Ukraine developed its own artificial intelligence platform after looking at ten others. It used a tailor-made system to avoid handing sensitive data to any company.

Amazon's $1.7 billion acquisition of Roomba manufacturer iRobot is under scrutiny not only in the US, but also in Europe. The European Commission has revealed that it has opened an in-depth investigation into the purchase out of concerns that the merger would restrict competition for robotic vacuum cleaners. In particular, the commission is concerned that Amazon might prevent iRobot's rivals from selling their robot vacuums on its marketplace. Amazon might favor iRobot's products in both paid and unpaid search results or charge competing products more to advertise their offerings, the commission said. In addition, authorities are worried about the possibility of Amazon preventing iRobot rivals from accessing future Alexa APIs and from getting the "Works with Alexa" certification, since voice control with the assistant is one of the most important selling points for robot vacuums.

Fox News chief national security correspondent Jennifer Griffin has more on Russian aggression in international skies after three warplanes reportedly harassed three American drones on'Special Report.' Russian fighter jets harassed United States Air Force drones over Syria for a second time in 24 hours, U.S. Air Forces Central said Thursday. A new video released Thursday showed the Russian aircraft flying dangerously close to and deploying flares near several U.S. drones. It was released the day after the U.S. military released similar footage on Wednesday. "Russian military aircraft engaged in unsafe and unprofessional behavior Thursday, 9:30 A.M. local time, while interacting with U.S. MQ-9 drones carrying out our D-ISIS mission in Syria," said Lt Gen Alexus Grynkewich, Commander, 9th AF and CFACC for CENTCOM.

Russian fighter jets have again flown dangerously close to several US MQ-9 Reaper drones operating over Syria – the second such incident of harassment in 24 hours – setting off flares and forcing Washington's unmanned aerial vehicles to take evasive manoeuvres, the United States air force said. The protest from the US air forces came as the French military said that two of its fighter jets on patrol over the Iraq-Syria border area were forced to manoeuvre "to control the risk of accident" involving a Russian Sukhoi SU-35 warplane on Thursday. The Russian aircraft had engaged in "non-professional interaction" with two of France's Rafale planes deployed to the region as part of "Operation Chammal", which seeks to contain the ISIL (ISIS) group in Iraq and Syria, the French military said. Two separate incidents on Wednesday and Thursday involving Russian warplanes and US Reaper drones were captured on video, the US said. "The events represent a new level of unprofessional and unsafe action by Russian air forces operating in Syria," the US military said.

The modeling of multi-scale and multi-physics complex systems typically involves the use of scientific software that can optimally leverage extreme scale computing. Despite major developments in recent years, these simulations continue to be computationally intensive and time consuming. Here we explore the use of AI to accelerate the modeling of complex systems at a fraction of the computational cost of classical methods, and present the first application of physics informed neural operators to model 2D incompressible magnetohydrodynamics simulations. Our AI models incorporate tensor Fourier neural operators as their backbone, which we implemented with the TensorLY package. Our results indicate that physics informed neural operators can accurately capture the physics of magnetohydrodynamics simulations that describe laminar flows with Reynolds numbers $Re\leq250$. We also explore the applicability of our AI surrogates for turbulent flows, and discuss a variety of methodologies that may be incorporated in future work to create AI models that provide a computationally efficient and high fidelity description of magnetohydrodynamics simulations for a broad range of Reynolds numbers. The scientific software developed in this project is released with this manuscript.

Deep learning is increasingly becoming a promising pathway to improving the accuracy of sub-grid scale (SGS) turbulence closure models for large eddy simulations (LES). We leverage the concept of differentiable turbulence, whereby an end-to-end differentiable solver is used in combination with physics-inspired choices of deep learning architectures to learn highly effective and versatile SGS models for two-dimensional turbulent flow. We perform an in-depth analysis of the inductive biases in the chosen architectures, finding that the inclusion of small-scale non-local features is most critical to effective SGS modeling, while large-scale features can improve pointwise accuracy of the a-posteriori solution field. The filtered velocity gradient tensor can be mapped directly to the SGS stress via decomposition of the inputs and outputs into isotropic, deviatoric, and anti-symmetric components. We see that the model can generalize to a variety of flow configurations, including higher and lower Reynolds numbers and different forcing conditions. We show that the differentiable physics paradigm is more successful than offline, a-priori learning, and that hybrid solver-in-the-loop approaches to deep learning offer an ideal balance between computational efficiency, accuracy, and generalization. Our experiments provide physics-based recommendations for deep-learning based SGS modeling for generalizable closure modeling of turbulence.

As AI technology advances rapidly, concerns over the risks of bigness in digital markets are also growing. The EU's Digital Markets Act (DMA) aims to address these risks. Still, the current framework may not adequately cover generative AI systems that could become gateways for AI-based services. This paper argues for integrating certain AI software as core platform services and classifying certain developers as gatekeepers under the DMA. We also propose an assessment of gatekeeper obligations to ensure they cover generative AI services. As the EU considers generative AI-specific rules and possible DMA amendments, this paper provides insights towards diversity and openness in generative AI services.

Prior knowledge and symbolic rules in machine learning are often expressed in the form of label constraints, especially in structured prediction problems. In this work, we compare two common strategies for encoding label constraints in a machine learning pipeline, regularization with constraints and constrained inference, by quantifying their impact on model performance. For regularization, we show that it narrows the generalization gap by precluding models that are inconsistent with the constraints. However, its preference for small violations introduces a bias toward a suboptimal model. For constrained inference, we show that it reduces the population risk by correcting a model's violation, and hence turns the violation into an advantage. Given these differences, we further explore the use of two approaches together and propose conditions for constrained inference to compensate for the bias introduced by regularization, aiming to improve both the model complexity and optimal risk.

Autonomous robotic systems have gained a lot of attention, in recent years. However, accurate prediction of robot motion in indoor environments with limited visibility is challenging. While vision-based and light detection and ranging (LiDAR) sensors are commonly used for motion detection and localization of robotic arms, they are privacy-invasive and depend on a clear line-of-sight (LOS) for precise measurements. In cases where additional sensors are not available or LOS is not possible, these technologies may not be the best option. This paper proposes a novel method that employs channel state information (CSI) from WiFi signals affected by robotic arm motion. We developed a convolutional neural network (CNN) model to classify four different activities of a Franka Emika robotic arm. The implemented method seeks to accurately predict robot motion even in scenarios in which the robot is obscured by obstacles, without relying on any attached or internal sensors.

Recent work has demonstrated the positive impact of incorporating linguistic representations as additional context and scaffolding on the in-domain performance of several NLP tasks. We extend this work by exploring the impact of linguistic representations on cross-domain performance in a few-shot transfer setting. An important question is whether linguistic representations enhance generalizability by providing features that function as cross-domain pivots. We focus on the task of relation extraction on three datasets of procedural text in two domains, cooking and materials science. Our approach augments a popular transformer-based architecture by alternately incorporating syntactic and semantic graphs constructed by freely available off-the-shelf tools. We examine their utility for enhancing generalization, and investigate whether earlier findings, e.g. that semantic representations can be more helpful than syntactic ones, extend to relation extraction in multiple domains. We find that while the inclusion of these graphs results in significantly higher performance in few-shot transfer, both types of graph exhibit roughly equivalent utility.