In this lesson and the next, we provide a brief history of AI. Although AI itself is a young field, it has inherited many ideas, viewpoints, and techniques from other disciplines. From over 2000 years of tradition in philosophy, theories of reasoning and learning have emerged, along with the viewpoint that the mind is constituted by the operation of a physical system. From over 400 years of mathematics, we have formal theories of logic, probability, decision making, and computation. From psychology, we have the tools with which to investigate the human mind, and a scientific language within which to express the resulting theories. From linguistics, we have theories of the structure and meaning of language. Finally, from computer science, we have the tools with which to make AI a reality.
Like any history, this one is forced to concentrate on a small number of people and events, and ignore others that were also important. We choose to arrange events to tell the story of how the various intellectual components of modern AI came into being. We certainly would not wish to give the impression, however, that the disciplines from which the components came have all been working toward AI as their ultimate fruition.
Philosophy (428 B.C.-Present)
The safest characterization of the European philosophical tradition is that it consists of a series of footnotes to Plato. —Alfred North Whitehead We begin with the birth of Plato in 428 B.C. His writings range across politics, mathematics, physics, astronomy, and several branches of philosophy. Together, Plato, his teacher Socrates, and his student Aristotle laid the foundation for much of western thought and culture. The philosopher Hubert Dreyfus (1979, p. 67) says that “The story of artificial intelligence might well begin around 450 B.C.” when Plato reported a dialogue in which Socrates asks Euthyphro, “I want to know what is characteristic of piety which makes all actions pious… that I may have it to turn to, and to use as a standard whereby to judge your actions and those of other men.” In other words, Socrates was asking for an algorithm to distinguish piety from non-piety. Aristotle went on to try to formulate more precisely the laws governing the rational part of the mind. He developed an informal system of syllogisms for proper reasoning, which in principle allowed one to mechanically generate conclusions, given initial premises. Aristotle did not believe all parts of the mind were governed by logical processes; he also had a notion of intuitive reason.
Now that we have the idea of a set of rules that can describe the working of (at least part of) the mind, the next step is to consider the mind as a physical system. We have to wait for Rene Descartes (1596-1650) for a clear discussion of the distinction between mind and matter, and the problems that arise. One problem with a purely physical conception of the mind is that it seems to leave little room for free will: if the mind is governed entirely by physical laws, then it has no more free will than a rock “deciding” to fall toward the center of the earth. Although a strong advocate of the power of reasoning, Descartes was also a proponent of dualism. He held that there is a part of the mind (or soul or spirit) that is outside of nature, exempt from physical laws. On the other hand, he felt that animals did not possess this dualist quality; they could be considered as if they were machines.
An alternative to dualism is materialism, which holds that all the world (including the brain and mind) operate according to physical law. Wilhelm Leibniz (1646-1716) was probably the first to take the materialist position to its logical conclusion and build a mechanical device intended to carry out mental operations. Unfortunately, his formulation of logic was so weak that his mechanical concept generator could not produce interesting results.
It is also possible to adopt an intermediate position, in which one accepts that the mind has a physical basis, but denies that it can be explained by a reduction to ordinary physical processes. Mental processes and consciousness are therefore part of the physical world, but inherently unknowable; they are beyond rational understanding. Some philosophers critical of AI have adopted exactly this position.
Barring these possible objections to the aims of AI, philosophy had thus established a tradition in which the mind was conceived of as a physical device operating principally by reasoning with the knowledge that it contained. The next problem is then to establish the source of knowledge. The empiricist movement, starting with Francis Bacon’s (1561-1626) Novwn Organum, is characterized by the dictum of John Locke (1632-1704): “Nothing is in the understanding, which was not first in the senses.” David Hume’s (1711-1776) A Treatise of Human Nature (Hume, 1978) proposed what is now known as the principle of induction: that general rules are acquired by exposure to repeated associations between their elements. The theory was given more formal shape by Bertrand Russell (1872-1970) who introduced logical positivism. This doctrine holds that all knowledge can be characterized by logical theories connected, ultimately, to observation sentences that correspond to sensory inputs. The confirmation theory of Rudolf Carnap and Carl Hempel attempted to establish the nature of the connection between the observation sentences and the more general theories—in other words, to understand how knowledge can be acquired from experience.
