MWC 2018 -- Telef--nica has launched its voice-activated "cognitive" assistant Aura in the six markets of Argentina, Brazil, Chile, Germany, Spain and the UK after teaming up with tech giants including Facebook, Google and Microsoft on the new platform. First announced at last year's Mobile World Congress, Aura works much like Amazon's Alexa, Apple's Siri and Microsoft's Cortana, allowing customers to request information and control devices through voice commands. Powered by artificial intelligence, the Aura technology will be available through apps that Telef--nica's customers can download onto their mobile devices. Unlike the digital assistants from Amazon.com Inc. (Nasdaq: AMZN), Apple Inc. (Nasdaq: AAPL) and Google, however, Aura has been designed around the communications services that Telef--nica provides to its customers. "It is a new customer relationship model based on cognitive intelligence and a one-stop shop for navigating through different channels," said Jose Maria Alvarez-Pallete, Telef--nica's CEO, during a press conference earlier today.

The post caught the attention of Facebook's AI system, which is programmed to spot potential suicidal language. The system decided it was an emergency and passed it along to moderators for review, who then alerted authorities in Buenos Aires. Before long, first responders were on the scene. "Artificial intelligence can be a very powerful tool," says Enrique del Carril, the investigations director in the district attorney's office in Buenos Aires. "We saved a woman far away in remote Argentina before something terrible happened. Facebook's suicide-alert system is just one of many efforts to use artificial intelligence to help identify people at risk for suicide as early as possible. In these programs, researchers use computers to comb through massive amounts of data, such as electronic health records, social-media posts, and audio and video recordings of patients, to find common threads among people who attempted suicide. Then algorithms can start to predict which new patients are more ...

Variational Bayesian neural nets combine the flexibility of deep learning with Bayesian uncertainty estimation. Unfortunately, there is a tradeoff between cheap but simple variational families (e.g.~fully factorized) or expensive and complicated inference procedures. We show that natural gradient ascent with adaptive weight noise implicitly fits a variational posterior to maximize the evidence lower bound (ELBO). This insight allows us to train full-covariance, fully factorized, or matrix-variate Gaussian variational posteriors using noisy versions of natural gradient, Adam, and K-FAC, respectively, making it possible to scale up to modern-size ConvNets. On standard regression benchmarks, our noisy K-FAC algorithm makes better predictions and matches Hamiltonian Monte Carlo's predictive variances better than existing methods. Its improved uncertainty estimates lead to more efficient exploration in active learning, and intrinsic motivation for reinforcement learning.

We propose a new algorithm for estimating NARMAX models with $L_1$ regularization for models represented as a linear combination of basis functions. Due to the $L_1$-norm penalty the Lasso estimation tends to produce some coefficients that are exactly zero and hence gives interpretable models. The novelty of the contribution is the inclusion of error regressors in the Lasso estimation (which yields a nonlinear regression problem). The proposed algorithm uses cyclical coordinate descent to compute the parameters of the NARMAX models for the entire regularization path. It deals with the error terms by updating the regressor matrix along with the parameter vector. In comparative timings we find that the modification does not reduce the computational efficiency of the original algorithm and can provide the most important regressors in very few inexpensive iterations. The method is illustrated for linear and polynomial models by means of two examples.

Despite efforts to tackle corruption around the world, progress is still frustratingly slow, according to the latest report from Transparency International. Its annual Corruption Perception index reveals some alarming trends. It shows public service corruption is still a huge problem for two-thirds of the world's economies. The report uses a scale of zero to 100 to rank countries: zero is highly corrupt and 100 is very clean. New Zealand comes out on top but with a score of 89.

Google Assistant had its coming out party at Mobile World Congress 2017 with the announcement that it was expanding beyond Google's own Pixel phones, and now it's ready to take on the world. Google has announced it will be expanding Assistant to nearly two dozen more languages this year as it expands its influence to some "95 percent of all eligible Android phones worldwide." Google Assistant already speaks English, French, German, Italian, Japanese, Korean, Spanish, and Portuguese (Brazil), but its linguist capabilities will be greatly expanded over the next 10 months as Google plans to add support for more than 30 languages. First up will be Danish, Dutch, Hindi, Indonesian, Norwegian, Swedish and Thai, all of which should arrive by summer. Along with the new languages comes support for multilingual speakers.

We propose several ways of reusing subword embeddings and other weights in subword-aware neural language models. The proposed techniques do not benefit a competitive character-aware model, but some of them improve the performance of syllable- and morpheme-aware models while showing significant reductions in model sizes. We discover a simple hands-on principle: in a multi-layer input embedding model, layers should be tied consecutively bottom-up if reused at output. Our best morpheme-aware model with properly reused weights beats the competitive word-level model by a large margin across multiple languages and has 20%-87% fewer parameters.

A novel Neural Network architecture is proposed using the mathematically and physically rich idea of vector fields as hidden layers to perform nonlinear transformations in the data. The data points are interpreted as particles moving along a flow defined by the vector field which intuitively represents the desired movement to enable classification. The architecture moves the data points from their original configuration to a new one following the streamlines of the vector field with the objective of achieving a final configuration where classes are separable. An optimization problem is solved through gradient descent to learn this vector field.

We are on the verge of a technological revolution that will fundamentally alter the way we live, work, and relate to one another unlike anything humankind has experienced before. The main driver for this technological revolution is Artificial Intelligence (AI). Technological change driven by AI will change not only what we do but also who we are. It will affect our identity and all the issues associated with it: our sense of privacy, our notions of ownership, our consumption patterns, the time we devote to work and leisure, and how we develop our careers, cultivate our skills, and nurture relationships. But the development and applications of artificial intelligence can also present a dystopian threat to our collective and individual well being. From SIRI to self-driving cars, artificial intelligence (AI) is progressing rapidly. While science fiction often portrays AI as robots with human-like characteristics, AI can encompass anything from Google's search algorithms to IBM's Watson to autonomous robots and weapons systems. Artificial intelligence today is often referred to as narrow AI (or weak AI), which is designed to perform a narrow task (eg:facial recognition or only internet searches or driving a car). The other kind of Artificial Intelligence is termed general AI (AGI or strong AI) which is designed to "think," and solve problems much like humans.

A decade ago, Pablo Graiver was working as a VP at Kayak, the online airfare aggregator, when he sat down to dinner with an old friend--a heart surgeon from his home country of Argentina. The talk turned to how tech was doing more to save folks a few bucks on a flight to Rome than to save people's lives. Right now, the US has exactly 19,816 clinical trials open and ready to recruit patients--trials of promising new therapeutics to fight everything from HIV to cancer to Alzheimer's. About 18,000 of them will get stuck on the tarmac because they won't get enough people enrolled. And a third of those will never get off the ground at all, for the same reason. So where are all the patients?