The Birth of AI (1956)
The Dartmouth Summer Research Project on Artificial Intelligence was an academic workshop sometimes considered to be the founding moment for artificial intelligence as a field of research. Held for eight weeks in Hanover, New Hampshire in 1956, the conference gathered twenty or so of the brightest minds in computer- and cognitive science for a workshop committed to the idea that:
"..every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer."
— A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence (McCarthy et al, 1955)
Now seventy or so years later, their vision seems as potent as ever before. This essay is about the 1956 workshop, its members and their various contributions to the nascent field of artificial intelligence.
Motivation
Prior to the conference, then-Assistant Professor of Mathematics at Dartmouth College John McCarthy (1927-2011) and Claude Shannon (1916-2001) at MIT had been co-editing the 34th volume of the journal Annals of Mathematics Studies, on Automata Studies (Shannon & McCarthy, 1956).
Automata are self-operating machines designed to automatically follow predetermined sequences of operations or respond to predetermined instructions. As engineering mechanisms they appear in a wide variety of everyday applications such as mechanical clocks where a hammer strikes a bell or a cuckoo appears to sing. Among those who contributed chapters to the book were John von Neumann (1903-57) and Stephen Cole Kleene (1909-94). According to James Moor (2006), though, McCarthy was ultimately disappointed in the book and its lack of emphasis on the possibilities of computers possessing intelligence beyond the rather trivial/simple behaviors of automata. As he later stated:
“At the time I believed if only we could get everyone who was interested in the subject together to devote time to it and avoid distractions, we could make real progress”
The initial group McCarthy had in mind included Marvin Minsky (1927-2016) whom he had known since they were graduate students together at Fine Hall in the early 1950s. The two had talked about artificial intelligence before, and Minsky’s Ph.D. dissertation (in mathematics) had been on neural nets (Moor, 2006) and the structure of the human brain (Nasar, 1998). Minsky and McCarthy had both been hired by Bell Labs in 1952. There, McCarthy learned that (the much more senior) Shannon too was interested in the topic. Finally, McCarthy had run into Nathaniel Rochester (1919-2001) at MIT when IBM was gifting the institution with its first computer. Rochester too showed interest in artificial intelligence, and so the four agreed to submit a proposal for a workshop (McCorduck, 1979).
The Proposal
“We propose that a two-month, ten-man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire”
The written proposal to the Rockefeller Foundation was first formulated by McCarthy at Dartmouth and Minsky, then at Harvard. They brought their proposal to Shannon at Bell and Rochester at IBM and got their signatures (Crevier, 1993). The proposal went as follows (McCarthy et al, 1955, reprinted in AI Magazine Volume 27, Number 4 pp. 12–14):
A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence (August 31st, 1955)
We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer. The following are some aspects of the artificial intelligence problem:
1. Automatic Computers. If a machine can do a job, then an automatic calculator can be programmed to simulate the machine. The speeds and memory capacities of present computers may be insufficient to simulate many of the higher functions of the human brain, but the major obstacle is not lack of machine capacity, but our inability to write programs taking full advantage of what we have.
2. How Can a Computer be Programmed to Use a Language. It may be speculated that a large part of human thought consists of manipulating words according to rules of reasoning and rules of conjecture. From this point of view, forming a generalization consists of admitting a new word and some rules whereby sentences containing it imply and are implied by others. This idea has never been very precisely formulated nor have examples been worked out.
3. Neuron Nets. How can a set of (hypothetical) neurons be arranged so as to form concepts. Considerable theoretical and experimental work has been done on this problem by Uttley, Rashevsky and his group, Farley and Clark, Pitts and McCulloch, Minsky, Rochester and Holland, and others. Partial results have been obtained but the problem needs more theoretical work.
4. Theory of the Size of a Calculation. If we are given a well-defined problem (one for which it is possible to test mechanically whether or not a proposed answer is a valid answer) one way of solving it is to try all possible answers in order. This method is inefficient, and to exclude it one must have some criterion for efficiency of calculation. Some consideration will show that to get a measure of the efficiency of a calculation it is necessary to have on hand a method of measuring the complexity of calculating devices which in turn can be done if one has a theory of the complexity of functions. Some partial results on this problem have been obtained by Shannon, and also by McCarthy.
5. Self-improvement. Probably a truly intelligent machine will carry out activities which may best be described as self-improvement. Some schemes for doing this have been proposed and are worth further study. It seems likely that this question can be studied abstractly as well.
6. Abstractions. A number of types of “abstraction” can be distinctly defined and several others less distinctly. A direct attempt to classify these and to describe machine methods of forming abstractions from sensory and other data would seem worthwhile.
7. Randomness and Creativity. A fairly attractive and yet clearly incomplete conjecture is that the difference between creative thinking and unimaginative competent thinking lies in the injection of a some randomness. The randomness must be guided by intuition to be efficient. In other words, the educated guess or the hunch include controlled randomness in otherwise orderly thinking.
Included with the proposal were short biographies of its authors and signatories.
