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Computational properties of use to biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
Feynman Lectures On Computation by Richard P. Feynman Pdf
When, in 1984?86, Richard P. Feynman gave his famous course on computation at the California Institute of Technology, he asked Tony Hey to adapt his lecture notes into a book. Although led by Feynman, the course also featured, as occasional guest speakers, some of the most brilliant men in science at that time, including Marvin Minsky, Charles Bennett, and John Hopfield. Although the lectures are now thirteen years old, most of the material is timeless and presents a ?Feynmanesque? overview of many standard and some not-so-standard topics in computer science such as reversible logic gates and quantum computers.
Computational properties of use to biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
Covering the theory of computation, information and communications, the physical aspects of computation, and the physical limits of computers, this text is based on the notes taken by one of its editors, Tony Hey, on a lecture course on computation given b
The last lecture course that Nobel Prize winner Richard P. Feynman gave to students at Caltech from 1983 to 1986 was not on physics but on computer science. The first edition of the Feynman Lectures on Computation, published in 1996, provided an overview of standard and not-so-standard topics in computer science given in Feynman’s inimitable style. Although now over 20 years old, most of the material is still relevant and interesting, and Feynman’s unique philosophy of learning and discovery shines through. For this new edition, Tony Hey has updated the lectures with an invited chapter from Professor John Preskill on “Quantum Computing 40 Years Later”. This contribution captures the progress made toward building a quantum computer since Feynman’s original suggestions in 1981. The last 25 years have also seen the “Moore’s law” roadmap for the IT industry coming to an end. To reflect this transition, John Shalf, Senior Scientist at Lawrence Berkeley National Laboratory, has contributed a chapter on “The Future of Computing beyond Moore’s Law”. The final update for this edition is an attempt to capture Feynman’s interest in artificial intelligence and artificial neural networks. Eric Mjolsness, now a Professor of Computer Science at the University of California Irvine, was a Teaching Assistant for Feynman’s original lecture course and his research interests are now the application of artificial intelligence and machine learning for multi-scale science. He has contributed a chapter called “Feynman on Artificial Intelligence and Machine Learning” that captures the early discussions with Feynman and also looks toward future developments. This exciting and important work provides key reading for students and scholars in the fields of computer science and computational physics.
Richard P. Feynman made profoundly important and prescient contributions to the physics of computing, notably with his seminal articles “There's Plenty of Room at the Bottom” and “Simulating Physics with Computers.” These two provocative papers (both reprinted in this volume) anticipated, decades before their time, several breakthroughs that have since become fields of science in their own right, such as nanotechnology and the newest, perhaps most exciting area of physics and computer science, quantum computing.The contributors to this book are all distinguished physicists and computer scientists, and many of them were guest lecturers in Feynman's famous CalTech course on the limits of computers. they include Charles Bennett on Quantum Information Theory, Geoffrey Fox on Internetics, Norman Margolus on Crystalline Computation, and Tommaso Toffoli on the Fungibility of Computation.Both a tribute to Feynman and a new exploration of the limits of computers by some of today's most influential scientists, Feynman and Computation continues the pioneering work started by Feynman and published by him in his own Lectures on Computation. This new computation volume consists of both original chapters and reprints of classic papers by leaders in the field. Feynman and Computation will generate great interest from the scientific community and provide essential background for further work in this field.
Feynman Lectures on Computation by Richard Phillips Feynman Pdf
"The last lecture course that Nobel Prize winner Richard P. Feynman gave to students at Caltech from 1983 to 1986 was not on physics but on computer science. The first edition of the Feynman Lectures on Computation published in 1996 and provided an overview of standard and not-so-standard topics in computer science given in Feynman's inimitable style. Although now over 20 years old, most of the material is still relevant and interesting, and Feynman's unique philosophy of learning and discovery shines through. For this new edition, Tony Hey has updated the lectures with an invited chapter from Professor John Preskill on "Quantum Computing 40 Years Later." This contribution captures the progress made towards building a quantum computer since Feynman's original suggestions in 1981. The last 25 years have also seen the "Moore's Law" roadmap for the IT industry coming to an end. To reflect this transition, John Shalf, Senior Scientist at Lawrence Berkeley National Laboratory, has contributed a chapter on "The Future of Computing Beyond Moore's Law." The final update for this edition is an attempt to capture Feynman's interest in Artificial Intelligence and Artificial Neural Networks. Eric Mjolsness, now a professor of Computer Science at the University of California Irvine, was a Teaching Assistant for Feynman's original lecture course and his research interests are now the application of Artificial Intelligence and Machine Learning for multi-scale science. He has contributed a chapter on "Feynman on Artificial Intelligence and Machine Learning" that captures the early discussions with Feynman and also looks towards future developments. This exciting and important work provides key reading for students and scholars in the fields of computer science and computational physics"--
Feynman Lectures On Gravitation by Richard Feynman Pdf
The Feynman Lectures on Gravitation are based on notes prepared during a course on gravitational physics that Richard Feynman taught at Caltech during the 1962-63 academic year. For several years prior to these lectures, Feynman thought long and hard about the fundamental problems in gravitational physics, yet he published very little. These lectures represent a useful record of his viewpoints and some of his insights into gravity and its application to cosmology, superstars, wormholes, and gravitational waves at that particular time. The lectures also contain a number of fascinating digressions and asides on the foundations of physics and other issues.Characteristically, Feynman took an untraditional non-geometric approach to gravitation and general relativity based on the underlying quantum aspects of gravity. Hence, these lectures contain a unique pedagogical account of the development of Einstein's general theory of relativity as the inevitable result of the demand for a self-consistent theory of a massless spin-2 field (the graviton) coupled to the energy-momentum tensor of matter. This approach also demonstrates the intimate and fundamental connection between gauge invariance and the principle of equivalence.
