At the Bristol Robotics Laboratory in southwest England, dozens of tiny metal machines were poised to dance over a shiny white surface in "swarming" experiments to track how they organize themselves and work together. The scene at the European Union-funded facility this week mirrored what the continent is now trying to do to compete with the U.S. and China in artificial intelligence, or AI. The European Commission said it wants to create a network of hundreds of so-called Digital Innovation Hubs like the one in Bristol. The aim is to help Europe's companies and scientists work together to boost research and the take-up of new technologies among the thousands of small and medium-sized companies that form the backbone of the economy. "The idea is that all these nodes will connect and communicate with each other, maximizing the impact of the technologies and expertise they develop," said Farid Dailami, an associate professor who runs the prototype hub in Bristol, which connects researchers and funding with companies that need robotics.

At the U.S. Army Research Laboratory, scientists are improving the rate of learning even with limited resources. It's possible to help Soldiers decipher hints of information faster and more quickly deploy solutions, such as recognizing threats like a vehicle-borne improvised explosive device, or potential danger zones from aerial war zone images. The researchers relied on low-cost, lightweight hardware and implemented collaborative filtering, a well-known machine learning technique on a state-of-the-art, low-power Field Programmable Gate Array platform to achieve a 13.3 times speedup of training compared to a state-of-the-art optimized multi-core system and 12.7 times speedup for optimized GPU systems. The new technique consumed far less power too. Consumption charted 13.8 watts, compared to 130 watts for the multi-core and 235 watts for GPU platforms, making this a potentially useful component of adaptive, lightweight tactical computing systems.

Jeff Brantingham is as close as it gets to putting a face on the controversial practice of "predictive policing." Over the past decade, the University of California-Los Angeles anthropology professor adapted his Pentagon-funded research in forecasting battlefield casualties in Iraq to predicting crime for American police departments, patenting his research and founding a for-profit company named PredPol, LLC. PredPol quickly became one of the market leaders in the nascent field of crime prediction around 2012, but also came under fire from activists and civil libertarians who argued the firm provided a sort of "tech-washing" for racially biased, ineffective policing methods. Now, Brantingham is using military research funding for another tech and policing collaboration with potentially damaging repercussions: using machine learning, the Los Angeles Police Department's criminal data, and an outdated gang territory map to automate the classification of "gang-related" crimes. Being classified as a gang member or related to a gang crime can result in additional criminal charges, heavier prison sentences, or inclusion in a civil gang injunction that restricts a person's movements and ability to associate with other people.

Attacks in cyber-physical systems (CPS) which manipulate sensor readings can cause enormous physical damage if undetected. Detection of attacks on sensors is crucial to mitigate this issue. We study supervised regression as a means to detect anomalous sensor readings, where each sensor's measurement is predicted as a function of other sensors. We show that several common learning approaches in this context are still vulnerable to \emph{stealthy attacks}, which carefully modify readings of compromised sensors to cause desired damage while remaining undetected. Next, we model the interaction between the CPS defender and attacker as a Stackelberg game in which the defender chooses detection thresholds, while the attacker deploys a stealthy attack in response. We present a heuristic algorithm for finding an approximately optimal threshold for the defender in this game, and show that it increases system resilience to attacks without significantly increasing the false alarm rate.

We consider multi-response and multitask regression models, where the parameter matrix to be estimated is expected to have an unknown grouping structure. The groupings can be along tasks, or features, or both, the last one indicating a bi-cluster or "checkerboard" structure. Discovering this grouping structure along with parameter inference makes sense in several applications, such as multi-response Genome-Wide Association Studies. This additional structure can not only can be leveraged for more accurate parameter estimation, but it also provides valuable information on the underlying data mechanisms (e.g. relationships among genotypes and phenotypes in GWAS). In this paper, we propose two formulations to simultaneously learn the parameter matrix and its group structures, based on convex regularization penalties. We present optimization approaches to solve the resulting problems and provide numerical convergence guarantees. Our approaches are validated on extensive simulations and real datasets concerning phenotypes and genotypes of plant varieties.

