Artificial Intelligence. Defining Artificial Intelligence The phrase “Artificial Intelligence” was first coined by John McCarthy four decades ago.
One representative definition is pivoted around comparing intelligent machines with human beings. Another definition is concerned with the performance of machines which historically have been judged to lie within the domain of intelligence. Yet none of these definitions have been universally accepted, probably because the reference of the word “intelligence” which is an immeasurable quantity. A better definition of artificial intelligence, and probably the most accurate would be: An artificial system capable of planning and executing the right task at the right time rationally. With all this a common questions arises: Does rational thinking and acting include all characteristics of an intelligent system? If so, how does it represent behavioral intelligence such as learning, perception and planning?
General Problem Solving Approaches in AI Begin AI Algorithm Fig. Neural Networks. Neural Network Tutorial. Introduction I have been interested in artificial intelligence and artificial life for years and I read most of the popular books printed on the subject.
I developed a grasp of most of the topics yet neural networks always seemed to elude me. Sure, I could explain their architecture but as to how they actually worked and how they were implemented… well that was a complete mystery to me, as much magic as science. I bought several books on the subject but every single one attacked the subject from a very mathematical and academic viewpoint and very few even gave any practical uses or examples.
So for a long long time I scratched my head and hoped that one day I would be able to understand enough to experiment with them myself. That day arrived some time later when - sat in a tent in the highlands of Scotland reading a book - I had a sudden blast of insight. The C++ source code for the tutorial and a pre-compiled executable can be found here. 2 3 4 5 6 7 8 Next Home. Neural Networks. Many concepts related to the neural networks methodology are best explained if they are illustrated with applications of a specific neural network program.
Therefore, this section contains many references to STATISTICA Neural Networks, a particularly comprehensive neural networks application available from StatSoft. Preface Neural networks have seen an explosion of interest over the last few years, and are being successfully applied across an extraordinary range of problem domains, in areas as diverse as finance, medicine, engineering, geology and physics. Indeed, anywhere that there are problems of prediction, classification or control, neural networks are being introduced. This sweeping success can be attributed to a few key factors: Power.
Neural networks are also intuitively appealing, based as they are on a crude low-level model of biological neural systems. Applications for Neural Networks Detection of medical phenomena. The Biological Inspiration The Basic Artificial Model Control.