Amazon's marketing material boasts that more than 7,500 government agencies worldwide use its cloud computing service AWS. Some of its investors fear those contracts include projects that see the company's technology contribute to human rights violations. Today a collective of 50 organizations working on digital and human rights called the Athena Coalition filed a proposal asking Amazon shareholders to force the company to investigate possible human rights violations by government clients. Athena works with owners of stock in the company who have the right to file shareholder resolutions on corporate governance. The proposal will be put to a vote at Amazon's annual meeting next year.

If you take the cosmic view of Sellafield, the superannuated nuclear facility in north-west England, its story began long before the Earth took shape. About 9bn years ago, tens of thousands of giant stars ran out of fuel, collapsed upon themselves, and then exploded. Flung out by such explosions, trillions of tonnes of uranium traversed the cold universe and wound up near our slowly materialising solar system. And here, over roughly 20m years, the uranium and other bits of space dust and debris cohered to form our planet in such a way that the violent tectonics of the young Earth pushed the uranium not towards its hot core but up into the folds of its crust. Within reach, so to speak, of the humans who eventually came along circa 300,000BC, and who mined the uranium beginning in the 1500s, learned about its radioactivity in 1896 and started feeding it into their nuclear reactors 70-odd years ago, making electricity that could be relayed to their houses to run toasters and light up Christmas trees. Sellafield compels this kind of gaze into the abyss of deep time because it is a place where multiple time spans – some fleeting, some cosmic – drift in and out of view. Laid out over six square kilometres, Sellafield is like a small town, with nearly a thousand buildings, its own roads and even a rail siding – all owned by the government, and requiring security clearance to visit. Sellafield's presence, at the end of a road on the Cumbrian coast, is almost hallucinatory. Then, having driven through a high-security gate, you're surrounded by towering chimneys, pipework, chugging cooling plants, everything dressed in steampunk. The sun bounces off metal everywhere. In some spots, the air shakes with the noise of machinery. It feels like the most manmade place in the world. Since it began operating in 1950, Sellafield has had different duties. First it manufactured plutonium for nuclear weapons.

Dozens of people, including a United States citizen, have been released in the latest prisoner swap between Russia and Ukraine, nearly 10 months into a grinding war that began after Moscow invaded its neighbour. The latest exchange included 64 Ukrainian soldiers and a US national living in Ukraine, Andriy Yermak, the head of President Volodymyr Zelenskyy's office said on Wednesday. Yermak identified the American as Suedi Murekezi who he said had been "helping our people" before he ended up in Russian custody. The White House national security spokesman, John Kirby, did not name the freed American, citing privacy concerns. "We certainly welcome that news," Kirby told reporters.

Machine learning is becoming an increasingly important tool in fusion research, allowing scientists to make new discoveries and improve fusion reaction efficiency. Researchers discussed the potential for using machine learning in fusion research at a recent workshop sponsored by the US Department of Energy, and identified several key areas for further study. One of the most difficult challenges in fusion research is accurately modeling and predicting the behavior of plasma, the superheated gas that powers fusion reactions. Traditional methods for simulating plasma rely on computationally intensive mathematical models, which can be difficult to solve and necessitate a significant amount of computational power. Machine learning algorithms, on the other hand, can be used to analyze large datasets and identify patterns and relationships that human experts would not be able to detect.

British people have a lot of questions, and these days all they have to do is shout at their voice assistant Alexa and they will probably get the answer. Amazon has now revealed its most asked questions for Alexa in Britain this year, ranging from the weird, wonderful and straight-up nosey. From the height of Prime Minister Rishi Sunak to Gordon Ramsay's net worth, hundreds of questions have been asked, with some being more popular than others. The net worth of the second richest man in the world, and new owner of Twitter, Elon Musk, was one of the most frequently asked question from Alexa owners. One of the most popular questions was'Alexa, how tall is Rishi Sunak'.

