Confused about AI and worried about what it means for your future and the future of the world? You’re not alone. AI is everywhere—and few things are surrounded by so much hype, misinformation, and misunderstanding. In AI Snake Oil, computer scientists Arvind Narayanan and Sayash Kapoor cut through the confusion to give you an essential understanding of how AI works, why it often doesn’t, where it might be useful or harmful, and when you should suspect that companies are using AI hype to sell AI snake oil—products that don’t work, and probably never will.

This book describes and explains the major events, personalities, conflicts, and convergences that have shaped the history of the Muslim world. The body of the work takes listeners from the origins of Islam to the eve of the nineteenth century, and an epilogue continues the story to the present day. Michael Cook thus provides a broad history of a civilization remarkable for both its unity and diversity.

This audiobook narrated by Davis Brooks reveals how the subtle but significant consequences of a hotter planet have already begun—from lower test scores to higher crime rates—and how we might tackle them today.

In this groundbreaking and practical guide, teachers will discover how to harness and manage AI as a powerful teaching tool. Jose Antonio Bowen and C. Edward Watson present emerging and powerful research on the seismic changes AI is already creating in schools and the workplace, providing invaluable insights into what AI can accomplish in the classroom and beyond.

In The Road to Freedom, Nobel prize winner Joseph E. Stiglitz dissects America's current economic system and the political ideology that created it, laying bare their twinned failure. Free and unfettered markets have exploited consumers, workers, and the environment alike. These movements now pose a real threat to true economic and political freedom.

Aram Sinnreich and Jesse Gilbert explore the many unpredictable, and often surprising, ways in which data surveillance, AI, and the constant presence of algorithms impact our culture and society in the age of global networks. The authors build on this basic premise: no matter what form data takes, and what purpose we think it’s being used for, data will always have a secret life. How this data will be used, by other people in other times and places, has profound implications for every aspect of our lives—from our intimate relationships to our professional lives to our political systems.

We are living through a revolution in machine learning-powered AI that shows no signs of slowing down. This technology is based on relatively simple mathematical ideas, some of which go back centuries, including linear algebra and calculus, the stuff of seventeenth- and eighteenth-century mathematics. It took the birth and advancement of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see today. In this enlightening book, Anil Ananthaswamy explains the fundamental math behind machine learning.

There is a robotics revolution underway. A record 3.1 million robots are working in factories right now, doing everything from assembling computers to packing goods and monitoring air quality and performance. A far greater number of smart machines impact our lives in countless other ways—improving the precision of surgeons, cleaning our homes, extending our reach to distant worlds—and we’re on the cusp of even more exciting opportunities.

When economist Angus Deaton immigrated to the United States from Britain in the early 1980s, he was awed by America’s strengths and shocked by the extraordinary gaps he witnessed between people. Economics in America explains in clear terms how the field of economics addresses the most pressing issues of our times—from poverty, retirement, and the minimum wage to the ravages of the nation’s uniquely disastrous health care system—and narrates Deaton’s own account of his experiences as a naturalized US citizen and academic economist.

The Arab Spring promised to end dictatorship and bring self-government to people across the Middle East. Yet everywhere except Tunisia it led to either renewed dictatorship, civil war, extremist terror, or all three. In The Arab Winter, Noah Feldman argues that the Arab Spring was nevertheless not an unmitigated failure, much less an inevitable one. Rather, it was a noble, tragic series of events in which, for the first time in recent Middle Eastern history, Arabic-speaking peoples took free, collective political action as they sought to achieve self-determination.

Spread through the public in the form of popular stories, ideas can go viral and move markets - whether it's the belief that tech stocks can only go up or that housing prices never fall. Whether true or false, stories like these - transmitted by word of mouth, by the news media, and increasingly by social media - drive the economy by driving our decisions about how and where to invest, how much to spend and save, and more. But despite the obvious importance of such stories, most economists have paid little attention to them. Narrative Economics sets out to change that.

Today's "machine-learning" systems, trained by data, are so effective that we've invited them to see and hear for us - and to make decisions on our behalf. But alarm bells are ringing. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole - and appear to assess black and white defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And autonomous vehicles on our streets can injure or kill.

Is artificial intelligence going to take over the world? Have big tech scientists created an artificial lifeform that can think on its own? Is it going to put authors, artists, and others out of business? Are we about to enter an age where computers are better than humans at everything? The answer to these questions, linguist Emily M. Bender and sociologist Alex Hanna make clear, is “no,” “they wish,” “LOL,” and “definitely not.” This kind of thinking is a symptom of a phenomenon known as “AI hype.”

How does a neural network become a brain? While neurobiologists investigate how nature accomplishes this feat, computer scientists interested in AI strive to achieve this through technology. The Self-Assembling Brain tells the stories of both fields, exploring the historical and modern approaches taken by the scientists pursuing answers to the quandary: What information is necessary to make an intelligent neural network? As Peter Robin Hiesinger argues, "the information problem" underlies both fields.

Central Asia is often seen as a remote and inaccessible land on the peripheries of modern history. Encompassing Uzbekistan, Tajikistan, Turkmenistan, Kyrgyzstan, Kazakhstan, and the Xinjiang province of China, it in fact stands at the crossroads of world events. Adeeb Khalid provides the first comprehensive history of Central Asia from the mid-18th century to today, shedding light on the historical forces that have shaped the region under imperial and Communist rule.