Feynman Lectures On Computation

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

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.

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.

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.

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.

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.

This book considers the basic ideas of quantum mechanics, treating the concept of amplitude and discusses relativity and the idea of anti-particles and explains quantum electrodynamics. It provides experienced researchers with an invaluable introduction to fundamental processes.