The final element in the philosophical picture of the mind is the connection between knowledge and action. What form should this connection take, and how can particular actions be justified? These questions are vital to AI, because only by understanding how actions are justified can we understand how to build an agent whose actions are justifiable, or rational. Aristotle provides an elegant answer in the Nicomachean Ethics (Book III. 3, 1112b):
We deliberate not about ends, but about means. For a doctor does not deliberate whether he shall heal, nor an orator whether he shall persuade, nor a statesman whether he shall produce law and order, nor does any one else deliberate about his end. They assume the end and consider how and by what means it is attained, and if it seems easily and best produced thereby; while if it is achieved by one means only they consider how it will be achieved by this and by what means this will be achieved, till they come to the first cause, which in the order of discovery is last .. . and what is last in the order of analysis seems to be first in the order of becoming. And if we come on an impossibility, we give up the search, e.g. if we need money and this cannot be got: but if a thing appears possible we try to do it.
Aristotle’s approach (with a few minor refinements) was implemented 2300 years later by Newell and Simon in their GPS program, about which they write (Newell and Simon, 1972):
The main methods of GPS jointly embody the heuristic of means-ends analysis. Means-ends analysis is typified by the following kind of common-sense argument:
I want to take my son to nursery school. What’s the difference between what I have and what I want? One of distance. What changes distance? My automobile. My automobile won’t work. What is needed to make it work? A new battery. What has new batteries? An auto repair shop. I want the repair shop to put in a new battery; but the shop doesn’t know I need one. What is the difficulty? One of communication. What allows communication? A telephone .. . and so on.
This kind of analysis—classifying things in terms of the functions they serve and oscillating among ends, functions required, and means that perform them—forms the basic system of heuristic of GPS.
Means-ends analysis is useful, but does not say what to do when several actions will achieve the goal, or when no action will completely achieve it. Arnauld, a follower of Descartes, correctly described a quantitative formula for deciding what action to take in cases like this (see Chapter 16). John Stuart Mill’s (1806-1873) book Utilitarianism (Mill, 1863) amplifies on this idea. The more formal theory of decisions is discussed in the following section.
Mathematics (c. 800-present)
Philosophers staked out most of the important ideas of AI, but to make the leap to a formal science required a level of mathematical formalization in three main areas: computation, logic, and probability. The notion of expressing a computation as a formal algorithm goes back to al-Khowarazmi, an Arab mathematician of the ninth century, whose writings also introduced Europe to Arabic numerals and algebra.
Logic goes back at least to Aristotle, but it was a philosophical rather than mathematical subject until George Boole (1815-1864) introduced his formal language for making logical inference in 1847. Boole’s approach was incomplete, but good enough that others filled in the gaps. In 1879, Gottlob Frege (1848-1925) produced a logic that, except for some notational changes, forms the first-order logic that is used today as the most basic knowledge representation system.8 Alfred Tarski (1902-1983) introduced a theory of reference that shows how to relate the objects in a logic to objects in the real world. The next step was to determine the limits of what could be done with logic and computation.
David Hilbert (1862-1943), a great mathematician in his own right, is most remembered for the problems he did not solve. In 1900, he presented a list of 23 problems that he correctly predicted would occupy mathematicians for the bulk of the century. The final problem asks if there is an algorithm for deciding the truth of any logical proposition involving the natural numbers—the famous Entscheidungsproblem, or decision problem. Essentially, Hilbert was asking if there were fundamental limits to the power of effective proof procedures. In 1930, Kurt Godel (1906-1978) showed that there exists an effective procedure to prove any true statement in the first-order logic of Frege and Russell; but first-order logic could not capture the principle of mathematical induction needed to characterize the natural numbers. In 1931, he showed that real limits do exist. His incompleteness theorem showed that in any language expressive enough to describe the properties of the natural numbers, there are true statements that are undecidable: their truth cannot be established by any algorithm.
