentiate between a photo of any object and a drawing of that object, or do we need to teach it the difference again for every different object there is? If I teach it to identify a simple two colour single-line drawing of a dog, like that Picasso picture, will it then be able to handle surrealist drawings of dogs, and impressionist, and cubist, and a picture of a sculpture, and watercolours and charcoal and all the other varieties of form and style, or do I need to teach it separately for everything? Don't forget slide puzzles! I can tell this is a dog - https://lh3.googleusercontent.com/oAtmNcl25MPQOZ5Occ_fr7_BKrxjQ_5uyZ13WKupm3MtZaXne-BLqlIQ8ZXk7n7augM=h900 https://lh3.googleusercontent.com/oAtmNcl25MPQOZ5Occ_fr7_BKr... These of course are hypothetical questions; I suspect the answer is that there is going to be an awful lot of teaching, with a few pleasant surprises when it gets one style of artwork from having seen enough of other styles. Except the first one; to the NN, a real dog is a picture of a dog - with no concept of real object behind the picture, the NN's universe is pictures and it will only ever be a simple machine for identifying things in a very very narrow universe. Nothing I'm saying here is news to anyone, of course, but sometimes it seems like these NNs are portrayed as general identifiers, when they're actually very narrow.
MrQuincle 9y ago
Holding it upside down would already be sufficient.
YeGoblynQueenne 9y ago
>> If we get to the point of having a human connectome to analyze-- or the kind of access to neural topology that a neural lace would provide-- could an optimizer generate an image of static that human would mistake for the president of the United States? Probably not, because our brain doesn't seem to work like Artificial Neural Networks do. Most notably, we learn to identify novel objects after only seeing a single example of them while ANNs may require many thousands of examples. We don't seem to learn to identify individual pixels, either (though what exactly our brain does when we learn to identify objects from their images is anyone's guess). Digital images are also not a very good analogy for what human eyes see: our vision doesn't have "pixels" and we don't even need images to be particularly clear to identify them with good accuracy (we can still tell what things are up to a point, even in the dark, when it rains, when our visual field is occluded etc). Generally, you can't expect the human brain to work like an ANN. Like others have said before [1], the "neural network" analogy is not a very good one. It often serves only to create confusion about the capabilities of ANNs and the human brain. ________________ [1] https://spectrum.ieee.org/automaton/robotics/artificial-intelligence/facebook-ai-director-yann-lecun-on-deep-learning https://spectrum.ieee.org/automaton/robotics/artificial-inte... Yann LeCun: My least favorite description is, “It works just like the brain.” I don’t like people saying this because, while Deep Learning gets an inspiration from biology, it’s very, very far from what the brain actually does. And describing it like the brain gives a bit of the aura of magic to it, which is dangerous. It leads to hype; people claim things that are not true. AI has gone through a number of AI winters because people claimed things they couldn’t deliver.
- amelius 9y ago
  Yes. Convolutional networks is probably not how the brain does it. I guess instead it takes a brute force approach, where more parameters need to be trained.
- xapata 9y ago
  Further, humans also use non-visual information when interpreting sights.
- Houshalter 9y ago
  I think the differences between ANNs and BNNs are exaggerated. I think they probably work on similar principles. Even if there some differences. But none of that is particularly relevant. Even if they are completely different, so what? The same procedure could still work. Take a biological brain and backprop through it to find exactly what inputs change the outputs by some small degree and tweak it bit by bit until you change the output. You can apply this to any function, it's general. Adversarial examples are exceedingly rare in natural data. They require tweaking exactly the right pixels in exactly the right direction. It's something stupid like a one in a billion billion chance of such a malicious example occurring by chance if you just randomly add noise to images. It requires a very precise optimization procedure. So if adversarial examples did exist for human vision, we probably wouldn't know it yet. They don't occur in nature. So there's no reason for the brain to have evolved defenses against them. (Though camouflage is an interesting natural analogy, it's not quite the same.)
dvanduzer 9y ago
As an avid reader of Neal Stephenson, I was surprised not to find the phrase "Magic Eye" in this thread yet. There are many known ways to create optic illusions that trick humans this quickly and thoroughly. Efficiency is the real question if you are concerned about deception and human brains.
coldsmoke 9y ago
This optical illusion [1] is the closest thing I've come across. It relies on the fact that there are apparently separate parts of the visual processing in the brain for fine details and for more coarse features. This illusion consists of a picture of the fine details of Albert Einsteins face, so when you are close enough to see the details, you see Albert. But there is also a blurry image of Marilyn Monroe, which isn't really possible to make out until you view the image from farther away, or squint your eyes, so that the finer details are lost. [1] http://www.123opticalillusions.com/pages/albert-einstein-marilyn-monroe.php http://www.123opticalillusions.com/pages/albert-einstein-mar...
- Sharlin 9y ago
  There are different processing stages, actually not unlike the layers in a DNN. Edge detection starts already in the retina.
coldsmoke 9y ago
Actually, come to think of it, every image is created to trick the brain's image processing. When viewing real scenery the eyes get hit by photons of all sorts of colors, but since there are only three different color receptors (with overlap) we can trick the brain into thinking an image with only red, green, and blue in it is the same. But for example some birds that can see ultra violet will probably not recognize an image of a flower since it lacks that spectrum.
Houshalter 9y ago
Relevant: http://ansible.uk/writing/c-b-faq.html http://ansible.uk/writing/c-b-faq.html

