Congress signaled its confidence in the Pentagon's young artificial intelligence office through a series of measures to increases its standing in the agency, including giving its director acquisition authority. The annual defense policy bill, called the fiscal 2021 National Defense Authorization Act, would alter the reporting structure of the Joint Artificial Intelligence Center, raising the office to report directly to the deputy secretary of defense, instead of the department's chief information officer. The bill, which still needs President Donald Trump's approval, establishes a board of advisers to give the center strategic advice and technical expertise on AI matters. The measures to bolster the importance of the Joint Artificial Intelligence Center come as the organization pivots from focusing on artificial intelligence projects to identifying and solving problems within the services using AI. The JAIC was established in 2018 to increase the adoption of AI across the Pentagon.

The mind of an experienced and dedicated cyber-criminal works like that of an entrepreneur: the relentless pursuit of profit guides every move they make. At each step of an attack, the same questions are asked: how can I minimise my time and resources? How can I mitigate against risk? What measures can I take which will return the best results? This way of thinking uncovers why attackers are turning to new technology in an attempt to maximise efficiency, and why a report from Forrester earlier this year revealed that 88 per cent of security leaders now consider the malicious use of AI in cyber-activity to be inevitable.

New Delhi: Iran on Sunday said that a satellite-controlled machine gun with "artificial intelligence" was used to kill its top nuclear scientist Mohsen Fakhrizadeh near the capital Tehran on November 27. The scientist was driving on a highway with a security detail of 11 Guards, when the machine gun "zoomed in" on his face and fired 13 rounds. Addressing a commemoration ceremony on Sunday for the scientist, Iran's deputy commander of the Revolutionary Guards (IRGC) Ali Fadavi told local media that the machine gun was mounted on a Nissan pickup and "focused only on martyr Fakhrizadeh's face in a way that his wife, despite being only 25 centimetres (10 inches) away, was not shot." He added, "It was being controlled online via a satellite and used an advanced camera and artificial intelligence to make the target. Fadavi also said that Fakhrizadeh's head of security took four bullets "as he threw himself" on the scientist and that there were "no terrorists at the scene".

South Tees Hospitals NHS Foundation Trust has become one of only three NHS trusts to have a team of surgical robots. Surgical teams at The James Cook University Hospital now have three da Vinci robots, allowing them to treat patients with minimally invasive procedures. The trust says it now has the potential to become a national centre of excellence. The three robots will be used across five specialities at the hospital including urology; thoratic services; gynaecology; general surgery; and ear, nose and throat services. James Cook currently provides robotic surgery to about 380 patients a year, but the expansion programme is expected to double that number.

Artificial intelligence tasks across numerous applications require accelerators for fast and low-power execution. Optical computing systems may be able to meet these domain-specific needs but, despite half a century of research, general-purpose optical computing systems have yet to mature into a practical technology. Artificial intelligence inference, however, especially for visual computing applications, may offer opportunities for inference based on optical and photonic systems. In this Perspective, we review recent work on optical computing for artificial intelligence applications and discuss its promise and challenges. Recent work on optical computing for artificial intelligence applications is reviewed and the potential and challenges of all-optical and hybrid optical networks are discussed.

Japan is seeking to boost its flagging birth rate by funding the use of artificial intelligence to help match lonely hearts, an official said Monday.Although it might not conjure thoughts of romance, AI tech can match a wider and smarter range of potential suitors, the Cabinet official said.Prime Minister Yoshihide Suga's government plans to allocate ¥2 billion ($19 million) in the next fiscal year to back local authorities that run programs to help their residents find love, he said.Around half of the nation's 47 prefectures offer matchmaking services and some of them have already introduced AI systems, according to the Cabinet Office.The human-run matchmaking services often use standardized forms to list people's interests and hobbies, and AI systems can perform more advanced analysis of this data."We

Clustering is a ubiquitous problem in data science and signal processing. In many applications where we observe noisy signals, it is common practice to first denoise the data, perhaps using wavelet denoising, and then to apply a clustering algorithm. In this paper, we develop a sparse convex wavelet clustering approach that simultaneously denoises and discovers groups. Our approach utilizes convex fusion penalties to achieve agglomeration and group-sparse penalties to denoise through sparsity in the wavelet domain. In contrast to common practice which denoises then clusters, our method is a unified, convex approach that performs both simultaneously. Our method yields denoised (wavelet-sparse) cluster centroids that both improve interpretability and data compression. We demonstrate our method on synthetic examples and in an application to NMR spectroscopy.

In 2016, the Defense Advanced Research Projects Agency (DARPA) hosted the Cyber Grand Challenge (Song & Alves-Foss, 2015), a competition which invited participating finalist teams to develop automated cyber defense systems that can self-discover, prove, and correct software vulnerabilities at real-time - without human intervention. For the first time, the world witnessed hackers being automated at scale, i.e. cyber autonomy (Brumley, 2018). As the competition progressed, the systems were not only able to auto-detect and correct their software, but also able to attack other systems (other participants' machines) in the network. Even though the competition did not catch much mainstream media attention, the DARPA Cyber Grand Challenge proved the feasibility of cyber autonomy, stretched the imagination of the national and cyber security industries and created a mix of perceptions ranging from hope to fear - the hope of increasingly secure computing systems at scale, and the fear of current jobs such as penetration testing being automated.

Managing inputs that are novel, unknown, or out-of-distribution is critical as an agent moves from the lab to the open world. Novelty-related problems include being tolerant to novel perturbations of the normal input, detecting when the input includes novel items, and adapting to novel inputs. While significant research has been undertaken in these areas, a noticeable gap exists in the lack of a formalized definition of novelty that transcends problem domains. As a team of researchers spanning multiple research groups and different domains, we have seen, first hand, the difficulties that arise from ill-specified novelty problems, as well as inconsistent definitions and terminology. Therefore, we present the first unified framework for formal theories of novelty and use the framework to formally define a family of novelty types. Our framework can be applied across a wide range of domains, from symbolic AI to reinforcement learning, and beyond to open world image recognition. Thus, it can be used to help kick-start new research efforts and accelerate ongoing work on these important novelty-related problems. This extended version of our AAAI 2021 paper included more details and examples in multiple domains.

Downscaling is necessary to generate high-resolution observation data to validate the climate model forecast or monitor rainfall at the micro-regional level operationally. Dynamical and statistical downscaling models are often used to get information at high-resolution gridded data over larger domains. As rainfall variability is dependent on the complex Spatio-temporal process leading to non-linear or chaotic Spatio-temporal variations, no single downscaling method can be considered efficient enough. In data with complex topographies, quasi-periodicities, and non-linearities, deep Learning (DL) based methods provide an efficient solution in downscaling rainfall data for regional climate forecasting and real-time rainfall observation data at high spatial resolutions. In this work, we employed three deep learning-based algorithms derived from the super-resolution convolutional neural network (SRCNN) methods, to precipitation data, in particular, IMD and TRMM data to produce 4x-times high-resolution downscaled rainfall data during the summer monsoon season. Among the three algorithms, namely SRCNN, stacked SRCNN, and DeepSD, employed here, the best spatial distribution of rainfall amplitude and minimum root-mean-square error is produced by DeepSD based downscaling. Hence, the use of the DeepSD algorithm is advocated for future use. We found that spatial discontinuity in amplitude and intensity rainfall patterns is the main obstacle in the downscaling of precipitation. Furthermore, we applied these methods for model data postprocessing, in particular, ERA5 data. Downscaled ERA5 rainfall data show a much better distribution of spatial covariance and temporal variance when compared with observation.