Yahoo is today announcing the release of a large-scale data set that describes people's usage of news feeds on several of the company's web services, including Yahoo News and Yahoo Finance. The idea is to empower machine learning researchers in academia with very rich data. The release of data is not, in and of itself, new for Yahoo -- there have been 56 previous releases in the Yahoo Labs Webscope program, which encompasses advertising, image, social, and ratings data, among other categories. This data set in particular covers 20 million people over the course of four months in 2015, and shows the types of devices people used to visit pages, how far down they got in the articles, and the top subjects of articles. There is data on people's locations, their ages (in some cases), and their gender -- all in an anonymized way.

Besides being a major player in the earthquake prediction method discussed here, the ionosphere is important because it's the layer of the atmosphere that reflects electromagnetic waves back to Earth and enables radio communication. There was increased ionization over Japan before the 2011 Tohoku earthquake and a spike in radio wave emissions near Haiti before the 2010 quake there. Enough historical data linking ionospheric activity to earthquakes needs to be collected in order to generate patterns, and the patterns then need to be matched to real-time data. When the Tohoku earthquake hit, Tokyo residents received a one-minute warning via Japan's earthquake early warning system.

Machine Learning Deep learning start-up MetaMind has published details of a system that is more accurate than other language processing methods. The company is developing technology designed to be capable of a range of different artificial-intelligence tasks. Google and Facebook, are investing huge sums into the research and development of improved artificial intelligence algorithms for processing language. Around the world, various research groups are making steady progress toward improving a computer's language skills especially using recent advances in machine learning. "The insight--and it's almost trivial--is that every task in NLP is actually a question-and-answer task."

This is another installment of Spare5's "Conversations in Machine Learning" blog series. Each week, our content human, Cassie, shares a summary of a recent conversation we had with a machine learning team and potential customer--what they're building, how they're handling training data today, etc. Read more about the series here. This week I'm featuring a technology that, when I learned about it I said, man I love when people think of stuff like this! Great idea, you technologically inclined people you. So imagine you're flipping through a magazine and see an ad for these great shoes and you're like, those are perfect shoes. You could head to Google, search for the brand, pull up their website, browse for the shoes there… Or you could just snap a picture of them with your phone and let the machine learning-based app we're highlighting today do all the work for you.

This time next week, on Tuesday 11 October at Bletchley Park, sees the launch of an initiative to celebrate women in maths and computing. As a new branch of the existing Suffrage Science scheme, it will encourage women into science, and to reach senior leadership roles. Women make up no more than four in ten undergraduates studying maths (London Mathematical Society), and fewer than two in ten of those studying computer science (WISE report, 2014). Despite much effort, there has been little sign of improvement. In fact, the number of women studying computer science at the undergraduate level has been in decline since the 1980s.

And – why we aren't ready for Superintelligence, DeepMind created an AI with memory, Facebook's ideas for VR and more! Last weekend we saw Cybathlon, the world's first "bionic Olympics", where disabled athletes assisted with exoskeletons, prosthetic robotic hands or brain-computer interfaces competed in a series of challenges. This article from BBC describes the games and lists all the winners. Some amputees want to have a prosthetic limb that can do a bit more or just looks better.Waterproof, dustproof, customized to client's skin color, matching to the owner's tattoos. And there are companies that are ready to help them for an appropriate price.

Scientists at Google have created an artificial intelligence program that can compute problems requiring strategic reasoning, The Guardian reports. The algorithm, part of an emerging field called deep learning, is able to master tasks independently using external memory, similar to the way humans work through a new recipe, according to the study published in Nature. In this case, it was able to figure out on its own the quickest route between stops on the London Underground and reassess if the destination was overshot. This could pave the way to more efficient virtual assistant applications, which might be bad news for Apple's sassy sidekick.

Using software to compare genetic information in bacterial isolates from animals and people, researchers have predicted that less than 10% of Escherichia coli 0157:H7 strains are likely to have the potential to cause human disease. According to Nadejda Lupolova, from the University of Edinburgh, Scotland, and colleagues, "machine-learning approaches have tremendous potential to interrogate complex genome information for which specific attributes of the organism, such as disease or isolation host, are known." The researchers published the results of their study in Proceedings of the National Academy of Sciences. Although most E. coli strains live in the gastrointestinal tracts of people and animals without causing disease, infection with E. coli 0157 is associated with serious illness in people. E. coli 0157 was first identified as a cause of disease in the United States in 1982, during an investigation into an outbreak of hemorrhagic colitis.

Despite computers performing increasingly impressive feats, when it comes to recognising real world objects, they have fallen short. But the machines are learning that just like the robots of sci-fi blockbusters, being able to tell one thing from another is a key skill. A team of UK researchers has created software which has taught itself to recognise objects around it, with impressive accuracy. UK researchers have created software, called RadarCat, which can teach itself to recognise objects around it with impressive accuracy . Called the RadarCat, short for radar categorisation for input and interaction, the software scans the world around it uses radar to scan the world around it.

While many advances have been made, it still has been difficult for non-programmers to create objects made of many materials (or mixtures of materials) without a more user-friendly interface. But this week, a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) will present "Foundry," a system for custom-designing a variety of 3-D printed objects with multiple materials. "In traditional manufacturing, objects made of different materials are manufactured via separate processes and then assembled with an adhesive or another binding process," says PhD student Kiril Vidim?e, who is first author on the paper. "Even existing multi-material 3-D printers have a similar workflow: parts are designed in traditional CAD [computer-aided-design] systems one at a time and then the print software allows the user to assign a single material to each part." In contrast, Foundry allows users to vary the material properties at a very fine resolution that hasn't been possible before.