Deep Blue beats Kasparov (1997): brute-force search on the world stage
On May 11, 1997, IBM’s Deep Blue defeated Garry Kasparov in a six-game match—a media milestone for AI.
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Support vector machines: the rise of statistical learning
SVMs from Vapnik and colleagues dominated 1990s–2000s classification with maximum margins and kernels.
Statistical NLP at IBM: translation leaves pure rules behind
IBM’s statistical machine-translation models based on alignments marked NLP’s data-driven turn.
TD-Gammon: reinforcement learning that rivaled humans
Gerald Tesauro trained a TD-lambda network for backgammon, reaching elite level without massive hand-coded expertise.
Yann LeCun and CNNs for digits: convolutions that learned to see
In the late 1980s–90s, LeCun and colleagues applied convolutional networks to zip-code and check recognition.
The second AI winter (late 1980s): Lisp machines and a crash
The Lisp-machine and expert-shell market contracted; AI retreated to labs while statistical ML grew quietly.
Judea Pearl and Bayesian networks: structured probability
Pearl popularized Bayesian networks for reasoning under uncertainty and later drove the causal-inference revolution.
Backpropagation 1986: the algorithm that reopened deep networks
Rumelhart, Hinton, and Williams popularized gradient descent via error backpropagation in multilayer networks.
Hopfield networks (1982): associative memory and statistical physics
John Hopfield linked recurrent nets to spin systems, inspiring a wave of energy-based models in AI.
Japan’s Fifth Generation project (1982): a bet on Prolog and logic
MITI launched FGCS to lead “intelligent” computers based on logic programming; the real outcome was mixed and costly.
Edward Feigenbaum and knowledge engineering
Feigenbaum argued that expert AI power lies in encoded domain knowledge, not only generic search mechanisms.
DENDRAL and MYCIN: the rise of expert systems
1970s–80s expert systems captured domain knowledge in rules; DENDRAL and MYCIN were emblematic cases.