Shannon, 40 years old in 1956, in his bio highlighted his contributions to the statistical theory of information, his work on switching circuits, the design of machines that learn, cryptography and theoretical computing (Turing machines). By this point an established senior researcher, Shannon had published A Mathematical Theory of Communication in 1948 (now among the most highest cited papers ever) and introduced the Shannon cipher in the 1949 paper Communication Theory and Secrecy Systems. He completed his Ph.D. at MIT in 1940 before moving to Princeton to be a National Research Fellow at the Institute for Advanced Study. There, he discussed his work with, among others, von Neumann and Hermann Weyl (1885-1955).
Like Shannon, McCarthy (29 years old in 1956) in his biography highlights his work on theoretical computing, as well as his work on the mathematical nature of the relationship between a brain model and its environment, and the use of languages by machines. While still an undergraduate at the California Institute of Technology, McCarthy in 1948 attended the Hixon Symposium on Cerebral Mechanisms in Behavior. There, von Neumann presented the paper The General and Logical Theory of Automata, which inspired McCarthy to think about developing machines that could think as people do. That same year, he entered graduate school for mathematics at Caltech, but decided to move to Princeton in 1949. There he met Minsky, who was also pursuing a Ph.D. in mathematics. After completing his Ph.D., in 1952 McCarthy, alongside Minsky, was invited by Shannon to work at Bell Labs for the summer.
Minsky had come to Princeton in 1950 after receiving a B.A. in mathematics from Harvard. He completed his Ph.D., on Neural Nets and the Brain Model Problem in 1954. In his biography for the proposal, Minsky highlights having “built a machine for simulating learning by nerve nets”. The result had impressed Shannon, von Neumann and Norbert Wiener (1894-1964) sufficiently for them all to recommend him for an appointment at Harvard, which he held from 1954-57.
Nathaniel Rochester was by 1954 a senior engineer at IBM and its manager of information research. There, he had worked on “radar technology for seven years and computing machinery for seven years”. Jointly with Jerrier Haddad (1922-2017), Rochester had been responsible for the design of the IBM 701, IBM’s first commercial scientific computer. Its design was based on the IAS machine at Princeton, sometimes referred to as the ‘von Neumann’ machine based on the von Neumann architecture. Rochester wrote the first assembler in 1951 and had by 1956 also worked on simulation of nerve nets for the purpose of testing theories in neurophysiology.
Attendees
Although they came from a wide variety of backgrounds (mathematics, psychology, electrical engineering and more) the attendees at the 1956 Dartmouth conference shared a common defining belief that the act of thinking is something not unique either to humans or indeed even biological beings. Rather, thinking is a form of computation, which is a formally deducible phenomenon that can be understood in a scientific way. The best nonhuman instrument for doing so is the digital computer (McCorduck, 1979). Shannon, McCarthy, Minsky and Rochester each invited people who shared their beliefs. According to notes written by Ray Solomonoff (1926-2009), twenty people attended over the eight week period. Among them were future Nobel laureates Nash and Herbert A. Simon (1916–2001). The former was likely invited by Minsky, as Nash had helped Minsky solve a problem for his dissertation. Simon was likely either invited by McCarthy himself or by extension through Allen Newell (1927-1992), as the two had worked together at IBM in 1955.
According to one of McCarthy’s communications from May of 1956, Simon was scheduled to attend for the first two weeks of the workshop. Then a Professor of Administration at Carnegie Mellon (then Carnegie Tech), Simon had been working on decision making problems (so-called “administrative behavior”) since receiving his Ph.D. in 1947 from the University of Chicago. He would go on to win the 1978 Nobel Memorial Prize in Economics for this work, and more broadly for “his pioneering research into the decision-making process within economic organizations”. At the time of the conference, he was collaborating with Newell and Cliff Shaw (1922-91) on the computer language ‘the Information Processing Language’ (IPL) and the pioneering computer programs Logic Theorist (1956) and General Problem Solver (1959). The former was the first program engineered to mimic the problem solving abilities of a human, and would eventually solve 38 of the first 52 theorems in Whitehead and Russell’s Principia Mathematica, even finding new and more elegant proofs than those proposed in 1912. The second was intended to work as a universal problem solving machine, which could solve any problem that is sufficiently formally expressed. It would evolve into the Soar architecture for artificial intelligence.
Simon’s collaborator Allen Newell also attended for the first two weeks of the seminar. The two had first met at the RAND Corporation in 1952 and created the IPL programming language together in 1956. The language was the first to introduce list manipulation, property lists, higher-order functions, computation with symbols and virtual machines to digital computing languages. Newell was also the programmer who first introduced list processing, the application of means-ends analysis to general reasoning, and using heuristics to limit the search space of a program. A year before the conference, Newell has published a paper entitled The Chess Machine: An Example of Dealing with a Complex Task by Adaptation, which included a theoretical outline of
"..an imaginative design for a computer program to play chess in humanoid fashion, incorporating notions of goals, aspiration levels for terminating search, satisfying with "good enough" moves, multidimensional evaluation functions, the generation of subgoals to implement goals and something like best first search. Information about the board was to be was to be expressed symbolically in a language resembling the predicate calculus."
