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Code to Joy

Why Everyone Should Learn a Little Programming

by Michael L. Littman

ISBN: 9780262546393

Publisher: The MIT Press

Pub Date: October 3, 2023

Forthcoming from the MIT Press

Person, Thing, Robot

A Moral and Legal Ontology for the 21st Century and Beyond

by David J. Gunkel

ISBN: 9780262546157

Publisher: The MIT Press

Pub Date: September 5, 2023

Forthcoming from the MIT Press

The Age of Prediction

Algorithms, AI, and the Shifting Shadows of Risk

by Igor Tulchinsky and Christopher E. Mason

ISBN: 9780262047739

Publisher: The MIT Press

Pub Date: August 22, 2023

The power of the ever-increasing tools and algorithms for prediction and their paradoxical effects on risk.

Probabilistic Machine Learning

Advanced Topics

by Kevin P. Murphy

ISBN: 9780262048439

Publisher: The MIT Press

Pub Date: August 15, 2023

An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in...

Evolutionary Intelligence

How Technology Will Make Us Smarter

by W. Russell Neuman

ISBN: 9780262048484

Publisher: The MIT Press

Pub Date: August 8, 2023

Forthcoming from the MIT Press

Gradient Expectations

Structure, Origins, and Synthesis of Predictive Neural Networks

by Keith L. Downing

ISBN: 9780262545617

Publisher: The MIT Press

Pub Date: July 18, 2023

An insightful investigation into the mechanisms underlying the predictive functions of neural networks—and their ability to chart a new path for AI.

Biological Intelligence for Biomimetic Robots

An Introduction to Synthetic Neuroethology

by Joseph Ayers

ISBN: 9780262048149

Publisher: The MIT Press

Pub Date: June 6, 2023

An introduction to how neuroethology can inform the development of robots controlled by synaptic networks instead of algorithms, from a pioneer in biorobotics.

Distributional Reinforcement Learning

by Marc G. Bellemare, Will Dabney and Mark Rowland

ISBN: 9780262048019

Publisher: The MIT Press

Pub Date: May 30, 2023

The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective.

Computational Formalism

Art History and Machine Learning

by Amanda Wasielewski

ISBN: 9780262545648

Publisher: The MIT Press

Pub Date: May 23, 2023

How the use of machine learning to analyze art images has revived formalism in art history, presenting a golden opportunity for art historians and computer scientists to learn from one another.

Living with Robots

What Every Anxious Human Needs to Know

by Ruth Aylett and Patricia A. Vargas

Foreword by Noel Sharkey

ISBN: 9780262546041

Publisher: The MIT Press

Pub Date: May 2, 2023

The truth about robots: two experts look beyond the hype, offering a lively and accessible guide to what robots can (and can't) do.

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