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AI winter: the story on HearLore | HearLore
— Ch. 1 · The Winter That Was Named —
AI winter.
~6 min read · Ch. 1 of 8
In 1984, the annual meeting of the American Association for Artificial Intelligence became a stage for a public debate that would define a generation of skepticism. Roger Schank and Marvin Minsky stood before business leaders to warn them about the dangers of unchecked enthusiasm in their field. They described a chain reaction similar to nuclear winter, where pessimism within the AI community would spread to the press and then trigger severe funding cuts. This cycle had already begun with disappointment in the press, followed by a cutback in funding, and finally the end of serious research. Three years later, the billion-dollar AI industry began to collapse under the weight of these predictions. The term AI winter entered the lexicon not as a prediction of doom, but as a description of a recurring pattern of hype and failure.
Machine Translation Collapse
Natural language processing research gained momentum after Warren Weaver published his influential memorandum on machine translation in 1949. IBM developed the first machine while MIT appointed its first full-time professor in the field. A public demonstration of the Georgetown-IBM machine garnered widespread attention in respected newspapers in 1954. Headlines proclaimed phrases like "the bilingual machine" and "robot brain translates Russian into King's English." The actual demonstration involved translating only 49 Russian sentences using a vocabulary limited to just 250 words. By 1964, the National Research Council formed the Automatic Language Processing Advisory Committee to investigate the lack of progress. Their famous 1966 report concluded that machine translation was more expensive, less accurate, and slower than human translation. After spending approximately 20 million dollars, the council ended all support for the program. Careers were destroyed and research came to an abrupt halt.
The AI winter began in 1984 when Roger Schank and Marvin Minsky warned business leaders about unchecked enthusiasm. This public debate triggered a chain reaction of pessimism that spread to the press and led to severe funding cuts.
What happened to machine translation research after 1966?
Machine translation research ended abruptly after the National Research Council released its famous report on the 2nd of May 1966. The council concluded that machine translation was more expensive, less accurate, and slower than human translation following an expenditure of approximately 20 million dollars.
Why did neural network projects lose funding during the 1970s and early 1980s?
Neural network projects lost major funding because Marvin Minsky and Seymour Papert published their book Perceptrons in 1969 which emphasized the limits of perceptron capabilities. Nobody knew how to train multilayered perceptrons at that time so interest faded until backpropagation algorithms emerged years later.
How did the UK Parliament impact British AI research in 1973?
Professor Sir James Lighthill delivered a report to the UK Parliament in 1973 that criticized the utter failure of AI to achieve grandiose objectives. This report led to the complete dismantling of AI research across most British universities until Alvey began funding again from a war chest of £350 million in 1983.
When did the commercial LISP-based AI hardware market collapse?
The market for specialized LISP-based AI hardware collapsed in 1987 three years after predictions made by Roger Schank and Marvin Minsky. An entire industry worth half a billion dollars was replaced in a single year as workstations offered powerful alternatives to LISP machines.
Simple networks or circuits of connected units had failed to deliver promised results and were abandoned in the late 1950s. Frank Rosenblatt invented perceptrons but kept interest alive through sheer force of personality alone. He optimistically predicted that the perceptron might eventually be able to learn, make decisions, and translate languages. Mainstream research into perceptrons ended partially because Marvin Minsky and Seymour Papert published their book Perceptrons in 1969. This work emphasized the limits of what perceptrons could do while nobody knew how to train multilayered perceptrons at that time. Major funding for neural network projects became difficult to find throughout the 1970s and early 1980s. Rosenblatt did not live to see the revival of large-scale interest which arrived in the middle 1980s when John Hopfield and David Rumelhart began their work. The winter of neural network approaches lasted until backpropagation algorithms finally emerged years later.
