For cognitive scientists, neurobiologists, and even some physicists, consciousness presents a unique and alluring problem. Although we know we are conscious, we know almost nothing about how it arises out of inanimate matter, and from where in the brain it comes from. Now, a team of researchers led by neurologists at Harvard Medical School's Beth Israel Deaconess Medical Center (BIDMC) believe it has discovered the physical foundations of consciousness. In a study published in the latest edition of the journal Neurology, the researchers pinpointed regions of the brain that appear to work together to create consciousness. "For the first time, we have found a connection between the brainstem region involved in arousal and regions involved in awareness, two prerequisites for consciousness," lead researcher Michael Fox from BIDMC, said in a statement.

Artificial Intelligence is not about statistics, is about experience. In this webinar Stuart Waplington, Co-Founder and CEO, will walk you through what deep learning really means and the possibilities that have been unlocked to the marketing world. The global market for AI is set to be worth $5.05 Billion by 2020 (Markets & Markets). Happy Finish is already ahead of the curve; we've just presented Shoegazer, a unique Proof of Concept that uses AI and Transfer Learning to identify the exact brand and style of trainers in real-time – with 95% accuracy and Buzzteam, our on-demand workforce resource platform which allows individual companies building teams by employing global network of resources that can be discovered by skill-set, experience, cost or rating. Join this webinar to understand AI and how its rapid adoption is set to transform a range of markets, from advertising and media to finance and retail, offering benefits such as improved productivity and increased customer satisfaction.

In August, Microsoft kickstarted the second wave of this current console generation, releasing its acclaimed Xbox One S to a largely receptive audience. Now, Sony is returning fire with the PlayStation 4 Pro, an updated version of the standard PS4, which – like Microsoft's machine – is designed to get the most out of the coming era of 4K televisions. Unlike the Xbox One S, this is no radical aesthetic departure. PS4 Pro looks like a vertically elongated version of the regular PS4, with slightly curved edges giving it a smoother outline. At 295 x 327 x 55mm, it is, of course, bigger and heavier than both the new PS4 Slim and the original model.

For more than a decade, neuroscientist Grégoire Courtine has been flying every few months from his lab at the Swiss Federal Institute of Technology in Lausanne to another lab in Beijing, China, where he conducts research on monkeys with the aim of treating spinal-cord injuries. The commute is exhausting -- on occasion he has even flown to Beijing, done experiments, and returned the same night. But it is worth it, says Courtine, because working with monkeys in China is less burdened by regulation than it is in Europe and the United States. And this week, he and his team report the results of experiments in Beijing, in which a wireless brain implant -- that stimulates electrodes in the leg by recreating signals recorded from the brain -- has enabled monkeys with spinal-cord injuries to walk. "They have demonstrated that the animals can regain not only coordinated but also weight-bearing function, which is important for locomotion. This is great work," says Gaurav Sharma, a neuroscientist who has worked on restoring arm movement in paralysed patients, at the non-profit research organization Battelle Memorial Institute in Columbus, Ohio.

Here is our new selection of featured articles and resources. Starred articles have interesting charts. The picture below is from the last article. Topics cover a salary survey, several programming languages with comparisons, a mathematical optimization technique widely used in machine learning algorithms (this article has great animated gifs that illustrate the convergence of various methods) and a methodology to make correct election forecasts.

Data Analytics is one of the most sought-after skill sets today, with students and professionals alike aspiring to be enabled with the necessary skills to derive data-driven business insights in their careers. It also helps organisations attain a competitive advantage over others. Data Analytics is not limited to mathematicians, statisticians or IT professionals with programming skills. The need to analyse data has become so elementary today that a professional in any business is expected to know the necessary skills. While professionals today are aware of the need to be trained, some are unaware of how to embark on a career in analytics.

The deep learning community has devised a diverse set of methods to make gradient optimization, using large datasets, of large and highly complex models with deeply cascaded nonlinearities, practical. Taken as a whole, these methods constitute a breakthrough, allowing computational structures which are quite wide, very deep, and with an enormous number and variety of free parameters to be effectively optimized. The result now dominates much of practical machine learning, with applications in machine translation, computer vision, and speech recognition. Many of these methods, viewed through the lens of algorithmic differentiation (AD), can be seen as either addressing issues with the gradient itself, or finding ways of achieving increased efficiency using tricks that are AD-related, but not provided by current AD systems. The goal of this paper is to explain not just those methods of most relevance to AD, but also the technical constraints and mindset which led to their discovery. After explaining this context, we present a "laundry list" of methods developed by the deep learning community. Two of these are discussed in further mathematical detail: a way to dramatically reduce the size of the tape when performing reverse-mode AD on a (theoretically) time-reversible process like an ODE integrator; and a new mathematical insight that allows for the implementation of a stochastic Newton's method.

Probabilistic Temporal Tensor Factorization (PTTF) is an effective algorithm to model the temporal tensor data. It leverages a time constraint to capture the evolving properties of tensor data. Nowadays the exploding dataset demands a large scale PTTF analysis, and a parallel solution is critical to accommodate the trend. Whereas, the parallelization of PTTF still remains unexplored. In this paper, we propose a simple yet efficient Parallel Probabilistic Temporal Tensor Factorization, referred to as P$^2$T$^2$F, to provide a scalable PTTF solution. P$^2$T$^2$F is fundamentally disparate from existing parallel tensor factorizations by considering the probabilistic decomposition and the temporal effects of tensor data. It adopts a new tensor data split strategy to subdivide a large tensor into independent sub-tensors, the computation of which is inherently parallel. We train P$^2$T$^2$F with an efficient algorithm of stochastic Alternating Direction Method of Multipliers, and show that the convergence is guaranteed. Experiments on several real-word tensor datasets demonstrate that P$^2$T$^2$F is a highly effective and efficiently scalable algorithm dedicated for large scale probabilistic temporal tensor analysis.

Importance sampling is often used in machine learning when training and testing data come from different distributions. In this paper we propose a new variant of importance sampling that can reduce the variance of importance sampling-based estimates by orders of magnitude when the supports of the training and testing distributions differ. After motivating and presenting our new importance sampling estimator, we provide a detailed theoretical analysis that characterizes both its bias and variance relative to the ordinary importance sampling estimator (in various settings, which include cases where ordinary importance sampling is biased, while our new estimator is not, and vice versa). We conclude with an example of how our new importance sampling estimator can be used to improve estimates of how well a new treatment policy for diabetes will work for an individual, using only data from when the individual used a previous treatment policy.

Social media services such as Twitter are a valuable source of information for decision support systems. Many studies have shown that this also holds for the medical domain, where Twitter is considered a viable tool for public health officials to sift through relevant information for the early detection, management, and control of epidemic outbreaks. This is possible due to the inherent capability of social media services to transmit information faster than traditional channels. However, the majority of current studies have limited their scope to the detection of common and seasonal health recurring events (e.g., Influenza-like Illness), partially due to the noisy nature of Twitter data, which makes outbreak detection and management very challenging. Within the European project M-Eco, we developed a Twitter-based Epidemic Intelligence (EI) system, which is designed to also handle a more general class of unexpected and aperiodic outbreaks. In particular, we faced three main research challenges in this endeavor: 1) dynamic classification to manage terminology evolution of Twitter messages, 2) alert generation to produce reliable outbreak alerts analyzing the (noisy) tweet time series, and 3) ranking and recommendation to support domain experts for better assessment of the generated alerts. In this paper, we empirically evaluate our proposed approach to these challenges using real-world outbreak datasets and a large collection of tweets. We validate our solution with domain experts, describe our experiences, and give a more realistic view on the benefits and issues of analyzing social media for public health.