Deep Learning AI Explained: Neural Networks

Ballyhooed artificial-intelligence technique known as "deep learning" revives 70-year-old idea. In the past 10 years, the best-performing artificial-intelligence systems -- such as the speech recognizers on smartphones or Google's latest automatic translator -- have resulted from a technique called "deep learning." Deep learning is in fact a new name for an approach to artificial intelligence called neural networks, which have been going in and out of fashion for more than 70 years. Neural networks were first proposed in 1944 by Warren McCullough and Walter Pitts, two University of Chicago researchers who moved to MIT in 1952 as founding members of what's sometimes called the first cognitive science department. Neural nets were a major area of research in both neuroscience and computer science until 1969, when, according to computer science lore, they were killed off by the MIT mathematicians Marvin Minsky and Seymour Papert, who a year later would become co-directors of the new MIT Artificial Intelligence Laboratory. Most applications of deep learning use "convolutional" neural networks, in which the nodes of each layer are clustered, the clusters overlap, and each cluster feeds data to multiple nodes (orange and green) of the next layer.