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> To my understanding, it's because deep learning is fundamentally aimed at pattern-recognition, but makes no attempt to have a model of "reality" underlying th

> To my understanding, it's because deep learning is fundamentally aimed at pattern-recognition, but makes no attempt to have a model of "reality" underlying that recognition. Isn't that just the issue that you're essentially making a crude analog of the first layers of the visual cortex and then overtrain them to only recognize specific patterns while ignoring the rest of reality because you simply don't have the higher-order facilities in current networks? In other words doesn't the critique boil down that ANNs are orders of magnitude simpler than the human brain and thus can only fulfill far more narrow task then many combined abstraction layers? New techniques are getting developed only as hardware advances in capability to actually run more complex networks.

jacobush 9y ago
In other words, current computers are hopelessly slow
- true_religion 9y ago
  Well, neural response in biological creatures is very slow---far slower than the clock cycles of an electrical computer. Yet somehow, biological animals seem to demonstrate more intelligence and adaptability than machines. Perhaps, the issue is more with the machine architecture, than the speed.
  - jacobush 9y ago
    Well not signal slow maybe, but we have MANY more signal pathways and more storage than any computer to date.
erikpukinskis 9y ago
> doesn't the critique boil down that ANNs are orders of magnitude simpler than the human brain No, it's not a difference of scale, it's a fundamentally different learning algorithm. Human brain trains interactively. From birth you being experiments controlling the environment around you. Your ability to perceive the world and your ability to control it are one in the same. You can't understand the world without participating in it. And if you are participating in it, you're stuck on the same slow timeline as every other animal. No more "train on 1 million hours of data in 30 seconds". You poke a quantum field, you wait for your bit of information back. There's no possible speedup because you're up against physics. Want to integrate the experiences of 1000 remote AIs? Fine, that'll be 1000 quanta please. In order, one at a time. And you're going to have to relearn all of the trillions of models our genes already learned over billions of years before you even catch up to a human baby.

jacobush 9y ago

In other words, current computers are hopelessly slow

true_religion 9y ago
Well, neural response in biological creatures is very slow---far slower than the clock cycles of an electrical computer. Yet somehow, biological animals seem to demonstrate more intelligence and adaptability than machines. Perhaps, the issue is more with the machine architecture, than the speed.
- jacobush 9y ago
  Well not signal slow maybe, but we have MANY more signal pathways and more storage than any computer to date.

erikpukinskis 9y ago

> doesn't the critique boil down that ANNs are orders of magnitude simpler than the human brain No, it's not a difference of scale, it's a fundamentally different learning algorithm. Human brain trains interactively. From birth you being experiments controlling the environment around you. Your ability to perceive the world and your ability to control it are one in the same. You can't understand the world without participating in it. And if you are participating in it, you're stuck on the same slow timeline as every other animal. No more "train on 1 million hours of data in 30 seconds". You poke a quantum field, you wait for your bit of information back. There's no possible speedup because you're up against physics. Want to integrate the experiences of 1000 remote AIs? Fine, that'll be 1000 quanta please. In order, one at a time. And you're going to have to relearn all of the trillions of models our genes already learned over billions of years before you even catch up to a human baby.