The use of drones is already growing across various sectors but an Ajman-based start-up -- Deep Aero -- is fuelling an autonomous drone economy driven by artificial intelligence and blockchain. Gurmeet Singh Anand, CEO of Deep Aero, told Gulf News that the use of unmanned aerial vehicles is increasing exponentially. "In the coming future, we will see millions or billions of commercial drones flying in the air. When that happens, there has to be an autonomous system to manage the drone traffic. Otherwise, it will be a nightmare for regulators," he said.

You should attend Adweek's Elevate: AI summit March 6 in New York. Hate speech isn't a new problem, but in the social media age, the rate at which it can be spread has exponentially increased. But can artificial intelligence help stop it? Agency Possible and its longtime partners at social media marketing software company Spredfast wanted to do something to slow the rate at which hate can spread online. That's why they launched WeCounterHate, a campaign which "counters" such messages on Twitter with a donation to a nonprofit organization fighting hate for every retweet.

On 25 April 2018, a new Communication was published that sets out the European Commission's (EC's) new strategy to boost Europe's artificial intelligence (AI) capabilities and related industries, while at the same time preparing for socioeconomic changes emanating from these emerging technologies. The Communication also poses questions as to whether – and, if so, where and how – the European legal and ethical framework needs to be adapted due to the advent of AI. The EC refers to AI as "systems that show intelligent behaviour by analysing their environment, and performing various tasks with some degree of autonomy to achieve specific goals."1 European leaders are considering AI as a top priority. On 10 April, 24 member states2 and Norway co-signed a Declaration which commits them to working together on AI.

The mass, or binding energy, is the basis property of the atomic nucleus. It determines its stability, and reaction and decay rates. Quantifying the nuclear binding is important for understanding the origin of elements in the universe. The astrophysical processes responsible for the nucleosynthesis in stars often take place far from the valley of stability, where experimental masses are not known. In such cases, missing nuclear information must be provided by theoretical predictions using extreme extrapolations. Bayesian machine learning techniques can be applied to improve predictions by taking full advantage of the information contained in the deviations between experimental and calculated masses. We consider 10 global models based on nuclear Density Functional Theory as well as two more phenomenological mass models. The emulators of S2n residuals and credibility intervals defining theoretical error bars are constructed using Bayesian Gaussian processes and Bayesian neural networks. We consider a large training dataset pertaining to nuclei whose masses were measured before 2003. For the testing datasets, we considered those exotic nuclei whose masses have been determined after 2003. We then carried out extrapolations towards the 2n dripline. While both Gaussian processes and Bayesian neural networks reduce the rms deviation from experiment significantly, GP offers a better and much more stable performance. The increase in the predictive power is quite astonishing: the resulting rms deviations from experiment on the testing dataset are similar to those of more phenomenological models. The empirical coverage probability curves we obtain match very well the reference values which is highly desirable to ensure honesty of uncertainty quantification, and the estimated credibility intervals on predictions make it possible to evaluate predictive power of individual models.

Machine learning is currently dominated by largely experimental work focused on improvements in a few key tasks. However, the impressive accuracy numbers of the best performing models are questionable because the same test sets have been used to select these models for multiple years now. To understand the danger of overfitting, we measure the accuracy of CIFAR-10 classifiers by creating a new test set of truly unseen images. Although we ensure that the new test set is as close to the original data distribution as possible, we find a large drop in accuracy (4% to 10%) for a broad range of deep learning models. Yet more recent models with higher original accuracy show a smaller drop and better overall performance, indicating that this drop is likely not due to overfitting based on adaptivity. Instead, we view our results as evidence that current accuracy numbers are brittle and susceptible to even minute natural variations in the data distribution.

Matt Pickett, Founder of the nonprofit, Oceans Unmanned, holds a DJI drone that will assist in the disentangling of humpback whales in Hawaii. In April 2018, the Washington Post reported that a sperm whale was found dead off the coast of Spain with 64 pounds of plastic debris in his stomach. Each fall, pods of endangered humpback whales numbering around 10,000, make their 3,000-mile journey towards Hawaii to winter over in the warm waters of the National Oceanic and Atmospheric Administration's (NOAA) Hawaiian Islands Humpback Whale National Marine Sanctuary. Along the way, these whales encounter man-made environmental hazards such as fishing gear lines and marine debris which become tangled around the whale's body and fins cutting into the whale's flesh and sometimes even dragging them down to the bottom of the sea where they drown. The work to untangle a 45-foot, 40-ton whale is dangerous to both the rescue team and the whale.

The U.S. Food and Drug Administration (FDA) has just approved an AI-based diagnostic tool that can accurately detect wrist fractures. Imagene's OsteoDetect uses machine learning algorithms to study 2D X-rays for the signs of wrist fractures. "Artificial intelligence algorithms have tremendous potential to help health care providers diagnose and treat medical conditions," said Robert Ochs, Ph.D., acting deputy director for radiological health, Office of In Vitro Diagnostics and Radiological Health in the FDA's Center for Devices and Radiological Health. "This software can help providers detect wrist fractures more quickly and aid in the diagnosis of fractures." OsteoDetect isn't about to replace doctors but it can help improve fracture detection and get the correct diagnosis and treatment quickly.

The Food and Drug Administration recently approved a software algorithm that helps doctors identify hand fractures in X-rays. On its own, it's not ground-breaking news, but it is a sign artificial intelligence in medicine is getting closer to making regular rounds. In addition to finding new health care veins to mine, AI developers also need operate within the business world of health care and meet basic requirements such as cost-effectiveness. On May 24, FDA gave the green light to market Imagen OsteoDetect, an AI algorithm that uses machine learning techniques to analyze wrist radiographs (X-ray images) to assist clinicians in locating areas of distal radius fracturing. It's designed to be used in a variety of settings, including primary care, emergency medicine, urgent care and specialized care such as orthopedics, FDA said.

The Lucid Robotic System is aimed at one of the central dilemmas of modern neurology: How to quickly identify patients with the most severe strokes who could benefit from being taken immediately to hospitals that can perform a complex clot-removal procedure, potentially helping to avoid major disability. The Lucid system, manufactured by Neural Analytics Inc. of Los Angeles, combines two technologies. One is a transcranial ultrasound, which shows whether a blood clot is blocking blood flow to the brain. The ultrasound, taken through a kind of natural window into the brain near a person's ear, is combined with the robotic part, which uses artificial intelligence to assess patients by instantaneously comparing them to thousands of earlier images of patients with severe strokes. "The goal is to have this in the ambulance soon," said Thomas G. Devlin, neurology chairman at the Erlanger Health System in Chattanooga.

In science news around the world, Congress approves a "right to try" bill giving patients with life-threatening illnesses a new means to obtain experimental treatments, a measure that President Donald Trump is expected to sign. A Swedish court blocks plans for a controversial new headquarters building for the Nobel Foundation on central Stockholm's waterfront, saying the design would harm the waterfront's historic character. NASA's Curiosity rover is once again sampling rocks after engineers found a new way to use a drill on its robotic arm that stopped operating properly in late 2016. The world's most sensitive dark matter detector fails to snare its quarry during a year of observations. A new package of instruments arrives at the International Space Station to help scientists study record-low temperatures and look for novel quantum effects.