Wouldn't it be wonderful to travel to a foreign country without having to worry about the nuisance of communicating in a different language? In a recent Wall Street Journal article, technology policy expert Alec Ross argued that, within a decade or so, we'll be able to communicate with one another via small earpieces with built-in microphones. No more trying to remember your high school French when checking into a hotel in Paris. Your earpiece will automatically translate "Good evening, I have a reservation" to Bon soir, j'ai une réservation - while immediately translating the receptionist's unintelligible babble to "I am sorry, Sir, but your credit card has been declined." Ross argues that because technological progress is exponential, it's only a matter of time.

Not only are ants impressively strong, they are also amazing team players. This research inspired by such teamwork examples how the ways that microrobots move effects their ability to work in teams. With careful consideration to robot gait, we demonstrate a team of 6 super strong microTug microrobots (https://www.youtube.com/watch?v _rWuU...) weighing 100 grams pulling the author's unmodified 3900lb (1800kg) car on polished concrete. Based on research published in Robotics and Automation Letters and to be presented at ICRA 2016 found here: http://ieeexplore.ieee.org/xpl/articl... Ant chain pulling millipede video reproduced with gracious permission from Stéphane De Greef. Original found at: https://www.youtube.com/watch?v SdTza... Music: Path to Follow by Jingle Punks, Available: https://www.youtube.com/audiolibrary/...

Larry Alton is an independent business consultant specializing in social media trends, business and entrepreneurship. Artificial intelligence (AI) is permeating our lives -- just not in the ways we might have expected from reading sci-fi novels or watching robot apocalypse-themed movies. Instead of having live robots walking around, doing our dishes and engaging us in conversation, AI exists primarily in web and mobile apps designed to help us with small intellectual chores, like finding out when the Civil War began or where the nearest taco restaurant is located. Until recently, most of these AI developments have been designed to make consumer processes easier; for example, digital assistants take on the role of an intermediary search engine to process a vocal request and fetch appropriate results. Now, the trend is starting to shift toward app development -- at least in an early stage.

George Ugras has joined IBM as managing director of IBM Ventures. That's a big job, considering how much money IBM throws into its strategic investments. Big Blue doesn't disclose exactly how much it puts into investments. But the company did say that in 2015 it invested more than 13 billion in research and development, capital expansions, acquisitions, and strategic investments. IBM has put billions into cognitive analytics, cloud, mobile, security, and social.

USA TODAY's Ed Baig tests Amazon Echo's personal digital assistant. It's not an easy product to get and you have to wait for an invitation to buy the product. Find out how Alexa relates to Siri, benefits and flaws. In this March 2, 2016 photo, David Limp, Amazon Senior Vice President of Devices, center, speaks behind an Amazon Echo in San Francisco. Amazon.com is introducing two devices, the Amazon Tap and Echo Dot, that are designed to amplify the role that its voice-controlled assistant Alexa plays in people's homes and lives.

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed theoretical definition, we formalize a heuristic method for approximating the interpretability of multivariate brain maps in a binary magnetoencephalography (MEG) decoding scenario. Third, we propose to combine the approximated interpretability and the performance of the brain decoding model into a new multi-objective criterion for model selection. Our results for the MEG data show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms in the future.

In this paper, we present a unified analysis of matrix completion under general low-dimensional structural constraints induced by {\em any} norm regularization. We consider two estimators for the general problem of structured matrix completion, and provide unified upper bounds on the sample complexity and the estimation error. Our analysis relies on results from generic chaining, and we establish two intermediate results of independent interest: (a) in characterizing the size or complexity of low dimensional subsets in high dimensional ambient space, a certain partial complexity measure encountered in the analysis of matrix completion problems is characterized in terms of a well understood complexity measure of Gaussian widths, and (b) it is shown that a form of restricted strong convexity holds for matrix completion problems under general norm regularization. Further, we provide several non-trivial examples of structures included in our framework, notably the recently proposed spectral $k$-support norm.

Asymptotic factorizations for the small-ball probability (SmBP) of a Hilbert valued random element $X$ are rigorously established and discussed. In particular, given the first $d$ principal components (PCs) and as the radius $\varepsilon$ of the ball tends to zero, the SmBP is asymptotically proportional to (a) the joint density of the first $d$ PCs, (b) the volume of the $d$-dimensional ball with radius $\varepsilon$, and (c) a correction factor weighting the use of a truncated version of the process expansion. Moreover, under suitable assumptions on the spectrum of the covariance operator of $X$ and as $d$ diverges to infinity when $\varepsilon$ vanishes, some simplifications occur. In particular, the SmBP factorizes asymptotically as the product of the joint density of the first $d$ PCs and a pure volume parameter. All the provided factorizations allow to define a surrogate intensity of the SmBP that, in some cases, leads to a genuine intensity. To operationalize the stated results, a non-parametric estimator for the surrogate intensity is introduced and it is proved that the use of estimated PCs, instead of the true ones, does not affect the rate of convergence. Finally, as an illustration, simulations in controlled frameworks are provided.

Joint state and parameter estimation is a core problem for dynamic Bayesian networks. Although modern probabilistic inference toolkits make it relatively easy to specify large and practically relevant probabilistic models, the silver bullet---an efficient and general online inference algorithm for such problems---remains elusive, forcing users to write special-purpose code for each application. We propose a novel blackbox algorithm -- a hybrid of particle filtering for state variables and assumed density filtering for parameter variables. It has following advantages: (a) it is efficient due to its online nature, and (b) it is applicable to both discrete and continuous parameter spaces . On a variety of toy and real models, our system is able to generate more accurate results within a fixed computation budget. This preliminary evidence indicates that the proposed approach is likely to be of practical use.

In statistical genetics an important task involves building predictive models for the genotype-phenotype relationships and thus attribute a proportion of the total phenotypic variance to the variation in genotypes. Numerous models have been proposed to incorporate additive genetic effects into models for prediction or association. However, there is a scarcity of models that can adequately account for gene by gene or other forms of genetical interactions. In addition, there is an increased interest in using marker annotations in genome-wide prediction and association. In this paper, we discuss an hybrid modeling methodology which combines the parametric mixed modeling approach and the non-parametric rule ensembles. This approach gives us a flexible class of models that can be used to capture additive, locally epistatic genetic effects, gene x background interactions and allows us to incorporate one or more annotations into the genomic selection or association models. We use benchmark data sets covering a range of organisms and traits in addition to simulated data sets to illustrate the strengths of this approach. The improvement of model accuracies and association results suggest that a part of the "missing heritability" in complex traits can be captured by modeling local epistasis.