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Introduction to Intelligent Systems.

The major categories of intelligent systems are expert systems, neural networks, fuzzy logic, genetic algorithms, and intelligent agents. You will learn about each of these systems in the following sections.

The term intelligent systems describes the various commercial applications of artificial intelligence. Artificial intelligence (AI) is a subfield of computer science that studies the thought processes of humans and re-creates the effects of those processes via machines, such as computers and robots.

One well-publicized definition of AI is "behavior by a machine that, if performed by a human being, would be considered intelligent." This definition raises the question, “What is intelligent behavior?" The following capabilities are considered to be signs of intelligence: learning or understanding from experience, making sense of ambiguous or contradictory messages, and responding quickly and successfully to new situations.

The ultimate goal of AI is to build machines that mimic human intelligence. A widely used test to determine whether a computer exhibits intelligent behavior was designed by Alan Turing, a British AI pioneer. The Turing test proposes a scenario in which a man and a computer both pretend to be women (or men), and a human interviewer has to identify which is the real human. Based on this standard, the intelligent systems exemplified in commercial AI products are far from exhibiting any significant intelligence.

We can better understand the potential value of AI by contrasting it with natural (human) intelligence. Al has several important commercial advantages over natural intelligence, but it also displays some limitations. Intelligent systems show up in a number of places, some of them surprising, as the following examples illustrate:

• A good session player is hard to find, but ujam (www.ujam.com) is always ready to rock. This Web app doubles as a studio band and a recording studio. It analyzes a melody and then produces sophisticated harmonics, bass lines, drum tracks, horn parts, and more.

Before ujmn can produce accompaniment, the app must figure out which notes the user is singing or playing. Once ujam recognizes these, its algorithms use a mix of statistical techniques and programmed musical rules to search for chords to match the tune.

• To the human eye, an x-ray is a murky puzzle. But to a machine, an x-ray-or a CT or MRI scan- is a dense data field that can be assessed down to the pixel level. AI techniques currently are being applied aggressively in the field of medical imaging.

New software gathers high-resolution image data from multiple sources- x-rays, MRI scans, ultrasounds, CT scans- and then groups together biological structures that share hard-to-detect similarities. For instance, the software can examine several images of the same breast to measure tissue density. The software then color-codes tissues of similar densities so humans can see the pattern as well.

The software finds and indexes pixels that share certain properties, even pixels that are far apart in one image or in a different image altogether. This process enables medical personnel to identify hidden features of diffuse structures as well as features within a region of tissue.

• The human brain receives visual information from two eyes. Google's AI system receives visual information from billions of smartphone camera lenses. The company collects these images from users of Google Goggles (www.google.com/mobilelgoggles), a mobile service that lets users run Web searches by taking pictures. Snap a barcode, and Goggles will shop for the item's best price. Take a picture of a book, and users will be linked to, for instance, a Wikipedia page about the book's, author. Photograph the Eiffel Tower, and Goggles will give you historical background on the landmark.

The software behind Goggles coordinates the efforts of multiple object-specific recognition data bases. There is a data base for text, one for landmarks, one for corporate logos, and so on. When an image arrives, Goggles transmits it to each of these databases, which in turn use a variety of visual-recognition techniques to identify potential matches and compute confidence scores. Goggles then applies its

own algorithm to decide which result(s), if any, go back to the user. Goggles' next category? Identifying plants.

• Building a model to run a major railroad is a complex task. One of the nation's largest freight carriers, Norfolk Southern (www.nscorp.com ), uses an intelligent system, the Princeton Locomotive and Shop Management System (PLASMA), to manage its huge operation. PLASMA uses algorithms to analyze the railroad's operations by tracking thousands of variables to predict the impact of changes in fleet size, maintenance policies, transit time, and other factors. The key breakthrough was refining PLASMA so that it could mimic the complex behavior of the company's dispatch center in Atlanta, Georgia.

PLASMA examines vast amounts of historical data from the railroad's data bases. It then uses this analysis to model the dispatch center's collective human decision making and suggest improvements.