In the 1970s, AI was subject to critiques and financial setbacks. AI researchers had failed to appreciate the difficulty of the problems they faced. Their tremendous optimism had raised public expectations impossibly high, and when the promised results failed to materialize, funding targeted at AI was severely reduced. The lack of success indicated that the techniques being used by AI researchers at the time were insufficient to achieve their goals. These setbacks did not affect the growth and progress of the field, however. The funding cuts only impacted a handful of major laboratories and the critiques were largely ignored. General public interest in the field continued to grow, the number of researchers increased dramatically, and new ideas were explored in
logic programming,
commonsense reasoning and many other areas. Historian Thomas Haigh argued in 2023 that there was no winter, and AI researcher
Nils Nilsson described this period as the most "exciting" time to work in AI.
Problems In the early seventies, the capabilities of AI programs were limited. Even the most impressive could only handle trivial versions of the problems they were supposed to solve; all the programs were, in some sense, "toys". AI researchers had begun to run into several limits that would be only conquered decades later, and others that still stymie the field in the 2020s: •
Limited computer power: There was not enough memory or processing speed to accomplish anything truly useful. For example: Ross Quillian's successful work on natural language was demonstrated with a vocabulary of only 20 words, because that was all that would fit in memory.
Hans Moravec argued in 1976 that computers were still millions of times too weak to exhibit intelligence. He suggested an analogy: artificial intelligence requires computer power in the same way that aircraft require
horsepower. Below a certain threshold, it's impossible, but, as power
increases, eventually it could become easy. "With enough horsepower," he wrote, "anything will fly". •
Intractability and the combinatorial explosion: In 1972
Richard Karp (building on
Stephen Cook's 1971
theorem) showed there are
many problems that can only be solved in
exponential time. Finding optimal solutions to these problems requires extraordinary amounts of computer time, except when the problems are trivial. This limitation applied to all symbolic AI programs that used search trees and meant that many of the "toy" solutions used by AI would never scale to useful systems. • '''
Moravec's paradox''': Early AI research had been very successful at getting computers to do "intelligent" tasks like proving theorems, solving geometry problems and playing chess. Their success at these intelligent tasks convinced them that the problem of intelligent behavior had been largely solved. However, they utterly failed to make progress on "unintelligent" tasks like recognizing a face or crossing a room without bumping into anything. By the 1980s, researchers would realize that symbolic reasoning was utterly unsuited for these perceptual and sensorimotor tasks and that there were limits to this approach. •
The breadth of commonsense knowledge: Many important artificial intelligence applications like
vision or
natural language require enormous amounts of information about the world: the program needs to have some idea of what it might be looking at or what it is talking about. This requires that the program know most of the same things about the world that a child does. Researchers soon discovered that this was a vast amount of information with billions of atomic facts. No one in 1970 could build a database large enough and no one knew how a program might learn so much information. •
Representing commonsense reasoning: Several related problems appeared when researchers tried to represent commonsense reasoning using formal logic or symbols. Descriptions of very ordinary deductions tended to get longer and longer the more one worked on them, as more and more exceptions, clarifications and distinctions were required. However, when people thought about ordinary concepts, they did not rely on precise definitions, rather they seemed to make hundreds of imprecise assumptions, correcting them when necessary using their entire body of commonsense knowledge.
Gerald Sussman observed that "using precise language to describe essentially imprecise concepts doesn't make them any more precise."
Decrease in funding The agencies that funded AI research, such as the
British government,
DARPA and the
National Research Council (NRC) became frustrated with the lack of progress and eventually cut off almost all funding for undirected AI research. The pattern began in 1966 when the
Automatic Language Processing Advisory Committee (ALPAC) report criticized machine translation efforts. After spending $20 million, the
NRC ended all support. In 1973, the
Lighthill report on the state of AI research in the UK criticized the failure of AI to achieve its "grandiose objectives" and led to the dismantling of AI research in that country. (The report specifically mentioned the
combinatorial explosion problem as a reason for AI's failings.) DARPA was deeply disappointed with researchers working on the
Speech Understanding Research program at CMU and canceled an annual grant of $3 million.
Hans Moravec blamed the crisis on the unrealistic predictions of his colleagues. "Many researchers were caught up in a web of increasing exaggeration." However, there was another issue: since the passage of the
Mansfield Amendment in 1969,
DARPA had been under increasing pressure to fund "mission-oriented direct research, rather than basic undirected research". Funding for the creative, freewheeling exploration that had gone on in the 60s would not come from DARPA, which instead directed money at specific projects with clear objectives, such as
autonomous tanks and
battle management systems. The major laboratories (MIT, Stanford, CMU and Edinburgh) had been receiving generous support from their governments, and when it was withdrawn, these were the only places that were seriously impacted by the budget cuts. The thousands of researchers outside these institutions and the many thousands that were joining the field were unaffected.
Philosophical and ethical critiques Several philosophers had strong objections to the claims being made by AI researchers. One of the earliest was
John Lucas, who argued that
Gödel's incompleteness theorem showed that a
formal system (such as a computer program) could never see the truth of certain statements, while a human being could.
Hubert Dreyfus ridiculed the broken promises of the 1960s and critiqued the assumptions of AI, arguing that human reasoning actually involved very little "symbol processing" and a great deal of
embodied,
instinctive, unconscious "
know how".
