AI effect

AI winter

See also History of artificial intelligence: the first AI winter and the second AI winter

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.

Early episodes

Machine translation and the ALPAC report of 1966

During the Cold War, the US government was particularly interested in the automatic, instant translation of Russian documents and scientific reports. The government aggressively supported efforts at machine translation starting in 1954. At the outset, the researchers were optimistic. Noam Chomsky's new work in grammar was streamlining the translation process and there were "many predictions of imminent 'breakthroughs'".

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.

The abandonment of perceptrons in 1969

See also: Perceptron and Frank Rosenblatt

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.

The setbacks of 1974

The S.U.R. debacle

DARPA was deeply disappointed with researchers working on the Speech Understanding Research program at Carnegie Mellon University. DARPA had hoped, and felt it had been promised, to get a system that could respond to voice commands from a pilot. The SUR team had developed a system which could recognize spoken English, but only if the words were spoken in a particular order. DARPA felt it had been duped and cancelled a three million dollar a year grant.

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.

The Lighthill report

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.

DARPA's funding cuts

After the passage of Mansfield Amendment in 1969, DARPA had been under increasing pressure to fund "mission-oriented direct research, rather than basic undirected research." Researchers now had to show that their work would soon produce some useful military technology. The Lighthill report and DARPA's own study (the American Study Group) suggested that most AI research was unlikely to produce anything truly useful in the foreseeable future. As a result, AI research proposals were held to a very high standard. Pure undirected research of the kind that had gone on in the 60s would not be funded by DARPA.

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.

The setbacks of the late 80s and early 90s

The collapse of the Lisp machine market in 1987

In the 1980s a form of AI program called an "expert system" was adopted by corporations around the world. The first commercial expert system was XCON, developed at Carnegie Mellon for Digital Equipment Corporation, and it was an enormous success: it was estimated to have saved the company 40 million dollars over just six years of operation. Corporations around the world began to develop and deploy expert systems and by 1985 they were spending over a billion dollars on AI, most of it to in-house AI departments. An industry grew up to support them, including software companies like Teknowledge and Intellicorp (KEE), and hardware companies like Symbolics and Lisp Machines Inc. who built specialized computers, called Lisp machines, that were optimized to process the programming language Lisp, the preferred language for AI.

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).

The fall of expert systems

Eventually the earliest successful expert systems, such as XCON, proved too expensive to maintain. They were difficult to update, they could not learn, they were "brittle" (i.e., they could make grotesque mistakes when given unusual inputs), and they fell prey to problems (such as the qualification problem) that had been identified years earlier in research in nonmonotonic logic. Expert systems proved useful, but only in a few special contexts. Another problem dealt with the computational hardness of truth maintenance efforts for general knowledge. KEE used an assumption-based approach (see NASA, TEXSYS) supporting multiple-world scenarios that was difficult to understand and apply.

The few remaining expert system shell companies were eventually forced to downsize and search for new markets and software paradigms, like case based reasoning or universal database access.

The fizzle of the fifth generation

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.

Lasting effects of the AI winters

The winter that wouldn't end

A survey of recent reports suggests that AI's reputation is still less than pristine:

  • Alex Castro in The Economist, 2007: "[Investors] were put off by the term 'voice recognition' which, like 'artificial intelligence', is associated with systems that have all too often failed to live up to their promises.
  • Patty Tascarella in Pittsburgh Business Times, 2006: "Some believe the word 'robotics' actually carries a stigma that hurts a company's chances at funding.
  • John Markoff in the New York Times, 2005: "At its low point, some computer scientists and software engineers avoided the term artificial intelligence for fear of being viewed as wild-eyed dreamers."

AI under different names

Many researchers in AI today deliberately call their work by other names, such as informatics, machine learning, knowledge-based systems, business rules management, cognitive systems, intelligent systems or computational intelligence, to indicate that their work emphasizes particular tools or is directed at a particular sub-problem. Although this may be partly because they consider their field to be fundamentally different from AI, it is also true that the new names help to procure funding by avoiding the stigma of false promises attached to the name "artificial intelligence."

