The United States and the European Union already restrict their exports of defense-related electronics to Russia, and have toughened those rules in recent years. Yet Russian networks have found ways around those obstacles. In 2015, several Russian agents were convicted of, or pleaded guilty to, federal charges of using a Texas-based company they set up to illegally export high-tech chips to Russian military and intelligence agencies.

Biases in artificial intelligence will aim to be eradicated in a world first as the NHS in England trials a new approach to the ethical adoption of AI in healthcare. AIAs designed by the Ada Lovelace Institute will be piloted to support researchers and developers to assess the possible risks and biases of AI systems to patients and the public before they can access NHS data. While artificial intelligence has the potential to support health and care workers to deliver better care for people, it could also exacerbate existing health inequalities if concerns such as algorithmic bias aren't accounted for. Innovation minister Lord Kamall said: "While AI has great potential to transform health and care services, we must tackle biases which have the potential to do further harm to some populations as part of our mission to eradicate health disparities. "This pilot once again demonstrates the UK is at the forefront of adopting new technologies in a way that is ethical and patient-centred.

"AI is performing well in back-end processing of security events, allowing for automation and speed of use-case development," says Doug Saylors, partner and cybersecurity co-lead with global technology research and advisory firm ISG. This is crucial because "in modern environments, ephemeral cloud assets turn on and off in minutes, work-from-home devices are hidden from view, and data centers are full of dusty corners," says Rosiek. "In what's being called XDR, AI/ML is just another tool in the toolbox to find anomalies -- methods of attack that aren't caught by traditional defense-in-depth technologies," says Patrick Orzechowski, vice president and distinguished engineer at managed cybersecurity vendor Deepwatch. "In cybersecurity, this is best reflected in areas such as intrusion detection and network monitoring -- it's fairly safe for administrators to allow AI to discover activity that is an outlier and may be malicious in these cases" says Sean O'Brien, founder and lead researcher at Privacy Lab at Yale and CSO at privacy-focused chat company Panquake. Cyber AI is "very hard," warns Aaron Sant-Miller, chief data scientist at consulting firm Booz Allen Hamilton, but it is key to building effective defenses.

Though we're living through a time of extraordinary innovation in GPU-accelerated machine learning, the latest research papers frequently (and prominently) feature algorithms that are decades, in certain cases 70 years old. Some might contend that many of these older methods fall into the camp of'statistical analysis' rather than machine learning, and prefer to date the advent of the sector back only so far as 1957, with the invention of the Perceptron. Given the extent to which these older algorithms support and are enmeshed in the latest trends and headline-grabbing developments in machine learning, it's a contestable stance. So let's take a look at some of the'classic' building blocks underpinning the latest innovations, as well as some newer entries that are making an early bid for the AI hall of fame. In 2017 Google Research led a research collaboration culminating in the paper Attention Is All You Need.

The presence of artificial intelligence in the federal workforce is poised to expand, with officials emphasizing the human component behind automation and machine learning technologies. Officials including Gil Alterovitz, the Veterans' Affairs National Artificial Intelligence Institute director, and Martin Stanley, the branch chief of Strategic Technology at the Cybersecurity and Infrastructure Security Agency, spoke during a Thursday panel and discussed digitization within their respective agencies. Alterovitz said that VA leadership has opened up new data scientist positions to serve as subject matter experts across the government. "We've been working toward building pathways toward developing and assessing that AI knowledge," he said. "We're working with a number of other agencies and really the idea there is to build that pipeline of talent with AI knowledge both from outside government [and] inside the government so that the result of that would be an agile and responsive federal workforce equipped with the necessary competencies for AI." Alterovitz also discussed the ethical parameters the VA has in place for its usage of automated technology.

