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There are varying levels of sophistication from which data can be learned from. Pigeons can learn non-trivial word concepts and statistics but they do so at a r
by Cybiote 10y ago
There are varying levels of sophistication from which data can be learned from. Pigeons can learn non-trivial word concepts and statistics but they do so at a rate that is much slower than a human infant.
Thus far, machines have not done well in scenarios of low stationarity. Those are scenarios where the past is not so good a predictor of the future or where the data manifold is rapidly changing. This occurs for example, when the act of predicting changes what is being predicted. The related notion of Antipredictable Sequences is the most interesting consequence of the oft misconstrued notion of the No free lunch theorem.
Humans are far from the ideal machine for this scenario but they vastly outmatch current algorithms. This is because humans are much better in the kind of generalization that is based off theory building. While not perfect, it does well enough at generalizing from observations (think Newton deriving the laws of gravitation from observations and Brahe's data) to scenarios that are not so close to the data.
This means that for now and in complex environments, simpler models that are adaptable to change online are better. Additionally, their inspectability means they are more easily modified to better match any very changed dynamics.
So your examples of: `speech recognition, face recognition, object classification and detection, speech`
Are of the type where a learner, such as a deep network, whose generalization ability mostly hails from interpolating to fill in missing structure based on data (implying poor out of sample performance) does well when you can give lots of examples which provide good coverage.
Language translation is sort of inbetween (the space is not as smooth but is still fairly stationary at the scale of years) and so we also find that the advantages of deep learning have not been as pronounced and dramatic as has been the case for image and speech.
The digital machine advantage is two. Their memory capacity is outmatched. Second, and related to the first, energy needs are not so pressing a concern.
- dougabug 10y agoNo one is suggesting that AI is a solved problem. I simply cited a handful of areas such as computer vision where progress in ML has been remarkably rapid after many years where the problems seemed daunting and skeptics had written off the prospects of the field. Citing Newton as a typical example of the superiority of human inferential abilities perhaps represents a case of cherry picking. How often do most human beings come to exhibiting the level of insight and reasoning power required to construct the calculus and discover the laws of classical mechanics? Inventing supernatural explanations for natural phenomena, burning sacrifices / witches / books (on Evolution or other heresies) seem more par for the course than Newtonian level revelations. Having said that, learning the laws of classical physics from observational data strikes me as a pretty natural task for the right kinds of machine architectures, since they represent essentially geometric symmetries which hold over a vast range of scales, space and time.
- eternalban 10y ago> Inventing supernatural explanations for natural phenomena If our mathamechanical minions ever start musing about 'The Supreme Eigenvector' that would be the time to start worrying about machines taking over.
- Cybiote 10y agoMy point was that you've underestimated the problem difficulty, when the signal is not of a smooth manifold and where the dynamics are far from stationary, it is incorrect to infer that techniques which have worked well in stable and smooth scenarios will continue to work well. In fact they have not and it looks, not for a while yet either. > Citing Newton as a typical example of the superiority of human inferential abilities perhaps represents a case of cherry picking Are hyperparameter searches across a sea of AWS instances cherry picking? Anyways, I don't think I cherry picked. There is not a single machine that can do the same yet. And more importantly, a learning algorithm that notices model failure and works out the surgery required to correct or even completely replace it. Newton is an example of the human brain at its best. But he was far from the only: Maxwell, Green, Einstein, Noether, Archimedes and so on the list goes. But we don't need to go that far since Crows are already capable of generalization that out matches what machines can do for the near future.
- 21 10y agoI would add a third big advantage to the digital machine: the ability to seamlessly connect to "classical" algorithms. For example, AlphaGo also used a classic MonteCarlo algorithm. A hypothetical "robot-cat-AI" could for example have access to a really precise physics engine, and also could run at a much higher "frame-rate", so it could see the world in slow-motion and execute really exquisite moves as a result.
- wuschel 10y agoHow do you know that cats, flies, and other lifeforms run their brains at the same "frequency" as humans? When it comes to comparing the sophistication, adaptivity and functions of life to the in-silico mashines we produce and use very, very primitive.