Anti-Differentiation and the Calculation of Feynman Amplitudes by Johannes Blümlein,Carsten Schneider Pdf
This volume comprises review papers presented at the Conference on Antidifferentiation and the Calculation of Feynman Amplitudes, held in Zeuthen, Germany, in October 2020, and a few additional invited reviews. The book aims at comprehensive surveys and new innovative results of the analytic integration methods of Feynman integrals in quantum field theory. These methods are closely related to the field of special functions and their function spaces, the theory of differential equations and summation theory. Almost all of these algorithms have a strong basis in computer algebra. The solution of the corresponding problems are connected to the analytic management of large data in the range of Giga- to Terabytes. The methods are widely applicable to quite a series of other branches of mathematics and theoretical physics.
Feynman's Tips on Physics by Richard P. Feynman,Michael A Gottlieb Pdf
Feynman's Tips on Physics is a delightful collection of Richard P. Feynman's insights and an essential companion to his legendary Feynman Lectures on Physics With characteristic flair, insight, and humor, Feynman discusses topics physics students often struggle with and offers valuable tips on addressing them. Included here are three lectures on problem-solving and a lecture on inertial guidance omitted from The Feynman Lectures on Physics. An enlightening memoir by Matthew Sands and oral history interviews with Feynman and his Caltech colleagues provide firsthand accounts of the origins of Feynman's landmark lecture series. Also included are incisive and illuminating exercises originally developed to supplement The Feynman Lectures on Physics, by Robert B. Leighton and Rochus E. Vogt. Feynman's Tips on Physics was co-authored by Michael A. Gottlieb and Ralph Leighton to provide students, teachers, and enthusiasts alike an opportunity to learn physics from some of its greatest teachers, the creators of The Feynman Lectures on Physics.
This book presents recent and ongoing research work aimed at understanding the mysterious relation between the computations of Feynman integrals in perturbative quantum field theory and the theory of motives of algebraic varieties and their periods. One of the main questions in the field is understanding when the residues of Feynman integrals in perturbative quantum field theory evaluate to periods of mixed Tate motives. The question originates from the occurrence of multiple zeta values in Feynman integrals calculations observed by Broadhurst and Kreimer.Two different approaches to the subject are described. The first, a ?bottom-up? approach, constructs explicit algebraic varieties and periods from Feynman graphs and parametric Feynman integrals. This approach, which grew out of work of Bloch?Esnault?Kreimer and was more recently developed in joint work of Paolo Aluffi and the author, leads to algebro-geometric and motivic versions of the Feynman rules of quantum field theory and concentrates on explicit constructions of motives and classes in the Grothendieck ring of varieties associated to Feynman integrals. While the varieties obtained in this way can be arbitrarily complicated as motives, the part of the cohomology that is involved in the Feynman integral computation might still be of the special mixed Tate kind. A second, ?top-down? approach to the problem, developed in the work of Alain Connes and the author, consists of comparing a Tannakian category constructed out of the data of renormalization of perturbative scalar field theories, obtained in the form of a Riemann?Hilbert correspondence, with Tannakian categories of mixed Tate motives. The book draws connections between these two approaches and gives an overview of other ongoing directions of research in the field, outlining the many connections of perturbative quantum field theory and renormalization to motives, singularity theory, Hodge structures, arithmetic geometry, supermanifolds, algebraic and non-commutative geometry.The text is aimed at researchers in mathematical physics, high energy physics, number theory and algebraic geometry. Partly based on lecture notes for a graduate course given by the author at Caltech in the fall of 2008, it can also be used by graduate students interested in working in this area.
