Nature and Scope of AI Techniques

Machine intelligence dates back to 1936, when Alan Turing proposed the idea of a universal mathematics machine, a theoretical concept in the mathematical theory of computability. Turing and Emil Post independently proved that determining the decidability of mathematical propositions is equivalent to asking what sort of sequences of a finite number of symbols can be recognized by an abstract machine with a finite set of instructions. Such a mechanism is now known as a Turing machine (Turing Machine, 2004). Turing’s research paper addressed the question of machine intelligence, assessing the arguments against the possibility of creating an intelligent computing machine and suggesting answers to those arguments; it proposed the Turing test as an empirical test of intelligence (Turing, 1950). The Turing test, called the imitation game by Alan Turing, measures the performance of a machine against that of a human being. The machine and a human (A) are placed in two rooms. A third person, designated the interrogator, is in a room apart from both the machine and the human. The interrogator cannot see or speak directly to either A or the machine, communicating with them solely through some text messages or even a chat window. The task of the interrogator is to distinguish between the human and the computer on the basis of questions he or she may put to both of them over the terminals. If the interrogator cannot distinguish the machine from the human, then, Turing argues, the machine may be assumed to be intelligent. In the 1960s, computers failed to pass the Turing test because of the low processing speed of the computers. The last few decades have seen a new era of artificial intelligence (AI) focusing on the principles, theoretical aspects, and design methodology of algorithms gleaned from nature. Examples are artificial neural networks inspired by mammalian neural systems, evolutionary computation inspired by natural selection in biology, simulated annealing inspired by thermodynamics principles, and swarm intelligence inspired by the collective behavior of insects or microorganisms, and so on, interacting locally with their environment, therein causing coherent functional global patterns to emerge. These techniques have found their way into solving real-world problems in science, business, technology, commerce, and also to a great extent in measuring systems. Computational intelligence is a well-established paradigm, where new theories with a sound biological understanding have been evolving. The current experimental systems have many of the characteristics of biological computers (brains, in other words) and are beginning to be built