The demands of UQ in computer prediction, a problem we believe to be NP-Hard, cannot be met on our current HPC technology path. We see that cognitive computing, defined through the technology convergence of AI, Big Data and HPC is an essential next step. With vendor technology decisions being made now and in the next few years in AI and HPC, it is urgent that broad classes of HW and SW are explored to best leverage commercial technology roadmaps. To that end, we are using precision medicine data as a force multiplier and accelerator. This rich, complex, unstructured, heterogeneous, curated, massive data is likely the richest class of data to work on today and brings with it unique partnerships that buys down risk in exploring the many splintered paths forward each with their own tough challenges and also shares costs.

It has been a rough few weeks for Facebook since the Observer reported the Cambridge Analytica data breach. The scandal revealed how the political consulting firm might have raked up the personal information of at least 87 million Facebook users in order to influence them with tailored political ads, sent the social network's stocks into a tailspin, triggered the #DeleteFacebook movement – and regaled the planet with the cringefest that was CEO Mark Zuckerberg's testimony before the US Senate. But if Facebook's reputation has seen better days, one of the company's most valuable assets has come out of the kerfuffle practically unscathed. Instagram, the photo-sharing platform Facebook acquired in 2012 for $715m, has not yet come up in the debate over Facebook's cavalier attitude to user data protection, despite being of a piece with the longer-running social network (and being headquartered just a few blocks from Facebook's Menlo Park campus in California). Prominent members of the #DeleteFacebook campaign, such as SpaceX's Elon Musk, singer Cher, and Playboy magazine, are still pretty much present and active on Instagram.

New York: A novel machine learning technique could help soldiers to learn 13 times faster than conventional methods as well as help save lives, say researchers, including one of Indian-origin. Using a low-cost, lightweight hardware and implementing collaborative filtering -- a well-known machine learning technique -- the team found that soldiers are able to decipher hints of information faster and more quickly deploy solutions, such as recognising threats like a vehicle-borne improvised explosive device, or potential danger zones from aerial war zone images. This technique could eventually become part of a suite of tools embedded on the next generation combat vehicle, offering cognitive services and devices for warfighters in distributed coalition environments, said Rajgopal Kannan, a researcher, from the US Army Research Laboratory. This work is part of Army's larger focus on artificial intelligence and machine learning research initiatives pursued to help to gain a strategic advantage and ensure warfighter superiority with applications such as on-field adaptive processing and tactical computing, he said. The paper on this new research won the best-paper award at the 26th ACM/SIGDA International Symposium on Field Programmable Gate Arrays in Monterey, California in February.

New York: A novel machine learning technique could help soldiers to learn 13 times faster than conventional methods as well as help save lives, say researchers, including one of Indian-origin. Using a low-cost, lightweight hardware and implementing collaborative filtering -- a well-known machine learning technique -- the team found that soldiers are able to decipher hints of information faster and more quickly deploy solutions, such as recognising threats like a vehicle-borne improvised explosive device, or potential danger zones from aerial war zone images. This technique could eventually become part of a suite of tools embedded on the next generation combat vehicle, offering cognitive services and devices for warfighters in distributed coalition environments, said Rajgopal Kannan, a researcher, from the US Army Research Laboratory. This work is part of Army's larger focus on artificial intelligence and machine learning research initiatives pursued to help to gain a strategic advantage and ensure warfighter superiority with applications such as on-field adaptive processing and tactical computing, he said. The paper on this new research won the best-paper award at the 26th ACM/SIGDA International Symposium on Field Programmable Gate Arrays in Monterey, California in February.

The Argonne Leadership Computing Facility (ALCF), a U.S. Department of Energy (DOE) Office of Science User Facility, is now accepting proposals for the ALCF Data Science Program (ADSP). Launched in 2016, the ADSP is targeted at "big data" science problems that require the scale and performance of leadership computing resources, such as the ALCF's two petascale supercomputers: Mira, an IBM Blue Gene/Q system, and Theta, an Intel-Cray system. From April 27 to June 20, 2018, the ADSP open call provides an opportunity for researchers to submit proposals for projects that will employ advanced data science and machine learning techniques to gain insights into very large datasets produced by experimental, simulation, or observational methods. The program, which currently supports eight projects, allocates computing time and supporting resources to research teams focused on using the ALCF's leadership-class systems and infrastructure to explore, demonstrate, and improve a wide range of data and learning techniques. These techniques include uncertainty quantification, statistics, machine learning, deep learning, databases, pattern recognition, image processing, graph analytics, data mining, real-time data analysis, and complex and interactive workflows.