Artificial Intelligence

I

Introduction

Artificial Intelligence (AI), the study and engineering of intelligent machines capable of performing the same kinds of functions that characterize human thought. The concept of AI dates from ancient times, but the advent of digital computers in the 20th century brought AI into the realm of possibility. AI was conceived as a field of computer science in the mid-1950s. The term AI has been applied to computer programs and systems capable of performing tasks more complex than straightforward programming, although still far from the realm of actual thought. While the nature of intelligence remains elusive, AI capabilities currently have far-reaching applications in such areas as information processing, computer gaming, national security, electronic commerce, and diagnostic systems.

II

Development of Artificial Intelligence

In 1956 American social scientist and Nobel laureate Herbert Simon and American physicist and computer scientist Allan Newell at Carnegie Mellon University in Pennsylvania devised a program called Logic Theorist that simulated human thinking on computers. The first AI conference occurred at Dartmouth College in New Hampshire in 1956. This conference inspired researchers to undertake projects that emulated human behavior in the areas of reasoning, language comprehension, and communications. In addition to Newell and Simon, computer scientists and mathematicians Claude Shannon, Marvin Minsky, and John McCarthy laid the groundwork for creating “thinking” machines from computers.

The search for AI has taken two major directions: psychological and physiological research into the nature of human thought, and the technological development of increasingly sophisticated computing systems. Some AI developers are primarily interested in learning more about the workings of the human brain and thus attempt to mimic its methods and processes. Other developers are more interested in making computers perform a specific task, which may involve computing methods well beyond the capabilities of the human brain.

Contemporary fields of interest resulting from early AI research include expert systems, cellular automata (treating pieces of data like biological cells), and artificial life (see Automata Theory). The search for AI goes well beyond computer science and involves cross-disciplinary studies in such areas as cognitive psychology, neuroscience, linguistics, cybernetics, information theory, and mechanical engineering, among many others. The search for AI has led to advancements in those fields, as well.

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III

Uses and Challenges of Artificial Intelligence

AI programs have a broad array of applications. They are used by financial institutions, scientists, psychologists, medical practitioners, design engineers, planning authorities, and security services, to name just a few. AI techniques are also applied in systems used to browse the Internet.

AI programs tend to be highly specialized for a specific task. They can play games, predict stock values, interpret photographs, diagnose diseases, plan travel itineraries, translate languages, take dictation, draw analogies, help design complex machinery, teach logic, make jokes, compose music, create drawings, and learn to do tasks better. AI programs perform some of these tasks well. In a famous example, a supercomputer called Deep Blue beat world chess champion Garry Kasparov in 1997. In developing its strategy, Deep Blue utilized parallel processing (interlinked and concurrent computer operations) to process 200 million chess moves per second. AI programs are often better than people at predicting stock prices, and they can create successful long-term business plans. AI programs are used in electronic commerce to detect possible fraud, using complex learning algorithms, and are relied upon to authorize billions of financial transactions daily. AI programs can also mimic creative human behavior. For example, AI-generated music can sound like compositions by famous composers.

Some of the most widely used AI applications involve information processing and pattern recognition. For example, one AI method now widely used is “data mining,” which can find interesting patterns in extremely large databases. Data mining is an application of machine learning, in which specialized algorithms enable computers to “learn.” Other applications include information filtering systems that discover user interests in an online environment. However, it remains unknown whether computer programs could ever learn to solve problems on their own, rather than simply following what they are programmed to do.

AI programs can make medical diagnoses as well as, or better than, most human doctors. AI programs have been developed that analyze the disease symptoms, medical history, and laboratory test results of a patient, and then suggest a diagnosis to the physician. The diagnostic program is an example of expert systems, which are programs designed to perform tasks in specialized areas as a human would. Expert systems take computers a step beyond straightforward programming, being based on a technique called rule-based inference, in which preestablished rule systems are used to process the data. Despite their sophistication, expert systems still do not approach the complexity of true intelligent thought.

Despite considerable successes AI programs still have many limitations, which are especially obvious when it comes to language and speech recognition. Their translations are imperfect, although good enough to be understood, and their dictation is reliable only if the vocabulary is predictable and the speech unusually clear. Research has shown that whereas the logic of language structure (syntax) submits to programming, the problem of meaning (semantics) lies far deeper, in the direction of true AI (or “strong” AI, in the parlance of developers). Developing natural-language capabilities in AI systems is an important focus of AI research. It involves programming computers to understand written or spoken information and to produce summaries, answer specific questions, or redistribute information to users interested in specific areas. Essential to such programs is the ability of the system to generate grammatically correct sentences and to establish linkages between words, ideas, and associations with other ideas. “Chatterbot” programs, although far from natural conversationalists, are a step in that direction. They attempt to simulate an intelligent conversation by scanning input keywords to come up with pre-prepared responses from a database.

Much work in AI models intellectual tasks, as opposed to the sensory, motor, and adaptive abilities possessed by all mammals. However, an important branch of AI research involves the development of robots, with the goal of creating machines that can perceive and interact with their surroundings. WABOT-2, a robot developed by Waseda University in Japan in the 1980s, utilized AI programs to play a keyboard instrument, read sheet music, and converse rudimentarily with people. It was a milestone in the development of “personal” robots, which are expected to be anthropomorphous—that is, to emulate human attributes. AI robots are being developed as personal assistants for hospitalized patients and disabled persons, among other purposes. Natural-language capabilities are integral to these efforts. In addition, scientists with the National Aeronautics and Space Administration (NASA) are developing robust AI programs designed to enable the next generation of Mars rovers to make decisions for themselves, rather than relying on (and waiting for) detailed instructions from teams of human controllers on Earth.

To match everything that people can do, AI systems would need to model the richness and subtlety of human memory and common sense. Many of the mechanisms behind human intelligence are still poorly understood, and computer programs can simulate the complex processes of human thought and cognition only to a limited extent. Even so, an AI system does not necessarily need to mimic human thought to achieve an intelligent answer or result, such as a winning chess move, as it may rely on its own “superhuman” computing power.

IV

Types of Artificial Intelligence

Work in AI has primarily focused on two broad areas: developing logic-based systems that perform common-sense and expert reasoning, and using cognitive and biological models to simulate and explain the information-processing capabilities of the human brain. In general, work in AI can be categorized within three research and development types: symbolic, connectionist, and evolutionary. Each has characteristic strengths and weaknesses.