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I think this will be pretty obvious to anyone who has meddled with machine learning. Suppose you have a working model of the world (based on your sense of touch

I think this will be pretty obvious to anyone who has meddled with machine learning. Suppose you have a working model of the world (based on your sense of touch) and you now suddenly get more data (sight): {255,254,254,254,255,255,254,253,254,255,254,255,255,255,248,247,255,255,255,254,255,249,228,221,207,204,210,216,247,255,255,236,146,169,184,166,127,88,231,255,255,241,154,155,155,106,96,105,236,255,255,243,156,159,160,113,105,100,238,255,255,247,170,157,164,104,55,103,243,255,254,255,244,214,179,120,182,237,255,254,255,255,255,255,246,244,255,255,255,255,255,255,254,254,255,255,253,254,255,255} Do you see the cube? I'd think not.

Sharlin 9y ago
This. Newly-sighted people have no idea whatsoever what to make of the torrent of confusing sensory data they're suddently receiving. Their brains simply haven't grown the really complicated pattern-recognition machinery required to reason about perspective projection, lighting and shading, shadows, colors and textures, and everything else people sighted from birth take for granted.
- heavenlyblue 9y ago
  Highly contextually dependent: most of the early-detection neurons are exactly what you describe: edge-detectors, color similarity detectors, etc. They become a thing right from the birth of a child and would never develop if the child could never see at all (they have a very rigid structure, so the random development is out of the equation). So you’re clearly touching upon the definition of what is it to “have the vision” and what is it to “obtain the vision” as it’s not binary anymore: there are many levels to “vision”, most of which overlap as well.
- deleted 9y ago
  [deleted]
__s 9y ago
More curious would be to show people a set of cube/sphere datasets & ask them to categorize
wcoenen 9y ago
Indeed. I had a light-bulb moment about this when thinking about how a 2D image is unrolled into a one-dimensional vector before processing by an artificial neural network. Before training, the NN doesn't even have a concept of certain pixels being related because of proximity. It needs to learn to see the 2D structure in the data from scratch, starting with edge detection. And this also means that the order in which the pixels are presented to the NN doesn't matter in the first place. You could pick a completely random (but fixed) permutation and the NN would still learn to "see" in that mess.
- llukas 9y ago
  NN has concept of 2D image built in - conv layer for example: http://cs231n.github.io/convolutional-networks/ http://cs231n.github.io/convolutional-networks/
  - wcoenen 9y ago
    Thanks, I don't think CNNs were mentioned in Andrew Ng's machine learning course. That's about the only background I have in machine learning. The NN examples of image recognition in that course did start from scratch without any architectural features designed for images.
Dylan16807 9y ago
You're solving the wrong problem. That's not being able to see. You need to have vision working enough to see that there are two objects before you can ask the question. It's a problem of distinguishing them, and it's not unreasonable to logic that out based on the sharpness of the transitions.

Sharlin 9y ago

This. Newly-sighted people have no idea whatsoever what to make of the torrent of confusing sensory data they're suddently receiving. Their brains simply haven't grown the really complicated pattern-recognition machinery required to reason about perspective projection, lighting and shading, shadows, colors and textures, and everything else people sighted from birth take for granted.

heavenlyblue 9y ago
Highly contextually dependent: most of the early-detection neurons are exactly what you describe: edge-detectors, color similarity detectors, etc. They become a thing right from the birth of a child and would never develop if the child could never see at all (they have a very rigid structure, so the random development is out of the equation). So you’re clearly touching upon the definition of what is it to “have the vision” and what is it to “obtain the vision” as it’s not binary anymore: there are many levels to “vision”, most of which overlap as well.
deleted 9y ago
[deleted]

__s 9y ago

More curious would be to show people a set of cube/sphere datasets & ask them to categorize

wcoenen 9y ago

Indeed. I had a light-bulb moment about this when thinking about how a 2D image is unrolled into a one-dimensional vector before processing by an artificial neural network. Before training, the NN doesn't even have a concept of certain pixels being related because of proximity. It needs to learn to see the 2D structure in the data from scratch, starting with edge detection. And this also means that the order in which the pixels are presented to the NN doesn't matter in the first place. You could pick a completely random (but fixed) permutation and the NN would still learn to "see" in that mess.

llukas 9y ago
NN has concept of 2D image built in - conv layer for example: http://cs231n.github.io/convolutional-networks/ http://cs231n.github.io/convolutional-networks/
- wcoenen 9y ago
  Thanks, I don't think CNNs were mentioned in Andrew Ng's machine learning course. That's about the only background I have in machine learning. The NN examples of image recognition in that course did start from scratch without any architectural features designed for images.

Dylan16807 9y ago

You're solving the wrong problem. That's not being able to see. You need to have vision working enough to see that there are two objects before you can ask the question. It's a problem of distinguishing them, and it's not unreasonable to logic that out based on the sharpness of the transitions.