Earlier quoted context omitted.
Machine learning is "learning from data." It is not the assumption that there are no dynamics, and that the future will simply be a repetition of the past. To the extent that the future is predictable, learning from data is the best that can be done. The reality is that speech recognition, language translation, face recognition, object classification and detection, semantic segmentation, speech and image synthesis ha…
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 exa…
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.