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Comment by jfields513 | Hacker News Reader

Your first statement here is not true.

Humans are excellent at learning from very few or even 1 example. Show a toddler a single image of an elephant and the toddler will generalize perfectly on new examples; show a machine a few thousand images of elephants and it might generalize decently if your machine is really clever.

There are very few tasks where machine systems achieve anything resembling human level performance. But on all such tasks, the machine requires far more data and still underperforms.

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That toddler has already processed lots of visual image data, examples of objects, nonliving and living, animals, mammals, etc. Don't you think that constitutes a large, important dataset for the problem of elephant recognition?

It does but it's unsorted.then post processed.

If you show a child 1000 images or animals. Then show different photographs of animals. And tell the child what animal each animal photograph is, you can now go back to the original 1000 and the explained ones will likely be recognized dispute them never beig initially sorted, or modeled as such.

Going from 5-10 to 1,000,000 is what computers have a problem with. They go from 1,000 to 1,000,000 easily, or even million to billions.

You're describing unsupervised training. It works with computers too, as in this article on using Google Brain to build an unsupervised image classifier. http://www.wired.com/2012/06/google-x-neural-network

Unsupervised training is the deep learning equal of context clues. It sees people talking about cats, sees an image guesses cat. Sees similar images + similar words, eventually builds a cat type prototype.

What I'm talking about is a child can see a photo of an unknown animal, I can show that child a cartoon elephant (which is the original animal). I then ask what the original animal is, the child likely responds correctly.

Reprocessing of already learned data as the scheme of the world changes based on new information.

This is the ability to abstract concepts and then recognize them in different settings (for instance, the idea of a child being a miniature version of a given animal, with less pronounced traits). In order to understand the clues you are talking about, an AI has first to be familiar with the terms used in the discussed topic, so as to be able to construct a definition by itself (what is "miniature", "traits", "pronounced"). These terms' definition must be synthetized somehow before hand, or perhaps as the discussion goes, but then the amount of necessary information in that discussion must be much larger, for the AI to untangle them properly.

Yeah i agree. When the child is shown the labeled example of an elephant and infers the traits that make an elephant an elephant, her previous visual experiences provide background knowledge that restricts the space of hypotheses she considers. After all, there's an infinite set of logically consistent hypotheses.

Nevertheless, if you provide your machine system with the video of all the child's visual input, it still won't generalize well from single examples, the way children do effortlessly.

This reminds me of the saying: It took me ten years to become an overnight success.

Humans generalize well from few examples because, well, they've already processed billions of examples. A toddler may have never seen an elephant before, but it may have seen cars, trucks, birds, dogs, people, trees, skies, buildings etc, giving it concepts for bigness, smallness, aliveness, humanness and much else. With all these concepts in place, then yes it becomes easy to see what makes an elephant distinct from a dog or a person. And it would be too for an artificial neural network.

An interesting fact is that newborns have very few concepts to begin with. It takes some months for them for instance to learn to differentiate between alive and dead things (the family cat vs a teddy bear for instance).

Using unsupervised learning (the same as humans) there are machines that can learn from a picture of a single elephant. You first learn a compressed "representation" of images with fewer dimensions than an entire bitmap. Then you can compare that small vector to others.

I can only speak for my situation, but my daughter saw easily hundreds and maybe thousands of examples of elephants, including real live ones at the zoo, before she could ever seem to use the abstract concept to identify new examples.

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