Filed under report | Tags: · artificial intelligence, computing, industry, machine learning, society, technology
“The Stanford One Hundred Year Study on Artificial Intelligence, a project that launched in December 2014, is designed to be a century-long periodic assessment of the field of Artificial Intelligence (AI) and its influences on people, their communities, and society. Colloquially referred to as “AI100″, the project issued its first report in September 2016. A Standing Committee works with the Stanford Faculty Director of AI100 in overseeing the project and designing its activities. A little more than two years after the first report appeared, we reflect on the decisions made in shaping it, the process that produced it, its major conclusions, and reactions subsequent to its release.
The inaugural AI100 report, which is titled “Artificial Intelligence and Life in 2030,” examines eight domains of human activity in which AI technologies are already starting to affect urban life. In scope, it encompasses domains with emerging products enabled by AI methods and ones raising concerns about technological impact generated by potential AI – enabled systems. The Study Panel members who authored the report and the AI100 Standing Committee, which is the body that directs the AI100 project, intend for it to act as a catalyst, spurring conversations on how we as a society might shape and share the potentially powerful technologies that AI could enable. In addition to influencing researchers and guiding decisions in industry and governments, the report aims to provide the general public with a scientifically and technologically accurate portrayal of the current state of AI and its potential. It aspires to replace conceptions rooted in science fiction books and movies with a realistic foundation for these deliberations.”
Publisher Stanford University, September 2016
Creative Commons BY-ND 4.0 International License
Commentary: Barbara J. Grosz & Peter Stone (Communications of the ACM, 2018).Comment (0)
Filed under book | Tags: · artificial intelligence, computation, computing, history of science, machine learning, neural networks, robotics
“This book traces the history of artificial intelligence, from the early dreams of eighteenth-century (and earlier) pioneers to the more successful work of today’s AI engineers. The book includes many diagrams and easy-to-understand descriptions of AI programs that will help the casual reader gain an understanding of how these and other AI systems actually work.”
Self-published 2009 (web version)
Publisher Cambridge University Press, 2009 (print version)
Open access (web version)
ISBN 9780521116398, 0521116392 (print version)
Review: Peter Norvig (Artificial Intelligence, 2010).
PDF (15 MB)Comment (0)
Filed under book | Tags: · abstraction, algorithm, archaeology, artificial intelligence, code, data, diagram, error, information science, information theory, knowledge, machine learning, mathematics, neural networks, programming, theory
“If machine learning transforms the nature of knowledge, does it also transform the practice of critical thought?
Machine learning—programming computers to learn from data—has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transaction processing, computer gaming, recommendation systems, finance, surveillance, and robotics use machine learning. Machine learning devices (sometimes understood as scientific models, sometimes as operational algorithms) anchor the field of data science. They have also become mundane mechanisms deeply embedded in a variety of systems and gadgets. In contexts from the everyday to the esoteric, machine learning is said to transform the nature of knowledge. In this book, Adrian Mackenzie investigates whether machine learning also transforms the practice of critical thinking.
Mackenzie focuses on machine learners—either humans and machines or human-machine relations—situated among settings, data, and devices. The settings range from fMRI to Facebook; the data anything from cat images to DNA sequences; the devices include neural networks, support vector machines, and decision trees. He examines specific learning algorithms—writing code and writing about code—and develops an archaeology of operations that, following Foucault, views machine learning as a form of knowledge production and a strategy of power. Exploring layers of abstraction, data infrastructures, coding practices, diagrams, mathematical formalisms, and the social organization of machine learning, Mackenzie traces the mostly invisible architecture of one of the central zones of contemporary technological cultures.
Mackenzie’s account of machine learning locates places in which a sense of agency can take root. His archaeology of the operational formation of machine learning does not unearth the footprint of a strategic monolith but reveals the local tributaries of force that feed into the generalization and plurality of the field.”
Publisher MIT Press, November 2017
ISBN 9780262036825, 0262036827
PDF (removed on 2018-8-20 upon request from publisher)
Draft and code samples on GIT