Many network systems lack a natural low-dimensional embedding from which we can readily extract their most prominent large-scale features. Instead, we have to infer this information from data, typically by decomposing the observed network into modules [1]. A principled approach to perform this task is to formulate generative models that allow this modular decomposition to be found via statistical inference [2]. The most fundamental model used for this purpose is the stochastic block model (SBM) [3], which groups nodes according to their probabilities of connection to the rest of the network. However, a central limitation of most SBM implementations is that they are defined strictly for simple or multigraphs. This means that they do not incorporate extra information on the edges, which are typically present in a variety of systems, and are required for an accurate representation of their structure. For example, to the existence of a route between two airports is associated a distance, to the biomass flow between two species in a food web is associated a flow magnitude, etc. In this work, we develop variations of the SBM that allow for this type of information on the edges to be incorporated into the network model and guide the partition of the nodes into groups in a statistically meaningful way. We follow the same basic idea put forth by Aicher et al. [4], who adapted the SBM to weighted networks by including edge values as additional covariates.

Nintendo's ingenious upcoming release for its Switch console uses augmented reality to make working cars, pianos and full-body robot suits – teaching coding and engineering principles through play Wed 17 Jan 2018 17.10 EST Last modified on Wed 17 Jan 2018 17.42 EST Nintendo has introduced a new product for its Switch games console: Nintendo Labo, an innovative augmented-reality game that turns cardboard models into fully functioning toys. Inside the Nintendo Labo box are 25 sheets of thick, brown, branded cardboard, and a little cartridge that pops into a Nintendo Switch console. Following Lego-like instructions on the Switch screen, you punch out the cardboard pieces and assemble them into contraptions of varying complexity. The first project, which takes maybe 15 minutes, is a simple little bug-like radio-controlled car; slot the Joy-Con controllers into its cardboard sides, pull up the controls on the Switch's screen, and the vibrations send it juddering across a flat surface with surprising speed. The more complex constructions are a telescopic fishing rod with a working reel, attached to a base with elastic bands and string for realistic tension; a cardboard model of a piano with springy keys; an abstract motorbike, with handles and a pedal; a little house.

More than 50 women in Sweden who said they used the Natural Cycles app as contraception ended up pregnant and reported their cases to the Medical Product Agency, according to a statement from Natural Cycles. The app, a certified contraceptive in Europe, has about the same failure rates as the pill, but only under certain circumstances, according to experts. "Natural methods, when used correctly, are very very effective," Richard J. Fehring, professor, and director of the Marquette University College of Nursing Institute for Natural Family Planning, told International Business Times. The Natural Cycles app uses an algorithm to calculate the days a woman is fertile and therefore most likely to get pregnant and when used properly, it claims to be about as effective as the pill. The app requires that users take their temperature each morning and input it into the app, then after some calculations, the app says whether users are "not fertile," or should "use protection."

Computers are built to process data, but there's a particular form of information so rich and dense in meaning that it's beyond the full comprehension of even the most advanced AI. It's also one that you and I process intuitively and deal in every day: language. Understanding the written and spoken word is a big an important challenge for computer scientists. This month, a small milestone was passed when a pair of teams from Microsoft and Alibaba independently created AI programs that can outperform humans in a reading comprehension test. As you might expect, this news resulted in a flurry of coverage.

People use chatbots to find homes, interact with their favorite brands, and schedule appointments. Many consumers are onboard with using chatbots to gather instant, personalized information. In many cases, chatbots are the first point of contact for individuals who feel unwell and need to decide whether to head to the doctor. As this technology becomes more prominent, people understandably begin to wonder if insurance companies will cover sessions with chatbot doctors. Given their innovative use of chatbots in the health care sector, it's looking like insurers and health organizations in the U.K. could be the first to establish insurance coverage for health consultations with chatbots.

Research recently conducted for The Times has presented a fairly negative snapshot of the public's perception of autonomous vehicle technology. In it, almost two thirds of motorists said they would not buy a driverless car, suggesting that people don't trust driverless technology… yet. Clearly, those involved in the burgeoning industry face an enormous challenge in reassuring those unnerved by the idea of not being in control. Failure to do so will see driverless vehicles join the scrap heap of failed transport modernisation projects. Safety is naturally top of the list when it comes to the prospect of driverless vehicles.

Google just made it a lot easier to build your very own custom AI system. A new service, called Cloud AutoML, uses several machine-learning tricks to automatically build and train a deep-learning algorithm that can recognize things in images. The technology is limited for now, but it could be the start of something big. Building and optimizing a deep neural network algorithm normally requires a detailed understanding of the underlying math and code, as well as extensive practice tweaking the parameters of algorithms to get things just right. The difficulty of developing AI systems has created a race to recruit talent, and it means that only big companies with deep pockets can usually afford to build their own bespoke AI algorithms.

It seems like only yesterday when everyone was talking about the impact of Big Data on the insurance industry. Talk about Big Data now and you will almost seem old-fashioned – it's all about artificial intelligence and InsurTech. Artificial intelligence (AI) will soon be everywhere. It is making decisions about what we buy, when we buy it and how much it costs. It is controlling interactions between customers and suppliers.

Early recognition of cardiac arrest is vitally important as the chance of survival decreases about 10 percent with each minute. In Denmark AI assistant Corti is listening in to phone calls to emergency services to help detect signs of a heart attack. With Corti implemented, the dispatcher gets a digital assistant that listens in on the conversation and helps to look for important signals in both verbal communication, as well as tone of voice and breathing patterns, while also considering other metadata. All the data provided during the emergency call is automatically analyzed by Corti and then compared to the millions of emergency calls – which Corti has already analysed –to find important patterns. As Corti's understanding of the incident increases, the assistant will try to predict the criticality of the patient's situation based on symptom descriptions and the signals gathered from voice and audio.

Britain's first passenger drone company claims it has found the answer to the dismal morning commute. Entrepreneur Martin Warner, 46, claims his new ultra-sleek drone will be able to transport commuters from Charing Cross to Heathrow in just 12 minutes. Scheduled for unveiling this Spring, the drone will be the first in the world to carry passengers - and could revolutionise city commutes. Britain's first passenger drone company claims it has found the answer to the dismal morning commute. Entrepreneur Martin Warner, 46, claims his new drone (artist's impression) will be able to transport commuters from Charing Cross to Heathrow in just 12 minutes Autonomous Flight is set to unveil Y6S, a two-seater electric vertical take-off and landing vehicle (VTOL), in London in March.