For example, deciphering handwriting is something that can’t be precisely described. They output other algorithms, that is, when given a number of input-output pairs, they find an algorithm to turn the inputs into outputs. However, machine learning algorithms are a little more abstract.

For example, for an algorithm designed to give directions, the input includes two destination points, and the output is the shortest route the algorithm computes between these two points. Simple, standard algorithms are designed to work on inputs of information, perform a task and then produce and output. To begin with, algorithms are defined as, “sequences of precise instructions that produce the same result every time.” They are present everywhere – in the scheduling of flights, for delivery of online consumer products, etc. It delves into understanding the implications these ever-evolving algorithms can have on the future. The Master Algorithm (2016) by Pedro Domingos dives into the algorithms that are currently in use, how these algorithms help, the problems they face, and solutions to those problems. Moreover, these algorithms influence every aspect of human life and could have the ability to surpass the human brain’s ability to comprehend and compute. That said, the machines conceived by the human brain and built by man are constantly evolving and getting more sophisticated than ever. Its capacity to learn from its surroundings is unparalleled. The human brain is perhaps the most fascinating and wondrous machine there is.

The master algorithm book