Computation Finite And Infinite Machines

This book constitutes the refereed proceedings of the 5th International Conference on Machines, Computations, and Universality, MCU 2007, held in Orleans, France, September 2007. The 18 revised full papers presented together with nine invited papers cover Turing machines, register machines, word processing, cellular automata, tiling of the plane, neural networks, molecular computations, BSS machines, infinite cellular automata, real machines, and quantum computing.

Computing Methodologies -- Artificial Intelligence.

The objective of the present book of essays is to convey to the intelligent nonmathematician something of the nature, development, and use of mathe matical concepts, particularly those that have found application in current scientific research. The idea of assembling such a volume goes back at least to 1974, when it was discussed by the then-newly-formed Joint Projects Committee for Mathematics (JPCM) of the American Mathematical Soci ety, the Mathematical Association of America, and the Society for Indus trial and Applied Mathematics. Currently, the nine members of the JPCM are Saunders Mac Lane (Chairman) of the University of Chicago, Frederick J. Almgren, Jr. of Princeton University, Richard D. Anderson of Louisiana State University, George E. Carrier of Harvard University, Hirsh G. Cohen ofthe International Business Machines Corporation, Richard C. DiPrima of Rensselaer Polytechnic Institute, Robion C. Kirby of the University of California at Berkeley, William H. Kruskal of the University of Chicago, and George D. Mostow of Yale University. The JPCM decided to make production of this volume its first major proj ect and requested the Conference Board of the Mathematical Sciences (CBMS), of which its three sponsoring societies are all member organiza tions, to approach the National Science Foundation on its behalffor support of the undertaking. A proposal submitted by the C BMS in December 1974 and in revised form in July 1975 was granted by the Foundation in May 1976, and work on assembling the volume got under way.

As a field, computer science occupies a unique scientific space, in that its subject matter can exist in both physical and abstract realms. An artifact such as software is both tangible and not, and must be classified as something in between, or "liminal." The study and production of liminal artifacts allows for creative possibilities that are, and have been, possible only in computer science. In It Began with Babbage, computer scientist and writer Subrata Dasgupta examines the distinct history of computer science in terms of its creative innovations, reaching back to Charles Babbage in 1819. Since all artifacts of computer science are conceived with a use in mind, the computer scientist is not concerned with the natural laws that govern disciplines like physics or chemistry; instead, the field is more concerned with the concept of purpose. This requirement lends itself to a type of creative thinking that, as Dasgupta shows us, has exhibited itself throughout the history of computer science. More than any other, computer science is the science of the artificial, and has a unique history to accompany its unique focus. The book traces a path from Babbage's Difference Engine in the early 19th century to the end of the 1960s by when a new academic discipline named "computer science" had come into being. Along the way we meet characters like Babbage and Ada Lovelace, Turing and von Neumann, Shannon and Chomsky, and a host of other people from a variety of backgrounds who collectively created this new science of the artificial. And in the end, we see how and why computer science acquired a nature and history all of its own.

The book constitutes the proceedings of the 24th International Conference on Artificial Neural Networks, ICANN 2014, held in Hamburg, Germany, in September 2014. The 107 papers included in the proceedings were carefully reviewed and selected from 173 submissions. The focus of the papers is on following topics: recurrent networks; competitive learning and self-organisation; clustering and classification; trees and graphs; human-machine interaction; deep networks; theory; reinforcement learning and action; vision; supervised learning; dynamical models and time series; neuroscience; and applications.