Deep learning techniques have recently shown promise in the field of anomaly detection, providing a flexible and effective method of modelling systems in comparison to traditional statistical modelling and signal processing-based methods. However, there are a few well publicised issues Neural Networks (NN)s face such as generalisation ability, requiring large volumes of labelled data to be able to train effectively and understanding spatial context in data. This paper introduces a novel NN architecture which hybridises the Long-Short-Term-Memory (LSTM) and Capsule Networks into a single network in a branched input Autoencoder architecture for use on multivariate time series data. The proposed method uses an unsupervised learning technique to overcome the issues with finding large volumes of labelled training data. Experimental results show that without hyperparameter optimisation, using Capsules significantly reduces overfitting and improves the training efficiency. Additionally, results also show that the branched input models can learn multivariate data more consistently with or without Capsules in comparison to the non-branched input models. The proposed model architecture was also tested on an open-source benchmark, where it achieved state-of-the-art performance in outlier detection, and overall performs best over the metrics tested in comparison to current state-of-the art methods.

This paper presents an approach to detect out-of-context (OOC) objects in an image. Given an image with a set of objects, our goal is to determine if an object is inconsistent with the scene context and detect the OOC object with a bounding box. In this work, we consider commonly explored contextual relations such as co-occurrence relations, the relative size of an object with respect to other objects, and the position of the object in the scene. We posit that contextual cues are useful to determine object labels for in-context objects and inconsistent context cues are detrimental to determining object labels for out-of-context objects. To realize this hypothesis, we propose a graph contextual reasoning network (GCRN) to detect OOC objects. GCRN consists of two separate graphs to predict object labels based on the contextual cues in the image: 1) a representation graph to learn object features based on the neighboring objects and 2) a context graph to explicitly capture contextual cues from the neighboring objects. GCRN explicitly captures the contextual cues to improve the detection of in-context objects and identify objects that violate contextual relations. In order to evaluate our approach, we create a large-scale dataset by adding OOC object instances to the COCO images. We also evaluate on recent OCD benchmark. Our results show that GCRN outperforms competitive baselines in detecting OOC objects and correctly detecting in-context objects.

Vulnerability of various machine learning methods to adversarial examples has been recently explored in the literature. Power systems which use these vulnerable methods face a huge threat against adversarial examples. To this end, we first propose a signal-specific method and a universal signal-agnostic method to attack power systems using generated adversarial examples. Black-box attacks based on transferable characteristics and the above two methods are also proposed and evaluated. We then adopt adversarial training to defend systems against adversarial attacks. Experimental analyses demonstrate that our signal-specific attack method provides less perturbation compared to the FGSM (Fast Gradient Sign Method), and our signal-agnostic attack method can generate perturbations fooling most natural signals with high probability. What's more, the attack method based on the universal signal-agnostic algorithm has a higher transfer rate of black-box attacks than the attack method based on the signal-specific algorithm. In addition, the results show that the proposed adversarial training improves robustness of power systems to adversarial examples. OWER quality refers to a variety of electromagnetic phenomena that characterize voltage and current measured at a given time instance and location in a power system [2]. Disturbance of power quality (PQ) signals can cause severe problems in electrical grids [3].

We present an oracle-efficient algorithm for boosting the adversarial robustness of barely robust learners. Barely robust learning algorithms learn predictors that are adversarially robust only on a small fraction $\beta \ll 1$ of the data distribution. Our proposed notion of barely robust learning requires robustness with respect to a "larger" perturbation set; which we show is necessary for strongly robust learning, and that weaker relaxations are not sufficient for strongly robust learning. Our results reveal a qualitative and quantitative equivalence between two seemingly unrelated problems: strongly robust learning and barely robust learning.

A US federal court has sentenced Canadian hacker Doug Bowser to 40 months in prison for his involvement in Switch hacking group Team Xecuter, the Department of Justice announced on Thursday. Not to be confused with Nintendo of America president Doug Bowser (or Mario's nemesis, for that matter), Bowser was part of a collective that developed and sold devices people could use to play pirated games on their consoles. The FBI arrested Bowser in 2020. One year later, he agreed to pay $10 million to Nintendo to settle a civil privacy lawsuit and another $4.5 million in restitution to the company. Leading up to today's sentencing announcement, Bowser faced up to 10 years in prison.