The Limitations of Deep Learning

Re:The Limitations of Deep Learning by geofft on Monday July 17, 2017 @04:02PM (#14791861)

That's about as interesting as saying that a Taylor series can approximate any analytic function arbitrarily well given time and space. Or that a lookup table can approximate any function arbitrarily well given time and space: see also the Chinese room example.

The first question is whether that neural network is learnable. Sure, some configuration of neurons may exist. Is it possible given enough time and space to discover what that configuration is, given a set of inputs and outputs?

The second question is whether "enough time and space" means "beyond the lifetime and resources of anyone alive," in which case it seems perfectly reasonable to me to call it a limitation. I generally want my software to work within my lifetime.

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Re:The Limitations of Deep Learning by latently on Monday July 17, 2017 @06:46PM (#14792897)

I like your comment. The real question is whether they are conscious.

The analogy between deep neural networks and the brain has proven to be very fruitful. Other analogies may as well. See our upcoming paper for more info.

https://grey.colorado.edu/mediawiki/sites/mingus/images/3/3a...

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Re:The Limitations of Deep Learning by nojvek on Tuesday July 18, 2017 @03:30PM (#14799355)

I think a lot of people end up mixing being alive with being conscious. Is a tree conscious? Is a self driving car conscious?

If we use the definition "Aware of its surroundings, responding and acting towards a certain goal" then a lot of things fit that definition.

When an AI plays the atari games, learns from it and plays at a human level, I would call it conscious. It's not a human level conscious agent but conscious nonetheless.

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Re:The Limitations of Deep Learning by latently on Wednesday July 19, 2017 @11:08AM (#14804828)

Consciousness has a specific meaning - https://en.wikipedia.org/wiki/Qualia

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