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.

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages. For eons they have carried out a huge variety of tasks, from manufacturing goods, to transporting people around, to helping us decipher the natural world, to simply entertaining us. Machines can fight, protect, heal, and even teach us. But what they have not been able to do until quite recently is to learn, make decisions, and act on their own. Today, intelligent machines are everywhere. From the Netflix recommendation en- gine to Google Translate to Appleâ s Siri voice-recognition system, artificial intelligence has become sufficiently accurate, reliable, and useful to find its way into numerous devices and applications. These technologies have taken off in parallel with a dramatic expan- sion of the amount and complexity of data, which provides fertile teaching ground from which machines can learn to make intelligent decisions on their own.

From harnessing artificial intelligence to understanding our origins, a panel of distinguished scientists outlined the grand challenges for science in the 21st century. Held at Nanyang Technological University and moderated by independent writer and lecturer Tor Norretranders, the panel session comprised Sydney Brenner, Nobel laureate in Physiology or Medicine; W. Brian Arthur, external professor at the Santa Fe Institute; Astronomer Royal Martin Rees; Terrence Sejnowski, Francis Crick Professor at the Salk Institute for Biological Studies; and Eörs Szathmáry, director of the Parmenides Center for the Conceptual Foundations of Science. The scientific industry has seen a marked shift towards industrial science--or science that directly benefits the economy--and away from basic research, said Sydney Brenner, a senior fellow at the Agency for Science, Technology and Research in Singapore. He lamented that scientists today lack a crucial truth-seeking mentality, by accepting the results of published research without challenging assumptions. In science, where research is built upon research--which potentially leads to an accumulation of mistakes--the practice of critical evaluation is all the more pertinent, Brenner said.

This issue features expanded versions of articles selected from the 2015 AAAI Conference on Innovative Applications of Artificial Intelligence held in Austin, Texas. We present a selection of four articles describing deployed applications plus two more articles that discuss work on emerging applications.

The 2015 conference continued the tradition with a selection of 6 deployed applications describing systems in use by their intended end users, 13 emerging applications describing works in progress, and three papers in a new category for challenge problems. In the first article, Activity Planning for a Lunar Orbital Mission, John Bresina describes a deployed application of current planning technology in the context of a NASA mission called LADEE (Lunar Atmospheric and Dust Environment Explorer). Bresina presents an approach taken to reduce the complexity of the activity-planning task in order to perform it effectively under the time pressures imposed by the mission requirements. One key aspect of this approach is the design of the activity-planning process based on principles of problem decomposition and planning abstraction levels. The second key aspect is the mixed-initiative system developed for this task, the LADEE activity scheduling system (LASS). The primary challenge for LASS was representing and managing the science constraints that were tied to key points in the spacecraft's orbit, given their dynamic nature due to the continually updated orbit determination solution. In our second article, Helping Novices Avoid the Hazards of Data: Leveraging Ontologies to Improve Model Generalization Automatically with Online Data Source, Sasin Janpuangtong and Dylan Shell describe an emerging application of an endto-end learning framework for large-scale data analytics that allows a novice to create models from data easily by helping structure the model-building process.

In the last decade, in the Semantic Web field, knowledge bases have attracted tremendous interest from both academia and industry and many large knowledge bases are now available. However, both generation of new knowledge and population of already existing knowledge bases with new facts face several challenges. Most of the time knowledge bases have been manually built, resulting in a highly specialistic and time consuming activity. Nevertheless, sources of unstructured and semi-structured data are still growing at a much faster rate than structured ones, as such it could be desirable to exploit such a large non-structured sources to populate structured knowledge bases. In the Semantic Web, a major cornerstone of knowledge bases are ontologies and schemas that play a key role for providing common vocabularies and for describing and constructing the Web of Data.

A host of leading industry experts gathered to discuss the launch of The Drum's Cannes Lions special edition guest edited by IBM's artificial intelligence (AI) technology Watson, which used machine learning to channel the creativity of David Ogilvy, arguably the godfather of advertising. The panel session, held in association with Quantcast, saw assembled marketers listen in on the thoughts of Amber Case, a cyborg anthropologist who examines the interaction between humans and technology; Oliver Cox, solutions architect, IBM Watson ecosystem; Konrad Feldman, CEO of Quantcast; David Shing, digital prophet for AOL; plus Todd Krugmann, president of O&M Japan. IBM's Cox added that the latest issue of The Drum bore testament to this potential union of data-led machine learning, and the creative process. Meanwhile, IBM's Cox further explained how such an offering could aid brands' communication strategies: "Watson would not create a personality - it will help you create the personality that's best for your brand [with elements of human moderation]."

It was a dark, overcast day, with clouds hovering low. The room was kept at the most appropriate temperatures and humidity, as usual. Yoko sat on a couch in an untidy manner, killing time with a silly game. But she would not talk to me. So begins a short story titled "A day when a computer writes fiction."

R is probably every data scientist's preferred programming language (besides Python and SAS) to build prototypes, visualize data, or run analyses on data sets. There are so many libraries, applications and techniques exist to explore data in R that I'm sure even experts don't know them all! Aspiring data scientists who are reading this though, fear not, for you are well on your way to understanding these secrets. The links provide the ability to download the pdfs of the books. Authored by: Trevor Hastie and Rob Tibshirani, recognized Stanford professors and authors of "The Elements of Statistical Learning" What you'll learn: Implementation of statistical and machine learning techniques in R This book will teach you what you need to know, without harassing you much about the math behind it all.

"Crowd Control: Heaven Makes a Killing," CNET's crowdsourced science fiction novel written and edited by readers, continues. To read past installments, learn more about the project or see our contributor list, visit the digital table of contents. The headlines on Meta's screens were uncharacteristically ominous in the weeks leading up to his final certification at the academy. Discussions in classes were more easily derailed by questions about the future of interversal trade and immigration asked by students who just weeks earlier were more likely to be drooling or snoring through sessions that were largely remedial, a last chance to catch up. "I don't understand why we can't just offer more positions to the subs," Zulema shouted in frustration during one class, surprising her fellow students with her use of a derogatory term for migrants. "Yea, we need help now," echoed Nara.