It is interesting to note that there is no agreed upon definition of artificial intelligence. Why is this interesting? Because government agencies ask for it, software shops claim to provide it, popular magazines and newspapers publish articles about it, dreamers base their fantasies on it, and pragmatists criticize and denounce it. Such a state of affairs has persisted since Newell, Simon and Shaw wrote their first chess program and proclaimed that in a few years, a computer would be the world champion. Not knowing exactly what we are talking about or expecting is typical of a new field; for example, witness the chaos that centered around program verification of security related aspects of systems a few years ago. The details are too grim to recount in mixed company. However, artificial intelligence has been around for 30 years, so one might wonder why our wheels are still spinning. Below, an attempt is made to answer this question and show why, in a serious sense, artificial intelligence can never demonstrate an outright success within its own discipline. In addition, we will see why the old bromide that "as soon as we understand how to solve a problem, it's no longer artificial intelligence" is necessarily true.

Artificial intelligence, or AI, is largely an experimental science—at least as much progress has been made by building and analyzing programs as by examining theoretical questions. MYCIN is one of several well-known programs that embody some intelligence and provide data on the extent to which intelligent behavior can be programmed. As with other AI programs, its development was slow and not always in a forward direction. But we feel we learned some useful lessons in the course of nearly a decade of work on MYCIN and related programs. In this book we share the results of many experiments performed in that time, and we try to paint a coherent picture of the work. The book is intended to be a critical analysis of several pieces of related research, performed by a large number of scientists. We believe that the whole field of AI will benefit from such attempts to take a detailed retrospective look at experiments, for in this way the scientific foundations of the field will gradually be defined. It is for all these reasons that we have prepared this analysis of the MYCIN experiments.

The complete book in a single file.

Today, surrounded by so many automatic machines industrial robots, and the R2-D2's of Star wars movies, most people think AI is much more advanced than it is. But still, many "computer experts" don't believe that machines will ever "really think." I think those specialists are too used to explaining that there's nothing inside computers but little electric currents. And there are many other reasons why so many experts still maintain that machines can never be creative, intuitive, or emotional, and will never really think, believe, or understand anything.

Practitioners of ethnography, in seeking to discover and describe complex patterns of behavior, face a number of serious problems.

Othello is a recent addition to the collection of games that have been examined within artificial intelligence. Advances have been rapid, yielding programs that have reached the level of world-championship play. This article describes the current champion Othello program, iago. The work described here includes: (1) a task analysis of Othello; (2) the implementation of a program based on this analysis and state-of-the-art AI game-playing techniques; and (3) an evaluation of the program's performance through games played against other programs and comparisons with expert human play.

... we began to program a computer understanding system thatwould attempt to process input texts. An item crucial to our ability to accomplishthis task was what we called a script. A script is a frequently repeated causalchain of events that describes a standard situation. In understanding, when it ispossible to notice that one of these standard event chains has been initiated,then it is possible to understand predictively. That is, if we know we are in arestaurant then we can understand where an "order" fits with what we justheard, who might be ordering what from whom, what preconditions (menu,sitting down) might have preceded the "order", and what is likely to happennext. All this information comes from the restaurant script.Hayes, J.E., D. Michie, and L. I. Mikulich (Eds.), Machine Intelligence 9, Ellis Horwood.

"Computing can change our ways of thinking about many things, mathematics, biology, engineering, administrative procedures, and many more. But my main concern is that it can change our thinking about ourselves: giving us new models, metaphors, and other thinking tools to aid our efforts to fathom the mysteries of the human mind and heart. The new discipline of Artificial Intelligence is the branch of computing most directly concerned with this revolution. By giving us new, deeper, insights into some of our inner processes, it changes our thinking about ourselves. It therefore changes some of our inner processes, and so changes what we are, like all social, technological and intellectual revolutions." This book, published in 1978 by Harvester Press and Humanities Press, has been out of print for many years, and is now online, produced from a scanned in copy of the original, digitised by OCR software and made available in September 2001. Since then a number of notes and corrections have been added. Atlantic Highlands, NJ: Humanities Press.

We have attempted to discover formal rules for transcribing into musical notation the fugue subjects of the Well-Tempered Clavier, as this might be done by an amanuensis listening to a'deadpan' performance on the keyboard. In this endeavour two kinds of problem arise: what are the harmonic relations between the notes, and what are the metrical units into which they are grouped? The harmonic problem is that the number of keyboard semitones between two notes does not define-- their harmonic relation, and we further develop an earlier theory of such relations, arriving at an algorithm which assigns every fugue to the right key and correctly notates every accidental in its subject.

Over the past few years, there has been a growing interest in statistics and machine learning. Today, machine learning can help us make smarter decisions, and big data controls everything in our lives. It influences how we work, shop and do business. Data even help the police determine when and where the next crime is likely to happen. But how is it all happening and how did it all start?

How often do you hear people use the terms "artificial intelligence" and "machine learning" interchangeably? The two are definitely related, and machine learning is actually a subset of artificial intelligence. However, as a greater number of businesses begin offering "intelligent" solutions, it becomes more vital than ever before to differentiate between these two concepts. After all, you may find yourself giving a presentation or speaking with someone who specializes in one of these fields, and you want to know what you're talking about. From cancer screenings to climate change, there are numerous applications for artificial intelligence.