Thomas Haigh (thomas.haigh@gmail.com) is a professor of history at the University of Wisconsin--Milwaukee, WI, USA, and a Comenius visiting professor at Siegen University, Germany.

In the early 1960s, computer scientist Ed Feigenbaum became interested in "creating models of the thinking processes of scientists, especially the processes of empirical induction by which hypotheses and theories were inferred from data." In April 1964, he met geneticist (and Noble-prize winner) Joshua Lederberg who told him how experienced chemists use their knowledge about how compounds tend to break up in a mass spectrometer to make guesses about a compound's structure. Recalling in 1987 the development of DENDRAL, the first expert system, Lederberg remarked: "…we were trying to invent AI, and in the process discovered an expert system. This shift of paradigm, 'that Knowledge IS Power' was explicated in our 1971 paper [On Generality and Problem Solving: A Case Study Using the DENDRAL Program], and has been the banner of the knowledge-based-system movement within AI research from that moment." Expert systems represented a new stage in the evolution of AI, shifting from its initial emphasis on general problem-solvers focused on expressing in code human reasoning, i.e., drawing inferences and arriving at logical conclusions.

As scientists laid the foundations of artificial intelligence, Pamela McCorduck was there. McCorduck, an author who wrote some of the first novels and histories about AI and was a generous friend of CMU, died Oct. 18. McCorduck described herself as an eyewitness to the birth and growth of AI. She was possibly best known for her 1979 book, "Machines Who Think," which chronicles the history of AI from the dreams and nightmares of ancient poets and prophets to the scientific discoveries of the 20th century. The novel contains the famous quote, "Artificial intelligence began with the ancient wish to forge the gods."

This article frames and presents the discussion at an invited panel “AI History: Expert Systems” held at the AAAI-17 conference held in San Francisco. The panel’s purpose was to open up this history of expert systems, its transformational aspects, and its connections to today’s AI awakening.

Hephaestus, the Greek god of blacksmiths, metalworking and carpenters, was said to have fashioned artificial beings in the form of golden robots. Myth finally moved toward truth in the 20th century, as AI developed in series of fits and starts, finally gaining major momentum--and reaching a tipping point--by the turn of the millennium. Here's how the modern history of AI and ML unfolded, starting in the years just following World War II. In 1950, while working at the University of Manchester, legendary code breaker Alan Turing (subject of the 2014 movie The Imitation Game) released a paper titled "Computing Machinery and Intelligence." It became famous for positing what became known as the "Turing test."

Hephaestus, the Greek god of blacksmiths, metalworking and carpenters, was said to have fashioned artificial beings in the form of golden robots. Myth finally moved toward truth in the 20th century, as AI developed in series of fits and starts, finally gaining major momentum--and reaching a tipping point--by the turn of the millennium. Here's how the modern history of AI and ML unfolded, starting in the years just following World War II. In 1950, while working at the University of Manchester, legendary code breaker Alan Turing (subject of the 2014 movie The Imitation Game) released a paper titled "Computing Machinery and Intelligence." It became famous for positing what became known as the "Turing test."

Artificial Intelligence (AI) is once again a promising technology. The last time this happened was in the 1980s, and before that, the late 1950s through the early 1960s. In between, commentators often described AI as having fallen into "Winter," a period of decline, pessimism, and low funding. Understanding the field's more than six decades of history is difficult because most of our narratives about it have been written by AI insiders and developers themselves, most often from a narrowly American perspective.[1] In addition, the trials and errors of the early years are scarcely discussed in light of the current hype around AI, heightening the risk that past mistakes will be repeated. How can we make better sense of AI's history and what might it tell us about the present moment?

Randy Davis announced the appointment of six new program managers at ARPA. He encouraged individuals to contact these managers to see where they can help. At IJCAI-95, Randall Davis assumed the office of president of the American Association for Artificial Intelligence (AAAI). Davis is a professor of electrical engineering and computer science and associate director of the AI Lab at the Massachusetts Institute of Technology (MIT). Davis succeeds Barbara Grosz, Gordon McKay professor of computer science in the Division of Applied Sciences at Harvard University.

The Japanese Fif-t,h Generation Project appears to be only a stimulus for this book. Clearly it is an important stimulus, and the book describes it in considerable detail as it covers both technical and managerial/social aspects of the Japanese project. But the book goes much beyond a description and an evaluation of the Fifth Generation Project In building the background of the project's significance, the book describes the current state of work in artificial intelligence (AI) in the US and abroad, it outlines the history of AI, it, focuses on developments in Expert Systems, it comments on the social and political environment in which AI is growing, and it provides glimpses of the type of future that AI may help us to create. Also, the relative state of industrial development in Japan and t,he US are analyzed, and many observations are made about styles of planning, value systems, and attitudes to education in the two countries. The book conveys very well the sense of intellectual excitement that characterizes work in AI, and the variety of viewpoints (and concerns) within the field about the possible impact on our lives of mass-produced knowledge technology.

The object is to bring the situation, or problem state, forward from its initial configuration to one satisfying a goal condition. For example, an initial situation might be the placement of chessmen on the board at the beginning of the game; the desired goal, any board configuration that is a checkmate; and the operators, rules for the legal moves in chess. This difference is then used to index the (forward) operator most relevant to reducing the difference. If this especially relevant operator cannot be immediately applied to the present problem state, subgoals are set up to change the problem state so that the relevant operator can be applied. After these subgoals are solved, the relevant operator is applied and the resulting, modified situation becomes a new starting point from which to solve for the original goal.