Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a professor of computer science, director of the Center for Intelligent Systems, and holder of the Smith–Zadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was cowinner of the Computers and Thought Award. He was a 1996 Miller Professor of the University of California and was appointed to a Chancellor's Professorship in 2000. In 1998, he gave the Forsythe Memorial Lectures at Stanford University. He is a Fellow and former Executive Council member of the American Association for Artificial Intelligence. He has published over 100 papers on a wide range of topics in artificial intelligence. His other books include The Use of Knowledge in Analogy and Induction and (with Eric Wefald) Do the Right Thing: Studies in Limited Rationality.

Leading organizations are taking a strategic approach to enabling their enterprise with AI technologies; they are solving complex problems, infusing intelligent capabilities into processes and investing to create a new and preferred future. This report, part of the 19th edition of our ongoing Global C-suite Study series, draws on input from 3,069 conversations with C-suite executives (CxOs) from April through June 2017. Here, we explore their perspective on artificial intelligence (AI) technologies that enable the Digital Reinvention of enterprises.

COVER A conceptual illustration of an artificial neuron evokes a technology that is transforming many fields of science: artificial intelligence (AI). One common form of AI is a neural network, which "learns" as connections between simulated neurons change in response to inputs. Such systems can find meaningful patterns in vast data sets, ranging from genomics to astronomy, and are even beginning to design experiments.

The Python Data Science Handbook provides a reference to the breadth of computational and statistical methods that are central to data-intensive science, research, and discovery. People with a programming background who want to use Python effectively for data science tasks will learn how to face a variety of problems: e.g., how can I read this data format into my script? How can I manipulate, transform, and clean this data? How can I visualize this type of data? How can I use this data to gain insight, answer questions, or to build statistical or machine learning models?

This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK. You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now. With restructured examples and code samples updated for the latest edition of Python, each chapter of this book introduces you to new algorithms and techniques.

This accessible, comprehensive book captures the essence of artificial intelligence -- solving the complex problems that arise wherever computer technology is applied. With his signature enthusiasm, George Luger demonstrates numerous techniques and strategies for addressing the many challenges facing computer scientists today. Diverse topics on this exciting and ever-evolving field range from perception and adaptation using neural networks and genetic algorithms, intelligent agents with ontologies, automated reasoning, natural language analysis, and stochastic approaches to machine learning. This book is ideal for a one - or two-semester university course on AI. "The style of writing and comprehensive treatment of the subject matter makes this a valuable addition to the AI literature." George Luger is currently a Professor of Computer Science, Linguistics, and Psychology at the University of New Mexico.

Who will remain a loyal customer and who won't? Which messages are most effective with which segments? How can customer value be maximized? This book supplies powerful tools for extracting the answers to these and other crucial business questions from the corporate databases where they lie buried. In the years since the first edition of this book, data mining has grown to become an indispensable tool of modern business.

Back issues are available on-line at www.aimagazine.org The purpose of AI Magazine is to disseminate timely and informative articles that represent the current state of the art in AI and to keep its readers posted on AAAI-related matters. Regular features in AI Magazine include feature articles, workshop, symposium, and conference summaries, book reviews, editorials, news about the Association for the Advancement of Artificial Intelligence, letters to the editor, forum discussions, calendar of events, recruitment and product advertising, and columns on various topics including AI in the news. AI Magazine publishes original articles that are reasonably self-contained and aimed at a broad spectrum of the AI community. The magazine welcomes the contribution of articles on the theory and practice of AI as well as general survey articles, tutorial articles on timely topics, conference or symposia or workshop reports, and reviews of books.

I'm often asked what I like best about my job. One of my top answers is public speaking, learning and networking at security and technology events around the world. Besides giving press interviews or speeches on cyberthreats, I really enjoy moderating panels and leading executive roundtables with public- and private-sector leaders at security and technology events. I often get asked to be a moderator for a few sessions at SecureWorld Expo events, InfraGard Conferences and regional technology forums, such as the upcoming MidWest Technology Leaders event. During these panel sessions, the participants typically talk about a range of (hopefully intriguing) topics that include top cybercrime trends, cyberthreat intelligence, attracting and retaining cybertalent, big industry security breaches, internal security incidents or the always interesting (but overused question) "what's keeping you up at night?" Inevitably, security and technology topics include well known themes that I have written about such as ransomware, IoT botnets, cloud computing, smart cities, smartphone security, government CISO plans, securing the smart grid, end-user training, etc. Hopefully, we get beyond the problems and spend a few minutes on solutions.