How Smart Machines Think

Everything you want to know about the breakthroughs in AI technology, machine learning, and deep learning—as seen in self-driving cars, Netflix recommendations, and more. The future is here: Self-driving cars are on the streets, an algorithm gives you movie and TV recommendations, IBM’s Watson triumphed on Jeopardy over puny human brains, computer programs can be trained to play Atari games. But how do all these things work? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today’s machines so smart. Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world—and to play Atari video games better than humans. He explains Watson’s famous victory on Jeopardy, and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution—at least for now. Gerrish weaves the stories behind these breakthroughs into the narrative, introducing readers to many of the researchers involved, and keeping technical details to a minimum. Science and technology buffs will find this book an essential guide to a future in which machines can outsmart people.

For ages, the idea of machines that think and act on their own has gripped scientists, thinkers, and the general public. This book explores the history of artificial intelligence (A.I.), and how science fiction is quickly becoming science fact. It examines the technologies involved in A.I. and its inevitable effects on work, life, health, and many other aspects of human society. Rooted in history and science, this book provides an inside look at a topic that captivates engineers, scientists, and dreamers, but also raises important ethical issues and challenges how we see ourselves and our mechanical and computer creations.

A public policy leader addresses how artificial intelligence is transforming the future of labor—and what we can do to protect the role of workers. As computer technology advances with dizzying speed, human workers face an ever-increasing threat of obsolescence. In Human Work In the Age of Smart Machines, Jamie Merisotis argues that we can—and must—rise to this challenge by preparing to work alongside smart machines doing that which only humans can: thinking critically, reasoning ethically, interacting interpersonally, and serving others with empathy. The president and CEO of Lumina Foundation, Merisotis offers a roadmap for the large-scale, radical changes we must make in order to find abundant and meaningful work for ourselves in the 21st century. His vision centers on developing our unique capabilities as humans through learning opportunities that deliver fair results and offer a broad range of credentials. By challenging long-held assumptions and expanding our concept of work, Merisotis argues that we can harness the population’s potential, encourage a deeper sense of community, and erase a centuries-long system of inequality.

We are crossing a new frontier in the evolution of computing and entering the era of cognitive systems. The victory of IBMÕs Watson on the television quiz show Jeopardy! revealed how scientists and engineers at IBM and elsewhere are pushing the boundaries of science and technology to create machines that sense, learn, reason, and interact with people in new ways to provide insight and advice. In Smart Machines, John E. Kelly III, director of IBM Research, and Steve Hamm, a writer at IBM and a former business and technology journalist, introduce the fascinating world of Òcognitive systemsÓ to general audiences and provide a window into the future of computing. Cognitive systems promise to penetrate complexity and assist people and organizations in better decision making. They can help doctors evaluate and treat patients, augment the ways we see, anticipate major weather events, and contribute to smarter urban planning. Kelly and HammÕs comprehensive perspective describes this technology inside and out and explains how it will help us conquer the harnessing and understanding of Òbig data,Ó one of the major computing challenges facing businesses and governments in the coming decades. Absorbing and impassioned, their book will inspire governments, academics, and the global tech industry to work together to power this exciting wave in innovation.

Weighing in from the cutting-edge frontiers of science, today’s most forward-thinking minds explore the rise of “machines that think.” Stephen Hawking recently made headlines by noting, “The development of full artificial intelligence could spell the end of the human race.” Others, conversely, have trumpeted a new age of “superintelligence” in which smart devices will exponentially extend human capacities. No longer just a matter of science-fiction fantasy (2001, Blade Runner, The Terminator, Her, etc.), it is time to seriously consider the reality of intelligent technology, many forms of which are already being integrated into our daily lives. In that spirit, John Brockman, publisher of Edge. org (“the world’s smartest website” – The Guardian), asked the world’s most influential scientists, philosophers, and artists one of today’s most consequential questions: What do you think about machines that think?

