On March 1, Chechnya's leader Ramzan Kadyrov posted a short video on Telegram, in which a cheery bearded soldier stood before a line of tanks clanking down a road under an overcast sky. In an accompanying post, Kadyrov assured Ukrainians that the Russian army doesn't hurt civilians and that Vladimir Putin wants their country to determine its own fate. In France, the CEO of a law enforcement and military training company called Tactical Systems took a screenshot of the soldier's face and got to work. Within about an hour, using face recognition services available to anyone online, he identified that the soldier was likely Hussein Mezhidov, a Chechen commander close to Kadyrov involved in Russia's assault on Ukraine, and found his Instagram account. "Just having access to a computer and internet you can basically be like an intelligence agency from a film," says the CEO, who asked to be identified as YC to avoid potential repercussions for his sleuthing.

Artificial intelligence (AI) is one of the most promising and exciting technological discoveries. Its applications range from smart music selection in personal devices to intelligent large data analysis and real-time fraud identification and aversion. The AI idea is based on the notion that if a computer system is given enough data, it can learn from it. The more data that is fed into it, the more complex its learning capacity grows. Fraud detection is a good application for machine learning, with a track record of success in industries such as banking and insurance. As people now buy what they used to buy in stores online, whether it's furniture, food, or apparel.

Today machine-learning and deep-learning techniques take part in our daily life under the name of AI. AI technology is being advanced to counter sophisticated and destructive cyberattacks. As AI cybersecurity is an emerging field, experts worry about the potential new threats that may emerge if vulnerabilities in AI technology are exposed. Without a certifying body regulating AI technology for the use of cybersecurity, will organizations find themselves more at risk and victim to manipulation? On April 21, 2021, the European Commission (EC) published a proposal describing the "first-ever legal framework on AI".

MILAN/LONDON – Italy's UniCredit and France's BNP Paribas were the latest banks to set out their Russian exposures, warning of billions of euros in potential costs from the financial fallout from Moscow's invasion of Ukraine. Banks, insurers and asset managers have been scrambling to distance themselves from Russia and assess their exposures after Moscow was hit with heavy sanctions by the West in the wake of the invasion of Ukraine that began last month. Russia calls its actions in Ukraine a "special operation." BNP Paribas has also cut off its Russia-based workforce from its internal computer systems as it seeks to bolster its defenses against any potential cyberattack, a source with direct knowledge of the matter told Reuters. The French lender is believed to be the first major bank to have excluded staff in Moscow from its IT networks.

RIYADH, March 9, 2022 – Pasqal, a developer of neutral atom-based quantum technology, and ARAMCO announced the signing of an MoU to collaborate on quantum computing capabilities and applications in the energy sector. Objectives include accelerating the design and development of quantum based machine learning models as well as identifying and advancing other use cases for the technology across the Saudi Aramco value chain. To that end, both companies plan to explore ways for collaborating and cultivating the quantum information sciences ecosystem in the Kingdom of Saudi Arabia. Quantum computing can be used to address a wide range of upstream, midstream and downstream challenges in the oil and gas industry including network optimization and management, reaction network generation and refinery linear programming. The collaboration will explore potential applications for quantum computing and artificial intelligence in these areas as well.

Algae has such immense potential as a biofuel source that scientists have long been studying it for sustainable energy. They even created 3D printed artificial leaves out of algae to produce oxygen for our investigations of Mars. Now, scientists from Texas A&M AgriLife Research are using artificial intelligence to break a new world record for producing algae as a reliable biofuel source, so that a greener and more economical fuel source for jet aircraft and other kinds of transportation could be achieved. The research project is conducted by Joshua Yuan, PhD., and funded by the U.S. Department of Energy Fossil Energy Office. One of the major problems with algaes' prominence was their growth limitations due to mutual shading and the high cost of harvest.

The war in Ukraine has highlighted two things to Russia and the outside world: that Russia's much-vaunted military revolution has been exaggerated and that Ukraine's resistance to the invasion is total. Russia's military capabilities have been built up in Western eyes, particularly after its modernisation programme in the wake of the 2008 Georgian conflict. New equipment was ordered and training focused on realism as Russia's armed forces were put on a more professional footing. A new doctrine, designed to give the military greater flexibility in responding to a variety of scenarios, was also developed. Russia's new "hybrid" military tactics were highlighted by the relatively bloodless takeover of the Crimean peninsula in 2014, when "grey" operations – those below the threshold of actual conflict – were seen.

Prototype drones capable of navigating dangerous and unpredictable environments without crashing could prove useful for search-and-rescue teams. Paolo De Petris at the Norwegian University of Science and Technology and his colleagues have developed a flying drone that aims to avoid crashes altogether. The robot, called RMF-Owl, made its debut while winning a competition hosted by the US Defense Advanced Research Projects Agency, in which it had to navigate an underground mine and perform rescue-related tasks.

Purpose: To explore the potential of Explainable Machine Learning in the prediction and detection of drivers of cleared homicides at the national- and state-levels in the United States. Methods: First, nine algorithmic approaches are compared to assess the best performance in predicting cleared homicides country-wise, using data from the Murder Accountability Project. The most accurate algorithm among all (XGBoost) is then used for predicting clearance outcomes state-wise. Second, SHAP, a framework for Explainable Artificial Intelligence, is employed to capture the most important features in explaining clearance patterns both at the national and state levels. Results: At the national level, XGBoost demonstrates to achieve the best performance overall. Substantial predictive variability is detected state-wise. In terms of explainability, SHAP highlights the relevance of several features in consistently predicting investigation outcomes. These include homicide circumstances, weapons, victims' sex and race, as well as number of involved offenders and victims. Conclusions: Explainable Machine Learning demonstrates to be a helpful framework for predicting homicide clearance. SHAP outcomes suggest a more organic integration of the two theoretical perspectives emerged in the literature. Furthermore, jurisdictional heterogeneity highlights the importance of developing ad hoc state-level strategies to improve police performance in clearing homicides.

Constructing accurate and generalizable approximators for complex physico-chemical processes exhibiting highly non-smooth dynamics is challenging. In this work, we propose new developments and perform comparisons for two promising approaches: manifold-based polynomial chaos expansion (m-PCE) and the deep neural operator (DeepONet), and we examine the effect of over-parameterization on generalization. We demonstrate the performance of these methods in terms of generalization accuracy by solving the 2D time-dependent Brusselator reaction-diffusion system with uncertainty sources, modeling an autocatalytic chemical reaction between two species. We first propose an extension of the m-PCE by constructing a mapping between latent spaces formed by two separate embeddings of input functions and output QoIs. To enhance the accuracy of the DeepONet, we introduce weight self-adaptivity in the loss function. We demonstrate that the performance of m-PCE and DeepONet is comparable for cases of relatively smooth input-output mappings. However, when highly non-smooth dynamics is considered, DeepONet shows higher accuracy. We also find that for m-PCE, modest over-parameterization leads to better generalization, both within and outside of distribution, whereas aggressive over-parameterization leads to over-fitting. In contrast, an even highly over-parameterized DeepONet leads to better generalization for both smooth and non-smooth dynamics. Furthermore, we compare the performance of the above models with another operator learning model, the Fourier Neural Operator, and show that its over-parameterization also leads to better generalization. Our studies show that m-PCE can provide very good accuracy at very low training cost, whereas a highly over-parameterized DeepONet can provide better accuracy and robustness to noise but at higher training cost. In both methods, the inference cost is negligible.