Prince Harry and a robot have been announced as two guest editors on Radio 4's Today Programme. Their fellow editors will be Baroness Trumpington, Tamara Rojo and Ben Okri. This is the 14th year control has been handed over to public figures between Christmas and New Year. Kensington Palace said Prince Harry would use the opportunity to "shine a spotlight on issues that are close to his heart". The palace added: "He is working closely with Today's team to produce segments on a range of topics, including youth violence, conservation and mental health."

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.

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. What can you learn from AI's leading organizations – the Reinventors? 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.

A constraint satisfaction problem (CSP) is a computational problem where the input consists of a finite set of variables and a finite set of constraints, and where the task is to decide whether there exists a satisfying assignment of values to the variables. Depending on the type of constraints that we allow in the input, a CSP might be tractable, or computationally hard. In recent years, general criteria have been discovered that imply that a CSP is polynomial-time tractable, or that it is NP-hard. Finite-domain CSPs have become a major common research focus of graph theory, artificial intelligence, and finite model theory. It turned out that the key questions for complexity classification of CSPs are closely linked to central questions in universal algebra. This thesis studies CSPs where the variables can take values from an infinite domain. This generalization enhances dramatically the range of computational problems that can be modeled as a CSP. Many problems from areas that have so far seen no interaction with constraint satisfaction theory can be formulated using infinite domains, e.g. problems from temporal and spatial reasoning, phylogenetic reconstruction, and operations research. It turns out that the universal-algebraic approach can also be applied to study large classes of infinite-domain CSPs, yielding elegant complexity classification results. A new tool in this thesis that becomes relevant particularly for infinite domains is Ramsey theory. We demonstrate the feasibility of our approach with two complete complexity classification results: one on CSPs in temporal reasoning, the other on a generalization of Schaefer's theorem for propositional logic to logic over graphs. We also study the limits of complexity classification, and present classes of computational problems provably do not exhibit a complexity dichotomy into hard and easy problems.

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.

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.

Always a highlight of the advertising year, The Festival of Media is an international celebration of excellence in media, attended by almost 250 global brands, as well as agencies, media owners, and adtech companies. With awards recognising everything from the Best Use of New Technology to Creative Use of Data, it is also the ideal setting to observe the synergy of technology and creativity first-hand. GingerMay PR attended this year's event at the Cavalieri Hotel in Rome to gain deeper insight into the topics currently impacting the industry. Measurement and verification of digital advertising was a key theme throughout the festival, with panel sessions indicating that advertisers are finally waking up to the need to implement the same safeguards in digital they have long expected in offline media. Because digital has grown without accepted standards for so long, even the most basic measurements such as impressions and CPM seem like a step-up, but advertisers should be aiming higher.

This is a useful overview of the current state of artificial intelligence. The author has clearly been swimming in these waters during his academic studies and professional career. I came away with the sense that his thoughts and observations were based on close personal experience and careful analysis. If the area interests you, I think that you'll find that Dr. Kaplan is an experienced guide. What are some of the major areas that the book examines?

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.