Would we get on better with clever machines if they understood what mood we were in? Many roboticists and computer engineers seem to think so, because they're always trying to make their creations more human. Take Solo, the "emotional radio", for example. A wall-mounted device that resembles a large clock, it features a liquid crystal display at its centre. When you approach it, the pictogram face shows a neutral expression.

Nvidia continued to see demand for its graphics processors in the emerging world of artificial intelligence in its fourth quarter earnings reported Thursday. In its fourth quarter earnings release, the Santa Clara, Calif.-based company reported revenue of $2.17 billion, up 55% year over year, on earnings per share of $1.13, up 117% a year ago. Wall Street analysts estimated $2.11 billion in revenue on EPS of 83 cents. Traditionally, the company's processors have been mostly used to power the latest gaming graphics, but the chips have become popular to run AI software in the data center and autonomous vehicles. A specific branch of AI, called deep learning, is where Nvidia's processors particularly shine.

I'm going to start this piece with a confession; I am a'trend' skeptic. Over the last few years I've developed something akin to list fatigue; growing weary of the constant stream of'six things you must know' clickbait headlines that pop up in my feed. If I was being kind I would say that as an industry we are sometimes guilty of exaggerating our predictions on the potential impact of emerging technologies (if I wasn't being kind I'd probably say that in truth a lot of our industry habitually over hypes everything'new' so that anything actually meaningful gets buried in the noise). Artificial Intelligence (AI) is one of those topics that you'll regularly have seen on lists throughout 2016. And editorially, it's fair to say that it's been covered across something of a broad spectrum. A quick Google search will either lead you to an article where AI will bring an end to the food going off in your fridge, or where AI will bring an end to humanity.

Nvidia CEO Jen-Hsun Huang introducing the Nvidia Spot, a USD 49.95 microphone and speaker that will let owners use Google Assistant anywhere in a home, at the company's CES 2017 keynote (Photo by Ethan Miller/Getty Images) Nvidia continued to see demand for its graphics processors in the emerging world of artificial intelligence in its fourth quarter earnings reported Thursday. In its fourth quarter earnings release, the Santa Clara, Calif.-based company reported revenue of $2.17 billion, up 55% year over year, on earnings per share of $1.13, up 117% a year ago. Wall Street analysts estimated $2.11 billion in revenue on EPS of 83 cents. Traditionally, the company's processors have been mostly used to power the latest gaming graphics, but the chips have become popular to run AI software in the data center and autonomous vehicles. A specific branch of AI, called deep learning, is where Nvidia's processors particularly shine.

We introduce a near-linear complexity (geometric and meshless/algebraic) multigrid/multiresolution method for PDEs with rough ($L^\infty$) coefficients with rigorous a-priori accuracy and performance estimates. The method is discovered through a decision/game theory formulation of the problems of (1) identifying restriction and interpolation operators (2) recovering a signal from incomplete measurements based on norm constraints on its image under a linear operator (3) gambling on the value of the solution of the PDE based on a hierarchy of nested measurements of its solution or source term. The resulting elementary gambles form a hierarchy of (deterministic) basis functions of $H^1_0(\Omega)$ (gamblets) that (1) are orthogonal across subscales/subbands with respect to the scalar product induced by the energy norm of the PDE (2) enable sparse compression of the solution space in $H^1_0(\Omega)$ (3) induce an orthogonal multiresolution operator decomposition. The operating diagram of the multigrid method is that of an inverted pyramid in which gamblets are computed locally (by virtue of their exponential decay), hierarchically (from fine to coarse scales) and the PDE is decomposed into a hierarchy of independent linear systems with uniformly bounded condition numbers. The resulting algorithm is parallelizable both in space (via localization) and in bandwith/subscale (subscales can be computed independently from each other). Although the method is deterministic it has a natural Bayesian interpretation under the measure of probability emerging (as a mixed strategy) from the information game formulation and multiresolution approximations form a martingale with respect to the filtration induced by the hierarchy of nested measurements.

Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in human language processing. In this paper, we investigate the factors that affect human prediction by building a computational model that can predict upcoming discourse referents based on linguistic knowledge alone vs. linguistic knowledge jointly with common-sense knowledge in the form of scripts. We find that script knowledge significantly improves model estimates of human predictions. In a second study, we test the highly controversial hypothesis that predictability influences referring expression type but do not find evidence for such an effect.

Given a training set with binary classification, the Support Vector Machine identifies the hyperplane maximizing the margin between the two classes of training data. This general formulation is useful in that it can be applied without regard to variance differences between the classes. Ignoring these differences is not optimal, however, as the general SVM will give the class with lower variance an unjustifiably wide berth. This increases the chance of misclassification of the other class and results in an overall loss of predictive performance. An alternate construction is proposed in which the margins of the separating hyperplane are different for each class, each proportional to the standard deviation of its class along the direction perpendicular to the hyperplane. The construction agrees with the SVM in the case of equal class variances. This paper will then examine the impact to the dual representation of the modified constraint equations.

In the field of exploratory data mining, local structure in data can be described by patterns and discovered by mining algorithms. Although many solutions have been proposed to address the redundancy problems in pattern mining, most of them either provide succinct pattern sets or take the interests of the user into account-but not both. Consequently, the analyst has to invest substantial effort in identifying those patterns that are relevant to her specific interests and goals. To address this problem, we propose a novel approach that combines pattern sampling with interactive data mining. In particular, we introduce the LetSIP algorithm, which builds upon recent advances in 1) weighted sampling in SAT and 2) learning to rank in interactive pattern mining. Specifically, it exploits user feedback to directly learn the parameters of the sampling distribution that represents the user's interests. We compare the performance of the proposed algorithm to the state-of-the-art in interactive pattern mining by emulating the interests of a user. The resulting system allows efficient and interleaved learning and sampling, thus user-specific anytime data exploration. Finally, LetSIP demonstrates favourable trade-offs concerning both quality-diversity and exploitation-exploration when compared to existing methods.

Do you have a racial bias? Is your moral compass intact? To find out what you think or feel, we usually have to take your word for it. But questionnaires and other explicit measures to reveal what's on your mind are imperfect: you may choose to hide your true beliefs or you may not even be aware of them. But now there is a technology that enables us to'read the mind' with growing accuracy: functional magnetic resonance imaging (fMRI). Mind-reading algorithms that use machine learning to reconstruct brain activity could reveal our innermost thoughts and could turns our society into a'Big Brother' world Experts from the University of Cambridge explore the uses of mind-reading algorithms and find the technology will be successful as a lie detector - which is already being tested.

Innovation in the white-hot digital performance management (DPM) market continues to accelerate, and it was clear from this week's Perform conference in Las Vegas that Dynatrace is setting the pace. In fact, Dynatrace's innovations are so cutting-edge and so flashy that on first glance they may seem to be gimmicks. For example, there's Dynatrace UFO, a saucer-shaped device with flashing lights that one might confuse with a drone. But instead of flying, it provides status reports with patterns of red and green lights. If normal-sized screens are good, the reasoning might go, then large ones would be better, right?