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Traditionally, with computer generated stuff, you could clearly see the math in the algorithms (the sine waves and fractals and whatnot). With AI generated stuf

by nothis 8y ago

Traditionally, with computer generated stuff, you could clearly see the math in the algorithms (the sine waves and fractals and whatnot). With AI generated stuff it looks... natural. Like someone actually drew this for abstract art class. It's entirely unpredictable and unexplainable, except maybe for very vaguely applying the key words. It's a computer no longer letting you see how he thinks.

TuringTest 8y ago
I think it actually shows this kind of image recognition AI doesn't actually think, i.e. it hasn't developed high-level concepts of the things in the pictures it's trained on. When these neural networks get an input image and spit out labels like "bird" or "car", they haven't actually recognized which parts of the image are a car, nor what pieces it's made of. Instead they have memorized some textures and simple shapes which go with the label. It provides the kind of knee-jerk reaction that allows your brain to make you jump when a large object approaches fast, or think there's a tiger hidden in the dirty laundry when you turn around in a dark room. That's why, when you reverse the process, it doesn't create meaningful images, but clumps of relatively common textures found in the training set. It lacks the hierarchy of concepts that allows you to identify objects and distinguish them from the background, which a baby learns in their first two years.
- fudoo 8y ago
  It's not so much that. It's because their training data is limited, the NNs haven't learned the constraints that typify birds or cars. They can recognise the features, and apply labels, but there has been no need for them to classify a car vs. the garbage you get out by generating images. So all these garbage outputs would be classified as cars, because it's happening in the space that the NN doesn't really have information about.

by nothis 8y ago

TuringTest 8y ago

I think it actually shows this kind of image recognition AI doesn't actually think, i.e. it hasn't developed high-level concepts of the things in the pictures it's trained on. When these neural networks get an input image and spit out labels like "bird" or "car", they haven't actually recognized which parts of the image are a car, nor what pieces it's made of. Instead they have memorized some textures and simple shapes which go with the label. It provides the kind of knee-jerk reaction that allows your brain to make you jump when a large object approaches fast, or think there's a tiger hidden in the dirty laundry when you turn around in a dark room. That's why, when you reverse the process, it doesn't create meaningful images, but clumps of relatively common textures found in the training set. It lacks the hierarchy of concepts that allows you to identify objects and distinguish them from the background, which a baby learns in their first two years.

fudoo 8y ago
It's not so much that. It's because their training data is limited, the NNs haven't learned the constraints that typify birds or cars. They can recognise the features, and apply labels, but there has been no need for them to classify a car vs. the garbage you get out by generating images. So all these garbage outputs would be classified as cars, because it's happening in the space that the NN doesn't really have information about.