Название: Mind Design III: Philosophy, Psychology, and Artificial IntelligenceАвтор: John Haugeland, Carl F. Craver, Colin Klein
Издательство: The MIT Press
Год: 2023
Страниц: 552
Язык: английский
Формат: epub
Размер: 10.1 MB
The essential reader on the philosophical foundations and implications of Artificial Intelligence (AI), now comprehensively updated for the twenty-first century.
In the quarter century since the publication of John Haugeland’s Mind Design II, computer scientists have hit many of their objectives for successful Artificial Intelligence. Computers beat chess grandmasters, driverless cars navigate streets, autonomous robots vacuum our homes, and ChatGPT answers existential queries in iambic pentameter on command. Engineering has made incredible strides. But have we made progress in understanding and building minds? Comprehensively updated by Carl Craver and Colin Klein to reflect the astonishing ubiquity of Machine Learning in modern life, Mind Design III offers an essential collection of classic and contemporary essays on the philosophical foundations and implications of Artificial Intelligence. Contributions from a diverse range of philosophers and computer scientists address the nature of computation, the nature of thought, and the question of whether computers can be made to think. With extensive new material reflecting the explosive growth and diversification of AI approaches, this classic reader equips students to assess the possibility of, and progress toward, building minds out of computers.
We have added new chapters that discuss advances in deep neural networks, reinforcement learning, and causal learning. Indeed, if one of the overarching themes of Mind Design II was the fight between old-fashioned symbolic AI and simple neural networks, then the main lesson of Mind Design III might be how much the field has settled into a kind of pragmatic pluralism—a willingness to mix and match techniques according to domains and aims.
Until recently, a type of DNN architecture called a “Deep Convolutional Neural Network” (hereafter DCNN) has been considered the most reliably successful tool on the widest range of problems. This architecture is a key component in models of image recognition (AlexNet), strategy gameplay (AlphaGo), scientific data analysis, and many other applications.
Over the past twenty-five years, Reinforcement Learning has had a tremendous impact on the development of Artificial Intelligence and has been a major driver in advancements in the so-called ‘decision sciences’—computational neuroscience, neuroscience, psychology, psychiatry, and economics. But even as we continue to advance the notion of reward maximization as a general solution to the problem of Artificial Intelligence, we have not yet embraced the full implications of Reinforcement Learning, together with the accompanying reward-prediction hypothesis, for our conceptions of the mind.
New edition highlights
New chapters on advances in deep neural networks, reinforcement learning, and causal learning
New material on the complementary intersection of neuroscience and AI
Organized thematically rather than chronologically
Brand new introductions to each section that include suggestions for coursework and further reading
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