When the field of Artificial Intelligence started, all the researchers were focussed on ‘solving’ a problem, as that was how they were trained. For example, automatically finding a solution to a maze. A paradigm shift in thinking had to happen before people started to approach problems in a different way.The new approach was not to solve a task but to ‘imitate’ its solution. Not all problems can be solved. This was known to mathematicians earlier. Well, one has to look what constitutes as a solution. For example,It had no solutions until the introduction of the concept of complex numbers. But, there are other problems which are truly unsolvable (in some sense). Real world problems are far too complex to find a solution. So, the concept of ‘imitating’ a solution was required for very complex real world tasks. The best example to compare these two paradigms would be the Deep Blue computer which beat Kasparov in 1996 and the AlphaGo computer which beat Lee Sedol in 2016. The former ‘searches’ for the best move in Chess, while the latter ‘imitates’ a strong player of Go.
What Is Deep Learning? — An Introduction