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.
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.
In the wake of the economic crisis, few questions are more pressing than those around the ethics of finance and economics. Theology and Economic Ethics expands the self-critical resources of contemporary theological economic ethics by bringing the method of a pre-modern thinker, Martin Luther (1483-1546), into interaction with that of a modern contribution to social ethics, the Swiss theologian Arthur Rich (1910-92). The work is undertaken through a close engagement with a selected publication of Luther (his 1519/20 Grosser Sermon von dem Wucher) and of Rich (his masterwork, Wirtschaftsethik, published in two volumes in 1984 and 1990 respectively). It is the first substantial treatment in English of Rich's magnum opus. Sean Doherty introduces Luther's sermon on usury, situates it in its context, then provides a commentary on this work, discussing how Luther brings key theological motifs to bear on a particular economic question. The study proceeds with a sketch of Arthur Rich's life and work, and presents Rich's method as set out in Wirtschaftsethik. Doherty illuminates Rich's understanding of ethics, his approach to Scripture, and his adoption of the thought of Max Weber and John Rawls. Bringing insights from the study of Luther to bear in an analysis of Rich's method, Doherty questions some of Rich's assumptions, and notes ways in which a more self-critical approach could have made his project more successful. Finally, the book makes tentative suggestions as to the wider applicability of these findings for a Christian approach to economic ethics.
A companion volume to 'Community Mental Health Nursing and Dementia Care'. Taken together the two volumes provide a rounded and evidence-based account of the complexity, breadth and diversity of community mental health nursing practice in this specialist field of care delivery.
Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people analyze and manage vast amounts of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans, and are accompanied by semantically rich content. As such, text data are especially valuable for discovering knowledge about human opinions and preferences, in addition to many other kinds of knowledge that we encode in text. In contrast to structured data, which conform to well-defined schemas (thus are relatively easy for computers to handle), text has less explicit structure, requiring computer processing toward understanding of the content encoded in text. The current technology of natural language processing has not yet reached a point to enable a computer to precisely understand natural language text, but a wide range of statistical and heuristic approaches to analysis and management of text data have been developed over the past few decades. They are usually very robust and can be applied to analyze and manage text data in any natural language, and about any topic. This book provides a systematic introduction to all these approaches, with an emphasis on covering the most useful knowledge and skills required to build a variety of practically useful text information systems. The focus is on text mining applications that can help users analyze patterns in text data to extract and reveal useful knowledge. Information retrieval systems, including search engines and recommender systems, are also covered as supporting technology for text mining applications. The book covers the major concepts, techniques, and ideas in text data mining and information retrieval from a practical viewpoint, and includes many hands-on exercises designed with a companion software toolkit (i.e., MeTA) to help readers learn how to apply techniques of text mining and information retrieval to real-world text data and how to experiment with and improve some of the algorithms for interesting application tasks. The book can be used as a textbook for a computer science undergraduate course or a reference book for practitioners working on relevant problems in analyzing and managing text data.
The city of Portland, Maine, has an extraordinary history as a prominent seaport dating back to early colonial times. Few realize how heavily intertwined this history is with fire and firefighting. The motto of the city, Resurgam, is Latin for "I will rise again." The city symbol has long included the phoenix, a mythological bird that is said to arise from the ashes of its predecessor. With over 20 major conflagrations and four different fires that destroyed the majority of the city, both the symbol and the motto directly reference Portland's perseverance despite catastrophic fire. As the Portland Fire Department celebrates the 250th anniversary of the inception of organized fire protection on March 29, 1768, Portland Firefighting takes the reader on a photographic tour encompassing not only the department's history but also the development of firefighting through the centuries.
This will help us customize your experience to showcase the most relevant content to your age group
Please select from below
Login
Not registered?
Sign up
Already registered?
Success – Your message will goes here
We'd love to hear from you!
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.