HIROSHIMA – Keiichi Tsukishiro, a veteran in the field of prosthetic engineering, will be in Rio de Janeiro this summer supporting athletes taking part in the Paralympics by repairing wheelchairs and artificial limbs. This will be the sixth Paralympics for the 51-year-old Japanese, who also teaches at Hiroshima International University as a member of the faculty of rehabilitation. "I will have to play a central role as I have participated in the event more often than most other staff members," Tsukishiro said. Born in the city of Kyoto, he first took up the family business of training dogs after graduating from high school. Keenly interested in craftsmanship, he later enrolled in a national training school for producing prosthetic devices.

Just when you thought you'd got your head around the whole Machine Learning thing…BAMN! There's a new tech buzzword in town rearing up to take it's place. And while it may seem like just another Silicon Valley buzzword that all the new startups will claim to be using, deep learning is actually already being used to make some really astounding advances. We caught up with deep learning expert, Andrew Ng, and asked him to explain what deep learning is and how we should expect to see it change the world in 2016. Deep learning is a subset of machine learning that essentially refers to trying to map neural networks (the same stuff that makes your brain work).

In this handout image provided by Google, South Korean professional Go player Lee Se-Dol (R) puts his first stone against Google's artificial intelligence program, AlphaGo, during the fourth Google DeepMind Challenge Match on March 13, 2016 in Seoul, South Korea. The ever-evolving field of artificial intelligence (AI) has proven its pervasiveness in humanity as it continues to revolutionize the lives of humans and make big strides when it comes to technological innovations. Hence, many several entrepreneurs and innovators are investing more time and money on AI-driven platforms and programs as well AI-oriented technologies. Despite the usefulness of artificial intelligence in almost all fields of sciences such as healthcare, education, businesses and becoming a "meta-solution" to the biggest issues of the society, many experts have warned about its impact to humanity. In fact, experts have predicted that AI will change everything about the way humans produce, manufacture and deliver.

Dr. Ben Choi, associate professor of computer science at Louisiana Tech University, will present his research on a groundbreaking new technology that has the potential to revolutionize the computing industry during a keynote speech next month at the International Conference on Measurement Instrumentation and Electronics. Choi will present on a foundational architecture for designing and building computers, which will utilize multiple values rather than binary as used by current computers. The many-valued logic computers should provide faster computation by increasing the speed of processing for microprocessors and the speed of data transfer between the processors and the memory as well as increasing the capacity of the memory. This technology has the potential to redefine the computing industry, which is constantly trying to increase the speed of computation and, in recent years, has run short of options. By providing a new hardware approach, the technology will push the speed limit of computing using a progressive approach which will move from two values to four values, then to eight values, then to 16 values, and so on. Future computers could be built using this many-valued approach.

We've entered the year where robots will start taking our orders. Pepper, a humanoid robot developed by SoftBank, will begin taking orders at Pizza Hut locations in Asia by the end of 2016, according to a company statement. It will be the first time Pepper is used for commerce purposes. You'll only be able to use the robot if you have MasterCard, though. Pizza Hut goers will be able to use Pepper by simply greeting the friendly-looking humanoid robot.

We present a new approach to estimating the interdependence of industries in an economy by applying data science solutions. By exploiting interfirm buyer--seller network data, we show that the problem of estimating the interdependence of industries is similar to the problem of uncovering the latent block structure in network science literature. To estimate the underlying structure with greater accuracy, we propose an extension of the sparse block model that incorporates node textual information and an unbounded number of industries and interactions among them. The latter task is accomplished by extending the well-known Chinese restaurant process to two dimensions. Inference is based on collapsed Gibbs sampling, and the model is evaluated on both synthetic and real-world datasets. We show that the proposed model improves in predictive accuracy and successfully provides a satisfactory solution to the motivated problem. We also discuss issues that affect the future performance of this approach.

A pdf version of this document created using latex can be downloaded by clicking here. Polymorphic malware detection is challenging due to the continual mutations miscreants introduce to successive instances of a particular virus. Such changes are akin to mutations in biological sequences. Recently, high-throughput methods for gene sequence classification have been developed by the bioinformatics and computational biology communities. In this paper, we argue that these methods can be usefully applied to malware detection. Unfortunately, gene classification tools are usually optimized for and restricted to an alphabet of four letters (nucleic acids). Consequently, we have selected the Strand gene sequence classifier, which offers a robust classification strategy that can easily accommodate unstructured data with any alphabet including source code or compiled machine code. To demonstrate Stand's suitability for classifying malware, we execute it on approximately 500GB of malware data provided by the Kaggle Microsoft Malware Classification Challenge (BIG 2015) used for predicting 9 classes of polymorphic malware.

Welcome to Visual Evidence, a new regular series about visualization in the real world! We'll take a look at unexpected datasets, cool design solutions or insightful graphics. We'll find examples of how visual information can help us solve real-world problems or save us from our own mistakes. And we'll illustrate all these ideas with charts, sketches, and of course, plenty of gifs. Chances are, you probably think your mind works pretty well. It might lead you astray now and then, but usually it helps you make good decisions and remember things reliably. At the very least, you're probably confident that it doesn't change depending on the time of day or what you had to eat.

Especially when those ideas stand in opposition to technological progress. I'm used to hearing, and quickly dismissing, fears related to our ongoing developments, especially when they come without factual basis or understanding of the underlying societal and technological aspects implicated. So keep all of that in mind when I tell you that I'm terrified of the future. Because I'm about to be fired and replaced by a robot. Yes, I'm afraid I've fallen off the bandwagon once again, knocked my head, and started to agree with some of the pundits you're hearing in the press. While I sense most people have no inkling of the huge tidal wave of change that's coming towards us, some of its effects are already starting to be felt.

Recently I visited the Innorobo show in Paris, a gathering of robot companies from around the world, which brought together industrial robots, service robots, toy robots, family robots, and many other robot types, all under one roof. One of the key highlights was SoftBank Robotics launching Pepper Partners Europe, inviting developers and companies in Europe to build applications for SoftBank's flagship robot, Pepper. This initiative is part of SoftBank's expansion of Pepper outside Japan, where more than 3,000 Pepper robots have already been deployed at over 1,000 companies. SoftBank Robotics, largely composed of the French robotics pioneer Aldebaran (of which Softbank owns 95%), has more than 500 employees globally, with the majority based in Paris at the Aldebaran facility. SoftBank has big ambitions for Pepper, and with more than 20,000 Pepper robots deployed worldwide in both consumer and enterprise environments, SoftBank is easily the leading player in this space.