BEST EXPLANATION*: THE HISTORY OF ARTIFICIAL INTELLIGENCE

With the background material behind us, we are now ready to outline the development of AI proper. We could do this by identifying loosely defined and overlapping phases in its development, or by chronicling the various different and intertwined conceptual threads that make up the field. In this section, we will take the former approach, at the risk of doing some degree of violence to the real relationships among subfields. The history of each subfield is covered in individual chapters later in the book.

The gestation of artificial intelligence (1943-1956)

The first work that is now generally recognized as AI was done by Warren McCulloch and Walter Pitts (1943). They drew on three sources: knowledge of the basic physiology and function of neurons in the brain; the formal analysis of propositional logic due to Russell and Whitehead; and Turing's theory of computation. They proposed a model of artificial neurons in which each neuron is characterized as being "on" or "off," with a switch to "on" occurring in response to stimulation by a sufficient number of neighboring neurons. 

The state of a neuron was conceived of as "factually equivalent to a proposition which proposed its adequate stimulus." They showed, for example, that any computable function could be computed by some network of connected neurons, and that all the logical connectives could be implemented by simple net structures. McCulloch and Pitts also suggested that suitably defined networks could learn. Donald Hebb (1949) demonstrated a simple updating rule for modifying the connection strengths between neurons, such that learning could take place. 

The work of McCulloch and Pitts was arguably the forerunner of both the logicist tradition i in AI and the connectionist tradition. In the early 1950s, Claude Shannon (1950) and Alan Turing (1953) were writing chess programs for von Neumann-style conventional computers.12 At the same time, two graduate students in the Princeton mathematics department, Marvin Minsky and Dean Edmonds, built the first neural network computer in 1951. The SNARC, as it was called, used 3000 vacuum tubes and a surplus automatic pilot mechanism from a B-24 bomber to simulate a network of 40 neurons. Minsky's Ph.D. committee was skeptical whether this kind of work should be considered mathematics, but von Neumann was on the committee and reportedly said, "If it isn't now it will be someday." Ironically, Minsky was later to prove theorems that contributed to the demise of much of neural network research during the 1970s.

12 Shannon actually had no real computer to work with, and Turing was eventually denied access to his own team's computers by the British government, on the grounds that research into artificial intelligence was surely frivolous.

Princeton was home to another influential figure in AI, John McCarthy. After graduation, McCarthy moved to Dartmouth College, which was to become the official birthplace of the field. McCarthy convinced Minsky, Claude Shannon, and Nathaniel Rochester to help him bring together U.S. researchers interested in automata theory, neural nets, and the study of intelligence. They organized a two-month workshop at Dartmouth in the summer of 1956. 

All together there were ten attendees, including Trenchard More from Princeton, Arthur Samuel from IBM, and Ray Solomonoff and Oliver Selfridge from MIT. Two researchers from Carnegie Tech,13 Alien Newell and Herbert Simon, rather stole the show. Although the others had ideas and in some cases programs for particular applications such as checkers, Newell and Simon already had a reasoning program, the Logic Theorist (LT), about which Simon claimed, "We have invented a computer program capable of thinking nonnumerically, and thereby solved the venerable mind-body problem."14 Soon after the workshop, the program was able to prove most of the theorems in Chapter 2 of Russell and Whitehead's Principia Mathematica. 

Russell was reportedly delighted when Simon showed him that the program had come up with a proof for one theorem that was shorter than the one in Principia. The editors of the Journal of Symbolic Logic were less impressed; they rejected a paper coauthored by Newell, Simon, and Logic Theorist. The Dartmouth workshop did not lead to any new breakthroughs, but it did introduce all the major figures to each other. For the next 20 years, the field would be dominated by these people and their students and colleagues at MIT, CMU, Stanford, and IBM. Perhaps the most lasting thing to come out of the workshop was an agreement to adopt McCarthy's new name for the field: artificial intelligence.

Early enthusiasm, great expectations (1952-1969)

The early years of AI were full of successes—in a limited way. Given the primitive computers and programming tools of the time, and the fact that only a few years earlier computers were seen as things that could do arithmetic and no more, it was astonishing whenever a computer did anything remotely clever. The intellectual establishment, by and large, preferred to believe that "a machine can never do X" (see Chapter 26 for a long list of X's gathered by Turing). 

