Although advances in computing technologies have made can making machine learning more popular than ever, it’s not a new concept. The origins of machine learning date back to 1950, according to a Forbes article. External link Speculating on how one could tell if they had developed a truly integrated artificial intelligence (AI), Alan Turing created what is now referred to as the Turing test, which suggests that one way of testing for whether or not the AI is capable of understanding language is to see if it is able to fool a human into thinking they are speaking to another person.

In 1952, Arthur Samuel wrote the first learning program for IBM, this time involving a game of checkers. The work of many other machine learning pioneers followed, including Frank Rosenblatt’s design of the first neural network in 1957 and Gerald DeJong’s introduction of explanation-based learning in 1981.

In the 1990s, a major shift occurred in machine learning when the focus moved away from a knowledge-based approach to one driven by data. This was a critical decade in the field’s evolution, as scientists began creating computer programs that could analyze large datasets and learn in the process.

The 2000s were marked by unsupervised learning becoming widespread, eventually leading to the advent of deep learning and the ubiquity of machine learning as a practice.

Milestones in machine learning are marked by instances in which an algorithm is able to beat the performance of a human being, including Russian chess grandmaster Garry Kasparov's defeat at the hands of IBM supercomputer Deep Blue in 1997 and, more recently, the 2016 victory of the Google DeepMind AI program AlphaGo over Lee Sedol playing Go, a game notorious for its massively large space of possibilities in game play.

Today, researchers are hard at work to expand on these achievements. As machine learning and artificial intelligence applications become more popular, they’re also becoming more accessible, moving from server-based systems to the cloud. At Google Next 2018, Google touted several new deep learning and machine learning capabilities, External link like Cloud AutoML, BigQuery ML, and more. During the past few years, Amazon, Microsoft, Baidu, and IBM have all unveiled machine learning platforms through open source projects and enterprise cloud services. Machine learning algorithms are here to stay, and they’re rapidly widening the parameters of what research and industry can accomplish.

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