The future is here: Self-driving cars are on the streets, an algorithm gives you movie and TV recommendations, IBM's Watson triumphed on Jeopardy over puny human brains, computer programs can be trained to play Atari games. But how do all these things work? Smart machines are no longer science-fiction. They are being used by businesses right here and now. And, every day, their capabilities are growing. So exactly what is a smart machine? Everything you've always wanted to know about smart machines other cognitive computing systems that are able to make decisions and solve problems without human intervention. By the end of reading this book, you will master the discussion about the following topics in Smart Machines: 01 - The Rise of Smart Machines: Smart machines are built on top of the breakthroughs in computer science. the advent of computers in the 20th century laid the modern groundwork for Smart Machines. Researchers, analysts and technology leaders agree that Smart Machines will change our lives 02 - Smart machines definitions: Why did Smart Machines emerge now and not later? General-purpose technology (GPT) is an economics term for innovation. Smart machines are devices that can teach themselves how to do things. Smart machines are built on top of existing technologies. The surge of progress in machine learning thanks to big data. 03 - Artificial intelligence of Smart Machines: Defense Advanced Research Projects Agency (DARPA) Projects. 04- Cognitive computing of Smart Machines: This categorization of Cognitive Computing and the discussion of the types of applications. Cognitive technologies are very good at analyzing large amounts of data and identifying anomalies. 05 - The real-world examples of Smart Machines: From DeepBlue to Watson. 06 - Smart Machine technologies: Smart Dust, Virtual Personal Assistants, Smart Data Discovery, Smart Workspace, Conversational User Interfaces, Smart Robots, Commercial UAVs (Drones), Autonomous Vehicles: Autonomous Vehicles. 07 - Smart machines go to school to learn:Central to machine learning is the process of feeding training data into a mathematical prediction model. The outlier cases are the best chances for learning 08 - Smart machines are wiser and smarter every day: Future machine learning, and the large increase in available data and the rise of crowdsourcing 09 - Smart machines speak multi-languages: One particularly complex problem facing machine translation today is dealing with rare languages 10 - Artificial Intelligence Levels: Support for Humans, Repetitive Task Automation, Context Knowledge and Learning, Self-awareness. 11 - Cognitive Task Types: Analyzing Numbers, Words and Images Analysis, Performing Digital Tasks, Performing Physical tasks 12 - The Great Convergence: The approach of key vendors to achieve the great convergence. The attributes of cognitive architecture. People will continue to have advantages over even the smartest machines. 13 - Smart robotics from vision to reality: Simultaneous Localization and Mapping. Funding the robotics projects. Robots navigate on planets. Developing robots for government and consumer-oriented applications. Robots in Japan. Robots Deployment. Social acceptance of robots. 14 - The business benefits of Smart Machines: Smart machines will change the way work. The use of Smart Machines in information technology. 15 - Smart machines are efficient: what do we really mean by machines replacing jobs? 16 - Smart machines raise challenging questions: The impact of Smart Machines on labor market. machines get smarter and more independent, and may not to our advantage. 17 - The future of Smart Machines: The growing role of industry labs and federal agencies. The advance of Smart Machines cannot be stopped so we must be ready for it.

A fascinating look at Artificial Intelligence, from its humble Cold War beginnings to the dazzling future that is just around the corner. When most of us think about Artificial Intelligence, our minds go straight to cyborgs, robots, and sci-fi thrillers where machines take over the world. But the truth is that Artificial Intelligence is already among us. It exists in our smartphones, fitness trackers, and refrigerators that tell us when the milk will expire. In some ways, the future people dreamed of at the World's Fair in the 1960s is already here. We're teaching our machines how to think like humans, and they're learning at an incredible rate. In Thinking Machines, technology journalist Luke Dormehl takes you through the history of AI and how it makes up the foundations of the machines that think for us today. Furthermore, Dormehl speculates on the incredible--and possibly terrifying--future that's much closer than many would imagine. This remarkable book will invite you to marvel at what now seems commonplace and to dream about a future in which the scope of humanity may need to broaden itself to include intelligent machines.

“Artificial intelligence has always inspired outlandish visions—that AI is going to destroy us, save us, or at the very least radically transform us. Erik Larson exposes the vast gap between the actual science underlying AI and the dramatic claims being made for it. This is a timely, important, and even essential book.” —John Horgan, author of The End of Science Many futurists insist that AI will soon achieve human levels of intelligence. From there, it will quickly eclipse the most gifted human mind. The Myth of Artificial Intelligence argues that such claims are just that: myths. We are not on the path to developing truly intelligent machines. We don’t even know where that path might be. Erik Larson charts a journey through the landscape of AI, from Alan Turing’s early work to today’s dominant models of machine learning. Since the beginning, AI researchers and enthusiasts have equated the reasoning approaches of AI with those of human intelligence. But this is a profound mistake. Even cutting-edge AI looks nothing like human intelligence. Modern AI is based on inductive reasoning: computers make statistical correlations to determine which answer is likely to be right, allowing software to, say, detect a particular face in an image. But human reasoning is entirely different. Humans do not correlate data sets; we make conjectures sensitive to context—the best guess, given our observations and what we already know about the world. We haven’t a clue how to program this kind of reasoning, known as abduction. Yet it is the heart of common sense. Larson argues that all this AI hype is bad science and bad for science. A culture of invention thrives on exploring unknowns, not overselling existing methods. Inductive AI will continue to improve at narrow tasks, but if we are to make real progress, we must abandon futuristic talk and learn to better appreciate the only true intelligence we know—our own.