Green computing involves reducing the electricity consumed and environmental waste generated when using a computer. • Strategies include: – Recycling. Discovering Computers Living in a View, navigate, and print Portable Document Format (PDF) files — documents formatted to look. Describe the various types of flash memory storage. Describe cloud storage and explain its advantages. Discovering Computers Fundamentals, Edition.
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Discovering Computers by Gary B Shelly, , available at Book Depository with free delivery worldwide. Discovering Computers book. Read reviews from world's largest community for readers. Students are guided through the latest trends in computer conce. Discovering Computers Living in a Digital World, Complete . tutorials, pdf, ebook, torrent, downloads, rapidshare, filesonic, hotfile.
How popular is entertainment software? Does text messaging improve typing skills? Can I add more RAM to my computer? How much music can I store on a portable media player?
How many PCs are in use worldwide? This book is our answer to the many requests we have Discovering Computers — Fundamentals, Edition is intended for use as a stand-alone textbook or in combination with an applications, Internet, or programming textbook in a full-semester introductory computer course.
No experience with computers is assumed. Hallmarks of Discovering Computers — Fundamentals To date, more than six million students have learned about computers using a Discovering Computers textbook. With the additional Web integration and interactivity, streaming up-to-date audio and video, extraordinary step-by-step visual drawings and photographs, unparalleled currency, and the Shelly and Cashman touch, this book will make your computer concepts course exciting and dynamic.
Hallmarks of this book include: A Proven Pedagogy Careful explanations of complex concepts, educationallysound elements, and reinforcement highlight this proven method of presentation. A Visually Appealing Book that Maintains Student Interest The latest technology, pictures, drawings, and text are combined artfully to produce a visually appealing and easyto-understand book. Many of the figures include a step-bystep presentation see page , which simplifies the more complex computer concepts.
If a machine could carry on a conversation over a teleprinter that was indistinguishable from a conversation with a human being, then it was reasonable to say that the machine was "thinking".
This simplified version of the problem allowed Turing to argue convincingly that a "thinking machine" was at least plausible and the paper answered all the most common objections to the proposition. Symbolic reasoning and the Logic Theorist[ edit ] When access to digital computers became possible in the middle fifties, a few scientists instinctively recognized that a machine that could manipulate numbers could also manipulate symbols and that the manipulation of symbols could well be the essence of human thought.
This was a new approach to creating thinking machines. Simon created the " Logic Theorist " with help from J. The program would eventually prove 38 of the first 52 theorems in Russell and Whitehead's Principia Mathematica , and find new and more elegant proofs for some. The proposal for the conference included this assertion: "every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it".
Simon , all of whom would create important programs during the first decades of AI research. The programs that were developed during this time were, to most people, simply "astonishing":  computers were solving algebra word problems, proving theorems in geometry and learning to speak English. Few at the time would have believed that such "intelligent" behavior by machines was possible at all.
Among the most influential were these: Reasoning as search[ edit ] Many early AI programs used the same basic algorithm. To achieve some goal like winning a game or proving a theorem , they proceeded step by step towards it by making a move or a deduction as if searching through a maze, backtracking whenever they reached a dead end.
This paradigm was called " reasoning as search ". Researchers would reduce the search space by using heuristics or " rules of thumb " that would eliminate those paths that were unlikely to lead to a solution.
The first AI program to use a semantic net was written by Ross Quillian  and the most successful and controversial version was Roger Schank 's Conceptual dependency theory.
She simply gave a canned response or repeated back what was said to her, rephrasing her response with a few grammar rules. ELIZA was the first chatterbot. They pointed out that in successful sciences like physics, basic principles were often best understood using simplified models like frictionless planes or perfectly rigid bodies.
Much of the research focused on a " blocks world ," which consists of colored blocks of various shapes and sizes arrayed on a flat surface.
At the same time, Minsky and Papert built a robot arm that could stack blocks, bringing the blocks world to life.
It could communicate in ordinary English sentences, plan operations and execute them. Simon and Allen Newell : "within ten years a digital computer will be the world's chess champion" and "within ten years a digital computer will discover and prove an important new mathematical theorem. Simon : "machines will be capable, within twenty years, of doing any work a man can do.
DARPA continued to provide three million dollars a year until the 70s. Licklider , then the director of ARPA , believed that his organization should "fund people, not projects! Its limb control system allowed it to walk with the lower limbs, and to grip and transport objects with hands, using tactile sensors.
Its vision system allowed it to measure distances and directions to objects using external receptors, artificial eyes and ears. And its conversation system allowed it to communicate with a person in Japanese, with an artificial mouth.
AI researchers had failed to appreciate the difficulty of the problems they faced. Their tremendous optimism had raised expectations impossibly high, and when the promised results failed to materialize, funding for AI disappeared.
Even the most impressive could only handle trivial versions of the problems they were supposed to solve; all the programs were, in some sense, "toys". Although some of these limits would be conquered in later decades, others still stymie the field to this day. For example, Ross Quillian 's successful work on natural language was demonstrated with a vocabulary of only twenty words, because that was all that would fit in memory. He suggested an analogy: artificial intelligence requires computer power in the same way that aircraft require horsepower.
Below a certain threshold, it's impossible, but, as power increases , eventually it could become easy. Intractability and the combinatorial explosion. In Richard Karp building on Stephen Cook 's theorem showed there are many problems that can probably only be solved in exponential time in the size of the inputs.
Finding optimal solutions to these problems requires unimaginable amounts of computer time except when the problems are trivial. This almost certainly meant that many of the "toy" solutions used by AI would probably never scale up into useful systems. Many important artificial intelligence applications like vision or natural language require simply enormous amounts of information about the world: the program needs to have some idea of what it might be looking at or what it is talking about.
This requires that the program know most of the same things about the world that a child does. Researchers soon discovered that this was a truly vast amount of information.
No one in could build a database so large and no one knew how a program might learn so much information.
This helps explain why research into vision and robotics had made so little progress by the middle s. AI researchers like John McCarthy who used logic discovered that they could not represent ordinary deductions that involved planning or default reasoning without making changes to the structure of logic itself.
They developed new logics like non-monotonic logics and modal logics to try to solve the problems. The pattern began as early as when the ALPAC report appeared criticizing machine translation efforts. After spending 20 million dollars, the NRC ended all support. Hans Moravec blamed the crisis on the unrealistic predictions of his colleagues. Funding for the creative, freewheeling exploration that had gone on in the 60s would not come from DARPA.
Instead, the money was directed at specific projects with clear objectives, such as autonomous tanks and battle management systems.
If the symbols have no meaning for the machine, Searle argued, then the machine can not be described as "thinking". Problems like intractability and commonsense knowledge seemed much more immediate and serious. It was unclear what difference " know how " or " intentionality " made to an actual computer program.