Investment in defences against cyberattacks has overtaken artificial intelligence as the main technology issue for law firms, a report reveals today. Cybersecurity was the area cited most often by leaders of the UK's biggest law firms when researchers asked how they were allocating their technology budgets. Artificial intelligence, broadly seen as the must-have technology function over the last 12 months, was relegated to fourth place on the investment league table. Law firm leaders at the top-50 practices in the country cited "client collaboration tools" -- software packages that share information between in-house lawyers and law firms -- and automated document production as being more important that AI.

This appears to be the first confirmed use of drones by an extremist group in Africa, according to the World of Drones project run by the Washington-based New America think tank. Its section on non-state actors notes that Libyan rebels are reported to have used drones for surveillance in that chaotic North African nation.

WASHINGTON – The U.S. Army is investing millions of dollars in experimental exoskeleton technology to make soldiers stronger and more resilient, in what experts say is part of a broader push into advanced gear to equip a new generation of "super-soldiers." The technology is being developed by Lockheed Martin Corp. with a license from Canada-based B-TEMIA, which first developed the exoskeletons to help people with mobility difficulties stemming from medical ailments like multiple sclerosis and severe osteoarthritis. Worn over a pair of pants, the battery-operated exoskeleton uses a suite of sensors, artificial intelligence and other technology to aid natural movements. For the U.S. military, the appeal of such technology is clear: Soldiers now deploy into war zones bogged down by heavy but critical gear like body armor, night-vision goggles and advanced radios. Altogether, that can weigh anywhere from 40 to 64 kilograms (90 to 140 pounds), when the recommended limit is just 23 kg (50 pounds).

The U.S. Army has awarded a $6.9m contract to develop an'Iron Man' exoskeleton to give soldiers superhuman strength and endurance. Called Onyx, the battery-operated exoskeleton uses a suite of sensors, artificial intelligence and other technology to aid natural movements. It is being built by Lockheed Martin, and was originally designed to help people with mobility problems. 'It supports and boosts leg capacity for physically demanding tasks that require lifting or dragging heavy loads, holding tools or equipment, repetitive or continuous kneeling or squatting, crawling, walking long distances, walking with load, walking up or down hills, or carrying loads on stairs,' Lockheed Martin said. 'When human strength is challenged, ONYX makes the difference, reducing muscle fatigue, increasing endurance, and reducing injury.'

This might seem bonkers given the reasonable fear that computers, robots and AI could wipe out half of all jobs in the next 20 years. It also might seem foolhardy from the C-suite perspective, since not all robots are suited for all jobs, and underused robots are costlier than a seasonal or on-demand human workforce. The bulk of economists argue that automation ultimately creates more jobs. That might be of little comfort to a Detroit assembly-line worker. Automation does eliminate jobs in the short term, with often painful and even permanent consequences.

If artificial intelligence (AI) was as developed two years ago as it is now, we could probably have predicted the result of the Brexit referendum. That is what AI excels at, analysing an immense amount of data from an array of sources to draw connections and predict outcomes. At its core, AI is all about crunching the data and coming up with insights that the human brain cannot. This is why it is emerging as a key competitive advantage and the decisive differentiator between companies. From boardroom to the factory floor, AI supports better decision making, increasing productivity and profit.

On Friday afternoons, Caitlin Kontgis and some of the other scientists at Descartes Labs convene in their Santa Fe, New Mexico, office and get down to work on a grassroots project that's not part of their jobs: watching hurricanes from above, and seeing if they can figure out what the storms will do.* They acquire data from GOES, the Geostationary Operational Environmental Satellite operated by NOAA and NASA, which records images of the Western Hemisphere every five minutes. That's about how long it takes the team to process each image through a deep learning algorithm that detects the eye of a hurricane and centers the image processor over that. Then, they incorporate synthetic aperture data, which uses long-wave radar to see through clouds, and can discern water beneath based on reflectivity. That, in turn, can show almost real-time flooding, tracked over days, of cities in the path of hurricanes.

This project presents the results of a partnership between the Data Science for Social Good fellowship, Jakarta Smart City and Pulse Lab Jakarta to create a video analysis pipeline for the purpose of improving traffic safety in Jakarta. The pipeline transforms raw traffic video footage into databases that are ready to be used for traffic analysis. By analyzing these patterns, the city of Jakarta will better understand how human behavior and built infrastructure contribute to traffic challenges and safety risks. The results of this work should also be broadly applicable to smart city initiatives around the globe as they improve urban planning and sustainability through data science approaches.

Detecting and mapping informal settlements encompasses several of the United Nations sustainable development goals. This is because informal settlements are home to the most socially and economically vulnerable people on the planet. Thus, understanding where these settlements are is of paramount importance to both government and non-government organizations (NGOs), such as the United Nations Children's Fund (UNICEF), who can use this information to deliver effective social and economic aid. We propose a method that detects and maps the locations of informal settlements using only freely available, Sentinel-2 low-resolution satellite spectral data and socio-economic data. This is in contrast to previous studies that only use costly very-high resolution (VHR) satellite and aerial imagery. We show how we can detect informal settlements by combining both domain knowledge and machine learning techniques, to build a classifier that looks for known roofing materials used in informal settlements. Please find additional material at https://frontierdevelopmentlab.github.io/informal-settlements/.

Detecting and mapping informal settlements encompasses several of the United Nations sustainable development goals. This is because informal settlements are home to the most socially and economically vulnerable people on the planet. Thus, understanding where these settlements are is of paramount importance to both government and non-government organizations (NGOs), such as the United Nations Children's Fund (UNICEF), who can use this information to deliver effective social and economic aid. We propose two effective methods for detecting and mapping the locations of informal settlements. One uses only low-resolution (LR), freely available, Sentinel-2 multispectral satellite imagery with noisy annotations, whilst the other is a deep learning approach that uses only costly very-high-resolution (VHR) satellite imagery. To our knowledge, we are the first to map informal settlements successfully with low-resolution satellite imagery. We extensively evaluate and compare the proposed methods. Please find additional material at https://frontierdevelopmentlab.github.io/informal-settlements/.