Artificial Intelligence

Artificial Intelligence is a branch of science (mostly, but not exclusively, computer science) concerned with making computers “think”. As a very broad topic, AI also relates to physiology, philosophy, mathematics and other scientific areas. The term “Artificial Intelligence” itself was coined in 1956 by John McCarthy from MIT at the now famous “Dartmouth summer research project on Artificial Intelligence.”

Now when we want to build “thinking” machines, it is also necessary to define intelligence. As it turns out, it is very difficult task, taking years of research into the areas of learning, language, sensory perception and others. British scientists Alan Turing stated that a computer can be called intelligent if it could deceive a human into believing that it was human. His test consists of a person asking questions via keyboard to both a person and an intelligent machine. A scaled down version of the Turing test, better known as a Loebner Prize, requires that machines have to “converse” with testers only on a limited topic. Dr. Hugh Loebner pledged a Grand Prize of $100,000 and a Gold Medal for the first computer whose responses were indistinguishable from a human’s. Each year an annual prize of $2000 and a bronze medal is awarded to the “most human” computer.

With the advent of the computer technology and more than 40 years of research, AI technologies will become important part of our everyday lives. Let us consider just a few of them:

  • expert systems: computer application that makes decisions in real-life situations that would otherwise be performed by a human expert.
  • neural networks: systems that simulate intelligence by reproducing the types of physical connections found in animal or even human brains. Because of the current technology limitations, the number of these connections is small (in terms of billions of connections found in human brain), but still capable of reproducing some very interesting behaviour in a number of disciplines such as voice or optical-character recognition and natural-language processing.
  • fuzzy logic: type of logic that recognizes more than simple true and false values. It represents a departure from classical two-valued sets and logic, that uses “soft” linguistic (e.g. large, small, hot, cold, warm) system variables and a continuous range of truth values in the interval [0,1], rather than strict binary (True or False) decisions and assignments.
  • natural language understanding: programming computers to understand and interact with users in natural languages like English. Related to the voice (speech) recognition which converts spoken dialogue to the computer-readable text, but without understanding the real meaning of that text.
  • computer games: development of computer games is a fast-growing, multi-billion business. “AI inside” feature is very desirable because it ensures increased profit and user satisfaction. It is also known that the best computer chess programs are now capable of beating humans. Some two years ago, the world chess champion Gary Kasparov was defeated by an IBM super-computer called Deep Blue.
  • agents: a computational entity which acts on behalf of other (most often human) entities in an autonomous fashion, performs its actions with some level of proactivity and/or reactiveness and exhibits some level of the key attributes of learning, co-operation and mobility. Imagine having your own “smart” agent that could watch new articles on the Usenet, and deliver only the most interesting ones (according to your preferences), instead of having to browse throuh thousands of new messages each day.
  • robotics: programming computers to see, hear and react to sensory stimuli. Probably the most attractive field of AI for newcomers. Includes several very different approaches: see BEAM robotics Web sites and MIT’s Cog project for more info on this.

One definition says that Artificial Intelligence is the simulation of human intelligence processes by machines. The relatively new field of Artificial Life takes a different approach in attempt to study and understand biological life by synthesizing artificial life forms. To paraphrase Chris Langton, the founder of the field, the goal of Artificial Life is to “model life as it could be so as to understand life as we know it”. Artificial Life is a very broad discipline which spans such diverse topics as artificial evolution, artificial ecosystems, genetic algorithms (search procedures that use the mechanics of natural selection and natural genetics) and many more.

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