{"title":"Artificial Intelligence","description":null,"products":[{"product_id":"machine-learning-for-data-streams-with-practical-examples-in-moa","title":"Machine Learning for Data Streams: with Practical Examples in MOA","description":"\u003cb\u003eA hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eToday many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations.\u003cbr\u003e\u003cbr\u003eThe book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. 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These tasks range from the design and control of the  underlying transport network to the filtering, interpretation and  manipulation of the transported media. The chapters focus on specific  applications, describe specific solutions and demonstrate the benefits  that neural networks can provide. By doing this, the authors  demonstrate that neural networks should be another tool in the  telecommunications engineer's toolbox. \u003cbr\u003e  Neural networks offer the computational power of nonlinear techniques,  while providing a natural path to efficient massively-parallel  hardware implementations. In addition, the ability of neural networks  to learn allows them to be used on problems where straightforward  heuristic or rule-based solutions do not exist. 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Traditionally, artificial intelligence has been concerned with reproducing the abilities of human brains; newer approaches take inspiration from a wider range of biological structures that that are capable of autonomous self-organization. Examples of these new approaches include evolutionary computation and evolutionary electronics, artificial neural networks, immune systems, biorobotics, and swarm intelligence—to mention only a few. This book offers a comprehensive introduction to the emerging field of biologically inspired artificial intelligence that can be used as an upper-level text or as a reference for researchers. Each chapter presents computational approaches inspired by a different biological system; each begins with background information about the biological system and then proceeds to develop computational models that make use of biological concepts. The chapters cover evolutionary computation and electronics; cellular systems; neural systems, including neuromorphic engineering; developmental systems; immune systems; behavioral systems—including several approaches to robotics, including behavior-based, bio-mimetic, epigenetic, and evolutionary robots; and collective systems, including swarm robotics as well as cooperative and competitive co-evolving systems. 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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.\u003cbr\u003e\u003cbr\u003eIn \u003ci\u003eThinking Machines\u003c\/i\u003e, 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. 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These and other AI applications raise complex ethical issues that are the subject of ongoing debate. This volume in the MIT Press Essential Knowledge series offers an accessible synthesis of these issues. Written by a philosopher of technology, \u003ci\u003eAI Ethics\u003c\/i\u003e goes beyond the usual hype and nightmare scenarios to address concrete questions.\u003c\/p\u003e\u003cp\u003eMark Coeckelbergh describes influential AI narratives, ranging from Frankenstein's monster to transhumanism and the technological singularity. He surveys relevant philosophical discussions: questions about the fundamental differences between humans and machines and debates over the moral status of AI. He explains the technology of AI, describing different approaches and focusing on machine learning and data science. 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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 \u003ci\u003eMachines like Us\u003c\/i\u003e, 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.\u003cbr\u003e\u003cbr\u003eUsing 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. 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The tools of artificial intelligence are being increasingly deployed in the battle for global sustainability. And yet, warns Peter Dauvergne, we should be cautious in declaring AI the planet's savior. In \u003ci\u003eAI in the Wild\u003c\/i\u003e, Dauvergne avoids the AI industry-powered hype and offers a critical view, exploring both the potential benefits and risks of using artificial intelligence to advance global sustainability.\u003c\/p\u003e\u003cp\u003eDauvergne finds that corporations and states often use AI in ways that are antithetical to sustainability. The competition to profit from AI is entrenching technocratic management, revving up resource extraction, and turbocharging consumption, as consumers buy new smart devices (and discard their old, less-smart ones). Smart technology is helping farmers grow crops more efficiently, but also empowering the agrifood industry. 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Laird.\u003c\/b\u003e\u003cp\u003eHerbert Simon's classic and influential \u003ci\u003eThe Sciences of the Artificial\u003c\/i\u003e declares definitively that there can be a science not only of natural phenomena but also of what is artificial. Exploring the commonalities of artificial systems, including economic systems, the business firm, artificial intelligence, complex engineering projects, and social plans, Simon argues that designed systems are a valid field of study, and he proposes a science of design. 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But the knowledge economy, based on the assumption that information is a scarce and precious commodity, is giving way to an economy in which information is ubiquitous, digital, and machine-generated. In \u003ci\u003eRunning with Robots\u003c\/i\u003e, Greg Toppo and Jim Tracy show how the technological advances that are already changing the world of work will transform the American high school as well.\u003cbr\u003e\u003cbr\u003eToppo and Tracy--a journalist and an education leader, respectively--look at developments in artificial intelligence and other fields that promise to bring us not only driverless cars but doctorless patients, lawyerless clients, and possibly even teacherless students. They visit schools from New York City to Iowa that have begun preparing for this new world. 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In \u003ci\u003eBots and Beasts\u003c\/i\u003e, Paul Thagard looks at how computers (\"bots\") and animals measure up to the minds of people, offering the first systematic comparison of intelligence across machines, animals, and humans.\u003cbr\u003e\u003cbr\u003eThagard explains that human intelligence is more than IQ and encompasses such features as problem solving, decision making, and creativity. He uses a checklist of twenty characteristics of human intelligence to evaluate the smartest machines--including Watson, AlphaZero, virtual assistants, and self-driving cars--and the most intelligent animals--including octopuses, dogs, dolphins, bees, and chimpanzees. Neither a romantic enthusiast for nonhuman intelligence nor a skeptical killjoy, Thagard offers a clear assessment. He discusses hotly debated issues about animal intelligence concerning bacterial consciousness, fish pain, and dog jealousy. 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