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None of these techniques are good in the context of human-style learning, where a child (or an adult) can learning how to do something new from only a few cases

None of these techniques are good in the context of human-style learning, where a child (or an adult) can learning how to do something new from only a few cases of trial and error, whereas a machine using any modern technique requires an immense number of examples to see first (often millions) before it can do the equivalent task but with less accuracy. NN and others are totally awesome, but we are so far away from true learning that is normally meant in the human context.

selectron 10y ago
People can't learn things which are truly new from only a few cases of trial and error either. For example, it takes a long time to learn a foreign language or become an expert at chess. Humans mainly learn things better because our training data is usually larger than that of the model for the task at hand.
- hellofunk 10y ago
  Suppose you want a computer vision model to start recognizing a particular object (and its subtle variations) in photos, but it has never seen this object before. How many of these objects must the model be trained on before it can generalize and recognize the object again? Now show a child some bizarre object just once, and see if it can find the object in any photos, even if the object is at different angles or subtly varied. The child will certainly do better, off just one training example.
sukilot 10y ago
Humans have a semi-hardwired architecture that evolved for classic human needs but is less general, less powerful , and less useful than artificial neural nets.
- hellofunk 10y ago
  >less general, less powerful , and less useful than artificial neural nets yet still much more capable in most cases. Handwriting recognition, for example. How many examples must a NN see to start accurately arriving at valid outputs for new handwritten cases? Now, how many examples must a human see to learn a particular letter before she starts recognizing it with 100% accuracy?

selectron 10y ago

People can't learn things which are truly new from only a few cases of trial and error either. For example, it takes a long time to learn a foreign language or become an expert at chess. Humans mainly learn things better because our training data is usually larger than that of the model for the task at hand.

hellofunk 10y ago
Suppose you want a computer vision model to start recognizing a particular object (and its subtle variations) in photos, but it has never seen this object before. How many of these objects must the model be trained on before it can generalize and recognize the object again? Now show a child some bizarre object just once, and see if it can find the object in any photos, even if the object is at different angles or subtly varied. The child will certainly do better, off just one training example.

sukilot 10y ago

Humans have a semi-hardwired architecture that evolved for classic human needs but is less general, less powerful , and less useful than artificial neural nets.

hellofunk 10y ago
>less general, less powerful , and less useful than artificial neural nets yet still much more capable in most cases. Handwriting recognition, for example. How many examples must a NN see to start accurately arriving at valid outputs for new handwritten cases? Now, how many examples must a human see to learn a particular letter before she starts recognizing it with 100% accuracy?