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Table of Contents -- July 07, 2017, 357 (6346)

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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.


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The Friday Cover p The Friday Cover is POLITICO Magazine's email of the week's best, delivered to your inbox every Friday morning. On Monday, Aug. 21, a solar eclipse will be visible across America. California produced so much solar power on those days that it paid Arizona to take excess electricity its residents weren't using to avoid overloading its own power … How HBO's insanely popular hit show turned a young British actress into a feminist, a fantasy icon and a royal fan favorite p On a recent Monday … Many people leave airport security feeling dehumanised, frightened, even violated. Edward Schwarzschild decided to find out p It was my first shift of on-the-job training as a transportation security officer at Albany International Airport's only checkpoint, and I was told to … Every Wednesday afternoon, in a windowless conference room in an office building at the tip of lower Manhattan, David Pecker decides what will be on the cover of the following week's i National Enquirer /i .


Innovation Nation Autumn 2016 Resource

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This edition of Innovation Nation focuses on the people behind digital disruption at Capgemini. We've assembled a number of articles in this issue, starting with "Next generation Global Business Services" that looks at how the human-machine relationship can be optimized to exceed individual customer expectations. Divya Kumar and Christopher Stancombe explore this relationship further in their respective articles on incremental artificial intelligence (AI) implementation and robotic process automation (RPA). Our expert insights this month, collated across the breadth of the business, touch on aspects of digital disruption in Business Services and the people it affects.


Distingusished Scientists Say These Are The Grand Challenges For Science

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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. A long-time champion of the multiverse theory of the origin of the universe, Rees hypothesizes that the Big Bang is merely one out of billions. Grand Challenges for Science in the 21st Century was a four-day panel discussion organized by Nanyang Technological University, taking place from June 13 to 16, 2016.


Call for papers: Special Issue on Machine Learning for Knowledge Base Generation and Population

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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. In order to cope with this issue, the availability of automatic methods for schema aware generation and population of knowledge bases results fundamental. The primary goal of the special issue is to provide novel machine learning/data mining methods for knowledge base generation, population, enrichment, evolution showing advances in the Semantic Web field. Please indicate in the cover letter that it is for the Special Issue on Machine Learning for Knowledge Base Generation and Population.


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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]."


Bards beware: Fiction-writing AI demanding spot at table of content The Japan Times

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Since 2013, a team of computer scientists led by Hitoshi Matsubara of Future University Hakodate in Hokkaido has been trying to get AI-based computers to write fiction. Matsubara, who has been researching AI since the 1980s, came up with the idea of AI-generated novels around 2010, when computers started beating chess and shogi (Japanese chess) champions. To create a story just like Hoshi would have, the team made the computer read and analyze 1,000 of his short works. That made possible the "formalistic" copying of Hoshi's writing style, including average sentence length and frequency of kanji used versus hiragana.


SafArtInt 2016

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The challenge also increases when AI systems are interacting in complex ways with other separately-developed AI systems that are themselves learning and adapting. The workshop, scheduled for June 28th, 2016, will include keynote talks and panel discussions that explore the potential future of AI and AI applications, the emerging technical means for constructing safe and secure systems, how safety might be assured, and how we can make progress on the challenges of safety and control for AI. In other words, how can we construct productive collaborations of the AI technical community, the application community, and the assurance community? In preparation for the technical workshop event, Carnegie Mellon will publish an open solicitation for white papers related to the workshop topics.


Table of Contents -- July 17, 2015, 349 (6245)

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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?