The evolution of unmanned aerial vehicles (UAVs), more commonly known as drones, is developing at a rapid pace. Not only is the technology progressing, but regulations are being adapted to encourage wider adoption. With the new FAA Part 107 Rules in the USA, users no longer need to have a commercial pilot license to operate a drone and in the UK, the National Air Traffic Control Service (NATS) is laying the foundation for drones to fly beyond their operators' line of sight – due to the development of new technology that can track small unmanned devices at low altitude. The release of applications is also starting to complement a wider variety of industries, inspiring further implementation. UAVs have come a long way since the Kettering Bug, a drone developed during the First World War.

Cluster analysis is a fundamental tool for pattern discovery of complex heterogeneous data. Prevalent clustering methods mainly focus on vector or matrix-variate data and are not applicable to general-order tensors, which arise frequently in modern scientific and business applications. Moreover, there is a gap between statistical guarantees and computational efficiency for existing tensor clustering solutions due to the nature of their non-convex formulations. In this work, we bridge this gap by developing a provable convex formulation of tensor co-clustering. Our convex co-clustering (CoCo) estimator enjoys stability guarantees and is both computationally and storage efficient. We further establish a non-asymptotic error bound for the CoCo estimator, which reveals a surprising "blessing of dimensionality" phenomenon that does not exist in vector or matrix-variate cluster analysis. Our theoretical findings are supported by extensive simulated studies. Finally, we apply the CoCo estimator to the cluster analysis of advertisement click tensor data from a major online company. Our clustering results provide meaningful business insights to improve advertising effectiveness.

Injecting adversarial examples during training, known as adversarial training, can improve robustness against one-step attacks, but not for unknown iterative attacks. To address this challenge, we first show iteratively generated adversarial images easily transfer between networks trained with the same strategy. Inspired by this observation, we propose cascade adversarial training, which transfers the knowledge of the end results of adversarial training. We train a network from scratch by injecting iteratively generated adversarial images crafted from already defended networks in addition to one-step adversarial images from the network being trained. We also propose to utilize embedding space for both classification and low-level (pixel-level) similarity learning to ignore unknown pixel level perturbation. During training, we inject adversarial images without replacing their corresponding clean images and penalize the distance between the two embeddings (clean and adversarial). Experimental results show that cascade adversarial training together with our proposed low-level similarity learning efficiently enhances the robustness against iterative attacks, but at the expense of decreased robustness against one-step attacks. We show that combining those two techniques can also improve robustness under the worst case black box attack scenario.

In February Olajide KSI Olatunji, a British YouTuber with more than 18.5 million followers managed to attract more viewers to a white collar boxing match between two amateur fighters than watched the FA Cup final. Now, a date and location for the sequel to that fight, due to be contested by two of online video's biggest stars has been released – though the future of the fight itself is in jeopardy. Twenty four-year-old KSI has been engaged in a war of words with Logan Paul, the 22-year-old YouTuber famous for uploading a video of a suicide victim to YouTube, triggering a temporary ban from making money on the site, since calling out the American after defeating Joe Weller, another YouTuber, in front of a sold-out crowd at the Copper Box Arena back in February. In tweets sent overnight on Thursday between the two YouTubers, who have more than 35 million subscribers between them, the pair revealed key details about their mooted meeting. The bout, due to be the first of two between the YouTubers, has been pencilled in for 25 August, a bank holiday weekend.

Data science is transforming many industries, from health care to banking to heavy manufacturing, and women are leading the charge. That was the crux of the Cambridge Women in Data Science Conference, held March 5 as part of a global event launched by Stanford University in 2015 to educate, inspire, and connect women in tech. The local conference, now in its second year, was hosted by the Institute of Applied Computational Science (IACS) at the Harvard John A. Paulson School of Engineering and Applied Sciences; the MIT Institute for Data, Systems, and Society; and Microsoft. Distinguished speakers from academia and industry presented technical talks to more than 240 female technologists, researchers, and students, highlighting research in such areas as deep learning applications in oncology, data science tools for pollution monitoring, and the challenges of preventing bias in algorithms. In addition, local winners of an international datathon/kaggle challenge, held in conjunction with Stanford's global conference were announced, and students presented posters and took advantage of networking and recruiting opportunities.

A new lightweight, low-cost agricultural robot could transform data collection and field scouting for agronomists, seed companies and farmers. The TerraSentia crop phenotyping robot, developed by a team of scientists at the University of Illinois, will be featured at the 2018 Energy Innovation Summit Technology Showcase in National Harbor, Maryland, on March 14. Traveling autonomously between crop rows, the robot measures the traits of individual plants using a variety of sensors, including cameras, transmitting the data in real time to the operator's phone or laptop computer. A custom app and tablet computer that come with the robot enable the operator to steer the robot using virtual reality and GPS. TerraSentia is customizable and teachable, according to the researchers, who currently are developing machine-learning algorithms to "teach" the robot to detect and identify common diseases, and to measure a growing variety of traits, such as plant and corn ear height, leaf area index and biomass.

China isn't just investing heavily in AI--its experts aim to set the global standards for the technology as well. Academics, industry researchers, and government experts gathered in Beijing last November to discuss AI policy issues. The resulting document, published in Chinese recently, shows that the country's experts are thinking in detail about the technology's potential impact. Together with the Chinese government's strategic plan for AI, it also suggests that China plans to play a role in setting technical standards for AI going forward. Chinese companies would be required to adhere to these standards, and as the technology spreads globally, this could help China influence the technology's course.

Senvol, a 3D printing data specialist based in New York City, is developing data-driven machine learning additive manufacturing (AM) software for the U.S. Navy's Office of Naval Research (ONR). The software will help the Navy cut out the process of trial and error during material development. New York's Senvol creates additive manufacturing software that analyzes the relationships between AM process parameters and material performance. With this software to hand, ONR will be able to develop what Senvol describes as "statistically substantiated material properties" in order to reduce the conventional material characterization and testing that is needed to develop design allowables (the statistically determined material property values ascertained from test data). "Our software's capabilities will allow ONR to select the appropriate process parameters on a particular additive manufacturing machine given a target mechanical performance," commented Senvol President Annie Wang.

General Motors said on Thursday it will invest more than $100 million in two facilities as it prepares to build production versions of its Cruise self-driving car next year at its Orion Township assembly plant in Michigan. The largest U.S. automaker also said roof modules for GM's self-driving vehicles will be assembled at its Brownstown Battery Assembly plant. In January, GM filed a petition seeking U.S. government approval for a fully autonomous car – one without a steering wheel, brake pedal or accelerator pedal – to enter the automaker's first commercial ride-sharing fleet in 2019. The U.S. National Highway Traffic Safety Administration (NHTSA) has been reviewing the petition for more than two months and has not yet deemed it "complete" – a step before it releases details of the proposal. A final decision might not come until later this year or 2019.

In 1959, Arthur Samuel, a pioneer in machine learning, defined it as the'field of study that gives computers the ability to learn without being explicitly programmed'. Machine learning can translate to using algorithms to parse through data, recognise patterns, and then make predictions and assessments based on what the algorithms have learnt. Machine learning can be used for fact checking and it can make archiving less of a tedious task for journalists. It can let voice assistants like Alexa or Google Assistant know you're pissed off based on the tone of your voice on a Monday morning and then play a song to cheer you up. It can also be used to explore scenes in Wes Anderson films and help uncover hidden spy planes. In short, machine learning systems could very well become essential journalism tools in the coming years.