Download or read book The Descent of Artificial Intelligence written by Kevin Padraic Donnelly and published by University of Pittsburgh Press. This book was released on 2024-07-15 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: The idea that a new technology could challenge human intelligence is as old as the warning from Socrates and Plato that written language eroded memory. With the emergence of generative artificial intelligence programs, we find ourselves once again debating how a new technology might influence human thought and behavior. Researchers, software developers, and “visionary” tech writers even imagine an AI that will equal or surpass human intelligence, adding to a sense of technological determinism where humanity is inexorably shaped by powerful new machines. But among the hundreds of essays, books, and movies that approach the question of AI, few have asked how exactly scientists and philosophers have codified human thought and behavior. Rather than focusing on technical contributions in machine building, The Descent of Artificial Intelligence explores a more diverse cast of thinkers who helped to imagine the very kind of human being that might be challenged by a machine. Kevin Padraic Donnelly argues that what we often think of as the “goal” of AI has in fact been shaped by forgotten and discredited theories about people and human nature as much as it has been by scientific discoveries, mathematical advances, and novel technologies. By looking at the development of artificial intelligence through the lens of social thought, Donnelly deflates the image of artificial intelligence as a technological monolith and reminds readers that we can control the narratives about ourselves.
Download or read book Artificial Intelligence in the Age of Neural Networks and Brain Computing written by Robert Kozma and published by Academic Press. This book was released on 2023-10-11 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition demonstrates that present disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity, and smart autonomous search engines. The book covers the major basic ideas of "brain-like computing" behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as possible future alternatives. The present success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel, and Amazon, can be interpreted using the perspective presented in this book by viewing the co-existence of a successful synergism among what is referred to as computational intelligence, natural intelligence, brain computing, and neural engineering. The new edition has been updated to include major new advances in the field, including many new chapters. - Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN - Authored by top experts, global field pioneers, and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making - Edited by high-level academics and researchers in intelligent systems and neural networks - Includes all new chapters, including topics such as Frontiers in Recurrent Neural Network Research; Big Science, Team Science, Open Science for Neuroscience; A Model-Based Approach for Bridging Scales of Cortical Activity; A Cognitive Architecture for Object Recognition in Video; How Brain Architecture Leads to Abstract Thought; Deep Learning-Based Speech Separation and Advances in AI, Neural Networks
Download or read book The Book of Why written by Judea Pearl and published by Basic Books. This book was released on 2018-05-15 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence "Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.
Download or read book Artificial Intelligence for Humans written by Jeff Heaton and published by Createspace Independent Publishing Platform. This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: « Artifical Intelligence for Humans is a book series meant to teach AI to those readers who lack an extensive mathematical background. The reader only needs knowledge of basic college algebra and computer programming. Additional topics are thoroughly explained. Every chapter also includes a programming example. Examples are currently provided in Java, C#, and Python. Other languages are planned. »--
Download or read book Artificial Intelligence in Healthcare written by Adam Bohr and published by Academic Press. This book was released on 2020-06-21 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
Download or read book The Myth of Artificial Intelligence written by Erik J. Larson and published by Harvard University Press. This book was released on 2021-04-06 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: “Artificial intelligence has always inspired outlandish visions—that AI is going to destroy us, save us, or at the very least radically transform us. Erik Larson exposes the vast gap between the actual science underlying AI and the dramatic claims being made for it. This is a timely, important, and even essential book.” —John Horgan, author of The End of Science Many futurists insist that AI will soon achieve human levels of intelligence. From there, it will quickly eclipse the most gifted human mind. The Myth of Artificial Intelligence argues that such claims are just that: myths. We are not on the path to developing truly intelligent machines. We don’t even know where that path might be. Erik Larson charts a journey through the landscape of AI, from Alan Turing’s early work to today’s dominant models of machine learning. Since the beginning, AI researchers and enthusiasts have equated the reasoning approaches of AI with those of human intelligence. But this is a profound mistake. Even cutting-edge AI looks nothing like human intelligence. Modern AI is based on inductive reasoning: computers make statistical correlations to determine which answer is likely to be right, allowing software to, say, detect a particular face in an image. But human reasoning is entirely different. Humans do not correlate data sets; we make conjectures sensitive to context—the best guess, given our observations and what we already know about the world. We haven’t a clue how to program this kind of reasoning, known as abduction. Yet it is the heart of common sense. Larson argues that all this AI hype is bad science and bad for science. A culture of invention thrives on exploring unknowns, not overselling existing methods. Inductive AI will continue to improve at narrow tasks, but if we are to make real progress, we must abandon futuristic talk and learn to better appreciate the only true intelligence we know—our own.
Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Download or read book Artificial Intelligence written by Leonidas Deligiannidis and published by Walter de Gruyter GmbH & Co KG. This book was released on 2024-08-05 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) revolves around creating and utilizing intelligent machines through science and engineering. This book delves into the theory and practical applications of computer science methods that incorporate AI across many domains. It covers techniques such as Machine Learning (ML), Convolutional Neural Networks (CNN), Deep Learning (DL), and Large Language Models (LLM) to tackle complex issues and overcome various challenges.
Download or read book The Economics of Artificial Intelligence written by Ajay Agrawal and published by University of Chicago Press. This book was released on 2024-03-05 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.
Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Download or read book Deep Learning written by John D. Kelleher and published by MIT Press. This book was released on 2019-09-10 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution. Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—major trends, possible developments, and significant challenges.
Download or read book Advances in Artificial Intelligence written by Ebrahim Bagheri and published by Springer. This book was released on 2018-04-23 with total page 403 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 31th Canadian Conference on Artificial Intelligence, Canadian AI 2018, held in Toronto, ON, Canada, in May 2018. The 16 regular papers and 18 short papers presented together with 7 Graduate Student Symposium papers and 4 Industry Track papers were carefully reviewed and selected from 72 submissions. The focus of the conference was on artificial intelligence research and advanced information and communications technology.
Download or read book Artificial Intelligence written by David L. Poole and published by Cambridge University Press. This book was released on 2017-09-25 with total page 821 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence presents a practical guide to AI, including agents, machine learning and problem-solving simple and complex domains.
Download or read book The Hundred page Machine Learning Book written by Andriy Burkov and published by . This book was released on 2019 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a practical guide to get started and execute on machine learning within a few days without necessarily knowing much about machine learning.The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue.
Download or read book Artificial Intelligence written by Richard E. Neapolitan and published by CRC Press. This book was released on 2018-03-12 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first edition of this popular textbook, Contemporary Artificial Intelligence, provided an accessible and student friendly introduction to AI. This fully revised and expanded update, Artificial Intelligence: With an Introduction to Machine Learning, Second Edition, retains the same accessibility and problem-solving approach, while providing new material and methods. The book is divided into five sections that focus on the most useful techniques that have emerged from AI. The first section of the book covers logic-based methods, while the second section focuses on probability-based methods. Emergent intelligence is featured in the third section and explores evolutionary computation and methods based on swarm intelligence. The newest section comes next and provides a detailed overview of neural networks and deep learning. The final section of the book focuses on natural language understanding. Suitable for undergraduate and beginning graduate students, this class-tested textbook provides students and other readers with key AI methods and algorithms for solving challenging problems involving systems that behave intelligently in specialized domains such as medical and software diagnostics, financial decision making, speech and text recognition, genetic analysis, and more.
Download or read book Beyond Zero and One written by Andrew Smart and published by OR Books. This book was released on 2015-12-03 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: “Andrew Smart deftly shows why it’s time for us to think deeply about thinking machines before they begin thinking deeply about us.” —Douglas Rushkoff, author, Escaping the Growth Trap,Present Shock, and Program or Be Programmed “Provocative and cool.” —Cory Doctorow “Forget the Turing test—will the supersmart AIs that we hear so much about these days pass the acid test? In this playful, informative, and prescient book, Andrew Smart brings psychedelics into dialogue with neuroscience in order to challenge the whiz-bang computational views of human and machine sentience that dominate the headlines. Giving robots LSD sounds like a joke, but Smart is dead serious in his critique of the hidden and sometimes dangerous biases that underlie both popular and scientific fantasies of digital minds.” —Erik Davis, host of “Expanding Mind” and author, Techgnosis: Myth, Magic, and Mysticism in the Age of Information “Philosophy, psychedelics, robots, and the future; consciousness and intelligence, what else do you desire? Here you will see why those machines that reach singularity will be smarter than us and take over the world—and shall need to be conscious…and maybe they can only be conscious if they are human enough. The thesis of the book, and the path shown us by Smart, leads to a great trip, of imagination and philosophy, of maths and neuroscience.” —Dr. Tristan Bekinschtein, Lecturer, Department of Psychology, University of Cambridge Can we build a robot that trips on acid? This is not a frivolous question, according to neuroscientist Andrew Smart. If we can’t, he argues, we haven’t really created artificial intelligence. In an exposition reminiscent of crossover works such as Gödel, Escher, Bach and Fermat’s Last Theorem, Andrew Smart weaves together Mangarevan binary numbers, the discovery of LSD, Leibniz, computer programming, and much more to connect the vast but largely forgotten world of psychedelic research with the resurgent field of AI and the attempt to build conscious robots. A book that draws on the history of mathematics, philosophy, and digital technology, Beyond Zero and One challenges fundamental assumptions underlying artificial intelligence. Is the human brain based on computation? Can information alone explain human consciousness and intelligence? Smart convincingly makes the case that true intelligence, and artificial intelligence, requires an appreciation of what is beyond the computational.
