Jumping robot spiders and swarms of robotic bees sounds like the stuff of science fiction, but researchers at The University of Manchester are already working on such projects and aiming to lead the world in micro robotics. But what will these kinds of robots be used for and is it something we should be worried? Dr Mostafa Nabawy is the Microsystems Research Theme Leader at The University of Manchester's School of Mechanical, Aerospace and Civil Engineering. He is presenting some of his research, "Spiders Attack: The rise of bioinspired microrobots" at Manchester's Industry 4.0 Summit on Thursday 1 March. Here Dr Nabawy explains why micro robots really aren't anything to worry about and, instead, could be the revolution in robotics that spearheads the next generation in manufacturing technology: 'For our robotic spiders research we are looking at a specific species of jumping spider called Phidippus regius.

Machine learning could be used to underwrite faster, reduce fraud and assess vehicle damages more accurately, Lawrence Wong, Munich Re Canada's director of application development, said Tuesday. Technology Conference in Toronto, Wong said that artificial intelligence (AI) is here to stay and will augment the way people do the business of insurance, whether insurers leverage it or not. "The power of machine learning is that you don't have to explicitly program it," Wong said during the session Case Studies in AI. "As you show [a self-learning machine] more and more pictures of bumper damage, it will, with experience, become more accurate at defining what's vehicle damage versus what's quirky vehicle design from the car manufacturer. "Notice I said the word experience," Wong added. "That's exactly how a human loss adjuster would learn about vehicle damage.

Recent advances in modern artificial intelligence demonstrated promising results in both biomarker development and drug discovery and the leading academic and research institutions are expanding their AI groups. The Buck Institute is the leading research institution with massive amounts of biological data coming from high-throughput experiments and with the unique multidisciplinary expertise in aging research. Alex Zhavoronkov works on the intersection of the next-generation of artificial intelligence and aging research and will help support the AI efforts at the Buck. "We are incredibly excited about the potential of AI to accelerate aging research," said Dr. Eric Verdin, President and CEO of the Buck Institute. "The Buck has been at the forefront of asking the most important questions in the field. Now, with the latest in bioinformatics and artificial intelligence, and with the involvement of world-class experts like Dr. Zhavoronkov, we will finally have the tools to answer them. Fully utilizing these powerful technologies, we will dramatically increase our understanding of how aging works, and what we can do about it."

NUTLEY, NEW JERSEY – Authorities say a New Jersey student accused of making an online threat against his high school posted a video created in the popular video game Minecraft showing a shooting at a replica of the school. NJ.com reports that Joseph Rafanello's avatar can be seen walking through the virtual school, complete with lockers and the sound of gunshots in the background. The new information was discussed Wednesday during a detention hearing for the 18-year-old Nutley High School student. A judge rejected prosecutors' request to keep him in custody until his trial, ordering that he instead be placed on home detention. Rafanello was charged with creating a false public alarm for a video that spurred the closure of Nutley schools Feb. 16.

The eyes may be the window to the soul, but they're also a window into heart health thanks to breakthrough health technology that combines artificial intelligence (AI) and machine learning. In a study published in February 2018 in Nature Biomedical Engineering, Google AI researchers and Verily (a health tech subsidiary of Google) teamed up to develop a non-invasive procedure to assess future heart health with retinal imaging techniques. Scientists used AI cameras to observe the blood vessels behind the eyes of more than 250,000 individuals from the United Kingdom and United States. From the collected data, an algorithm predicted the next five years of cardiovascular health for each patient with over 70 percent accuracy. "This discovery is particularly exciting because it suggests we might discover even more ways to diagnose health issues from retinal images," Lily Peng, a product manager for the Google Brain AI research group, said on their blog.

Artificial Intelligence is on the cusp of transforming our world in ways many of us can barely imagine. While there's much excitement about emerging technologies, a new report by 26 of the world's leading AI researchers warns of the potential dangers that could emerge over the coming decade, as AI systems begin to surpass levels of human performance. Automated hacking is identified as one of the most imminent applications of AI, especially so-called "phishing" attacks. "That part used to take a lot of human effort – you had to study your target, make a profile of them, craft a particular message – that's known as phishing. We are now getting to the point where we can train computers to do the same thing. So you can model someone's topics of interest or preferences, their writing style, the writing style of a close friend, and have a machine automatically create a message that looks a lot like something they would click on," says report co-author Shahar Avin of the Center for the Study of Existential Risk at Britain's University of Cambridge.

Optimal Transport (OT) is a mathematical gem at the interface between probability, analysis and optimization. The goal of that theory is to define geometric tools that are useful to compare probability distributions. Earlier contributions originated from Monge's work in the 18th century, to be later rediscovered under a different formalism by Tolstoi in the 1920's, Kantorovich, Hitchcock and Koopmans in the 1940's. The problem was solved numerically by Dantzig in 1949 and others in the 1950's within the framework of linear programming, paving the way for major industrial applications in the second half of the 20th century. OT was later rediscovered under a different light by analysts in the 90's, following important work by Brenier and others, as well as in the computer vision/graphics fields under the name of earth mover's distances. Recent years have witnessed yet another revolution in the spread of OT, thanks to the emergence of approximate solvers that can scale to sizes and dimensions that are relevant to data sciences. Thanks to this newfound scalability, OT is being increasingly used to unlock various problems in imaging sciences (such as color or texture processing), computer vision and graphics (for shape manipulation) or machine learning (for regression,classification and density fitting). This short book reviews OT with a bias toward numerical methods and their applications in data sciences, and sheds lights on the theoretical properties of OT that make it particularly useful for some of these applications.

High-dimensional data requires scalable algorithms. We propose and analyze three scalable and related algorithms for semi-supervised discriminant analysis (SDA). These methods are based on Krylov subspace methods which exploit the data sparsity and the shift-invariance of Krylov subspaces. In addition, the problem definition was improved by adding centralization to the semi-supervised setting. The proposed methods are evaluated on a industry-scale data set from a pharmaceutical company to predict compound activity on target proteins. The results show that SDA achieves good predictive performance and our methods only require a few seconds, significantly improving computation time on previous state of the art.

We discuss general perturbation inequalities when the perturbation is random. As applications, we obtain several new results concerning two important problems: matrix sparsification and matrix completion.

Often, when dealing with real-world recognition problems, we do not need, and often cannot have, knowledge of the entire set of possible classes that might appear during operational testing. Moreover, sometimes some of these classes may be ill-sampled, not sampled at all or undefined. In such cases, we need to think of robust classification methods able to deal with the "unknown" and properly reject samples belonging to classes never seen during training. Notwithstanding, almost all existing classifiers to date were mostly developed for the closed-set scenario, i.e., the classification setup in which it is assumed that all test samples belong to one of the classes with which the classifier was trained. In the open-set scenario, however, a test sample can belong to none of the known classes and the classifier must properly reject it by classifying it as unknown. In this work, we extend upon the well-known Support Vector Machines (SVM) classifier and introduce the Specialized Support Vector Machines (SSVM), which is suitable for recognition in open-set setups. SSVM balances the empirical risk and the risk of the unknown and ensures that the region of the feature space in which a test sample would be classified as known (one of the known classes) is always bounded, ensuring a finite risk of the unknown. The same cannot be guaranteed by the traditional SVM formulation, even when using the Radial Basis Function (RBF) kernel. In this work, we also highlight the properties of the SVM classifier related to the open-set scenario, and provide necessary and sufficient conditions for an RBF SVM to have bounded open-space risk. An extensive set of experiments compares the proposed method with existing solutions in the literature for open-set recognition and the reported results show its effectiveness.