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"This book offers a high interdisciplinary exchange of ideas pertaining to the philosophy of computer science, from philosophical and mathematical logic to epistemology, engineering, ethics or neuroscience experts and outlines new problems that arise with new tools"--Provided by publisher.
"This book offers a high interdisciplinary exchange of ideas pertaining to the philosophy of computer science, from philosophical and mathematical logic to epistemology, engineering, ethics or neuroscience experts and outlines new problems that arise with new tools"--Provided by publisher.
A fascinating look at Artificial Intelligence, from its humble Cold War beginnings to the dazzling future that is just around the corner. When most of us think about Artificial Intelligence, our minds go straight to cyborgs, robots, and sci-fi thrillers where machines take over the world. But the truth is that Artificial Intelligence is already among us. 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. In Thinking Machines, 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. This remarkable book will invite you to marvel at what now seems commonplace and to dream about a future in which the scope of humanity may need to broaden itself to include intelligent machines.
Colburn (computer science, U. of Minnesota-Duluth) has a doctorate in philosophy and an advanced degree in computer science; he's worked as a philosophy professor, a computer programmer, and a research scientist in artificial intelligence. Here he discusses the philosophical foundations of artificial intelligence; the new encounter of science and philosophy (logic, models of the mind and of reasoning, epistemology); and the philosophy of computer science (touching on math, abstraction, software, and ontology).
An argument that—despite dramatic advances in the field—artificial intelligence is nowhere near developing systems that are genuinely intelligent. In this provocative book, Brian Cantwell Smith argues that artificial intelligence is nowhere near developing systems that are genuinely intelligent. Second wave AI, machine learning, even visions of third-wave AI: none will lead to human-level intelligence and judgment, which have been honed over millennia. Recent advances in AI may be of epochal significance, but human intelligence is of a different order than even the most powerful calculative ability enabled by new computational capacities. Smith calls this AI ability “reckoning,” and argues that it does not lead to full human judgment—dispassionate, deliberative thought grounded in ethical commitment and responsible action. Taking judgment as the ultimate goal of intelligence, Smith examines the history of AI from its first-wave origins (“good old-fashioned AI,” or GOFAI) to such celebrated second-wave approaches as machine learning, paying particular attention to recent advances that have led to excitement, anxiety, and debate. He considers each AI technology's underlying assumptions, the conceptions of intelligence targeted at each stage, and the successes achieved so far. Smith unpacks the notion of intelligence itself—what sort humans have, and what sort AI aims at. Smith worries that, impressed by AI's reckoning prowess, we will shift our expectations of human intelligence. What we should do, he argues, is learn to use AI for the reckoning tasks at which it excels while we strengthen our commitment to judgment, ethics, and the world.
This book is a history of artificial intelligence, that audacious effort to duplicate in an artifact what we consider to be our most important property—our intelligence. It is an invitation for anybody with an interest in the future of the human race to participate in the inquiry.
An exhaustive work that represents a landmark exploration of both the philosophical and methodological issues surrounding the search for true artificial intelligence. Distinguished psychologists, computer scientists, philosophers, and programmers from around the world debate weighty issues such as whether a self-conscious computer would create an internet ‘world mind’. This hugely important volume explores nothing less than the future of the human race itself.
Human intuition and perception are basic and essential phenomena of consciousness. As such, they will never be replicated by computers. This is the challenging notion of Hubert Dreyfus, Ph. D., archcritic of the artificial intelligence establishment. It's important to emphasize that he doesn't believe that AI is fundamentally impossible, only that the current research program is fatally flawed. Instead, he argues that to get a device (or devices) with human-like intelligence would require them to have a human-like being in the world, which would require them to have bodies more or less like ours, and social acculturation (i.e. a society) more or less like ours. This helps to explain the practical problems in implementing artificial intelligence algorithms.
“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.
Could a computer have a mind? What kind of machine would this be? Exactly what do we mean by 'mind' anyway?The notion of the 'intelligent' machine, whilst continuing to feature in numerous entertaining and frightening fictions, has also been the focus of a serious and dedicated research tradition. Reflecting on these fictions, and on the research tradition that pursues 'Artificial Intelligence', raises a number of vexing philosophical issues. Minds and Computers introduces readers to these issues by offering an engaging, coherent, and highly approachable interdisciplinary introduction to the Philosophy of Artificial Intelligence.Readers are presented with introductory material from each of the disciplines which constitute Cognitive Science: Philosophy, Neuroscience, Psychology, Computer Science, and Linguistics. Throughout, readers are encouraged to consider the implications of this disparate and wide-ranging material for the possibility of developing machines with minds. And they can expect to de