An AI Winter is a collapse in the perception of artificial intelligence research. The term was coined by analogy with the relentless spiral of a nuclear winter: a chain reaction of pessimism in the AI community, followed by pessimism in the press, followed by a severe cutback in funding, followed by the end of serious research.
It first appeared in 1984 as the topic of a public debate at the annual meeting of AAAI (then called the "American Association of Artificial Intelligence"). Two leading AI researchers, Roger Schank and Marvin Minsky, warned the business community that enthusiasm for AI had spiraled out of control and that disappointment would certainly follow. They were right. Just three years later, the billion-dollar AI industry began to collapse.
The process of hype, disappointment and funding cuts are common in many emerging technologies (consider the railway mania or the dot-com bubble), but the problem has been particularly acute for AI. The pattern has occurred many times:
The worst times for AI have been 1974−80 and 1987-93. Sometimes one or the other of these periods (or some part of them) is referred to as the AI winter.
The historical episodes known as AI winters are collapses only in the perception of AI by government bureaucrats and venture capitalists. Despite the rise and fall of AI's reputation, it has continued to develop new and successful technologies. AI researcher Rodney Brooks would complain in 2002 that "there's this stupid myth out there that AI has failed, but AI is around you every second of the day. Ray Kurzweil agrees: "Many observers still think that the AI winter was the end of the story and that nothing since come of the AI field. Yet today many thousands of AI applications are deeply embedded in the infrastructure of every industry." He adds unequivocally: "the AI winter is long since over.
However, researchers had underestimated the profound difficulty of disambiguation. In order to translate a sentence, a machine needed to have some idea what the sentence was about, otherwise it made ludicrous mistakes. An anecdotal example was "the spirit is willing but the flesh is weak." Translated back and forth with Russian, it became "the vodka is good but the meat is rotten. Similarly, "out of sight, out of mind" became "blind idiot." Later researchers would call this the commonsense knowledge problem.
By 1964, National Research Council had become concerned about the lack progress and formed the Automatic Language Processing Advisory Committee (ALPAC) to look into the problem. They concluded, in a famous 1966 report, that machine translation was more expensive, less accurate and slower than human translation. After spending some 20 million dollars, the NRC ended all support. Careers were destroyed and research ended.
Machine translation is still an open research problem in the 21st century.
A perceptron is a form of neural network introduced in 1958 by Frank Rosenblatt, who had been a schoolmate of Marvin Minsky at the Bronx High School of Science. Like most AI researchers, he made optimistic claims about their power, predicting that "perceptron may eventually be able to learn, make decisions, and translate languages." An active research program into the paradigm was carried out throughout the 60s but came to a sudden halt with the publication of Minsky and Papert's 1969 book Perceptrons. They showed that there were severe limitations to what perceptrons could do and that Frank Rosenblatt's claims had been grossly exaggerated. The effect of the book was devastating: virtually no research at all was done in connectionism for 10 years.
Eventually, the work of Hopfield, David Rumelhart and others would revive the field and thereafter it would become a vital and useful part of artificial intelligence. The specific problems brought up by Perceptrons were ultimately addressed using backpropagation and other modern machine learning techniques, developed by Paul Werbos in 1974 and championed by Rumelhart in the early 80s. Rosenblatt would not live to see this, as he died in a boating accident shortly after the book was published.
Many years later, successful commercial speech recognition systems would use the technology developed by the Carnegie Mellon team (such as hidden Markov models) and the market for speech recognition systems would reach $4 billion by 2001.
In 1973, professor Sir James Lighthill was asked by Parliament to evaluate the state of AI research in the United Kingdom. His report, now called the Lighthill report, criticized the utter failure of AI to achieve its "grandiose objectives." He concluded that nothing being done in AI couldn't be done in other sciences. He specifically mentioned the problem of "combinatorial explosion" or "intractability", which implied that many of AI's most successful algorithms would grind to a halt on real world problems and were only suitable for solving "toy" versions. (John McCarthy would later write in response that "the combinatorial explosion problem has been recognized in AI from the beginning.)
The report led to the complete dismantling of AI research in England. AI research continued in only two or three top universities. This "created a bow-wave effect that led to funding cuts across Europe," writes James Hendler. Research would not revive on a large scale until 1983, when Alvey (a research project of the British Government) began to fund AI from a war chest of £350 million.
By 1974, funding for AI projects was hard to find. AI researcher Hans Moravec believed that "it was literally phrased at DARPA that 'some of these people were going to be taught a lesson [by] having their two-million-dollar-a-year contracts cut to almost nothing!'" and he blamed the crisis on the unrealistic predictions of his colleagues: "Many researchers were caught up in a web of increasing exaggeration. Their initial promises to DARPA had been much too optimistic. Of course, what they delivered stopped considerably short of that. But they felt they couldn't in their next proposal promise less than in the first one, so they promised more.
