Applying machine thinking to a human problem

"An efficient manager has a high evaluation quotient. A machine has a better one, in chess and a number of increasing fields. The problem now is to keep up with what the machines are learning!"
                                                                                                                                                     

-Denis Rothman

Evaluation is one of the major keys to efficient decision making in all fields: from chess, production management, rocket launching, and self-driving cars to data center calibration, software development, and airport schedules. Chess engines are not high-level deep-learning-based software. They rely heavily on evaluations and calculations. They evaluate much better than humans, and there is a lot to learn from them. The question now is to know whether any human can beat a chess engine or not. The answer is no.

To evaluate a position in chess, you need to examine all the pieces, their quantitative value, their qualitative value, cooperation between pieces, who owns each of the 64 squares, the king's safety, bishop pairs, knight positioning, and many other factors.