Human perception involves considerable amounts of abstraction and symbolic reasoning -- unlike the input-output matching performed by machine "perception" models.pic.twitter.com/REdksXpB1z
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A state-of-the-art convnet trained on millions of images and videos of prehistoric animals in the wild could not recognize an auroch in this picture. But you can. In fact, you can, *even though you have never seen one*.
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True. But humans can describe this as "a bull with a beard" and a net could only classify it as a bull without additional features.
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This is one reason that some of us have been saying for many years that deep learning has nothing to do with AGI. In fact it's a distraction. The brain doesn't model the world. It learns how to sense it. The world is its own model. We can sense objects we have never seen before.
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The brain needs to model future events because reality is a dangerous place and we can't afford to reload from a save point.
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"Aurochs" is singular.
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Faceid can recognize a person after capturing ~1 min of their video
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1) That's a non-standard usage of "recognize". You can't recognize something you've ever seen. I can recognize that there's a mammal there, but not that there's an auroch. 2) I think you're mistaken about human recognition of abstract/symbolic imagery on 1st exposure.
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The opposite direction will make things clearer. Trained on nothing but natural images, a GAN could never crisply produce such an image or the stick figure drawing of a toddler. That stick figure represents generalization and reified abstracting away.
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That's in fact inaccurate/natural language. There's no auroch in that picture, it's an schematic drawing of an auroch. Several problems there, including AI narrowness.
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This would have been even better if aurochs were not an animal. Consider this interaction replacing auroch with brapmoth. I would have trusted your tweet and reply equivalently.
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