John Searle's
Chinese Room argument, presented in 1980, attempted to show that a program could not be said to "understand" the symbols that it uses (a quality called "
intentionality"). If the symbols have no meaning for the machine, Searle argued, then the machine can not be described as "thinking". These critiques were not taken seriously by AI researchers. Problems like
intractability and
commonsense knowledge seemed much more immediate and serious. It was unclear what difference "
know how" or "
intentionality" made to an actual computer program. MIT's
Minsky said of Dreyfus and Searle, "they misunderstand, and should be ignored." Dreyfus, who also taught at
MIT, was given a cold shoulder: he later said that AI researchers "dared not be seen having lunch with me."
Joseph Weizenbaum, the author of
ELIZA, was also an outspoken critic of Dreyfus' positions, but he "deliberately made it plain that [his AI colleagues' treatment of Dreyfus] was not the way to treat a human being," and was unprofessional and childish. Weizenbaum began to have serious ethical doubts about AI when
Kenneth Colby wrote a "computer program which can conduct
psychotherapeutic dialogue" based on ELIZA. Weizenbaum was disturbed that Colby saw a mindless program as a serious therapeutic tool. A feud began, and the situation was not helped when Colby did not credit Weizenbaum for his contribution to the program. In 1976,
Weizenbaum published
Computer Power and Human Reason which argued that the misuse of artificial intelligence has the potential to devalue human life.
Logic at Stanford, CMU, and Edinburgh Logic was introduced into AI research as early as 1958, by
John McCarthy in his
Advice Taker proposal. In 1963,
J. Alan Robinson had discovered a simple method to implement deduction on computers, the
resolution and
unification algorithms. However, straightforward implementations, like those attempted by McCarthy and his students in the late 1960s, were especially intractable: the programs required astronomical numbers of steps to prove simple theorems. A more fruitful approach to logic was developed in the 1970s by
Robert Kowalski at the
University of Edinburgh, and soon this led to the collaboration with French researchers
Alain Colmerauer and who created the successful logic programming language
Prolog. Prolog uses a subset of logic (
Horn clauses, closely related to "
rules" and "
production rules") that permits tractable computation. Rules would continue to be influential, providing a foundation for
Edward Feigenbaum's
expert systems and the continuing work by
Allen Newell and
Herbert A. Simon that would lead to
Soar and their
unified theories of cognition. Critics of the logical approach noted, as
Dreyfus had, that human beings rarely used logic when they solved problems. Experiments by psychologists like
Peter Wason,
Eleanor Rosch,
Amos Tversky,
Daniel Kahneman and others provided proof. McCarthy responded that what people do is irrelevant. He argued that what is really needed are machines that can solve problems—not machines that think as people do.
MIT's "anti-logic" approach Among the critics of
McCarthy's approach were his colleagues across the country at
MIT.
Marvin Minsky,
Seymour Papert and
Roger Schank were trying to solve problems like "story understanding" and "object recognition" that required a machine to think like a person. To use ordinary concepts like "chair" or "restaurant" they had to make all the same illogical assumptions that people normally made. Unfortunately, imprecise concepts like these are hard to represent in logic. MIT chose instead to focus on writing programs that solved a given task without using high-level abstract definitions or general theories of cognition, and measured performance by iterative testing, rather than arguments from first principles.
Schank described their "anti-logic" approaches as
"scruffy", as opposed to the
"neat" paradigm used by
McCarthy,
Kowalski,
Feigenbaum,
Newell and
Simon. In 1975, in a seminal paper,
Minsky noted that many of his fellow researchers were using the same kind of tool: a framework that captures all our
common sense assumptions about something. For example, if we use the concept of a bird, there is a constellation of facts that immediately come to mind: we might assume that it flies, eats worms and so on (none of which are true for all birds). Minsky associated these assumptions with the general category and they could be
inherited by the frames for subcategories and individuals, or over-ridden as necessary. He called these structures
frames.
Schank used a version of frames he called "
scripts" to successfully answer questions about short stories in English. Frames would eventually be widely used in
software engineering under the name
object-oriented programming. The logicians rose to the challenge.
Pat Hayes claimed that "most of 'frames' is just a new syntax for parts of first-order logic." But he noted that "there are one or two apparently minor details which give a lot of trouble, however, especially defaults".
Ray Reiter admitted that "conventional logics, such as first-order logic, lack the expressive power to adequately represent the knowledge required for reasoning by default". He proposed augmenting first-order logic with a
closed world assumption that a conclusion holds (by default) if its contrary cannot be shown. He showed how such an assumption corresponds to the common-sense assumption made in reasoning with frames. He also showed that it has its "procedural equivalent" as
negation as failure in
Prolog. The closed world assumption, as formulated by Reiter, "is not a first-order notion. (It is a meta notion.)" However,
Keith Clark showed that negation as
finite failure can be understood as reasoning implicitly with definitions in first-order logic including a
unique name assumption that different terms denote different individuals. During the late 1970s and throughout the 1980s, a variety of logics and extensions of first-order logic were developed both for negation as failure in
logic programming and for default reasoning more generally. Collectively, these logics have become known as
non-monotonic logics. ==Boom (1980–1987)==