This footage shows an Israeli drone defense system shooting a series of moving targets out of the sky with a Star Wars-like laser beam. Manufacturers of the Drone Dome C-UAS hailed the system a '100 per cent success' after it brought down drones that were zig-zagging sharply during tests. The so-called Counter Unmanned Aircraft System can detect hostile drones from more than two miles away and fire a destructive laser beam'within seconds'. In this video published by defence manufacturer Rafael, three drones (circled) are flying in formation over the desert but are brought down by the Drone Dome's laser beam The footage published by Rafael, the defense manufacturer which is based in Haifa, showed drones falling out of the sky after they were intercepted by the Drone Dome. One target drone was hit and destroyed despite veering sharply from left to right as it flew over the desert.

NEW DELHI: The government will soon come up with a national policy to reskill and upskill millions of youth in the country to create a workforce capable of handling emerging trends such as artificial intelligence (AI), internet of things (IoT) and machine learning. The idea is to strengthen the government's Skill India mission through dedicated policy measures. "Reskilling and upskilling is big on the incoming government's agenda," a senior government official told ET. "There will be renewed focus on reskilling." The idea is to create a workforce that can access new opportunities and to insulate it from technological shocks. "We would like to ensure that individuals have access to economic opportunities by remaining competitive in the new world of work and that businesses have access to the talent they need for the jobs of the future," the official added.

Last Tuesday, 14th January 2020, was a big day for the Norwegian IT sector as the government's national strategy for artificial intelligence was presented at a breakfast meeting at MESH, central Oslo. Over 160 people from business, academia and the public sector participated in the launch, as well as many who followed the event online. The presented strategy claims to serve as a framework for both public and private sectors that aim to develop and use artificial intelligence, especially in areas where Norway already is greatly positioned and has strong foundations, such as in health, oil and gas, energy, and marine industry. Regarding the current digital development, we see many countries have high ambitions where one worth mentioning is the UK. Their AI strategy was initiated in 2017, which has by 2019 opened 16 New Centres for Doctoral Training in AI at universities across the country, industry funding for new Masters positions and numerous governmental funded scholar-ships.

A curated list of amazingly awesome tools and resources related to the use of machine learning for cyber security. Please read CONTRIBUTING if you wish to add tools or resources. This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International license.

The US Air Force Life Cycle Management Center has awarded L3Harris Technologies a multimillion-dollar contract to develop a software platform to make it easier for analysts to use artificial intelligence (AI) to identify objects in large data sets, the company announced on 13 February. The US military and intelligence community are inundated with massive amounts of data generated by remote sensing systems. Automated searches using algorithms that can identify pre-loaded images of objects makes pinpointing them easier. However, in order to train these algorithms, real images are often unavailable, because they are either rare or do not exist. The L3Harris tool creates sample images used to train search algorithms to identify hard-to-find objects in the data, which will help make it easier for the community to adopt AI. "L3Harris is a premier provider of modelling and simulation capabilities that provide risk reduction for our customers who rely on advanced geospatial systems and data," commented Ed Zoiss, President, Space and Airborne Systems, L3Harris.

Two Democratic U.S. Senators today proposed legislation that requires a moratorium on facial recognition use by federal agencies, government employees, and law enforcement until a Congressional commission can act to recommend guidelines and place limits on use of the technology. The bill, named the Ethical Use of Artificial Intelligence Act, is being introduced today by Senators Cory Booker (D-NJ) and Jeff Merkley (D-OR). It would establish a 13-member Congressional Commission made up of people appointed by the president, members of Congress, federal immigration and law enforcement officers, and privacy and tech experts. Six committee members come from communities most impacted by use of facial recognition. That Commission's goal would be to ensure facial recognition does not produce bias or inaccurate results, stand in the way of law enforcement's efforts to identify missing and exploited children, or "create a constant state of surveillance of individuals in the United States that does not allow for a level of reasonable anonymity."

NASA Jet Propulsion Laboratory's (JPL) Mars 2020 rover is heading to the Red Planet armed with a high-powered laser to assist in its search for fossils. The technology, called SuperCam, is fitted at the robot's mast and shoots pulses capable of vaporizing rocks from up to 20 feet away. The laser beam heats the target to 18,000 degrees Fahrenheit, which is hot enough to transform the solid rock into plasma that can be imaged by a camera for further analysis. Using this instrument will help researchers identify minerals that are beyond the reach of the rover's robotic arm or in areas too steep for the rover to go. NASA Jet Propulsion Laboratory's (JPL) Mars 2020 rover is heading to the Red Planet armed with a high-powered laser to assist in its search for fossils. The technology, called SuperCam, is fitted at the robot's mast and shoots pulses capable of vaporizing rocks from up to 20 feet away NASA is set to launch the Mars 2020 rover in July with the goal of finding signs of ancient microbial life.

The success of Convolutional Neural Networks (CNNs) in image classification has prompted efforts to study their use for classifying image data obtained in Particle Physics experiments. Here, we discuss our efforts to apply CNNs to 2D and 3D image data from particle physics experiments to classify signal from background. In this work we present an extensive convolutional neural architecture search, achieving high accuracy for signal/background discrimination for a HEP classification use-case based on simulated data from the Ice Cube neutrino observatory and an ATLAS-like detector. We demonstrate among other things that we can achieve the same accuracy as complex ResNet architectures with CNNs with less parameters, and present comparisons of computational requirements, training and inference times.

Audio processing models based on deep neural networks are susceptible to adversarial attacks even when the adversarial audio waveform is 99.9% similar to a benign sample. Given the wide application of DNN-based audio recognition systems, detecting the presence of adversarial examples is of high practical relevance. By applying anomalous pattern detection techniques in the activation space of these models, we show that 2 of the recent and current state-of-the-art adversarial attacks on audio processing systems systematically lead to higher-than-expected activation at some subset of nodes and we can detect these with up to an AUC of 0.98 with no degradation in performance on benign samples.

We consider a wireless communication system that consists of a transmitter, a receiver, and an adversary. The transmitter transmits signals with different modulation types, while the receiver classifies its received signals to modulation types using a deep learning-based classifier. In the meantime, the adversary makes over-the-air transmissions that are received as superimposed with the transmitter's signals to fool the classifier at the receiver into making errors. While this evasion attack has received growing interest recently, the channel effects from the adversary to the receiver have been ignored so far such that the previous attack mechanisms cannot be applied under realistic channel effects. In this paper, we present how to launch a realistic evasion attack by considering channels from the adversary to the receiver. Our results show that modulation classification is vulnerable to an adversarial attack over a wireless channel that is modeled as Rayleigh fading with path loss and shadowing. We present various adversarial attacks with respect to availability of information about channel, transmitter input, and classifier architecture. First, we present two types of adversarial attacks, namely a targeted attack (with minimum power) and non-targeted attack that aims to change the classification to a target label or to any other label other than the true label, respectively. Both are white-box attacks that are transmitter input-specific and use channel information. Then we introduce an algorithm to generate adversarial attacks using limited channel information where the adversary only knows the channel distribution. Finally, we present a black-box universal adversarial perturbation (UAP) attack where the adversary has limited knowledge about both channel and transmitter input.