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Is part of an investigation into the extremely complex processes that are involved in intelligent, adaptive, and creative behavior. Many kinds of information can aid in solving problems: information may . . suggest the order in which possible solutions should be examined; it may rule out a whole class of solutions previously thought possible; it may provide a cheap test to distinguish likely from unlikely possibilities; and so on. All these kinds things that aid discovery. Heuristics seldom provide infallible guidance.

Now they play games well, understand simple conversations, weigh many factors in decisions. What next? Today, machines solve problems mainly according to the principles we build into them. Before long, we may learn how to set them to work upon the very special problem of improving their own capacity to solve problems. Once a certain threshold is passed, this could lead to a spiral of acceleration and it may be hard to perfect a reliable 'governor' to restrain it. 31 It seems that there may be no limit to the range and brilliance of the no wonder that among philosoproperly programmed computer.

We therefore have to ask what assumptions underlie this persistent optimism in the face of repeated failress, never, ures. Part II attempts to bring to light four deeply rooted assumptions or prejudices which mask the gravity of the current impasse, and to lay bare the conceptual confusion to which these prejudices give rise. But these prejudices are so deeply rooted in our thinking that the only them seems to be an obscurantist rejection of the possibility of a science of human behavior. Part III attempts to answer this objecalternative to tion, insofar as it can be answered, by presenting an alternative to these drawing on the ideas of twentieth-century an implicit critique of artificial reason, although traditional assumptions, thinkers it whose work is has not before been read in this We light.

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Artificial Neural Networks - A Tutorial by Jain

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