They consist of multiple weak classifiers that together create a strong learning model. Today, this algorithm is commonly used for AI-driven recommendations and price predictions.ġ990 – Robert Schapire introduced boosting algorithms for improving AI models. With written English text and phonetic transcriptions as input, it learned to “talk” like a baby.ġ986 – Paul Smolensky invented the restricted Boltzmann machine (RBM) for predicting probabilities of various possible outcomes based on input data. His work later inspired a convolution neural network (CNN) for deep learning.ġ981 – Gerald DeJong proposed EBL (Explanation-Based Learning), a method that an ML algorithm can use when analyzing data to create general rules and ignore irrelevant data points.ġ985 – Terrence Sejnowski invented NETtalk, an ML-based computer program that could perform cognitive tasks like a human.
It was designed to help study the remote control of a Moon rover.ġ979 – Kunihiko Fukushima published a research paper on the Neocognitron, an artificial neural network (ANN) with multiple layers for detecting complex patterns. Hart came up with the nearest neighbor algorithm, which later became the foundation for pattern recognition.ġ979 – Stanford students built the Stanford Cart, a remotely-controlled, autonomous cart that could navigate on its own and avoid bumping into objects. Let’s see where it all began and how it has evolved over the years.ġ949 – Donald Hebb published “The Organization of Behavior,” introducing theories on the interaction between neurons, which were later crucial in developing machine learning.ġ950 – Alan Turing invented the Turing Test, or the imitation game, to determine if a computer can pass for a human-based on its written linguistic fluency.ġ951 – Dean Edmonds and Marvin Minsky built the SNARC machine, the first machine with an artificial neural network, based on Hebb’s model.ġ952 – Arthur Samuel developed a computer game of checkers.ġ957 – Frank Rosenblatt used Hebb’s model and Samuel’s ML algorithms to develop the perceptron, a computer program with human-like thought processes, primarily designed for image recognition.ġ967 – Thomas Cover and Peter E. The history of machine learning is quite impressive. We may have deep learning and AI-powered technology now, but those wouldn’t have been possible had it not been for machine learning. However, they are all based on the first ML algorithms by Arthur Samuel. Since then, many scientists and researchers have jumped on the ML bandwagon and started developing new programs and algorithms. The chess computer beat Kasparov in 1997, proving that machines were indeed capable of human-like intelligence. The showdown between Deep Blue and world chess champion Garry Kasparov was all everyone talked about back then. It didn’t take off until the late 1990s when IBM developed its Deep Blue supercomputer. There have been numerous machine learning initiatives to date, helping ML evolve to a great extent since the ‘50s. Has Machine Learning Changed Much Since Then? The more the program played the game, the more it learned from its experience, thanks to a minimax algorithm for studying moves to come up with winning strategies. That was when he designed a computer program for playing checkers. Many genius individuals contributed to its development.īut there’s one person who stands out when thinking about when ML was invented.Īrthur Samuel, a computer scientist at IBM and a pioneer in AI and computer gaming, coined the term “Machine Learning” in 1952. We can’t say that a single person invented machine learning, especially the advanced ML algorithms we know today.
Who Came up with Machine Learning and When? However, it wasn’t until the 1950s that we saw how ML works for the first time. While machine learning may seem like a very recent concept, you may be surprised to know that the history of machine learning dates back to the 1940s. Many services we use daily, such as social media and Netflix, use ML to analyze consumer behavior and recommend trending content. Businesses increasingly rely on tools that use ML algorithms to provide them with accurate data for improving and growing their organization. It seems like machine learning is everywhere these days.