Current "machine learning" algorithms such as decision trees, SVM, regression etc are in a way cheating. In all these algorithms you need to design features and

Current "machine learning" algorithms such as decision trees, SVM, regression etc are in a way cheating. In all these algorithms you need to design features and they contain much of the smarts that you see from "machine learning" algorithms. In other words, an impressive machine learned model is typically a result of an impressive features gymnastic manually programmed by humans after lot of sweat and blood. A true machine learning algorithms shouldn't require humans to encode their smarts and knowledge. If these algorithms existed then they would just look at stream of symbols or sensory inputs that occurs in the nature and extract patterns from it. Deep learning gets closer to this by eliminating need for developing features (to some extent so far). I think it is first of many steps towards true machine learning.