While many existing graph neural networks (GNNs) have been proven to perform $\ell_2$-based graph smoothing that enforces smoothness globally, in this work we aim to further enhance the local smoothness adaptivity of GNNs via $\ell_1$-based graph smoothing. As a result, we introduce a family of GNNs (Elastic GNNs) based on $\ell_1$ and $\ell_2$-based graph smoothing. In particular, we propose a novel and general message passing scheme into GNNs. This message passing algorithm is not only friendly to back-propagation training but also achieves the desired smoothing properties with a theoretical convergence guarantee. Experiments on semi-supervised learning tasks demonstrate that the proposed Elastic GNNs obtain better adaptivity on benchmark datasets and are significantly robust to graph adversarial attacks. The implementation of Elastic GNNs is available at \url{https://github.com/lxiaorui/ElasticGNN}.

Understanding and representing traffic patterns are key to detecting anomalies in the maritime domain. To this end, we propose a novel graph-based traffic representation and association scheme to cluster trajectories of vessels using automatic identification system (AIS) data. We utilize the (un)clustered data to train a recurrent neural network (RNN)-based evidential regression model, which can predict a vessel's trajectory at future timesteps with its corresponding prediction uncertainty. This paper proposes the usage of a deep learning (DL)-based uncertainty estimation in detecting maritime anomalies, such as unusual vessel maneuvering. Furthermore, we utilize the evidential deep learning classifiers to detect unusual turns of vessels and the loss of AIS signal using predicted class probabilities with associated uncertainties. Our experimental results suggest that using graph-based clustered data improves the ability of the DL models to learn the temporal-spatial correlation of data and associated uncertainties. Using different AIS datasets and experiments, we demonstrate that the estimated prediction uncertainty yields fundamental information for the detection of traffic anomalies in the maritime and, possibly in other domains.

An AI-controlled fighter jet will battle a US Air Force pilot in a simulated dogfight next week -- and you can watch the action online. The clash is the culmination of DARPA's AlphaDogfight competition, which the Pentagon's "mad science" wing launched to increase trust in AI-assisted combat. DARPA hopes this will raise support for using algorithms in simpler aerial operations, so pilots can focus on more challenging tasks, such as organizing teams of unmanned aircraft across the battlespace. The three-day event was scheduled to take place in-person in Las Vegas from August 18-20, but the COVID-19 pandemic led DARPA to move the event online. Attend the tech festival of the year and get your super early bird ticket now!

The advent of technology has revolutionized how the world functions. People and businesses alike have adopted modern technology to enhance their prospects and lifestyle. The business that is yet to adopt technology in its functions does not get favor from customers. Hence, they are overtaken by their competitors who have already adopted it. Advanced technologies like IoT (Internet of Things), Machine Learning, Artificial Intelligence, etc. have managed to overpower outdated systems. However, it is the current Covid-19 pandemic that has compelled almost all businesses to go online and adopt the top technology trends.

In the late 1970s I lived and worked briefly in the Netherlands. Often, on Sundays, I would travel to Amsterdam, go to the morning concert in the Spiegelzaal of the Concertgebouw, and afterwards walk over to the Rijksmuseum, Holland's national gallery, and spend a couple of hours there. The museum is a wonderful storehouse of Dutch art and there was always much to explore. But on nearly every visit I found myself being drawn back to one of Rembrandt's most famous pictures – The Night Watch – which I guess is to the Rijksmuseum what the Mona Lisa is to the Louvre. Its official title is Militia Company of District II under the Command of Captain Frans Banninck Cocq.

People who think about the long-term existential risks of artificial intelligence sometimes discuss the notion of an "AI box." To prevent a superintelligent computer from starting a nuclear war or otherwise wreaking havoc, its minders would seal it off from direct interaction with the outside world by keeping it offline. The only output would be communication with its operators. But, people worry, it might still escape, not through hacking but through "social engineering"--manipulating someone into setting it free. Such a scenario dramatically played out in the 2014 sci-fi thriller Ex Machina, in which a wily imprisoned robot seduces a hapless human into helping it break out.

The recent emergence of U.S. Navy videos of UFOs--and the fact that the government is addressing them seriously--will no doubt generate larger than average buzz around "Roswell: The Final Verdict," although the title suggests something like "Final Destination 6": Will the question of intergalactic life ever really be resolved until extraterrestrials can walk comfortably among us? "Final Verdict" is hooked to the 74th anniversary of the incidents at Roswell. It's safe to expect similar celebrations next year. Meanwhile, this Discovery production is an ambitious if somewhat overheated summing-up of what happened near the New Mexico city in 1947, the stuff of both scientific speculation and folklore: Did the government cover up the crash landing of an alien spaceship, replete with otherworldly visitors? Or did the "witnesses" who claimed that it all happened construct an elaborate hoax?

Four years ago, organizers created the international AI City Challenge to spur the development of artificial intelligence for real-world scenarios like counting cars traveling through intersections or spotting accidents on freeways. In the first years, teams representing American companies or universities took top spots in the competition. Last year, Chinese companies won three out of four competitions. Last week, Chinese tech giants Alibaba and Baidu swept the AI City Challenge, beating competitors from nearly 40 nations. Chinese companies or universities took first and second place in all five categories.

To help managers ensure accountability and responsible use of artificial intelligence (AI) in government programs and processes, GAO developed an AI accountability framework. This framework is organized around four complementary principles, which address governance, data, performance, and monitoring. For each principle, the framework describes key practices for federal agencies and other entities that are considering, selecting, and implementing AI systems. Each practice includes a set of questions for entities, auditors, and third-party assessors to consider, as well as procedures for auditors and third- party assessors. AI is a transformative technology with applications in medicine, agriculture, manufacturing, transportation, defense, and many other areas. It also holds substantial promise for improving government operations.

Anyone working in data management and data science can attest to the challenge and time-consuming nature of mapping a set of data from a new source into a platform where it can be cleaned, validated, and ultimately analyzed and used to train algorithms. After all, your algorithms are only as good as the data used to train them. Now imagine if these data sets are coming from hundreds of external users who have employed any number of systems to collect this data, from Excel files to actual shoeboxes full of photos. That is the challenge that non-profit wildlife conservation machine learning and artificial intelligence service provider Wild Me has faced over its more than a decade of operation. The organization builds open software and AI for the conservation research community.