The long-awaited sequel to Top Gun is due to hit cinemas in December, but the virtuoso fighter pilots at its heart could soon be a thing of the past. The trustworthy wingman will soon be replaced by artificial intelligence, built into a drone, or an existing fighter jet with no one in the cockpit. Since 2010, the US Air Force and Boeing's QF-16 programme has been converting old F-16 fighter jets into unmanned drones, which can fly preset routes without a pilot. This year, 32 of these autonomous planes – rescued from retirement in the "boneyard" at an Air Force base near Arizona – will be used as targets in weapons testing over the Gulf of Mexico. In the future, self-flying fighter jets such as these could transform aerial combat. The Air Force's Skyborg programme, which could be in operation as soon as 2023, is developing AI systems for its unmanned Valkyrie drones which would enable them to communicate with and operate in tandem with a manned F-35 jet.

The U.S. is using every tool at its disposal to defeat the novel coronavirus, including artificial intelligence. American laboratories are harnessing AI to discover new therapeutics. The Food and Drug Administration approved an AI tool to help detect coronavirus in CT scans. And the White House led an initiative to create a database with more than 128,000 articles that scientists can analyze using AI to help understand the virus better and develop treatments. At the same time, AI is being twisted by authoritarian regimes to violate rights. The Chinese Communist Party is reportedly using AI to uncover and punish those who criticize the regime's pandemic response and to institute a type of coronavirus social-credit score--assigning people color codes to determine who is free to go out and who will be forced into quarantine.

As lawmakers in Brasilia debated a controversial pension overhaul for months, a robot more than 5,000 miles away in London kept a close eye on all 513 of them. The algorithm, designed by technology startup Arkera Inc., tracked their comments in Brazilian newspapers and government web pages each day to predict the likelihood the bill would pass. Weeks before the legislation cleared its biggest obstacle in July, the machine's data crunching allowed Arkera analysts to predict the result almost to the letter, giving hedge fund clients in New York and London the insight to buy the Brazilian real near eight-month lows in May. It's since rallied more than 8%. This is the kind of edge that a new generation of researchers are betting will upend the research marketplace.

In times of crisis, we all want to know where the robots are! And young roboticists just starting their careers, or simply thinking about robotics as a career, ask us'How can robotics help?' and'What can I do to help?'. Cluster organizations like Silicon Valley Robotics can serve as connection points between industry and academia, between undergrads and experts, between startups and investors, which is why we rapidly organized a weekly discussion with experts about "COVID-19, robots and us" (video playlist). During our online series, we heard from roboticists directly helping with all sorts of COVID-19 response, like Gui Cavalcanti of Open Source Medical Supplies and Alder Riley of Helpful Engineering. Both groups are great examples of the incredible power of people working together.

In August this year, the Wall Street Journal reported on an unusual case of AI being used in hacking. Using AI, the attackers were able to impersonate a CEO's voice convincingly enough to compel a colleague to transfer nearly a quarter of a million dollars into their accounts. As unusual as it may seem, this "vishing" or voice phishing attack – a more sophisticated take on conventional phishing scams – wasn't exactly unprecedented. Vishing attacks have grown nearly 350% since 2013 and recent predictions indicate that the phenomenon will only become more commonplace in 2020 and more sophisticated as ML/AI technologies mature. Emerging AI-enabled cyberthreats such as vishing represent a new challenge for cybersecurity analysts and CISOs.

Boston will become the second largest city in the US to ban facial recognition software for government use after a unanimous city council vote. Following San Francisco, which banned facial recognition in 2019, Boston will bar city officials from using facial recognition systems. The ordinance will also bar them from working with any third party companies or organizations to acquire information gathered through facial recognition software. The ordinance was co-sponsored by Councilors Ricardo Arroyo and Michelle Wu, who were especially concerned about the potential for racial bias in the technology, according to a report from WBUR. 'Boston should not be using racially discriminatory technology and technology that threatens our basic rights,' Wu said at a hearing before the vote.

As AI models and services are used in a growing number of highstakes areas, a consensus is forming around the need for a clearer record of how these models and services are developed to increase trust. Several proposals for higher quality and more consistent AI documentation have emerged to address ethical and legal concerns and general social impacts of such systems. However, there is little published work on how to create this documentation. This is the first work to describe a methodology for creating the form of AI documentation we call FactSheets. We have used this methodology to create useful FactSheets for nearly two dozen models. This paper describes this methodology and shares the insights we have gathered. Within each step of the methodology, we describe the issues to consider and the questions to explore with the relevant people in an organization who will be creating and consuming the AI facts in a FactSheet. This methodology will accelerate the broader adoption of transparent AI documentation.

Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs. However, recent studies show that GNNs are vulnerable to carefully-crafted perturbations, called adversarial attacks. Adversarial attacks can easily fool GNNs in making predictions for downstream tasks. The vulnerability to adversarial attacks has raised increasing concerns for applying GNNs in safety-critical applications. Therefore, developing robust algorithms to defend adversarial attacks is of great significance. A natural idea to defend adversarial attacks is to clean the perturbed graph. It is evident that real-world graphs share some intrinsic properties. For example, many real-world graphs are low-rank and sparse, and the features of two adjacent nodes tend to be similar. In fact, we find that adversarial attacks are likely to violate these graph properties. Therefore, in this paper, we explore these properties to defend adversarial attacks on graphs. In particular, we propose a general framework Pro-GNN, which can jointly learn a structural graph and a robust graph neural network model from the perturbed graph guided by these properties. Extensive experiments on real-world graphs demonstrate that the proposed framework achieves significantly better performance compared with the state-of-the-art defense methods, even when the graph is heavily perturbed. We release the implementation of Pro-GNN to our DeepRobust repository for adversarial attacks and defenses (footnote: https://github.com/DSE-MSU/DeepRobust). The specific experimental settings to reproduce our results can be found in https://github.com/ChandlerBang/Pro-GNN.

Twitter users had some fun on Friday upon seeing that tweets that contained the words "frequency" and "oxygen" were automatically slapped with a coronavirus fact-check label. The tech giant has been cracking down in recent months on tweets it perceives as spreading misinformation, most notably the fact-check it had placed on President Trump's tweets on mail-in voting. However, Twitter raised eyebrows when it labeled any tweet that had the two words "frequency" and "oxygen" with label that read "Get the facts about COVID-19," which takes users to a page from May 11 addressing a conspiracy theory that 5G technology was responsible for the spread of the virus. Many took the opportunity to get creative with tweets that prompted the automatic labeling. NEWSWEEK MOCKED FOR CLAIMING CONSERVATIVES ARE'WEAPONIZING' CANCEL CULTURE TO'TAME ANTI-TRUMP CELEBRITIES' "This is a fun new meme," journalist Tim Pool began.

NASA is asking for your help to guide its Curiosity rover around sand traps, sharp rocks and other obstacles on the Red Planet. A new online tool called AI4Mars, hosted on Zooniverse, allows anyone to label parts of the terrain in the landscape surrounding Curiosity, which has been roving on Mars since 2012. The tool is a form of "machine learning" that allows rover planners assisting with Curiosity's movements to train the rover's intelligence for safe route planning. Picking an appropriate pathway is a pressing problem for Martian rovers. Curiosity's wheels wore down in the early years of its mission from driving over sharp rocks, while another Mars rover called Spirit got permanently stuck in a sand trap in 2010.