This fundamental result can also be interpreted as showing that there are some functions on the integers that cannot be represented by an algorithm—that is, they cannot be computed. This motivated Alan Turing (1912-1954) to try to characterize exactly which functions are capable of being computed. This notion is actually slightly problematic, because the notion of a computation or effective procedure really cannot be given a formal definition. However, the Church-Turing thesis, which states that the Turing machine (Turing, 1936) is capable of computing any computable function, is generally accepted as providing a sufficient definition. Turing also showed that there were some functions that no Turing machine can compute. For example, no machine can tell in general whether a given program will return an answer on a given input, or run forever.
Although undecidability and noncomputability are important to an understanding of computation, the notion of intractability has had a much greater impact. Roughly speaking, a class of problems is called intractable if the time required to solve instances of the class grows at least exponentially with the size of the instances. The distinction between polynomial and exponential growth in complexity was first emphasized in the mid-1960s (Cobham, 1964; Edmonds, 1965). It is important because exponential growth means that even moderate-sized instances cannot be solved in any reasonable time. Therefore, one should strive to divide the overall problem of generating intelligent behavior into tractable subproblems rather than intractable ones. The second important concept in the theory of complexity is reduction, which also emerged in the 1960s (Dantzig, 1960; Edmonds, 1962). A reduction is a general transformation from one class of problems to another, such that solutions to the first class can be found by reducing them to problems of the second class and solving the latter problems.
How can one recognize an intractable problem? The theory of NP-completeness, pioneered by Steven Cook (1971) and Richard Karp (1972), provides a method. Cook and Karp showed the existence of large classes of canonical combinatorial search and reasoning problems that are NP-complete. Any problem class to which an NP-complete problem class can be reduced is likely to be intractable. (Although it has not yet been proved that NP-complete problems are necessarily intractable, few theoreticians believe otherwise.) These results contrast sharply with the “Electronic Super-Brain” enthusiasm accompanying the advent of computers. Despite the ever-increasing speed of computers, subtlety and careful use of resources will characterize intelligent systems. Put crudely, the world is an extremely large problem instance!
Besides logic and computation, the third great contribution of mathematics to AI is the theory of probability. The Italian Gerolamo Cardano (1501-1576) first framed the idea of I probability, describing it in terms of the possible outcomes of gambling events. Before his time, the outcomes of gambling games were seen as the will of the gods rather than the whim of chance, Probability quickly became an invaluable part of all the quantitative sciences, helping to deal with uncertain measurements and incomplete theories. Pierre Fermat (1601-1665), Blaise Pascal I (1623-1662), James Bernoulli (1654-1705), Pierre Laplace (1749-1827), and others advanced the theory and introduced new statistical methods. Bernoulli also framed an alternative view of probability, as a subjective “degree of belief” rather than an objective ratio of outcomes. Subjective probabilities therefore can be updated as new evidence is obtained. Thomas Bayes j (1702-1761) proposed a rule for updating subjective probabilities in the light of new evidence! (published posthumously in 1763). Bayes’ rule, and the subsequent field of Bayesian analysis, form the basis of the modern approach to uncertain reasoning in AI systems. Debate still rages between supporters of the objective and subjective views of probability, but it is not clear if the! difference has great significance for AI. Both versions obey the same set of axioms. Savage’sJ (1954) Foundations of Statistics gives a good introduction to the field.
As with logic, a connection must be made between probabilistic reasoning and action.Decision theory, pioneered by John Von Neumann and Oskar Morgenstern (1944), combines probability theory with utility theory (which provides a formal and complete framework for specifying the preferences of an agent) to give the first general theory that can distinguish good! actions from bad ones. Decision theory is the mathematical successor to utilitarianism, and provides the theoretical basis for many of the agent designs in this lesson.
