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Deep learning's role in the evolution of machine learning Posted on : Jun 30 - 2020

Machine learning has continued to evolve since its beginnings some seven decades ago. Learn how deep learning has catalyzed a new phase in the evolution of machine learning.

Machine learning had a rich history long before deep learning reached fever pitch. Researchers and vendors were using machine learning algorithms to develop a variety of models for improving statistics, recognizing speech, predicting risk and other applications.

While many of the machine learning algorithms developed over the decades are still in use today, deep learning -- a form of machine learning based on multilayered neural networks -- catalyzed a renewed interest in AI and inspired the development of better tools, processes and infrastructure for all types of machine learning.

Here, we trace the significance of deep learning in the evolution of machine learning, as interpreted by people active in the field today.

The birth of machine learning

The story of machine learning starts in 1943 when neurophysiologist Warren McCulloch and mathematician Walter Pitts introduced a mathematical model of a neural network. The field gathered steam in 1956 at a summer conference on the campus of Dartmouth College. There, 10 researchers came together for six weeks to lay the ground for a new field that involved neural networks, automata theory and symbolic reasoning.

The distinguished group, many of whom would go on to make seminal contributions to this new field, gave it the name artificial intelligence to distinguish it from cybernetics, a competing area of research focused on control systems. In some ways these two fields are now starting to converge with the growth of IoT, but that is a topic for another day.

Early neural networks were not particularly useful -- nor deep. Perceptrons, the single-layered neural networks in use then, could only learn linearly separable patterns. Interest in them waned after Marvin Minsky and Seymour Papert published the book Perceptrons in 1969, highlighting the limitations of existing neural network algorithms and causing the emphasis in AI research to shift.

"There was a massive focus on symbolic systems through the '70s, perhaps because of the idea that perceptrons were limited in what they could learn," said Sanmay Das, associate professor of computer science and engineering at Washington University in St. Louis and chair of the Association for Computing Machinery's special interest group on AI. View More