If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Carnegie-Mellon University, Pittsburgh, Pennsylvania 15213 The Hearsay-H system, developed during the DARPAsponsored five-year speechunderstanding research program, represents both a specific solution to the speechunderstanding problem and a general framework for coordinating independent processes to achieve cooperative problem-solving behavior. As a computational problem, speech understanding reflects a large number of intrinsically interesting issues. Spoken sounds are achieved by a long chain of successive transformations, from intentions, through semantic and syntactic structuring, to the eventually resulting audible acoustic waves. As a consequence, interpreting speech means effectively inverting these transformations to recover the speaker's intention from the sound. At each step in the interpretive process, ambiguity and uncertainty arise.
SESSION 4B PAPER 3 TO WHAT EXTENT CAN ADMINISTRATION BE MECHANIZED? Mr. J. H. H. Merriman was educated at King's College School, Wimbledon, and King's College, University of London. He obtained his B.Sc. (Hons.) in 1935 and did Postgraduate Research at King's College London obtaining his M.Sc. Engineering Department, Radio Research Branch, Dollis Hill, in 1936 and was associated with development of long distance radio communication systems. He was Officer-in-charge Castleton radio research station 1940-8, and from 1948-5 in the Office of Engineer-in- Chief G.P.O. and responsible for microwave system development and planning.
Summary--There is frequently more or less acrimonious discussion about artificial intelligence and intelligent machines and their place in science. Usually the discussion settles down to the reiteration of two points of view. This paper is concerned with the difference between them. Do they merely reflect two emotional or ethical biases, or is there an underlying technical judgment on which they disagree? The authors claim the latter and purport to show what that judgment is.
Oliver Selfridge Using a knowledge-based architecture for a design automation application Facing Web content distribution challenges What makes a compelling empirical evaluation? Driven by his curiosity about the nature of learning, Oliver Selfridge has spent over a half century enmeshed in the most exciting developments in artificial intelligence, communications, and computer science. A participant at the original conference at Dartmouth in 1956 (and at the Western Joint Computer Conference in Los Angeles the year before, which he considers the true start of Al), Selfridge formed working relationships and cemented friendships with AI's founding members--John McCarthy, Marvin Minsky, and Allen Newell, among others--as he went on to become a true AI pioneer himself. Before retiring in 1993 after 10 years as Chief Scientist at GTE Laboratories, Computer and Information Systems Lab, he served as a member of the National Security Agency's Advisory Board for 20 years, chairing its Data Processing Panel for the last 15 of those. He also served on various advisory panels to the White House, as well as on the peer review committee for the National Institute of Health (NIH), directed Project MAC and the Cambridge Project at MIT's Lincoln Labs, and was Staff Scientist at Bolt, Beranek, and Neuman (BBN).
For the past ten years we have been working on the problem of getting a computer to understand natural language. We built an early version of a parser that mapped from English into a language-free representation of the meaning of input sentences (Schank and Tesler, 1969). Simultaneously we worked on the meaning representation itself. We developed Conceptual Dependency which represents meaning as a network of concepts independent of the actual words that might be used to express those concepts (Schank, 1969). Over the years the parser and the representation evolved as we began to understand the complexity of the problem with which we were dealing.
The intention is as follows. When x is printed it is (3) and y when printed is (2, 3) rather than (2, 1) as it would have been had the last assignment left it undisturbed. How are we to prove assertions about such programs? Our task will be to obtain a more formal means of making inferences, which, unlike the picture language, will deal with general propositions about lists. We will extend Floyd's proof system for flow diagrams to handle commands Which process lists.
The study of perception is divided among many established sciences: physiology, experimental psychology and machine intelligence; with several others making contributions. But each of the contributing sciences tends to have its own concepts, and ways of considering problems. Each -- to use T. S. Kuhn's term (1962) -- has its own'paradigm', within which its science is respectable. This can make cooperation difficult, as misunderstandings (and even distrust) can be generated by paradigm differences. This paper is a plea to consider perceptual phenomena from many points of view, and to consider whether a general paradigm for perception might be found.
Motor control systems are complex systems that process information. Orientation behaviour, posture control, and the manipulation of objects are examples of motor control systems which involve one or more sensory modality and various central neural processes, as well as effector systems and their immediate neuronal control mechanisms. Like all complex information processing systems, they must be analysed and understood at several different levels (see, e.g., Marr & Poggio 1977). At the lowest level there is the analysis of basic components and circuits, the neurons, their synapses, etc. At the other extreme, there is the study of the computations performed by the system -- the problems it solves and the ways that it solves them -- and the analysis of its logical organization in terms of its primary modules.
Machine intelligence, more commonly known by the misnomer artifical intelligence, is now about twenty-five years old as a scientific field. In contrast with early predictions, its practical applicability has been frustratingly slow to develop. It appears, however, that we are now (finally!) on the verge of practicality in a number of specialities within machine intelligence more or less simultaneously. This can be expected to result in the short term in a qualitative shift in the nature of the field itself, and to result in the longer term in a shift in the way certain industries go about their business. Machine Intelligence Corporation (MIC) was founded in 1978 as a vehicle for bringing the more practical aspects of the field into widespread use.
This paper describes a program for extracting an accurate outline of a man's head from a digital picture. The program accepts as input digital, grey scale pictures containing people standing in front of various backgrounds. The output of the program is an ordered list of the points which form the outline of the head. The edges of background objects and the interior details of the head have been suppressed. The program is successful because of an improved method for edge detection which uses heuristic planning, a technique drawn from artificial intelligence research in problem solving.