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.

In 2008, Political Analysis published a groundbreaking special issue on the analysis of political text, examining some of the initial efforts in political science to consider text as a data source and to develop methods for analyzing text data. In their introduction to the special issue, Monroe and Schrodt (2008) note that text one of the most common mediums through which political phenomenon are documented is underutilized in the social sciences and they argue for further research. They suggest the research discussed in the special issue should be a jumping-off point, or "departure lounge" for future text as data research. Answering their call, in the last eight years, the fi eld of "text as data" in social science has grown dramatically. As the number of sources and types of textual data documenting social science phenomenon has exploded, so too have methods for, and the use of, text analysis in social science research.

COVER Intelligence is hard to define, but you know it when you see it … Or do you? Artificial intelligence researchers can now design algorithms with almost humanlike abilities to perceive images, communicate with language, and learn from experience. Can we learn anything about how our neuron-based minds work from these machines? Do we need to worry about what these algorithmic minds might be learning about us? On the cover is a visualization of human brain connectivity from MRI diffusion imaging, with superimposed computer connectors.

The articles in this special issue of AI Magazine include those that propose specific tests, and those that look at the challenges inherent in building robust, valid, and reliable tests for advancing the state of the art in AI.

Within the field, the test is widely recognized as a pioneering landmark, but also is now seen as a distraction, designed over half a century ago, and too crude to really measure intelligence. Intelligence is, after all, a multidimensional variable, and no one test could possibly ever be definitive truly to measure it. Moreover, the original test, at least in its standard implementations, has turned out to be highly gameable, arguably an exercise in deception rather than a true measure of anything especially correlated with intelligence. The much ballyhooed 2015 Turing test winner Eugene Goostman, for instance, pretends to be a thirteen-year-old foreigner and proceeds mainly by ducking questions and returning canned one-liners; it cannot see, it cannot think, and it is certainly a long way from genuine artificial general intelligence.

If you have been searching for AI books to help you with as good start then you have come to the right place these book covers the basics to high end stuff. Machine learning is the study of computer systems that learn from data and experience. It is applied in an incredibly wide variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential customer of machine learning. An introduction to Prolog programming for artificial intelligence covering both basic and advanced AI material.

With the increasingly global nature of our everyday interactions, the need for multilin- gual technologies to support efficient and effective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross- language in order to create multilingual technologies rapidly. The goal of this JAIR special issue on Cross-Language Algorithms and Applications (CLAA) is to present leading re- search in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.