The input data features set for many data driven tasks is high-dimensional while the intrinsic dimension of the data is low. Data analysis methods aim to uncover the underlying low dimensional structure imposed by the low dimensional hidden parameters by utilizing distance metrics that consider the set of attributes as a single monolithic set. However, the transformation of the low dimensional phenomena into the measured high dimensional observations might distort the distance metric, This distortion can effect the desired estimated low dimensional geometric structure. In this paper, we suggest to utilize the redundancy in the attribute domain by partitioning the attributes into multiple subsets we call views. The proposed methods utilize the agreement also called consensus between different views to extract valuable geometric information that unifies multiple views about the intrinsic relationships among several different observations. This unification enhances the information that a single view or a simple concatenations of views provides.

Contagions such as the spread of popular news stories, or infectious diseases, propagate in cascades over dynamic networks with unobservable topologies. However, "social signals" such as product purchase time, or blog entry timestamps are measurable, and implicitly depend on the underlying topology, making it possible to track it over time. Interestingly, network topologies often "jump" between discrete states that may account for sudden changes in the observed signals. The present paper advocates a switched dynamic structural equation model to capture the topology-dependent cascade evolution, as well as the discrete states driving the underlying topologies. Conditions under which the proposed switched model is identifiable are established. Leveraging the edge sparsity inherent to social networks, a recursive $\ell_1$-norm regularized least-squares estimator is put forth to jointly track the states and network topologies. An efficient first-order proximal-gradient algorithm is developed to solve the resulting optimization problem. Numerical experiments on both synthetic data and real cascades measured over the span of one year are conducted, and test results corroborate the efficacy of the advocated approach.

Mumbai: There is tremendous buzz around machine learning, broadly described as a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. However, despite an exponential increase in power, computers have typically proved incompetent at things that are really simple to human beings--like recognizing the dog in a picture containing a dog, or understanding speech. The trend, however, is changing. Consider'Deep Learning', which describes a collection of techniques that allow computational tasks that were previously thought impossible. Facebook Inc, for instance, uses it to identify faces, and when Google Inc recently announced that their algorithms could not only'see' a dog but also identify it as a Pomeranian, they heralded the maturity of Deep Learning techniques.

Using deep learning techniques, a group of researchers has trained a computer to recognise events in videos on YouTube -- even the ones the software has never seen before like riding a horse, baking cookies or eating at a restaurant. Researchers from Disney Research and Shanghai's Fudan University used both scene and object features from the video and enabled link between these visual elements and each type of event to be automatically determined by a machine-learning architecture known as neural network. "Notably, this approach not only works better than other methods in recognising events in videos, but is significantly better at identifying events that the computer programme has never or rarely encountered previously," said Leonid Sigal, senior research scientist at Disney Research. Automated techniques are essential for indexing, searching and analysing the incredible amount of video being created and uploaded daily to the Internet. "With multiple hours of video being uploaded to YouTube every second, there is no way to describe all of that content manually.

Cozmo is a 179 artificially intelligent robot that can recognize faces and react to human gestures. SAN FRANCISCO – At first glance, the palm-sized toy on the table looks unexceptional, an odd cross between a bulldozer and a forklift. Except that the toy is snoring. Suddenly, it wakes up, motors over to its human inquisitor and lets out a happy squawk as its digital eyes go wide. Made by Anki, the start-up that has found success with its self-driving Anki Drive racing cars, Cozmo goes on sale today for 179 with orders shipping this fall.

Cozmo is a 179 artificially intelligent robot that can recognize faces and react to human gestures. SAN FRANCISCO – At first glance, the palm-sized toy on the table looks unexceptional, an odd cross between a bulldozer and a forklift. Except that the toy is snoring. Suddenly, it wakes up, motors over to its human inquisitor and lets out a happy squawk as its digital eyes go wide. Made by Anki, the start-up that has found success with its self-driving Anki Drive racing cars, Cozmo goes on sale today for 179 with orders shipping this fall.

Sega has revealed that a new title in the Sonic the Hedgehog franchise is in development for release in 2017. The news was confirmed by Sonic Team executive Takashi Iizuka during a 25th anniversary event in Tokyo, according to Gematsu. It was also revealed that more information on the mysterious new game will be shown at San Diego Comic Con next month, all of which will be livestreamed online. "We at Sonic Team are developing a completely new game," Iizuka said. "The most important thing is not the fact that the series survived for 25 years, but how many games [were] developed.

In a match last October, the AlphaGo program developed by Google's "DeepMind" subsidiary beat, 5 games to 0, the French professional player Fan Hui,1 who is ranked 2 dan (on the professional scale from 1 dan to 9 dan) and is today Europe's best player. The story was related by the journal Nature.2 This was the first time that a computer beats a professional player. But in the world of artificial intelligence, the progress demonstrated by the AlphaGo victory wasn't expected for another ten years or so. The moment of truth, however, will take place between March 9-15 in Seoul, where AlphaGo will face the South Korean Lee Se-dol, who is 9 dan, and is considered the best player in the world as well as a Go living legend.

Experts at the Annual Meeting of the New Champions tackled the issue of artificial intelligence and what it means for humans, concluding that they can and should coexist. The pertinent issue is how humans can leverage artificial intelligence to enhance the outcome of new technologies and improve quality of life, and not focus on the narrative of human vs machine. However, rapid technological advances underline the urgency for policy-makers to redesign educational systems so that younger generations are adequately prepared for a workplace that will see more automated processes. "By some estimates, 47% of existing jobs in the US could be replaced by automation," said Wendell Wallach, Scholar, Interdisciplinary Center for Bioethics, Yale University, USA. "When the World Bank used similar methodology, it came up with 69% in India, and 77% in China.

But this hand-wringing is a distraction from the very real problems with artificial intelligence today, which may already be exacerbating inequality in the workplace, at home and in our legal and judicial systems. Sexism, racism and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many "intelligent" systems that shape how we are categorized and advertised to. Take a small example from last year: Users discovered that Google's photo app, which applies automatic labels to pictures in digital photo albums, was classifying images of black people as gorillas. Google apologized; it was unintentional. This is fundamentally a data problem.