In March 2016, machine learning (ML) took a big leap forward when AlphaGo, a ML program for the game Go, defeated the world's best player, Lee Sedol, in Korea. This historic match has attracted significant attention in both the scientific and popular press because of the tough computational challenges associated with playing Go proficiently.  Go has a huge number of cases, which make its complexity vastly greater than that of chess; therefore, it was regarded to be almost impossible to handle by explicit mathematical means.  However, AlphaGo seemingly handled this huge complexity without explicit programming.

 What is ML? Arthur Lee Samuel(1901-1990), who wrote an early TEX manual (typing system)  in 1983, defined ML as the "field of study that gives computers the ability to learn without being explicitly programmed".  Numerous mathematical models with differing integrative levels have been developed to solve various real-world problems systematically and quantitatively. However, the corresponding problems in many cases are ill posed, with modeling inaccuracies and data uncertainties, which make them difficult to deal with using solely numerical means. ML has the potential to deal with these ill-posed problems using statistical reasoning. Supervised ML is concerned with training a program using existing data so as to enhance its ability to make the best predictions (or decisions) when faced with new data. In ML, the first step should be the formulation of forward models. Before programming, we should understand the information loss of the models and also potential data uncertainties, practical limitations associated with the data, and so on. 

George Box(1919-2013) emphasized a motivated iteration between theory and practice:   All models are wrong, but some are useful. The scientist cannot obtain a correct one by excessive elaboration. If you want to know what happens to a process when you interfere with it, you have to interfere with it, not passively observe it.   Science is a means whereby learning is achieved, not by mere theoretical speculation on the one hand, nor by the undirected accumulation of practical facts on the other, but rather by a motivated iteration between theory and practice. Given that many researchers are susceptible to biased hypotheses, the rule of parsimony seems to be necessary to falsify incorrect theories. A critical view is an important part of the scientific method, and acts as an early warning system to alleviate biased and erroneous development.