Artificial Intelligence and Learning Computers

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Artificial Intelligence
&
Learning Computers
Presented by: S.DEEPAKKUMAR

Abstract

The term artificial intelligence is used to describe a property of machines or programs: the intelligence that the system demonstrates. Among the traits that researchers hope machines will exhibit are reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. Constructing robots that perform intelligent tasks has always been a highly motivating factor for the science and technology of information processing. Unlike philosophy and psychology, which are also concerned with intelligence, AI strives to build intelligent entities such as robots as well as understand them. Although no one can predict the future in detail, it is clear that computers with human-level intelligence (or better) would have a huge impact on our everyday lives and on the future course of civilization Neural Networks have been proposed as an alternative to Symbolic Artificial Intelligence in constructing intelligent systems. They are motivated by computation in the brain. Small Threshold computing elements when put together produce powerful information processing machines. In this paper, we put forth the foundational ideas in artificial intelligence and important concepts in Search Techniques, Knowledge Representation, Language Understanding, Machine Learning, Neural Computing and such other disciplines.

Artificial Intelligence

Starting from a modest but an over ambitious effort in the late 50’s, AI has grown through its share of joys, disappointments and self-realizations. AI deals in science, which deals with creation of machines, which can think like humans and behave rationally. AI has a goal to automate every machine.

AI is a very vast field, which spans:
·Many application domains like Language Processing, Image Processing, Resource Scheduling, Prediction, Diagnosis etc. ·Many types of technologies like Heuristic Search, Neural Networks, and Fuzzy Logic etc. ·Perspectives like solving complex problems and understanding human cognitive processes. ·Disciplines like Computer Science, Statistics, Psychology, etc.

DEFINITION OF INTELLIGENCE & TURING TEST

The Turing Test, proposed by Alan Turing (1950), was designed to provide a satisfactory definition of intelligence. Turing defined intelligent behavior as the ability to achieve human-level performance in all cognitive tasks, sufficient to fool an interrogator. Roughly speaking, the test he proposed is that the computer should be interrogated by a human via a teletype, and passes the test if the interrogator cannot tell if there is a computer or a human at the other end. His theorem (the Church-Turing thesis) states that “Any effective procedure (or algorithm) can be implemented through a Turing machine. “ Turing machines are abstract mathematical entities that are composed of a tape, a read-write head, and a finite-state machine. The head can either read or write symbols onto the tape, basically an input-output device. The head can change its position, by either moving left or right. The finite state machine is a memory/central processor that keeps track of which of finitely many states it is currently in. By knowing which state it is currently in, the finite state machine can determine which state to change to next, what symbol to write onto the tape, and which direction the head should move.

Requirement of an Artificial Intelligence system

No AI system can be called intelligent unless it learns & reasons like a human. Reasoning derives new information from given ones.

Areas of Artificial Intelligence

Knowledge Representation...
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