At the 1900 International Congress of Mathematicians, held that year in Paris, the German mathematician David Hilbert put forth a list of 23 unsolved problems that he saw as being the greatest challenges for twentieth-century mathematics. Hilbert's 10th problem, to find a method (what we now call an algorithm) for deciding whether a Diophantine equation has an integral solution, was solved by Yuri Matiyasevich in 1970.
Parallel distributed processing is transforming the field of cognitive science. Microcognition provides a clear, readable guide to this emerging paradigm from a cognitive philosopher's point of view. It explains and explores the biological basis of PDP, its psychological importance, and its philosophical relevance.
The broad range of material included in these volumes suggests to the newcomer the nature of the field of artificial intelligence, while those with some background in AI will appreciate the detailed coverage of the work being done at MIT. The results presented are related to the underlying methodology. Each chapter is introduced by a short note outlining the scope of the problem begin taken up or placing it in its historical context.
Animation provides a rich environment for actively exploring algorithms. Multiple, dynamic, graphical displays of an algorithm reveal properties that might otherwise be difficult to comprehend or even remain unnoticed. This exciting new approach to the study of algorithms is taken up by Marc Brown in Algorithm Animation.
Teaching the theory of error correcting codes on an introductory level is a difficult task. The theory, which has immediate hardware applications, also concerns highly abstract mathematical concepts. This text explains the basic circuits in a refreshingly practical way that will appeal to undergraduate electrical engineering students as well as to engineers and technicians working in industry.Arazi's truly commonsense approach provides a solid grounding in the subject, explaining principles intuitively from a hardware perspective.
Perceptrons - the first systematic study of parallelism in computation - has remained a classical work on threshold automata networks for nearly two decades. It marked a historical turn in artificial intelligence, and it is required reading for anyone who wants to understand the connectionist counterrevolution that is going on today.
Scientific discovery is often regarded as romantic and creative—and hence unanalyzable—whereas the everyday process of verifying discoveries is sober and more suited to analysis. Yet this fascinating exploration of how scientific work proceeds argues that however sudden the moment of discovery may seem, the discovery process can be described and modeled.
Today's computers must perform with increasing reliability, which in turn depends on the problem of determining whether a circuit has been manufactured properly or behaves correctly. However, the greater circuit density of VLSI circuits and systems has made testing more difficult and costly.
The development of parallel processing, with the attendant technology of advanced software engineering, VLSI circuits, and artificial intelligence, now allows high-performance computer systems to reach the speeds necessary to meet the challenge of future complex scientific and commercial applications. This collection of articles documents the design of one such computer, a single instruction multiple data stream (SIMD) class supercomputer with 16,834 processing units capable of over 6 billion 8 bit operations per second.