This page is a timeline of machine learning. Major discoveries, achievements, milestones, and other major events in machine learning are included.
Overview Decade Summary pre-1950 Statistical methods are discovered and refined. 1950s Pioneering machine learning research is conducted using simple algorithms. 1960s Bayesian methods are introduced for probabilistic inference in machine learning. 1970s 'AI winter' caused by pessimism about machine learning effectiveness. 1980s Rediscovery of backpropagation causes a resurgence in machine learning research. 1990s Work on Machine learning shifts from a knowledge-driven approach to a data-driven approach. Scientists begin creating programs for computers to analyze large amounts of data and draw conclusions or "learn" from the results.. Support-vector machines (SVMs) and recurrent neural networks (RNNs) become popular. The fields of computational complexity via neural networks and super-Turing computation started. 2000s Support-Vector Clustering and other kernel methods and unsupervised machine learning methods become widespread. 2010s Deep learning becomes feasible, which leads to machine learning becoming integral to many widely used software services and applications. Deep learning spurs huge advances in vision and text processing.2020sGenerative AI leads to revolutionary models, creating a proliferation of foundation models, both proprietary and open source, notably enabling products such as advanced chatbots and text to image models. Machine learning and AI enter the wider public consciousness. The commercial potential of AI based on machine learning causes large increases in the valuations of companies linked to AI.
Timeline