AI behind the scenes

"Many observers still think that the AI winter was the end of the story and that nothing since come of the AI field," writes Ray Kurzweil, "yet today many thousands of AI applications are deeply embedded in the infrastructure of every industry." In the late 90s and early 21st century, AI technology became widely used as elements of larger systems, but the field is rarely credited for these successes. Nick Bostrom explains "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore. Rodney Brooks adds "there's this stupid myth out there that AI has failed, but AI is around you every second of the day.

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.

AI funding

Primarily the way researchers and economists judge the status of an AI winter is by reviewing which AI projects are being funded, how much and by whom. Trends in funding are often set by major funding agencies in the developed world. Currently, DARPA and a civilian funding program called EU-FP7 provide much of the funding for AI research in the US and European Union.

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)

Fear of another winter

Concerns are sometimes raised that a new AI winter could be triggered by any overly ambitious or unrealistic promise by prominent AI scientists. For example, some researchers feared that the widely publicised promises in the early 1990s that Cog would show the intelligence of a human two-year-old might lead to an AI winter. In fact, the Cog project and the success of Deep Blue seems to have led to an increase of interest in strong AI in that decade from both government and industry.

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.

Hope of another spring

There are also constant reports that another AI spring is imminent:

  • Raj Reddy, in his presidential address to AAAI, 1988: "[T]he field is more exciting than ever. Our recent advances are significant and substantial. And the mythical AI winter may have turned into an AI spring. I see many flowers blooming.
  • Pamela McCorduck in Machines Who Think: "In the 1990s, shoots of green broke through the wintry AI soil.
  • Jim Hendler and Devika Subramanian in AAAI Newsletter, 1999: "Spring is here! Far from the AI winter of the past decade, it is now a great time to be in AI.
  • Ray Kurzweil in his book The Singularity is Near, 2005: "The AI Winter is long since over
  • Heather Halvenstein in Computerworld, 2005: "Researchers now are emerging from what has been called an 'AI winter'
  • John Markoff in The New York Times, 2005: "Now there is talk about an A.I. spring among researchers"

Underlying causes behind AI winters

Several explanations have been put forth for the cause of AI winters in general. As AI progressed from government funded applications to commercial ones, new dynamics came into play. While hype is the most commonly cited cause, the explanations are not necessarily mutually exclusive.


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.

Institutional Factors

Another factor is AI's place in the organisation of universities. Research on AI often take the form of interdisciplinary research. One example is the Master of Artificial Intelligence program at K.U. Leuven which involve lecturers from Philosophy to Mechanical Engineering. AI is therefore prone to the same problems other types of interdisciplinary research face. Funding is channeled through the established departments and during budget cuts, there will be a tendency to shield the "core contents" of each department, at the expense of interdisciplinary and less traditional research projects.

Economic Factors

Downturns in the national economy cause budget cuts in universities. The "core contents" tendency worsen the effect on AI research and investors in the market are likely to put their money into less risky ventures during a crisis. Together this may amplify an economic downturn into an AI winter. It is worth noting that the Lighthill report came at a time of economic crisis in the UK, when universities had to make cuts and the question was only which programs should go.

Empty pipeline

It is common to see the relationship between basic research and technology as a pipeline. Advances in basic research give birth to advances in applied research, which in turn leads to new commercial applications. From this it is often argued that a lack of basic research will lead to a drop in marketable technology some years down the line. This view was advanced by James Hendler in 2008, claiming that the fall of expert systems in the late 80's were not due to and inherent and unavoidable brittleness of expert systems, but to funding cuts in basic research in the 70's. These expert systems advanced in the 80's through applied research and product development, but by the end of the decade, the pipeline had run dry and expert systems were unable to produce improvements that could have overcome the brittleness and secured further funding.

Failure to adapt

The fall of the Lisp machine market and the failure of the fifth generation computers were cases of expensive advanced products being overtaken by simpler and cheaper alternatives. This fits the definition of a low-end disruptive technology, with the Lisp machine makers being marginalized. Expert systems were carried over to the new desktop computers by for instance CLIPS, so the fall of the Lisp machine market and the fall of expert systems are strictly speaking two separate events. Still, the failure to adapt to such a change in the outside computing milieu is cited as one reason for the 80's AI winter.

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