Scientific psychology can be said to have begun with the work of the German physicist Hermann von Helmholtz (1821-1894) and his student Wilhelm Wundt (1832-1920). Helmholtz applied the scientific method to the study of human vision, and his Handbook of Physiological Optics is even now described as “the single most important treatise on the physics and physiology of human vision to this day” (Nalwa, 1993, p.15). In 1879, the same year that Frege launched firstorder logic, Wundt opened the first laboratory of experimental psychology at the University of Leipzig. Wundt insisted on carefully controlled experiments in which his workers would perform a perceptual or associative task while introspecting on their thought processes. The careful controls went a long way to make psychology a science, but as the methodology spread, a curious phenomenon arose: each laboratory would report introspective data that just happened to match the theories tint were popular in that laboratory. The behaviorism movement of John Watson (1878-1958) aid Edward Lee Thorndike (1874-1949) rebelled against this subjectivism, rejecting any theory involving mental processes on the grounds that introspection could not provide reliable evidence. Behiviorists insisted on studying only objective measures of the percepts (or stimulus) given to an animal and its resulting actions (or response). Mental constructs such as knowledge, beliefs, goals, md reasoning steps were dismissed as unscientific “folkpsychology.” Behaviorism discovered a let about rats and pigeons, but had less success understanding humans. Nevertheless, it had a stronghold on psychology (especially in the United States) from about 1920 to 1960.
The view that the brain possesses and processes information, which is the principal characteristic of cognitive psychology, can be traced back at least to the works of William James9 (1842-1910). Helmholtz also insisted that perception involved a form of unconscious logical inference. The cognitive viewpoint was largely eclipsed by behaviorism until 1943, when Kenneth Craik published The Nature of Explanation. Craik put back the missing mental step between stimulus and response. He claimed that beliefs, goals, and reasoning steps could be useful valid components of a theory of human behavior, and are just as scientific as, say, using pressure and temperature to talk about gases, despite their being made of molecules that have neither. Craik specified the tlree key steps of a knowledge-based agent: (1) the stimulus must be translated into an internal representation, (2) the representation is manipulated by cognitive processes to derive new internal representations, and (3) these are in turn retranslated back into action. He clearly explained why this was a good design for an agent:
If the orgmism carries a “small-scale model” of external reality and of its own possible actions within its head, it is able to try out various alternatives, conclude which is the best of them, react to fiture situations before they arise, utilize the knowledge of past events in dealing with the present and future, and in every way to react in a much fuller, safer, and more competent manner to the emergencies which face it. (Craik, 1943)
An agent designed this way can, for example, plan a long trip by considering various possible routes, comparing them, and choosing the best one, all before starting the journey. Since the 1960s, the information-processing view has dominated psychology. It it now almost taken for granted among many psychologists that “a cognitive theory should be like a computer program” (Andersen, 1980). By this it is meant that the theory should describe cognition as consisting of well-define transformation processes operating at the level of the information carried by the input signals.
For most of the early history of AI and cognitive science, no significant distinction was drawn between the two fields, and it was common to see AI programs described as psychological results without any claim as to the exact human behavior they were modelling. In the last decade or so, however, the methodological distinctions have become clearer, and most work now falls into one field or the other.
Computer engineering (1940-present)
For artificial intelligence to succeed, we need two things: intelligence and an artifact. The computer has been unanimously acclaimed as the artifact with the best chance of demonstrating intelligence. The modern digital electronic computer was invented independently and almost simultaneously by scientists in three countries embattled in World War II. The first operational modern computer was the Heath Robinson,10 built in 1940 by Alan Turing’s team for the single purpose of deciphering German messages. When the Germans switched to a more sophisticated code, the electromechanical relays in the Robinson proved to be too slow, and a new machine called the Colossus was built from vacuum tubes. It was completed in 1943, and by the end of the war, ten Colossus machines were in everyday use.
The first operational programmable computer was the Z-3, the invention of Konrad Zuse in Germany in 1941. Zuse invented floating-point numbers for the Z-3, and went on in 1945 to develop Plankalkul, the first high-level programming language. Although Zuse received some support from the Third Reich to apply his machine to aircraft design, the military hierarchy did not attach as much importance to computing as did its counterpart in Britain.
