Intel is a company that powers the cloud and billions of smart, connected computing devices. Thanks to the pervasive reach of cloud computing, the ever decreasing cost of compute enabled by Moore's Law, and the increasing availability of connectivity, these connected devices are generating millions of terabytes of data every single day. The ability to analyze and derive value from that data is one of the most exciting opportunities for us all. Central to that opportunity is artificial intelligence. While artificial intelligence is often equated with great science fiction, it isn't relegated to novels and movies.

Can artificial intelligence be emotionally intelligent? In Boston, researchers have programed BB-8, the little droid from "Star Wars: The Force Awakens," to detect expressions and determine how people are feeling. And that technology is being adapted for marketing, video games, even therapy for children diagnosed with autism.

You don't need to have aced physics at school to understand and predict how objects behave in the real world. Now scientists have discovered why we possess this innate ability, by pinpointing the brain's'physics engine'. The'engine' comes alive when we watch physical events unfold – such as a ball being thrown towards us – and is not in the brain's vision centre, but in a set of devoted to planning actions. Scientists have discovered the brain's'physics engine' (coloured in this illustration). The'engine' comes alive when we watch physical events unfold, the scientists said The physics engine is located in several regions of the brain.

Three of my movie recommendations this time around are about thinking: There's an aging detective trying to recover the memory of his last case, a wife who discovers the true nature of her relationship after repeatedly lying to him, and a group of smart people who sit around drinking coffee, smoking cigarettes, and talking about life. Since we're in the midst of the Olympic Games, I'd also encourage you to catch T-Rex, a documentary that's not about a terrible lizard, but a terrific female middleweight boxer named Claressa Shields. Other new and notable movies this week involve physical activities of their characters and subjects, whether it's making punk music or folk music, shooting an erotic movie, exploring a terrifying underworld, or defending yourself with a machete. Sometimes the act of escaping can be physical, with the fear of getting caught increasing the adrenaline flow. Characters this week escape from a tyrannical dystopian future and with a stolen baby.

If only walking through the airport always looked this dreamy. When everyone wants their 30 seconds of Internet fame, what happens to our IRL relationships? A whole lot of drama and angst, as imagined by Dmitry Nikiforov and Aleksei Korneev, the makers of Delete My Photos, a Russian film following a young man's dreams of developing a new dating app. As many parts of our lives are these days, their trailer is bathed in filters -- a unique approach to a tool that's as become second-nature as selfies. They ran key scenes through mobile photo editor Prisma, which uses convolutional neural networks to combine photos with artistic styles, modeled on, for instance, Van Gogh and Roy Lichtenstein.

In accessibility tests for digital preservation, over time we experience drifts of localized and labelled content in statistical models of evolving semantics represented as a vector field. This articulates the need to detect, measure, interpret and model outcomes of knowledge dynamics. To this end we employ a high-performance machine learning algorithm for the training of extremely large emergent self-organizing maps for exploratory data analysis. The working hypothesis we present here is that the dynamics of semantic drifts can be modeled on a relaxed version of Newtonian mechanics called social mechanics. By using term distances as a measure of semantic relatedness vs. their PageRank values indicating social importance and applied as variable `term mass', gravitation as a metaphor to express changes in the semantic content of a vector field lends a new perspective for experimentation. From `term gravitation' over time, one can compute its generating potential whose fluctuations manifest modifications in pairwise term similarity vs. social importance, thereby updating Osgood's semantic differential. The dataset examined is the public catalog metadata of Tate Galleries, London.

We study indeterminacies in realization of ornaments and how they can be incorporated in a stochastic performance model applicable for music information processing such as score-performance matching. We point out the importance of temporal information, and propose a hidden Markov model which describes it explicitly and represents ornaments with several state types. Following a review of the indeterminacies, they are carefully incorporated into the model through its topology and parameters, and the state construction for quite general polyphonic scores is explained in detail. By analyzing piano performance data, we find significant overlaps in inter-onset-interval distributions of chordal notes, ornaments, and inter-chord events, and the data is used to determine details of the model. The model is applied for score following and offline score-performance matching, yielding highly accurate matching for performances with many ornaments and relatively frequent errors, repeats, and skips.

We develop a unified and systematic framework for performing online nonnegative matrix factorization under a wide variety of important divergences. The online nature of our algorithm makes it particularly amenable to large-scale data. We prove that the sequence of learned dictionaries converges almost surely to the set of critical points of the expected loss function. We do so by leveraging the theory of stochastic approximations and projected dynamical systems. This result substantially generalizes the previous results obtained only for the squared-$\ell_2$ loss. Moreover, the novel techniques involved in our analysis open new avenues for analyzing similar matrix factorization problems. The computational efficiency and the quality of the learned dictionary of our algorithm are verified empirically on both synthetic and real datasets. In particular, on the tasks of topic learning, shadow removal and image denoising, our algorithm achieves superior trade-offs between the quality of learned dictionary and running time over the batch and other online NMF algorithms.

The Republican National Convention already seems to have solidified its position as the most dramatic political event in recent memory -- and yes, that's saying a lot. From accusations of plagiarism to a speaker lineup that reads more like a who's who of reality television than the American political system, there's a lot to cover at the 2016 RNC. And at both the Washington Post and Buzzfeed, some of that coverage is coming from bots. While the two publications may seem like rather dissimilar outlets with disparate readerships, both seem to have recognized the value in utilizing bot technology to cover the highly buzzed-about convention. The Washington Post's bot will work with Twitter and Double Robotics, to provide a live stream of the goings-on in Cleveland by way of Periscope. Moreover, readers (or rather, viewers) will be able to ask questions about the convention through a Periscope chat.

Last week, I was in San Francisco's Mission District, betwixt testing Prisma filters on my photos, and enjoying the fine cuisine, when I noticed an actual painting of the Golden Gate Bridge on the wall. "It's like Prisma in real life," I reacted. In today's technology-fueled hyper-sensualized world, one of the many symptoms is a blurring of the line between art and design. Design is function-oriented, and though some readily fetishize consumer electronics as art objects, the bounds of design are neatly wrapped in utility. Art, on the other hand, is characterized by an effort that redefines the confines of knowledge with a particular emphasis on questioning the boundaries of emotions, politics, and society.