British And American Funding Cuts
In 1973, Professor Sir James Lighthill was asked by the UK Parliament to evaluate the state of AI research within the United Kingdom. His report criticized the utter failure of AI to achieve its grandiose objectives and concluded that nothing being done in AI could not be done in other sciences. The report led to the complete dismantling of AI research across most British universities. Research continued only in a few institutions like Edinburgh, Essex, and Sussex until Alvey began funding again from a war chest of £350 million in 1983. Meanwhile, DARPA changed its attitude after the passage of the Mansfield Amendment in 1969. This legislation required the agency to fund mission-oriented direct research rather than basic undirected research. By 1974, funding for AI projects became hard to find as money was directed at specific projects with identifiable goals such as autonomous tanks and battle management systems. Hans Moravec blamed the crisis on unrealistic predictions made by his colleagues who felt they could not promise less than in their first proposal.
LISP Machine Market Crash
The first commercial expert system XCON developed at Carnegie Mellon for Digital Equipment Corporation saved the company an estimated 40 million dollars over six years. Corporations around the world began developing and deploying expert systems while spending over a billion dollars on AI by 1985. An industry grew up to support them including hardware companies like Symbolics and LISP Machines Inc. who built specialized computers optimized to process the programming language LISP. In 1987, three years after Minsky and Schank's prediction, the market for specialized LISP-based AI hardware collapsed. Workstations by companies like Sun Microsystems offered a powerful alternative to LISP machines while desktop computers built by Apple and IBM offered simpler architectures. Benchmarks were published showing workstations maintaining a performance advantage over LISP machines. An entire industry worth half a billion dollars was replaced in a single year. By the early 1990s, most commercial LISP companies had failed including Symbolics, LISP Machines Inc., and Lucid Inc.
Expert System Limitations
By the early 1990s, the earliest successful expert systems proved too expensive to maintain and difficult to update. They could not learn and were brittle enough to make grotesque mistakes when given unusual inputs. Expert systems fell prey to problems such as the qualification problem that had been identified years earlier in research into nonmonotonic logic. The few remaining expert system shell companies were eventually forced to downsize and search for new markets and software paradigms. KEE used an assumption-based approach supporting multiple-world scenarios that was difficult to understand and apply. Other systems moved from LISP to C++ variants on personal computers to help establish object-oriented technology. The field struggled with computational hardness of truth maintenance efforts for general knowledge which made many applications impractical for widespread use.
Fifth Generation Project End
In 1981, the Japanese Ministry of International Trade and Industry set aside 850 million dollars for the Fifth Generation computer project. Their objectives included writing programs and building machines that could carry on conversations, translate languages, interpret pictures, and reason like human beings. By 1991, the impressive list of goals penned in 1981 had not been met according to HP Newquist's account. On the 1st of June 1992, the Fifth Generation Project ended not with a successful roar but with a whimper. Expectations had run much higher than what was actually possible for researchers at the time. Jack Schwarz ascended to leadership of DARPA's Information Processing Technology Office in 1987 and dismissed expert systems as clever programming. He cut funding to AI deeply and brutally while feeling strongly that AI was not the next wave of technological development. A few projects survived including pilot assistants and autonomous land vehicles though these were never delivered successfully.
Modern Resurgence And Boom
AI has reached the highest levels of interest and funding in its history during the 2020s by every possible measure. Total investment reached 50 billion dollars in 2022 with predictions of 364 billion dollars by 2025 from large tech companies. Job openings numbered 800,000 across the United States in 2022 alone. A turning point occurred in 2012 when AlexNet won the ImageNet Large Scale Visual Recognition Challenge with half as many errors as the second place winner. The field saw advances in language translation through Google Translate and image recognition commercialized by Google Image Search. Game-playing systems such as AlphaZero and AlphaGo became champions in chess and Go respectively. Watson emerged as a Jeopardy champion while OpenAI released ChatGPT in 2022 which had over 100 million users by January 2023. This dramatic increase in funding and public interest began around 2012 driven by deep learning breakthroughs and widespread adoption of large language models.