3 ms·

No 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

No 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 ago
My 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.

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 ago

My 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.