The world's most valuable and dominant internet companies are based in the US, but the nation's unproductive lawmakers and business-friendly courts have effectively outsourced the regulation of tech giants to the EU. That has given tremendous power to Didier Reynders, the European commissioner for justice, who is in charge of crafting and enforcing laws that apply across the 27-nation bloc. After nearly four years on the job, he's tired of hearing big talk from the US with little action. Ahead of his latest round of biannual meetings with US officials, including attorney general Merrick Garland in Washington, DC, tomorrow, Reynders told WIRED why the US needs to finally step up, where a probe into ChatGPT is headed, and why he made contentious comments about one of the world's most prominent privacy activists. His bicoastal tour began with a Waymo robotaxi ride through San Francisco (he gave it a rave review) and include meetings with Google and California's privacy czar.

Russia's air defence forces and fleet in the Black Sea have shot down eight Ukrainian drones over the Crimean port of Sevastopol, according to a Moscow-installed official. Mikhail Razvozhayev, the Russian-installed governor of Sevastopol, said the drone attack took place early on Sunday over the port of Sevastopol and the city's Balaklava and Khersones districts. "No objects, either in the city or in the water area were damaged," he said on the Telegram messaging app. One drone was shot down over the sea, five were intercepted by Russia's electronic warfare forces and two water surface drones were destroyed on the outer shore, he added. There was no immediate comment from Kyiv on the attack on the Crimean Peninsula, which Russia annexed from Ukraine in 2014.

The potential of realistic and useful synthetic data is significant. However, current evaluation methods for synthetic tabular data generation predominantly focus on downstream task usefulness, often neglecting the importance of statistical properties. This oversight becomes particularly prominent in low sample scenarios, accompanied by a swift deterioration of these statistical measures. In this paper, we address this issue by conducting an evaluation of three state-of-the-art synthetic tabular data generators based on their marginal distribution, column-pair correlation, joint distribution and downstream task utility performance across high to low sample regimes. The popular CTGAN model shows strong utility, but underperforms in low sample settings in terms of utility. To overcome this limitation, we propose MargCTGAN that adds feature matching of de-correlated marginals, which results in a consistent improvement in downstream utility as well as statistical properties of the synthetic data.

Face recognition from image sets acquired under unregulated and uncontrolled settings, such as at large distances, low resolutions, varying viewpoints, illumination, pose, and atmospheric conditions, is challenging. Face feature aggregation, which involves aggregating a set of N feature representations present in a template into a single global representation, plays a pivotal role in such recognition systems. Existing works in traditional face feature aggregation either utilize metadata or high-dimensional intermediate feature representations to estimate feature quality for aggregation. However, generating high-quality metadata or style information is not feasible for extremely low-resolution faces captured in long-range and high altitude settings. To overcome these limitations, we propose a feature distribution conditioning approach called CoNAN for template aggregation. Specifically, our method aims to learn a context vector conditioned over the distribution information of the incoming feature set, which is utilized to weigh the features based on their estimated informativeness. The proposed method produces state-of-the-art results on long-range unconstrained face recognition datasets such as BTS, and DroneSURF, validating the advantages of such an aggregation Figure 1: Low resolution faces acquired from large distance strategy.

Machine learning (ML) models deployed in healthcare systems must face data drawn from continually evolving environments. However, researchers proposing such models typically evaluate them in a time-agnostic manner, splitting datasets according to patients sampled randomly throughout the entire study time period. This work proposes the Evaluation on Medical Datasets Over Time (EMDOT) framework, which evaluates the performance of a model class across time. Inspired by the concept of backtesting, EMDOT simulates possible training procedures that practitioners might have been able to execute at each point in time and evaluates the resulting models on all future time points. Evaluating both linear and more complex models on six distinct medical data sources (tabular and imaging), we show how depending on the dataset, using all historical data may be ideal in many cases, whereas using a window of the most recent data could be advantageous in others. In datasets where models suffer from sudden degradations in performance, we investigate plausible explanations for these shocks. We release the EMDOT package to help facilitate further works in deployment-oriented evaluation over time.

Fish locomotion emerges from a diversity of interactions among deformable structures, surrounding fluids and neuromuscular activations, i.e., fluid-structure interactions (FSI) controlled by fish's motor systems. Previous studies suggested that such motor-controlled FSI may possess embodied traits. However, their implications in motor learning, neuromuscular control, gait generation, and swimming performance remain to be uncovered. Using robot models, we studied how swimming behaviours emerged from the FSI and the embodied traits. We developed modular robots with various designs and used Central Pattern Generators (CPGs) to control the torque acting on robot body. We used reinforcement learning to learn CPG parameters to maximize the swimming speed. The results showed that motor frequency converged faster than other parameters, and the emergent swimming gaits were robust against disruptions applied to motor control. For all robots and frequencies tested, swimming speed was proportional to the mean undulation velocity of body and caudal-fin combined, yielding an invariant, undulation-based Strouhal number. The Strouhal number also revealed two fundamental classes of undulatory swimming in both biological and robotic fishes. The robot actuators also demonstrated diverse functions as motors, virtual springs, and virtual masses. These results provide novel insights into the embodied traits of motor-controlled FSI for fish-inspired locomotion.

