Hospi-R is the aggressively friendly robot that may deliver your medication during your next hospital stay. Panasonic has created an aggressively pleasant Dalek-nurse to whir silently between hospital beds, smile, and drop off medications and meals to patients. Japan and other countries have already given the wifi-camera-sensor driven bot proper certification to chip in with menial hospital duties. While good bedside manor is definitely appreciated by anyone stuck in a hospital for more than a few hours, something about the over-the-top grin on Hospi-R, the pink robo-servant, just seems a little off. Thankfully Hospi-R seems to lack arms, teeth, or articulated needle jabbers that might make the prospect of it wheeling into your room late at night extra scary.

The ACM constitution provides that our Association hold a general election in the even-numbered years for the positions of President, Vice President, Secretary/Treasurer, and Members-at-Large. Biographical information and statements of the candidates appear on the following pages (candidates' names appear in random order). In addition to the election of ACM's officers--President, Vice President, Secretary/Treasurer--five Members-at-Large will be elected to serve on ACM Council. Please refer to the instructions posted at https://www.esc-vote.com/acm2016. To access the secure voting site, you will need to enter your email address (the email address associated with your ACM member record) and your unique PIN provided by Election Services Co. Please return your ballot in the enclosed envelope, which must be signed by you on the outside in the space provided. The signed ballot envelope may be inserted into a separate envelope for mailing if you prefer this method. All ballots must be received by no later than 16:00 UTC on 24 May 2016. Validation by the Tellers Committee will take place at 14:00 UTC on 26 May 2016. Vicki Hanson is a Distinguished Professor of Computing at Rochester Institute of Technology, U.S. (since 2013), Professor and Chair of Inclusive Technologies, Computing, University of Dundee, U.K. (since 2009), and an IBM Research Staff Member Emeritus (since 2009). Previously, she was Research Staff Member and Manager, IBM T.J. Watson Research Center (1986–2008), Research Associate, Haskins Laboratories, New Haven, CT (1980–86), and Postdoctoral Fellow at the Salk Institute for Biological Studies (1978–80). Vicki is the ACM Vice President. She also currently serves as a member of the ACM Executive Committee and Council, on the ACM-W Europe Executive Committee, and on the ACM Fellows Awards Committee (Chair, 2015). She is Vice President at Large of ACM SIGCHI and an ACM Distinguished Speaker. She has served on the SIG Governing Board Executive Committee (2005–14; SGB Chair 2010–12), and as Chair of SIGACCESS, where she revitalized the SIG and established a successful annual conference (ASSETS). She co-founded the field's premier archival journal (ACM Transactions on Accessible Computing) and served as Associate Editor of ACM TWEB. She was on the organizing committee for several SIGPLAN OOPSLA conferences, chaired the recent ACM CEO Search Committee, and currently serves on the Royal Society of Edinburgh Fellows committee (since 2013; Convener 2015). She is an ACM Fellow, a Chartered Fellow of the British Computer Society, a Fellow of the Royal Society of Edinburgh, and a Senior Member of IEEE.

The big news on March 12 of this year was of the Go-playing AI-system AlphaGo securing victory against 18-time world champion Lee Se-dol by winning the third straight game of a five-game match in Seoul, Korea. After Deep Blue's victory against chess world champion Gary Kasparov in 1997, the game of Go was the next grand challenge for game-playing artificial intelligence. Go has defied the brute-force methods in game-tree search that worked so successfully in chess. In 2012, Communications published a Research Highlight article by Sylvain Gelly et al. on computer Go, which reported that "Programs based on Monte-Carlo tree search now play at human-master levels and are beginning to challenge top professional players." AlphaGo combines tree-search techniques with search-space reduction techniques that use deep learning. Its victory is a stunning achievement and another milestone in the inexorable march of AI research.

Using humanoid robots to study human behavior.

Most existing abstractions and implementations for parallel computing were developed for computational science applications in which the main parallelism pattern is data parallelism; an example of a data-parallel operation is "map," which applies a function to each element of a set, producing a new set. Systems like Hadoop enable programmers to express and exploit data parallelism without having to write low-level parallel code. Some graph analytics algorithms have data parallelism, but others exhibit a more complex parallelism pattern called "amorphous" data-parallelism,29 described later in this article. Unlike in data-parallel programs, tasks in amorphous data-parallel programs may or may not be able to run in parallel, depending on the graph structure and values known only at runtime. To exploit this parallelism pattern, systems need to find opportunities for parallel execution while executing the program in parallel.

Samuel Greengard is an author and journalist based in West Linn, OR.

The research on human cognition has recently benefited from the use of the mathematical formalism of quantum theory in Hilbert space. However, cognitive situations exist which indicate that the Hilbert space structure, and the associated Born rule, would be insufficient to provide a satisfactory modeling of the collected data, so that one needs to go beyond Hilbert space. In Part I of this paper we follow this direction and present a general tension-reduction (GTR) model, in the ambit of an operational and realistic framework for human cognition. In this Part II we apply this non-Hilbertian quantum-like model to faithfully reproduce the probabilities of the 'Clinton/Gore' and 'Rose/Jackson' experiments on question order effects. We also explain why the GTR-model is needed if one wants to deal, in a fully consistent way, with response replicability and unpacking effects.

An underlying assumption in conventional multi-view learning algorithms is that all views can be simultaneously accessed. However, due to various factors when collecting and pre-processing data from different views, the streaming view setting, in which views arrive in a streaming manner, is becoming more common. By assuming that the subspaces of a multi-view model trained over past views are stable, here we fine tune their combination weights such that the well-trained multi-view model is compatible with new views. This largely overcomes the burden of learning new view functions and updating past view functions. We theoretically examine convergence issues and the influence of streaming views in the proposed algorithm. Experimental results on real-world datasets suggest that studying the streaming views problem in multi-view learning is significant and that the proposed algorithm can effectively handle streaming views in different applications.

Drawing a sample from a discrete distribution is one of the building components for Monte Carlo methods. Like other sampling algorithms, discrete sampling suffers from the high computational burden in large-scale inference problems. We study the problem of sampling a discrete random variable with a high degree of dependency that is typical in large-scale Bayesian inference and graphical models, and propose an efficient approximate solution with a subsampling approach. We make a novel connection between the discrete sampling and Multi-Armed Bandits problems with a finite reward population and provide three algorithms with theoretical guarantees. Empirical evaluations show the robustness and efficiency of the approximate algorithms in both synthetic and real-world large-scale problems.

That's the reason why I was shocked by a piece of news that came out of London on January 27 this year. AlphaGo, a program created by Google subsidiary DeepMind, defeated the European Go champion, five games to nothing. Maybe you think that's no big deal. After all, it's almost 20 years since IBM's Deep Blue beat Kasparov at chess in 1997. Chess is about logic; Go involves imagination and intuition.