JAPANESE carmaker Honda Motor Co. has chosen to headquarter its artificial intelligence (AI) research hub in Tokyo, saying its own home ground will enable closer interactions between scientists and researchers compared to Silicon Valley. According to Bloomberg, the research and development (R&D) center will launch in 2017 and will consolidate all the company's current AI teams from Silicon Valley, Europe, and Japan in Tokyo. Yoshiyuki Matsumoto, president of Honda's research arm, said in an interview that the carmakers chose Tokyo due to saturation in the San Francisco Bay Area, which is home to thousands of tech companies and startups. Honda chooses Tokyo over Silicon Valley for AI research centre – THE BUSINESS TIMES https://t.co/r25wCfNHKy Matsumoto was quoted saying: "We won't make much difference if we did the same things as everyone else in Silicon Valley. And not everyone has succeeded there."

Suboptimal heuristic search algorithms such as weighted A* and greedy best-first search are widely used to solve problems for which guaranteed optimal solutions are too expensive to obtain. These algorithms crucially rely on a heuristic function to guide their search. However, most research on building heuristics addresses optimal solving. In this paper, we illustrate how established wisdom for constructing heuristics for optimal search can fail when considering suboptimal search. We consider the behavior of greedy best-first search in detail and we test several hypotheses for predicting when a heuristic will be effective for it. Our results suggest that a predictive characteristic is a heuristic's goal distance rank correlation (GDRC), a robust measure of whether it orders nodes according to distance to a goal. We demonstrate that GDRC can be used to automatically construct abstraction-based heuristics for greedy best-first search that are more effective than those built by methods oriented toward optimal search. These results reinforce the point that suboptimal search deserves sustained attention and specialized methods of its own.

Dr. John R. Talburt is Professor of Information Science at the University of Arkansas at Little Rock (UALR) where he is the Coordinator for the Information Quality Graduate Program and the Executive Director of the UALR Center for Advanced Research in Entity Resolution and Information Quality (ERIQ). He is also the Chief Scientist for Black Oak Partners, LLC, an information quality solutions company. Prior to his appointment at UALR he was the leader for research and development and product innovation at Acxiom Corporation, a global leader in information management and customer data integration. Professor Talburt holds several patents related to customer data integration and the author of numerous articles on information quality and entity resolution, and is the author of Entity Resolution and Information Quality (Morgan Kaufmann, 2011). He also holds the IAIDQ Information Quality Certified Professional (IQCP) credential.

About 17,000 people attended this week's IBM World of Watson conference. Many of them were trying to figure out what exactly to do with the cognitive computing engine. After an initial Watson-specific conference drew just over 1,000 people to Brooklyn, N.Y., in May 2015, IBM this year combined that event into its much larger IBM Insight analytics conference and changed the name to World of Watson. But the growing interest shown in Watson by attendees is a testament to how hot all things related to artificial intelligence are right now. At the same time, many businesses are just starting to think about how they can use cognitive applications like Watson.

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Adversarial input can fool a machine-learning algorithm into misperceiving images. Over the past five years, machine learning has blossomed from a promising but immature technology into one that can achieve close to human-level performance on a wide array of tasks. In the near future, it is likely to be incorporated into an increasing number of technologies that directly impact society, from self-driving cars to virtual assistants to facial-recognition software. Yet machine learning also offers brand-new opportunities for hackers. Malicious inputs specially crafted by an adversary can "poison" a machine learning algorithm during its training period, or dupe it after it has been trained.

The BoniRob is a multipurpose robotic platform for agricultural applications featuring independently steerable drive wheels and adjustable track width. Field farming is "the world's oldest profession," and not just because food plants have been cultivated for over 10,000 years. Its individual practitioners are old as well, the median age rising rapidly as young people abandon the farming lifestyle (the U.S. Department of Agriculture reports a median age of 58 in 2012, up from 55 in 2002, with other countries showing similar data). Those who remain face the same repetitive work of seeding, weeding, feeding, and harvesting, the tedium of each task increasing as farms grow ever larger. However, today's agricultural robots excel at repetitive tasks, letting farmers tend to more strategic matters.

No one likes being reduced to a number. For example, there is much more to my financial picture than my credit score alone. There is even scholarly work on weaknesses in the system to compute this score. Everyone may agree the number is far from perfect, yet it is used to make decisions that matter to me, as Moshe Y. Vardi discussed in his Editor's Letter "Academic Rankings Considered Harmful!" (Sept. So I care what my credit score is.

Adi Livnat (alivnat@univ.haifa.ac.il) is a Senior Lecturer in the Department of Evolutionary and Environmental Biology, and Institute of Evolution at the University of Haifa, Israel. Christos Papadimitriou (christos@cs.berkeley.edu) is the C. Lester Hogan Professor in the Computer Science Division of the University of California at Berkeley.

The theory of computability was launched in the 1930s, by a group of logicians who proposed new characterizations of the ancient idea of an algorithmic process. The most prominent of these iconoclasts were Kurt Gödel, Alonzo Church, and Alan Turing. The theoretical and philosophical work that they carried out in the 1930s laid the foundations for the computer revolution, and this revolution in turn fueled the fantastic expansion of scientific knowledge in the late 20th and early 21st centuries. Thanks in large part to these groundbreaking logico-mathematical investigations, unimagined number-crunching power was soon boosting all fields of scientific enquiry. The motivation of these three revolutionary thinkers was not to pioneer the disciplines now known as theoretical and applied computer science, although with hindsight this is indeed what they did.