Nhà Xuất Bản The MIT Press

Code to Joy: Why Everyone Should Learn a Little Programming

How we can get more joy from our machines by telling them what our hearts desire.

In this informative, accessible, and very funny book, Michael L. Littman inspires readers to learn how to tell machines what to do for us. Rather than give in to the fear that computers will steal our jobs, spy on us and control what we buy and whom we vote for, we can improve our relationship with them just by learning basic programming skills. Our devices will help us, Littman writes, if we can say what we want in a way they can understand.

Each chapter of the book focuses on a particular element of what can be said, providing examples of how we use similar communication in our daily interactions with people. Littman offers ways readers can experiment with these ideas right away, using publicly available systems that might also make us more productive as a welcome side effect. Each chapter also reflects on how the use of these programming components can be expedited by machine learning. With humor and teacherly guidance, Code to Joy brings into view a future where programming is like reading—something everyone can learn.

Living With Robots: What Every Anxious Human Needs To Know

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

There's a lot of hype about robots; some of it is scary and some of it utopian. In this accessible book, two robotics experts reveal the truth about what robots can and can't do, how they work, and what we can reasonably expect their future capabilities to be. It will not only make you think differently about the capabilities of robots; it will make you think differently about the capabilities of humans.

Ruth Aylett and Patricia Vargas discuss the history of our fascination with robots—from chatbots and prosthetics to autonomous cars and robot swarms. They show us the ways in which robots outperform humans and the ways they fall woefully short of our superior talents. They explain how robots see, feel, hear, think, and learn; describe how robots can cooperate; and consider robots as pets, butlers, and companions. Finally, they look at robots that raise ethical and social issues: killer robots, sexbots, and robots that might be gunning for your job. Living with Robots equips readers to look at robots concretely—as human-made artifacts rather than placeholders for our anxieties.

Find out:

- Why robots can swim and fly but find it difficult to walk

- Which robot features are inspired by animals and insects

- Why we develop feelings for robots

- Which human abilities are hard for robots to emulate

Working With AI - Real Stories Of Human-Machine Collaboration

Two management and technology experts show that AI is not a job destroyer, exploring worker-AI collaboration in real-world work settings.

This book breaks through both the hype and the doom-and-gloom surrounding automation and the deployment of artificial intelligence-enabled—“smart”—systems at work. Management and technology experts Thomas Davenport and Steven Miller show that, contrary to widespread predictions, prescriptions, and denunciations, AI is not primarily a job destroyer. Rather, AI changes the way we work—by taking over some tasks but not entire jobs, freeing people to do other, more important and more challenging work. By offering detailed, real-world case studies of AI-augmented jobs in settings that range from finance to the factory floor, Davenport and Miller also show that AI in the workplace is not the stuff of futuristic speculation. It is happening now to many companies and workers.

These cases include a digital system for life insurance underwriting that analyzes applications and third-party data in real time, allowing human underwriters to focus on more complex cases; an intelligent telemedicine platform with a chat-based interface; a machine learning-system that identifies impending train maintenance issues by analyzing diesel fuel samples; and Flippy, a robotic assistant for fast food preparation. For each one, Davenport and Miller describe in detail the work context for the system, interviewing job incumbents, managers, and technology vendors. Short “insight” chapters draw out common themes and consider the implications of human collaboration with smart systems.

Machines Like Us

How we can create artificial intelligence with broad, robust common sense rather than narrow, specialized expertise.

It’s sometime in the not-so-distant future, and you send your fully autonomous self-driving car to the store to pick up your grocery order. The car is endowed with as much capability as an artificial intelligence agent can have, programmed to drive better than you do. But when the car encounters a traffic light stuck on red, it just sits there—indefinitely. Its obstacle-avoidance, lane-following, and route-calculation capacities are all irrelevant; it fails to act because it lacks the common sense of a human driver, who would quickly figure out what’s happening and find a workaround. InMachines like Us, Ron Brachman and Hector Levesque—both leading experts in AI—consider what it would take to create machines with common sense rather than just the specialized expertise of today’s AI systems.

Using the stuck traffic light and other relatable examples, Brachman and Levesque offer an accessible account of how common sense might be built into a machine. They analyze common sense in humans, explain how AI over the years has focused mainly on expertise, and suggest ways to endow an AI system with both common sense and effective reasoning. Finally, they consider the critical issue of how we can trust an autonomous machine to make decisions, identifying two fundamental requirements for trustworthy autonomous AI systems: having reasons for doing what they do, and being able to accept advice. Both in the end are dependent on having common sense.

Evolutionary Intelligence

A surprising vision of how human intelligence will coevolve with digital technology and revolutionize how we think and behave.

It is natural for us to fear artificial intelligence. But does Siri really want to kill us? Perhaps we are falling into the trap of projecting human traits onto the machines we might build. InEvolutionary Intelligence,Neuman offers a surprisingly positive vision in which computational intelligence compensates for the well-recognized limits of human judgment, improves decision making, and actually increases our agency. In artful, accessible, and adventurous prose, Neuman takes the reader on an exciting, fast-paced ride, all the while making a convincing case about a revolution in computationally augmented human intelligence.

Neuman argues that, just as the wheel made us mobile and machines made us stronger, the migration of artificial intelligence from room-sized computers to laptops to our watches, smart glasses, and even smart contact lenses will transform day-to-day human decision making. If intelligence is the capacity to match means with ends, then augmented intelligence can offer the ability to adapt to changing environments as we face the ultimate challenge of long-term survival.

Tapping into a global interest in technology’s potential impacts on society, economics, and culture,Evolutionary Intelligencedemonstrates that our future depends on our ability to computationally compensate for the limitations of a human cognitive system that has only recently graduated from hunting and gathering.