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Author: Stephen Muggleton
Publisher: Oxford University Press
Published: 2021
Total Pages: 533
ISBN-13: 0198862539
DOWNLOAD EBOOKThis book, authored by an array of internationally recognised researchers, is of direct relevance to all those involved in Academia and Industry wanting to obtain insights into the topics at the forefront of the revolution in Artificial Intelligence and Cognitive Science.
Author: Stephen Muggleton
Publisher:
Published: 2021
Total Pages: 533
ISBN-13: 9780191895333
DOWNLOAD EBOOKThis book, authored by an array of internationally recognised researchers, is of direct relevance to all those involved in Academia and Industry wanting to obtain insights into the topics at the forefront of the revolution in Artificial Intelligence and Cognitive Science.
Author: Kartik Hosanagar
Publisher: Viking Adult
Published: 2019
Total Pages: 274
ISBN-13: 0525560882
DOWNLOAD EBOOKIn his new book, Kartik Hosanagar surveys the brave new world of algorithmic decision-making and reveals the potentially dangerous biases they can give rise to as they increasingly run our lives.
Author: Melanie Mitchell
Publisher: Farrar, Straus and Giroux
Published: 2019-10-15
Total Pages: 336
ISBN-13: 0374715238
DOWNLOAD EBOOKMelanie Mitchell separates science fact from science fiction in this sweeping examination of the current state of AI and how it is remaking our world No recent scientific enterprise has proved as alluring, terrifying, and filled with extravagant promise and frustrating setbacks as artificial intelligence. The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it. In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go. Interweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all.
Author: Stuart Jonathan Russell
Publisher: Penguin Books
Published: 2019
Total Pages: 354
ISBN-13: 0525558616
DOWNLOAD EBOOKA leading artificial intelligence researcher lays out a new approach to AI that will enable people to coexist successfully with increasingly intelligent machines.
Author: Erik J. Larson
Publisher: Harvard University Press
Published: 2021-04-06
Total Pages: 321
ISBN-13: 0674983513
DOWNLOAD EBOOK“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.
Author: Jianlong Zhou
Publisher: Springer
Published: 2018-06-07
Total Pages: 485
ISBN-13: 3319904035
DOWNLOAD EBOOKWith an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications. This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making. This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.
Author: Amir Husain
Publisher: Simon and Schuster
Published: 2017-11-21
Total Pages: 224
ISBN-13: 1501144677
DOWNLOAD EBOOKExplores universal questions about humanity's capacity for living and thriving in the coming age of sentient machines and AI, examining debates from opposing perspectives while discussing emerging intellectual diversity and its potential role in enabling a positive life.
Author: Robert Munro
Publisher: Simon and Schuster
Published: 2021-07-20
Total Pages: 422
ISBN-13: 1617296740
DOWNLOAD EBOOKMachine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster. Human-in-the-loop machine learning lays out methods for humans and machines to work together effectively. You'll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You'll learn to dreate training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows.
Author: Ben Shneiderman
Publisher: Oxford University Press
Published: 2022
Total Pages: 390
ISBN-13: 0192845292
DOWNLOAD EBOOKThe remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits those who build on their working methods by including HCAI strategies of design and testing. As many technology companies and thought leaders have argued, the goal is not to replace people, but to empower them by making design choices that give humans control over technology. In Human-Centered AI, Professor Ben Shneiderman offers an optimistic realist's guide to how artificial intelligence can be used to augment and enhance humans' lives. This project bridges the gap between ethical considerations and practical realities to offer a road map for successful, reliable systems. Digital cameras, communications services, and navigation apps are just the beginning. Shneiderman shows how future applications will support health and wellness, improve education, accelerate business, and connect people in reliable, safe, and trustworthy ways that respect human values, rights, justice, and dignity.