In the United States, the first electronic computer, the ABC, was assembled by John Atanasoff and his graduate student Clifford Berry between 1940 and 1942 at Iowa State University. The project received little support and was abandoned after Atanasoff became involved in military research in Washington. Two other computer projects were started as secret military research: the Mark I, If, and III computers were developed at Harvard by a team under Howard Aiken; and the ENIAC was developed at the University of Pennsylvania by a team including John Mauchly and John Eckert. ENIAC was the first general-purpose, electronic, digital computer. One of its first applications was computing artillery firing tables. A successor, the EDVAC, followed John Von Neumann’s suggestion to use a stored program, so that technicians would not have to scurry about changing patch cords to run a new program.
But perhaps the most critical breakthrough was the IBM 701, built in 1952 by Nathaniel Rochester and his group. This was the first computer to yield a profit for its manufacturer. IBM went on to become one of the world’s largest corporations, and sales of computers have grown to $150 billion/year. In the United States, the computer industry (including software and services) now accounts for about 10% of the gross national product.
Each generation of computer hardware has brought an increase in speed and capacity, and I a decrease in price. Computer engineering has been remarkably successful, regularly doubling performance every two years, with no immediate end in sight for this rate of increase. Massively j parallel machines promise to add several more zeros to the overall throughput achievable.
Of course, there were calculating devices before the electronic computer. The abacus \ is roughly 7000 years old. In the mid-17th century, Blaise Pascal built a mechanical addingand subtracting machine called the Pascaline. Leibniz improved on this in 1694. building a mechanical device that multiplied by doing repeated addition. Progress stalled for over a century unti 1 Charles Babbage (1792-1871) dreamed that logarithm tables could be computed by machine. He designed a machine for this task, but never completed the project. Instead, he turned to the design of the Analytical Engine, for which Babbage invented the ideas of addressable memory. stored programs, and conditional jumps. Although the idea of programmable machines was not new—in 1805. Joseph Marie Jacquard invented a loom that could be programmed using punched cards—Babbage’s machine was the first artifact possessing the characteristics necessary for universal computation. Babbage’s colleague Ada Lovelace, daughter of the poet Lord Byron, wrote programs for the Analytical Engine and even speculated that the machine could play chess or compose music. Lovelace was the world’s first programmer, and the first of many to endure massive cost overruns and to have an ambitious project ultimately abandoned.” Babbage’s basic design was proven viable by Doron Swade and his colleagues, who built a working model using only the mechanical techniques available at Babbage’s time (Swade. 1993). Babbage had the right idea, but lacked the organizational skills to get his machine built.
AI also owes a debt to the software side of computer science, which has supplied the operating systems, programming languages, and tools needed to write modern programs (and papers about them). But this is one area where the debt has been repaid: work in AI has pioneered many ideas that have made their way back to “mainstream” computer science, including time sharing, interactive interpreters, the linked list data type, automatic storage management, and some of the key concepts of object-oriented programming and integrated program development environments with graphical user interfaces.
In 1957. B. F. Skinner published Verbal Behavior. This was a comprehensive, detailed account of the behaviorist approach to language learning, written by the foremost expert in the field. But curiously, a review of the book became as well-known as the book itself, and served to almost kill off interest in behaviorism. The author of the review was Noam Chomsky, who had just published a book on his own theory. Syntactic Structures. Chomsky showed how the behaviorist theory did not address the notion of creativity in language—it did not explain how a child could understand and make up sentences that he or she had never heard before. Chomsky’s theory—based on syntactic models going back to the Indian linguist Panini (c. 350 B.C.)—could explain this, and unlike previous theories, it was formal enough that it could in principle be programmed.
Later developments in linguistics showed the problem to be considerably more complex than it seemed in 1957. Language is ambiguous and leaves much unsaid. This means that understanding language requires an understanding of the subject matter and context, not just an understanding of the structure of sentences. This may seem obvious, but it was not appreciated until the early 1960s. Much of the early work in knowledge representation (the study of how to put knowledge into a form that a computer can reason with) was tied to language and informed by research in linguistics, which was connected in turn to decades of work on the philosophical analysis of language.
Modern linguistics and AI were “born” at about the same time, so linguistics does not play a large foundational role in the growth of AI. Instead, the two grew up together, intersecting in a hybrid field called computational linguistics or natural language processing, which concentrates on the problem of language use.