In full:
Project on Mathematics and Computation

Project MAC, a collaborative computer endeavour in the 1960s that sought to to create a functional time-sharing system. Project MAC, founded in 1963 at the Massachusetts Institute of Technology (MIT), was funded by the U.S. Department of Defense’s Advanced Research Projects Agency (ARPA) and the National Science Foundation. The goal for the project was to allow many users access to the programs of a single computer from various locations. Project MAC’s pioneering exploration of the working methods of multiple-user access became a foundation for modern computer networking and online collaboration.

Project MAC was first directed by MIT computer scientist Robert M. Fano, with computer scientist Fernando José Corbató as a founding member. The term project was used rather than laboratory to inspire individuals at MIT to join the effort without disaffiliating themselves from their current laboratories. One of the project’s first contributions was to expand and provide hardware for Corbató’s 1961 Compatible Time-Sharing System (CTSS) software, which permitted multiple users at dispersed terminals to run programs centrally located on one machine. Innovative computer scientist and ARPA group leader J.C.R. Licklider contributed immensely to the expansion of that system and believed that CTSS would facilitate greater efficiency, reduce costs, and save time by permitting many users to share one large computer instead of employing individual small machines.

Within six months of Project MAC’s creation, 200 users were able to access the system in 10 different MIT departments. By 1967 Project MAC had become its own interdepartmental laboratory, separated from its earlier Department of Electrical Engineering home. In 1969 Project MAC, Bell Laboratories, and General Electric jointly developed Multics, the Multiplexed Information and Computing Service. Multics evolved from computer time-sharing into an online computer system and incorporated features such as file sharing and management and system security into its design. The complex system could support 300 simultaneous users on 1,000 MIT terminals and prompted Bell Labs to employ a simpler form of the UNIX operating system.

Project MAC became the Laboratory for Computer Science (LCS) at MIT in 1976 and broadened its focus. Lab director Michael L. Dertouzos pushed for developing more-intelligent programs to run on the computer systems. In addition, to promote computer use, the laboratory studied how to develop cost-effective user-friendly systems and explored the theoretical foundations in computer science that sought to understand limitations on space and time. Advancing the role of the computer system, the LCS focused on creating applications that would foster online computing in several academic disciplines, including architecture, biology, medicine, and library sciences. LCS joined with MIT’s Artificial Intelligence Laboratory (AI Lab) in 2004 to become the Computer Science and Artificial Intelligence Laboratory (CSAIL), the largest research laboratory at MIT.

James Pyfer
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artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since their development in the 1940s, digital computers have been programmed to carry out very complex tasks—such as discovering proofs for mathematical theorems or playing chess—with great proficiency. Despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match full human flexibility over wider domains or in tasks requiring much everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in executing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, voice or handwriting recognition, and chatbots.

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All but the simplest human behavior is ascribed to intelligence, while even the most complicated insect behavior is usually not taken as an indication of intelligence. What is the difference? Consider the behavior of the digger wasp, Sphex ichneumoneus. When the female wasp returns to her burrow with food, she first deposits it on the threshold, checks for intruders inside her burrow, and only then, if the coast is clear, carries her food inside. The real nature of the wasp’s instinctual behavior is revealed if the food is moved a few inches away from the entrance to her burrow while she is inside: on emerging, she will repeat the whole procedure as often as the food is displaced. Intelligence—conspicuously absent in the case of the wasp—must include the ability to adapt to new circumstances.

Psychologists generally characterize human intelligence not by just one trait but by the combination of many diverse abilities. Research in AI has focused chiefly on the following components of intelligence: learning, reasoning, problem solving, perception, and using language.

Learning

There are a number of different forms of learning as applied to artificial intelligence. The simplest is learning by trial and error. For example, a simple computer program for solving mate-in-one chess problems might try moves at random until mate is found. The program might then store the solution with the position so that, the next time the computer encountered the same position, it would recall the solution. This simple memorizing of individual items and procedures—known as rote learning—is relatively easy to implement on a computer. More challenging is the problem of implementing what is called generalization. Generalization involves applying past experience to analogous new situations. For example, a program that learns the past tense of regular English verbs by rote will not be able to produce the past tense of a word such as jump unless the program was previously presented with jumped, whereas a program that is able to generalize can learn the “add -ed” rule for regular verbs ending in a consonant and so form the past tense of jump on the basis of experience with similar verbs.

(Read Ray Kurzweil’s Britannica essay on the future of “Nonbiological Man.”)

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