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> 1) sounds like exactly what deep learning is...map more complex abstractions in each succeeding layer Only at such a high level of abstraction as to be meani

> 1) sounds like exactly what deep learning is...map more complex abstractions in each succeeding layer Only at such a high level of abstraction as to be meaningless. > 2) are computers that can understand speech, recognize faces, drive cars, beat humans at Jeopardy really 'nothing at all like human intelligence?' They are not. Hundreds of man years worth of engineering time go into each of those systems, and none of those systems generalizes to anything other than the task it was created for. That's nothing like human intelligence.

jimfleming 10y ago
> Only at such a high level of abstraction as to be meaningless. I'm not sure what this means or how the abstractions are meaningless? From Gabor filters to concepts like "dog", the abstractions are quite meaningful (in that they function well), even if not to us. > They are not. Hundreds of man years worth of engineering time go into each of those systems, and none of those systems generalizes to anything other than the task it was created for. That's nothing like human intelligence. This isn't strictly true if we look at the ability to generalize as a sliding scale. The level of generalization has actually increased significantly from expert systems to machine learning to deep learning. We have not reached human levels of generalization but we are approaching. Consider that DL can identify objects, people, animals in unique photos never seen before and that more generally the success of modern machine learning is it's ability generalize from training to test time rather than hand engineering for each new case. Newer work is even able to learn from just a few examples[0] and then generalize beyond that. Or the Atari work from DeepMind that can generalize to dozens/hundreds of games. None of those networks are created specifically for Break Out or Pong. It's also not entirely fair to discount the hundreds of years of engineering considering most of these systems are trained from scratch (randomness). Humans, however, benefit from the preceding evolution which has a time scale that far exceeds any human engineering effort. :) [0] https://arxiv.org/abs/1605.06065 https://arxiv.org/abs/1605.06065
bobdole1234 10y ago
We spend a lifetime building the skills that we use in our day to day lives. And most of them don't transfer.
eva1984 10y ago
It beats human with accuracy. Which makes it more practical. Computer doesn't need to be strong AI to replace human.
armitron 10y ago
This is a very superficial point-of-view. What matters here is the concept itself (deep learning as a generic technique) but also scalability. Not the specifics that we have today, but the specifics that we will have 20 years from now. The concept is proven, all that matters now is time...
- daveguy 10y ago
  > The concept is proven, all that matters now is time... This is a very naive point of view. You could deep-learn with a billion times more processing power and a billion times more data for 20 years and it would not produce a general artificial intelligence. Deep learning is a set of neural network tweaks that is insufficient to produce AGI. Within 20 years we may have enough additional tweaks to make an AGI, but I doubt that algorithm will look anything like the deep learning we have today.
  - throwawaysocks 10y ago
    This is basically exactly what I was trying to say with my original comment; thanks for stating it in a clearer way.

jimfleming 10y ago

> Only at such a high level of abstraction as to be meaningless. I'm not sure what this means or how the abstractions are meaningless? From Gabor filters to concepts like "dog", the abstractions are quite meaningful (in that they function well), even if not to us. > They are not. Hundreds of man years worth of engineering time go into each of those systems, and none of those systems generalizes to anything other than the task it was created for. That's nothing like human intelligence. This isn't strictly true if we look at the ability to generalize as a sliding scale. The level of generalization has actually increased significantly from expert systems to machine learning to deep learning. We have not reached human levels of generalization but we are approaching. Consider that DL can identify objects, people, animals in unique photos never seen before and that more generally the success of modern machine learning is it's ability generalize from training to test time rather than hand engineering for each new case. Newer work is even able to learn from just a few examples[0] and then generalize beyond that. Or the Atari work from DeepMind that can generalize to dozens/hundreds of games. None of those networks are created specifically for Break Out or Pong. It's also not entirely fair to discount the hundreds of years of engineering considering most of these systems are trained from scratch (randomness). Humans, however, benefit from the preceding evolution which has a time scale that far exceeds any human engineering effort. :) [0] https://arxiv.org/abs/1605.06065 https://arxiv.org/abs/1605.06065

bobdole1234 10y ago

We spend a lifetime building the skills that we use in our day to day lives. And most of them don't transfer.

eva1984 10y ago

It beats human with accuracy. Which makes it more practical. Computer doesn't need to be strong AI to replace human.

armitron 10y ago

This is a very superficial point-of-view. What matters here is the concept itself (deep learning as a generic technique) but also scalability. Not the specifics that we have today, but the specifics that we will have 20 years from now. The concept is proven, all that matters now is time...

daveguy 10y ago
> The concept is proven, all that matters now is time... This is a very naive point of view. You could deep-learn with a billion times more processing power and a billion times more data for 20 years and it would not produce a general artificial intelligence. Deep learning is a set of neural network tweaks that is insufficient to produce AGI. Within 20 years we may have enough additional tweaks to make an AGI, but I doubt that algorithm will look anything like the deep learning we have today.
- throwawaysocks 10y ago
  This is basically exactly what I was trying to say with my original comment; thanks for stating it in a clearer way.