by tbabb 9y ago

halflings 9y ago

The human brain is incredibly more complex than a "simple" (artificial) neural network, so there might very well be no equivalent for a real brain... but I think optical illusions would be a good approximation, even if less severe: tricking the brain in elaborate ways to see something that is different from what it would usually see.

govg 9y ago
The brain has some inherent biases and prejudices - these arise from just how we grow up and perceive objects. So optical illusions are a deviation from whatever visuals we are used to, and you can say similar events occur with stuff like tasting an unknown object, or finding a different smell.
TeMPOraL 9y ago
I'd look at the superset containing both optical illusions and the the various funny brain exploits lkie rerarnagnig ltetres in wrods, or using more than one "the" in a sentence without reader noticing it. I don't know what's the the name of that superset, but I don't think we should limit study of our visual processing stack to object recognition - the brain does so much more!

Peaker 9y ago

I'd expect redundancies in analysis in the brain's NN make this impossible.

IanCal 9y ago

We have optical illusions, which can make you answer a lot of questions wrong like Is this a face? Are these lines straight? Is this stationary? What colour is this? These even work across vast numbers of people. With full knowledge of a person's brain and the connections, we would surely at least be able to enormously improve on this. What other things might be possible? Could we make people move in a certain way, do or say certain things? There's no fundamental reason we'd be limited to affecting the visual processing sections of the brain. And now I need to read snow crash again.

Nition 9y ago
> What other things might be possible? Could we make people move in a certain way, do or say certain things? At least to some extent it's possible, with hypnotism.
- StavrosK 9y ago
  Does anyone have conclusive evidence that it works? My perception is that it's between lies and pseudoscience.
  - anchpop 9y ago
    Hypnosis is essentially placebo. I can elaborate on this if you're interested
    - StavrosK 9y ago
      I am, please do.
Lorkki 9y ago
They also tend to implicitly make viewers aware that their sensory apparatus is doing something strange, because people aren't structured to only answer those single questions.
- 9y ago

Jabbles 9y ago

Does your brain confuse this squiggly line for a dog? http://www.pablopicasso.net/dog/ http://www.pablopicasso.net/dog/ It doesn't look anything like a dog!

EliRivers 9y ago
That's a good question; I can look at that and say it's not a dog, but it is a deliberate representation of a dog. It doesn't confuse me into thinking it is a dog. Do the NNs differentiate between an actual object and a representation of an object? Given that their input is (I understand) just a 2D image, I could imagine the question itself makes no sense in the context of a NN. To an NN, a picture of a dog is a dog.
- IshKebab 9y ago
  Nope you could easily train a CNN to distinguish photos of real dogs from drawings of dogs.
  - EliRivers 9y ago
    How about a real dog and a photo of a real dog? Having been taught the difference between a photo of a dog and a drawing of a dog, would it then be able to differentiate between a photo of any object and a drawing of that object, or do we need to teach it the difference again for every different object there is? If I teach it to identify a simple two colour single-line drawing of a dog, like that Picasso picture, will it then be able to handle surrealist drawings of dogs, and impressionist, and cubist, and a picture of a sculpture, and watercolours and charcoal and all the other varieties of form and style, or do I need to teach it separately for everything? Don't forget slide puzzles! I can tell this is a dog - https://lh3.googleusercontent.com/oAtmNcl25MPQOZ5Occ_fr7_BKrxjQ_5uyZ13WKupm3MtZaXne-BLqlIQ8ZXk7n7augM=h900 https://lh3.googleusercontent.com/oAtmNcl25MPQOZ5Occ_fr7_BKr... These of course are hypothetical questions; I suspect the answer is that there is going to be an awful lot of teaching, with a few pleasant surprises when it gets one style of artwork from having seen enough of other styles. Except the first one; to the NN, a real dog is a picture of a dog - with no concept of real object behind the picture, the NN's universe is pictures and it will only ever be a simple machine for identifying things in a very very narrow universe. Nothing I'm saying here is news to anyone, of course, but sometimes it seems like these NNs are portrayed as general identifiers, when they're actually very narrow.

