Make way for this MONSTEROUS construction crew!

How ed tech was born: Twentieth-century teaching machines--from Sidney Pressey's mechanized test-giver to B. F. Skinner's behaviorist bell-ringing box. Contrary to popular belief, ed tech did not begin with videos on the internet. The idea of technology that would allow students to "go at their own pace" did not originate in Silicon Valley. In Teaching Machines, education writer Audrey Watters offers a lively history of predigital educational technology, from Sidney Pressey's mechanized positive-reinforcement provider to B. F. Skinner's behaviorist bell-ringing box. Watters shows that these machines and the pedagogy that accompanied them sprang from ideas--bite-sized content, individualized instruction--that had legs and were later picked up by textbook publishers and early advocates for computerized learning. Watters pays particular attention to the role of the media--newspapers, magazines, television, and film--in shaping people's perceptions of teaching machines as well as the psychological theories underpinning them. She considers these machines in the context of education reform, the political reverberations of Sputnik, and the rise of the testing and textbook industries. She chronicles Skinner's attempts to bring his teaching machines to market, culminating in the famous behaviorist's efforts to launch Didak 101, the "pre-verbal" machine that taught spelling. (Alternate names proposed by Skinner include "Autodidak," "Instructomat," and "Autostructor.") Telling these somewhat cautionary tales, Watters challenges what she calls "the teleology of ed tech"--the idea that not only is computerized education inevitable, but technological progress is the sole driver of events.

Everythingyou need to know about how machines...

Use the Magic Lens to reveal the inner workings of the machines all around us

Mobile Working Machines are defined by three characteristics. These machines have a cer-tain task of doing a working process, they are mobile, and they have a signifi cant energy share in their working functions. The machines should be as productive, efficient and of high quality as possible. All these machines in the fi eld of agriculture, forestry, construction, logistics, municipal sector, and in other special applications work in different applications. But, many technologies placed in the machines are the same, similar or comparable; therefore, different branches can learn from each other. Mobile Working Machines provides a wide and deep view into the technologies used in these machines. Appropriate for new engineers as well as those who wish to increase their knowledge in this field, this book brings together all the latest research and development into one place.

Open out the giant fold-out pages to find out about some of the world's biggest, strongest and tallest machines. Full of the world’s biggest machines found on building sites, farms, airports and dockyards including one of the biggest machines ever, the bucket-wheel excavator used in mining. For the biggest of machines, the book includes two giant foldout pages. This attractive picture book format replaces the original board book format, ISBN 9781409507314.

No detailed description available for "On Machine Translation".

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. In Machines 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.

This is a story of how work gets done. It is also a study of how field service technicians talk about their work and how that talk is instrumental in their success. In his innovative ethnography, Julian E. Orr studies the people who repair photocopiers and shares vignettes from their daily lives. He characterizes their work as a continuous highly skilled improvisation within a triangular relationship of technician, customer, and machine. The work technicians do encompasses elements not contained in the official definition of the job yet vital to its success. Orr's analysis of the way repair people talk about their work reveals that talk is, in fact, a crucial dimension of their practice. Diagnosis happens through a narrative process, the creation of a coherent description of the troubled machine. The descriptions become the basis for technicians' discourse about their experience, and the circulation of stories among the technicians is the principal means by which they stay informed of the developing subtleties of machine behavior. Orr demonstrates that technical knowledge is a socially distributed resource stored and diffused primarily through an oral culture.Based on participant observation with copier repair technicians in the field and strengthened by Orr's own years as a technician, this book explodes numerous myths about technicians and suggests how technical work differs from other kinds of employment.