AI researchers naturally responded by demonstrating one X after another. Some modern AI researchers refer to this period as the "Look, Ma, no hands!" era. Newell and Simon's early success was followed up with the General Problem Solver, or GPS. Unlike Logic Theorist, this program was designed from the start to imitate human problem-solving protocols. Within the limited class of puzzles it could handle, it turned out that the order in which the program considered subgoals and possible actions was similar to the way humans approached the same problems. Thus, GPS was probably the first program to embody the "thinking humanly" approach. The combination of AI and cognitive science has continued at CMU up to the present day.

13 Now Carnegie Mellon University (CMU). 14 Newell and Simon also invented a list-processing language, IPL, to write LT. They had no compiler, and translated it into machine code by hand. To avoid errors, they worked in parallel, calling out binary numbers to each other as they wrote each instruction to make sure they agreed.

At IBM, Nathaniel Rochester and his colleagues produced some of the first AI programs. Herbert Gelernter (1959) constructed the Geometry Theorem Prover. Like the Logic Theorist, it proved theorems using explicitly represented axioms. Gelernter soon found that there were too many possible reasoning paths to follow, most of which turned out to be dead ends. To help focus the search, he added the capability to create a numerical representation of a diagram—a particular case of the general theorem to be proved. 

Before the program tried to prove something, it could first check the diagram to see if it was true in the particular case. Starting in 1952, Arthur Samuel wrote a series of programs for checkers (draughts) that eventually learned to play tournament-level checkers. Along the way, he disproved the idea that computers can only do what they are told to, as his program quickly learned to play a better game than its creator. The program was demonstrated on television in February 1956, creating a very strong impression. Like Turing, Samuel had trouble finding computer time. Working at night, he used machines that were still on the testing floor at IBM's manufacturing plant. 

Chapter 5 covers game playing, and Chapter 20 describes and expands on the learning techniques used by Samuel. John McCarthy moved from Dartmouth to MIT and there made three crucial contributions in one historic year: 1958. In MIT AI Lab Memo No. 1, McCarthy defined the high-level language Lisp, which was to become the dominant AI programming language. Lisp is the second-oldest language in current use.15 With Lisp, McCarthy had the tool he needed, but access to scarce and expensive computing resources was also a serious problem. Thus, he and others at MIT invented time sharing. After getting an experimental time-sharing system up at MIT, McCarthy eventually attracted the interest of a group of MIT grads who formed Digital Equipment Corporation, which was to become the world's second largest computer manufacturer, thanks to their time-sharing minicomputers. Also in 1958, McCarthy published a paper entitled Programs with Common Sense, in which he described the Advice Taker, a hypothetical program that can be seen as the first complete AI system. 

Like the Logic Theorist and Geometry Theorem Prover, McCarthy's program was designed to use knowledge to search for solutions to problems. But unlike the others, it was to embody general knowledge of the world. For example, he showed how some simple axioms would enable the program to generate a plan to drive to the airport to catch a plane. The program was also designed so that it could accept new axioms in the normal course of operation, thereby allowing it to achieve competence in new areas without being reprogrammed. The Advice Taker thus embodied the central principles of knowledge representation and reasoning: that it is useful to have a formal, explicit representation of the world and the way an agent's actions affect the world, and to be able to manipulate these representations with deductive processes. 

It is remarkable how much of the 1958 paper remains relevant after more than 35 years. 1958 also marked the year that Marvin Minsky moved to MIT. For years he and McCarthy were inseparable as they defined the field together. But they grew apart as McCarthy stressed representation and reasoning in formal logic, whereas Minsky was more interested in getting programs to work, and eventually developed an anti-logical outlook. In 1963, McCarthy took the opportunity to go to Stanford and start the AI lab there. His research agenda of using logic to build the ultimate Advice Taker was advanced by J. A. Robinson's discovery of the resolution method (a complete theorem-proving algorithm for first-order logic; see Section 9.6). Work at Stanford emphasized general-purpose methods for logical reasoning. Applications of

15 FORTRAN is one year older than Lisp.

logic included Cordell Green's question answering and planning systems (Green, 1969b), and the Shakey robotics project at the new Stanford Research Institute (SRI). The latter project, discussed further in Chapter 25, was the first to demonstrate the complete integration of logical reasoning and physical activity.