MrQuincle 9y ago

Holding it upside down would already be sufficient.

YeGoblynQueenne 9y ago

>> If we get to the point of having a human connectome to analyze-- or the kind of access to neural topology that a neural lace would provide-- could an optimizer generate an image of static that human would mistake for the president of the United States? Probably not, because our brain doesn't seem to work like Artificial Neural Networks do. Most notably, we learn to identify novel objects after only seeing a single example of them while ANNs may require many thousands of examples. We don't seem to learn to identify individual pixels, either (though what exactly our brain does when we learn to identify objects from their images is anyone's guess). Digital images are also not a very good analogy for what human eyes see: our vision doesn't have "pixels" and we don't even need images to be particularly clear to identify them with good accuracy (we can still tell what things are up to a point, even in the dark, when it rains, when our visual field is occluded etc). Generally, you can't expect the human brain to work like an ANN. Like others have said before [1], the "neural network" analogy is not a very good one. It often serves only to create confusion about the capabilities of ANNs and the human brain. ________________ [1] https://spectrum.ieee.org/automaton/robotics/artificial-intelligence/facebook-ai-director-yann-lecun-on-deep-learning https://spectrum.ieee.org/automaton/robotics/artificial-inte... Yann LeCun: My least favorite description is, “It works just like the brain.” I don’t like people saying this because, while Deep Learning gets an inspiration from biology, it’s very, very far from what the brain actually does. And describing it like the brain gives a bit of the aura of magic to it, which is dangerous. It leads to hype; people claim things that are not true. AI has gone through a number of AI winters because people claimed things they couldn’t deliver.

amelius 9y ago
Yes. Convolutional networks is probably not how the brain does it. I guess instead it takes a brute force approach, where more parameters need to be trained.
xapata 9y ago
Further, humans also use non-visual information when interpreting sights.
Houshalter 9y ago
I think the differences between ANNs and BNNs are exaggerated. I think they probably work on similar principles. Even if there some differences. But none of that is particularly relevant. Even if they are completely different, so what? The same procedure could still work. Take a biological brain and backprop through it to find exactly what inputs change the outputs by some small degree and tweak it bit by bit until you change the output. You can apply this to any function, it's general. Adversarial examples are exceedingly rare in natural data. They require tweaking exactly the right pixels in exactly the right direction. It's something stupid like a one in a billion billion chance of such a malicious example occurring by chance if you just randomly add noise to images. It requires a very precise optimization procedure. So if adversarial examples did exist for human vision, we probably wouldn't know it yet. They don't occur in nature. So there's no reason for the brain to have evolved defenses against them. (Though camouflage is an interesting natural analogy, it's not quite the same.)

dvanduzer 9y ago

As an avid reader of Neal Stephenson, I was surprised not to find the phrase "Magic Eye" in this thread yet. There are many known ways to create optic illusions that trick humans this quickly and thoroughly. Efficiency is the real question if you are concerned about deception and human brains.

coldsmoke 9y ago

This optical illusion [1] is the closest thing I've come across. It relies on the fact that there are apparently separate parts of the visual processing in the brain for fine details and for more coarse features. This illusion consists of a picture of the fine details of Albert Einsteins face, so when you are close enough to see the details, you see Albert. But there is also a blurry image of Marilyn Monroe, which isn't really possible to make out until you view the image from farther away, or squint your eyes, so that the finer details are lost. [1] http://www.123opticalillusions.com/pages/albert-einstein-marilyn-monroe.php http://www.123opticalillusions.com/pages/albert-einstein-mar...

Sharlin 9y ago
There are different processing stages, actually not unlike the layers in a DNN. Edge detection starts already in the retina.

coldsmoke 9y ago

Actually, come to think of it, every image is created to trick the brain's image processing. When viewing real scenery the eyes get hit by photons of all sorts of colors, but since there are only three different color receptors (with overlap) we can trick the brain into thinking an image with only red, green, and blue in it is the same. But for example some birds that can see ultra violet will probably not recognize an image of a flower since it lacks that spectrum.

Houshalter 9y ago

Relevant: http://ansible.uk/writing/c-b-faq.html http://ansible.uk/writing/c-b-faq.html