In 1955, a summer study project at Dartmouth College was proposed by McCarthy (with Marvin Minsky and Claude Shannon, who is said to have proposed it). The proposal is credited with introducing the term 'artificial intelligence'.

“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 brain clearly contains "information structures" and does something like information processing, particularly storage and retrieval. McCarthy's conjecture is that a machine can simulate every aspect of human learning, can use human language, can solve problems and form abstract concepts. The deep question is whether it uses computer algorithms as argued by McCarthy and his colleagues.

Information philosophy hopes to show that man is not a machine and the brain is not a computer. A neural network is not a computer network.

Attendees at the Dartmouth summer project included: W. Ross Ashby, Alex Bernstein, Julian Bigelow, Tom Etter, John Holland, Donald Mackay, John McCarthy, W.S. McCulloch, Marvin Minsky, Trenchard More, John Nash, Allen Newell, Abraham Robinson, Nathanial Rochester, Arthur Samuel, David Sayre, Oliver Selfridge, Claude Shannon, Kenneth R. Shoulders, Herbert Simon, and Ray Solomonoff, who gave us the most complete record of the workshop proceedings.

The most complete record of the workshop was that of Ray Solomonoff, who, with founders John McCarthy and Marvin Minsky, were the only ones to attend the entire eight weeks.

The absence at the Dartmouth Summer Project of the pioneer of cybernetics, MIT's Norbert Wiener, was most conspicuous. The conference proposal deliberately did not mention cybernetics. That was the basis of the famous Macy Conferences, which ran from the mid 1940's to around 1960, and included several of the AI pioneers mentioned above. McCarthy wanted to avoid discussions of simple automata theory. By avoiding cybernetics, which focused on analog feedback, it meant he also avoided debates with the powerful Wiener.

In the first two weeks of the summer workshop, Newell and Simon put forward the idea of a two-stage "General Problem Solver," which was partial inspiration for Daniel Dennett's "Valerian Model" of free will in 1978.

McCarthy thought mathematical logic should be the basis for the new field. His slogan was "He who refuses to do arithmetic is doomed to talk nonsense."

A common feature of free will is that a person has choices among alternative actions and chooses the action with the apparently most preferred consequences. In a determinist theory, the mechanism that makes the choice among the alternatives is determinist. The sensation of free will comes from the fact that the mechanism that generates the choices uses a non-determinist theory as a computational device and that the stage in which the choices have been identified is introspectable. The present formalism is based on work in artificial intelligence (AI).

We present a theory of simple deterministic free will (SDFW) in a deterministic world. The theory splits the mechanism that determines action into two parts. The first part computes possible actions and their consequences. Then the second part decides which action is most preferable and does it.

We formalize SDFW by two sentences in situation calculus, a mathematical logical theory often used in AI. The situation calculus formalization makes the notion of free will technical. According to this notion, almost no animal behavior exhibits free will, because exercising free will involves considering the consequences of alternative actions. A major advantage of our notion of free will is that whether an animal does have free will may be determinable by experiment. Some computer programs, e.g. chess programs, exhibit SDFW. Almost all do not. At least SDFW seems to be required for effective chess performance and also for human-level AI.