- Excerpt, "Allen Newell" in Biographical Memoirs (1997) by Herbert A Simon.
Newell attributed the idea that lead to the paper to a “conversion experience” he had while in a seminar in 1954 listening to Oliver Selfridge (1926-2008), student of Wiener, describing “running a computer program that learned to recognize letters and other patterns” (Simon, 1997). Selfridge attended the workshop for four weeks. He would go on to write important early papers on neural networks, pattern recognition and machine learning. His Pandemonium paper (1959) is considered a classic in artificial intelligence circles.
Pioneering computer engineer Julian Bigelow (1913-2003) also attended. Bigelow had worked with Wiener on one the founding papers on cybernetics and was hired by von Neumann to build the IAS machine (or “the MANIAC”) at the Institute for Advanced study in 1946, on the former’s recommendation.
Other attendees included Trenchard More (1930-2019), W. Ross Ashby (1903-72), W.S. McCulloch (1898-1969), Abraham Robinson (1918-74), David Sayre (1924-2012), Arthur Samuel (1901-1990) and Kenneth R. Shoulders (1927-2013).
Outcomes
Somewhat ironically, the “intense and sustained two months of scientific exchange” envisioned by McCarthy prior to the workshop never actually quite took place As McCorduck (1979) writes:
“Anybody who was there was pretty stubborn about pursuing the ideas that he had before he came, nor was there, as far as I could see, any real exchange of ideas. People came for different periods of time. The idea was that everyone would agree to come for six weeks, and the people came for periods ranging from two days to the whole six weeks, so not everybody was there at once. It was a great disappointment to me because it really meant that we couldn’t have regular meetings.”
Some tangible outcomes can however still be deduced. First of all, the term artificial intelligence (AI) itself was first coined by McCarthy during the conference. Of important work directly related to the same period of time as the conference, McCarthy later emphasized the works of Newell, Shaw and Simon on the Information Processing Language (IPL) and their Logic Theory Machine (Moor, 2006).
One of the conference’s attendees, Arthur Samuel, would go on to coin the term “machine learning” in 1959 and create the Samuel Checkers-playing program, one of the world’s first successful self-learning programs. Selfridge is now often referred to as “Father of Machine Perception” for his research into pattern-recognition. Minsky would go on to win the Turing award in 1969 for his “central role in creating, shaping, promoting and advancing the field of artificial intelligence”. Newell and Simon would go on to win the same award in 1975 for their contributions to “artificial intelligence and the psychology of human cognition”.
The entire proposal, including its budget, is available on Stanford’s website here.
I hope you enjoyed this essay,
Best
Jørgen
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References
Crevier, D., 1993. AI: The Tumultuous Search for Artificial Intelligence*. New York, NY: BasicBooks.
Guizzo, E.M., 2003. The essential message: Claude Shannon and the making of information theory (Doctoral dissertation, Massachusetts Institute of Technology).
McCarthy, J. Minsky, M., Rochester, N., Shannon, C.E., 1955. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence., http://raysolomonoff.com/dartmouth/boxa/dart564props.pdf August.
McCorduck, P., 2004. Machines Who Think*, A.K. Peters, Ltd, Second Edition.
Moor, J., 2006. "The Dartmouth College Artificial Intelligence Conference: The Next Fifty years", AI Magazine, Vol 27, No. 4, pp. 87–89.
Nasar, S., 2011. A Beautiful Mind*, Simon and Schuster. New York, NY.
* This essay contains Amazon Affiliate Links
Thank you for sharing and summarizing this remarkable little episode. Here are two things that struck me:
(1) Once upon a time, it seems, the idea was to *simulate* human intelligence, not to *replicate* or *re-create* it. It seems, then, that theorists used to understand that models are illuminating because they are partial, not because they are exhaustive. I wonder whether theorists today are as keen not to confuse the map with the territory.
(2) By contrast to the judiciousness mentioned above, it's jarring to see the application of some astonishingly unilluminating dichotomies: deduction and induction, rule-guidedness and randomness, reason and intuition. The most paradigmatically human reasoning we humans do is abductive reasoning — broadly and roughly, reasoning to make sense of things. And that kind of reasoning undermines each of these dichotomies by sublating each pole. Erik Larson, who writes over at Colligo here on Substack, has written about this in a wonderful book, The Myth of Artificial Intelligence (Belknap Press, 2022).
John McCarthy was hired at Dartmouth in 1955 by John Kemeny after Kemeny became the chair of the Mathematics department. 29-year-old Kemeny was just a year older than McCarthy. They overlapped at Princeton. Kemeny participated in the 1956 summer project and helped McCarthy obtain a Sloan Foundation Fellowship, which took the latter to MIT. Later in his career, Kemeny co-created the BASIC programming language and served as the president of Dartmouth. Remarkable people!