In 1987, three years after Minsky and Schank's prediction, the market for specialized AI hardware collapsed. Desktop computers with a simpler architecture, from Apple and IBM had been steadily gaining speed and power and in 1987 they became more powerful than the more expensive Lisp machines. The desktop computers had rule-based engines such as CLIPS available which left no reason to buy a Lisp machine. An entire industry worth half a billion dollars was demolished overnight.
Commercially, many Lisp machine companies failed, like Symbolics, Lisp Machines Inc., Lucid Inc., etc. Other companies, like Texas Instruments and Xerox abandoned the field. However, a number of customer companies (that is, companies using systems written in Lisp and developed on Lisp machine platforms) continued to maintain systems. In some cases, this maintenance involved the assumption of the resulting support work. The maturation of Common Lisp saved many systems such as ICAD which found application in Knowledge-based engineering. Other systems, such as Intellicorp's KEE, moved from Lisp to a C++ (variant) on the PC via object-oriented technology and helped establish the o-o technology (including providing major support for the development of UML).
In 1981, the Japanese Ministry of International Trade and Industry set aside $850 million dollars for the Fifth generation computer project. Their objectives were to write programs and build 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. Indeed, some of them had not been met in 2001. As with other AI projects, expectations had run much higher than what was actually possible.
Technologies developed by AI researchers have achieved commercial success in a number of domains, such as machine translation, data mining, industrial robotics, logistics, speech recognition, banking software, medical diagnosis and Google's search engine.
Fuzzy logic controllers have been developed for automatic gearboxes in automobiles (the 2006 Audi TT, VW Toureg and VW Caravell feature the DSP transmission which utilizes Fuzzy logic, a number of Škoda variants (Škoda Fabia) also currently include a Fuzzy Logic based controller). Camera sensors widely utilize fuzzy logic to enable focus (ironically).
Heuristic search and data analytics are both technologies that have developed from the evolutionary computing and machine learning subdivision of the AI research community. Again, these techniques have been applied to a wide range of real world problems with considerable commercial success.
In the case of Heuristic Search, ILOG has developed a large number of applications including deriving job shop schedules for many manufacturing installations Many telecommunications companies also make use of this technology in the management of their workforces, for example BT Group has deployed heuristic search in a scheduling application that provides the work schedules of 20000 engineers.
Data analytics technology utilizing algorithms for the automated formation of classifiers that were developed in the supervised machine learning community in the 1990s (for example, TDIDT, Support Vector Machines, Neural Nets, IBL) are now used pervasively by companies for marketing survey targeting and discovery of trends and features in data sets.
As of 2007 DARPA is soliciting AI research proposals under a number of programs including The Grand Challenge Program, Cognitive Technology Threat Warning System (CT2WS), "Human Assisted Neural Devices (SN07-43)", "Autonomous Real-Time Ground Ubiquitous Surveillance-Imaging System (ARGUS-IS)" and "Urban Reasoning and Geospatial Exploitation Technology (URGENT)"
Perhaps best known, is DARPA's Grand Challenge Program which has developed fully automated road vehicles that can successfully navigate real world terrain in a fully autonomous fashion.
DARPA has also supported programs on the Semantic Web with a great deal of emphasis on intelligent management of content and automated understanding. However James Hendler, the manager of the DARPA program at the time, expressed some disappointment with the outcome of the program.
The EU-FP7 funding program, provides financial support to researchers within the European Union. Currently it funds AI research under the Cognitive Systems: Interaction and Robotics Programme (€193m), the Digital Libraries and Content Programme (€203m) and the FET programme (€185m)
James Hendler in 2008, observed that AI funding both in the EU and the US were being channeled more into applications and cross-breeding with traditional sciences, such as bioinformatics. This shift away from basic research is happening at the same time as there's a drive towards applications of e.g. the semantic web. Invoking the pipeline argument, (see underlying causes) Hendler saw a parallell with the 80's winter and warned of a coming AI winter in the 10's.
The AI winters can be partly understood as a sequence of over-inflated expectations and subsequent crash seen in stock-markets and examplified by the railway mania and dotcom bubble. The hype cycle concept for new technology looks at perception of technology in more detail. It describes a common pattern in development of new technology, where an event, typically a technological breakthrough, creates publicity which feeds on itself to create a "peak of inflated expectations" followed by a "trough of disillusionment" and later recovery and maturation of the technology. The key point is that since scientific and technological progress can't keep pace with the publicity-fueled increase in expectations among investors and other stakeholders, a crash must follow. AI technology seems to be no exception to this rule.