Massive data corpora like WebText, Wikipedia, Conceptual Captions, WebImageText, and LAION have propelled recent dramatic progress in AI. Large neural models trained on such datasets produce impressive results and top many of today's benchmarks. A notable omission within this family of large-scale datasets is 3D data. Despite considerable interest and potential applications in 3D vision, datasets of high-fidelity 3D models continue to be mid-sized with limited diversity of object categories. Addressing this gap, we present Objaverse 1.0, a large dataset of objects with 800K+ (and growing) 3D models with descriptive captions, tags, and animations. Objaverse improves upon present day 3D repositories in terms of scale, number of categories, and in the visual diversity of instances within a category. We demonstrate the large potential of Objaverse via four diverse applications: training generative 3D models, improving tail category segmentation on the LVIS benchmark, training open-vocabulary object-navigation models for Embodied AI, and creating a new benchmark for robustness analysis of vision models. Objaverse can open new directions for research and enable new applications across the field of AI.

Active galactic nuclei (AGN) are supermassive black holes with luminous accretion disks found in some galaxies, and are thought to play an important role in galaxy evolution. However, traditional optical spectroscopy for identifying AGN requires time-intensive observations. We train a convolutional neural network (CNN) to distinguish AGN host galaxies from non-active galaxies using a sample of 210,000 Sloan Digital Sky Survey galaxies. We evaluate the CNN on 33,000 galaxies that are spectrally classified as composites, and find correlations between galaxy appearances and their CNN classifications, which hint at evolutionary processes that effect both galaxy morphology and AGN activity. With the advent of the Vera C. Rubin Observatory, Nancy Grace Roman Space Telescope, and other wide-field imaging telescopes, deep learning methods will be instrumental for quickly and reliably shortlisting AGN samples for future analyses.

Machine learning (ML) has found broad applicability in quantum information science in topics as diverse as experimental design, state classification, and even studies on quantum foundations. Here, we experimentally realize an approach for defining custom prior distributions that are automatically tuned using ML for use with Bayesian quantum state estimation methods. Previously, researchers have looked to Bayesian quantum state tomography due to its unique advantages like natural uncertainty quantification, the return of reliable estimates under any measurement condition, and minimal mean-squared error. However, practical challenges related to long computation times and conceptual issues concerning how to incorporate prior knowledge most suitably can overshadow these benefits. Using both simulated and experimental measurement results, we demonstrate that ML-defined prior distributions reduce net convergence times and provide a natural way to incorporate both implicit and explicit information directly into the prior distribution. These results constitute a promising path toward practical implementations of Bayesian quantum state tomography.

Flooding is one of the most disastrous natural hazards, responsible for substantial economic losses. A predictive model for flood-induced financial damages is useful for many applications such as climate change adaptation planning and insurance underwriting. This research assesses the predictive capability of regressors constructed on the National Flood Insurance Program (NFIP) dataset using neural networks (Conditional Generative Adversarial Networks), decision trees (Extreme Gradient Boosting), and kernel-based regressors (Gaussian Process). The assessment highlights the most informative predictors for regression. The distribution for claims amount inference is modeled with a Burr distribution permitting the introduction of a bias correction scheme and increasing the regressor's predictive capability. Aiming to study the interaction with physical variables, we incorporate Daymet rainfall estimation to NFIP as an additional predictor. A study on the coastal counties in the eight US South-West states resulted in an $R^2=0.807$. Further analysis of 11 counties with a significant number of claims in the NFIP dataset reveals that Extreme Gradient Boosting provides the best results, that bias correction significantly improves the similarity with the reference distribution, and that the rainfall predictor strengthens the regressor performance.

Vulnerability to adversarial attacks is a well-known weakness of Deep Neural Networks. While most of the studies focus on natural images with standardized benchmarks like ImageNet and CIFAR, little research has considered real world applications, in particular in the medical domain. Our research shows that, contrary to previous claims, robustness of chest x-ray classification is much harder to evaluate and leads to very different assessments based on the dataset, the architecture and robustness metric. We argue that previous studies did not take into account the peculiarity of medical diagnosis, like the co-occurrence of diseases, the disagreement of labellers (domain experts), the threat model of the attacks and the risk implications for each successful attack. In this paper, we discuss the methodological foundations, review the pitfalls and best practices, and suggest new methodological considerations for evaluating the robustness of chest xray classification models. Our evaluation on 3 datasets, 7 models, and 18 diseases is the largest evaluation of robustness of chest x-ray classification models.