100 Books Like Artificial Intelligence

'This is the most important conversation of our time, and Tegmark's thought-provoking book will help you join it' Stephen Hawking

THE INTERNATIONAL BESTSELLER. DAILY TELEGRAPH AND THE TIMES BOOKS OF THE YEAR
SELECTED AS ONE OF BARACK OBAMA'S FAVOURITE BOOKS OF 2018

AI is the future - but what will that future look like? Will superhuman intelligence be our slave, or become our god?

Taking us to the heart of the latest thinking about AI, Max Tegmark, the MIT professor whose work has helped mainstream research on how to keep AI beneficial, separates myths from reality, utopias from dystopias, to… show more.

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”
—Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all… show more.

The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. Other animals have stronger muscles or sharper claws, but we have cleverer brains.

If machine brains one day come to surpass human brains in general intelligence, then this new superintelligence could become very powerful. As the fate of the gorillas now depends more on us humans than on the gorillas themselves, so the fate of our species then would come to depend on the actions of the machine superintelligence.

But we have one advantage:… show more.

From the New York Times bestselling authors of Abundance and Bold comes a practical playbook for technological convergence in our modern era.

In their book Abundance, bestselling authors and futurists Peter Diamandis and Steven Kotler tackled grand global challenges, such as poverty, hunger, and energy. Then, in Bold, they chronicled the use of exponential technologies that allowed the emergence of powerful new entrepreneurs. Now the bestselling authors are back with The Future Is Faster Than You Think, a blueprint for how our world will change in response to the next ten years of rapid technological disruption.

Technology is accelerating far… show more.

*An Economist, Financial Times, Guardian, Prospect and Sunday Times Book of the Year*
Shortlisted for the FT and Schroders Business Book of the Year

This is the only book you need to understand our new world - from the ultimate AI insider, the CEO of Microsoft AI and co-founder of the pioneering AI company DeepMind.

'Important' YUVAL NOAH HARARI
'Excellent' BILL GATES
'Astonishing' STEPHEN FRY
'Stunning' RORY STEWART

Soon you will live surrounded by AIs. In a world of quantum computers, robot assistants and abundant energy, they will organise your life, operate your business, and run government services.

None of… show more.

Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models.

Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you'll… show more.

Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation pro- gation. Similarly, new models… show more.

THE WALL STREET JOURNAL BESTSELLERImpromptu: Amplifying Our Humanity Through AI, written by Reid Hoffman with GPT-4, takes readers on a travelog of the future – exploring how AI, and especially Large Language Models like GPT-4, can elevate humanity across key areas like education, business, and creativity.But, it's not just a book, it's a conversation. In a first, Hoffman doesn’t just write about GPT-4; he interacts and writes with GPT-4, letting readers see the technology’s capabilities — its strengths and limitations alike. Using GPT-4 as his “author’s co-pilot,” Hoffman paints an intriguing, challenging, and often entertaining picture of what’s possible: where… show more.

A leading artificial intelligence researcher lays out a new approach to AI that will enable us to coexist successfully with increasingly intelligent machines

In the popular imagination, superhuman artificial intelligence is an approaching tidal wave that threatens not just jobs and human relationships, but civilization itself. Conflict between humans and machines is seen as inevitable and its outcome all too predictable.

In this groundbreaking book, distinguished AI researcher Stuart Russell argues that this scenario can be avoided, but only if we rethink AI from the ground up. Russell begins by exploring the idea of intelligence in humans and in machines.… show more.

As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role… show more.