Tony Hey – författare

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 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.