The AI Winters
Two boom-and-bust cycles, and the people who kept working through the frost
Twice now, artificial intelligence has gone from boundless funding to near-total collapse. The pattern repeats: bold promises, government money, a damning report, a freeze. This trail follows both winters end to end - not as cautionary trivia, but because every era of AI optimism, including this one, gets measured against them.
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The workshop that named artificial intelligence
The 1955 Dartmouth proposal by McCarthy, Minsky, Rochester, and Shannon coined 'artificial intelligence' and launched the field.
The field is born already optimistic: the Dartmouth proposal suggests a significant advance can be made in one summer if a carefully selected group works on it together.
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Simon and Newell's 1957 ten-year predictions
In 1957 Herbert Simon and Allen Newell predicted that within ten years a computer would be world chess champion and prove a new theorem.
Within two years the field's leaders are making dated, falsifiable promises. Some eventually come true - decades late. The pattern of overpromising starts here.
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The perceptron: a machine that learns from examples
Frank Rosenblatt's perceptron was an early trainable neural network that adjusted its own connections to classify patterns.
The perceptron arrives with Navy-funded fanfare and newspaper stories about machines that will walk, talk, and be conscious. The hype writes checks the hardware cannot cash.
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1966 ALPAC report ends a decade of machine-translation funding
The 1966 ALPAC report found no near-term prospect of useful machine translation and urged cutting funding, collapsing US support for roughly two decades.
The first bill comes due in machine translation: a government committee concludes there is no near-term prospect of useful MT, and funding collapses for twenty years.
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Perceptrons and the first neural network winter
Minsky and Papert's book exposed the limits of single-layer perceptrons, helping freeze neural network funding for years.
Then the mathematics: Minsky and Papert prove what single-layer perceptrons can never compute. Fair as written, devastating as read - neural networks become a career dead end.
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The 17-year neural network freeze
Minsky and Papert 1969 book Perceptrons exposed what single-layer networks cannot do, helping freeze neural-network research until backprop revived it in 1986.
The freeze lasts seventeen years. The lesson is not that the critics were wrong, but that a true negative result about a narrow case can take down an entire research direction.
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The Lighthill Report triggers the first AI winter in the UK
Sir James Lighthill's survey for the UK Science Research Council judged AI a disappointment, cutting British AI funding for a decade.
The UK runs the same play with a single author: Lighthill's report judges AI a disappointment and British funding is cut for a decade. The phrase 'AI winter' now has a textbook case.
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XCON/R1, the first big commercial expert system
R1, later called XCON, was a rule-based expert system that configured DEC's VAX computer orders and became one of the first to pay off in business.
The thaw comes from an unexpected direction - not learning, but rules. XCON configures DEC's computer orders and proves AI can pay its own way. The expert-systems boom ignites.
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Japan launches the Fifth Generation Computer Systems project
Japan MITI launched a ten-year national project in 1982, run by the ICOT institute, to build knowledge-processing computers based on logic programming.
Japan goes all-in with a ten-year national project, and Western governments panic-fund their own programs in response. The second boom is now geopolitical.
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The rise and fall of the Lisp machine industry
Around 1980 the MIT AI Lab spun out Symbolics and LMI to sell purpose-built Lisp computers; both collapsed within a decade as cheap workstations caught up.
An entire hardware industry grows up to run AI workloads - and is then undercut by ordinary workstations that got fast enough. Specialized AI hardware would have to wait for GPUs.
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The expert systems boom and bust
In the early 1980s 'knowledge engineering' was sold as AI's future, sparking a wave of expert-systems firms; by the decade's end the market had collapsed.
Expert systems hit their ceiling: brittle rules, expensive maintenance, and knowledge that walks out the door when the expert does. The market collapses by decade's end.
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The second AI winter
In the late 1980s the commercial AI boom collapsed as the Lisp-machine market crashed and expert systems failed their hype, starting a long downturn near 1987.
Boom number two ends like boom number one. 'AI' becomes a word grant writers avoid; survivors rebrand as machine learning, informatics, or just software.
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CIFAR Funds Deep Learning Through the Winter (2004)
In 2004 Canada's CIFAR launched a program directed by Geoffrey Hinton that sustained Hinton, Bengio, and LeCun through the neural-network winter.
What ends a winter is patient money: a Canadian institute quietly funds Hinton, Bengio, and LeCun to keep working on the unfashionable idea of deep neural networks.
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AlexNet wins ImageNet 2012
A deep convolutional neural network crushed the ImageNet contest, proving deep learning could outperform hand-built computer vision.
Eight years later the unfashionable idea wins ImageNet by a margin nobody can ignore. The thaw is instant, and the field has not cooled since - so far.