Temporal credit assignment is crucial for learning and skill development in natural and artificial intelligence. While computational methods like the TD approach in reinforcement learning have been proposed, it's unclear if they accurately represent how humans handle feedback delays. Cognitive models intend to represent the mental steps by which humans solve problems and perform a number of tasks, but limited research in cognitive science has addressed the credit assignment problem in humans and cognitive models. Our research uses a cognitive model based on a theory of decisions from experience, Instance-Based Learning Theory (IBLT), to test different credit assignment mechanisms in a goal-seeking navigation task with varying levels of decision complexity. Instance-Based Learning (IBL) models simulate the process of making sequential choices with different credit assignment mechanisms, including a new IBL-TD model that combines the IBL decision mechanism with the TD approach. We found that (1) An IBL model that gives equal credit assignment to all decisions is able to match human performance better than other models, including IBL-TD and Q-learning; (2) IBL-TD and Q-learning models underperform compared to humans initially, but eventually, they outperform humans; (3) humans are influenced by decision complexity, while models are not. Our study provides insights into the challenges of capturing human behavior and the potential opportunities to use these models in future AI systems to support human activities.

We present a neural network approach to compute stream functions, which are scalar functions with gradients orthogonal to a given vector field. As a result, isosurfaces of the stream function extract stream surfaces, which can be visualized to analyze flow features. Our approach takes a vector field as input and trains an implicit neural representation to learn a stream function for that vector field. The network learns to map input coordinates to a stream function value by minimizing the inner product of the gradient of the neural network's output and the vector field. Since stream function solutions may not be unique, we give optional constraints for the network to learn particular stream functions of interest. Specifically, we introduce regularizing loss functions that can optionally be used to generate stream function solutions whose stream surfaces follow the flow field's curvature, or that can learn a stream function that includes a stream surface passing through a seeding rake. We also discuss considerations for properly visualizing the trained implicit network and extracting artifact-free surfaces. We compare our results with other implicit solutions and present qualitative and quantitative results for several synthetic and simulated vector fields.

On-orbit close proximity operations involve robotic spacecraft maneuvering and making decisions for a growing number of mission scenarios demanding autonomy, including on-orbit assembly, repair, and astronaut assistance. Of these scenarios, on-orbit assembly is an enabling technology that will allow large space structures to be built in-situ, using smaller building block modules. However, robotic on-orbit assembly involves a number of technical hurdles such as changing system models. For instance, grappled modules moved by a free-flying "assembler" robot can cause significant shifts in system inertial properties, which has cascading impacts on motion planning and control portions of the autonomy stack. Further, on-orbit assembly and other scenarios require collision-avoiding motion planning, particularly when operating in a "construction site" scenario of multiple assembler robots and structures. These complicating factors, relevant to many autonomous microgravity robotics use cases, are tackled in the ReSWARM flight experiments as a set of tests on the International Space Station using NASA's Astrobee robots. RElative Satellite sWarming and Robotic Maneuvering, or ReSWARM, demonstrates multiple key technologies for close proximity operations and on-orbit assembly: (1) global long-horizon planning, accomplished using offline and online sampling-based planner options that consider the system dynamics; (2) on-orbit reconfiguration model learning, using the recently-proposed RATTLE information-aware planning framework; and (3) robust control tools to provide low-level control robustness using current system knowledge. These approaches are detailed individually and in an "on-orbit assembly scenario" of multi-waypoint tracking on-orbit. Additionally, detail is provided discussing the practicalities of hardware implementation and unique aspects of working with Astrobee in microgravity.

Over the course of the six movies and nearly 30 years leading up to this one, Mission: Impossible's Ethan Hunt has fought double agents and shadowy terrorist networks, scaled skyscrapers, and thrown himself out of planes. But he's never fought an adversary like the Entity, the rogue artificial intelligence he takes on in Dead Reckoning Part One. For one thing, it has no physical form, which means Tom Cruise can't catch it no matter how fast he runs. And for another, the Entity doesn't just want to defeat Ethan: It wants to replace him. In a briefing of the U.S. top intelligence officials in which exposition is passed from one actor to the next like a red-hot baton, one alphabet-agency higher-up describes the Entity as a "godless, stateless, amoral" being that can infiltrate any system in the world--not unlike the Impossible Mission Force itself, which, though nominally a branch of the U.S. government, doesn't take orders from the military-industrial complex so much as consider its suggestions.