Mathematicians have never been so sought after by the world's richest people. At universities across the world, academics are seeing their colleagues mysteriously disappear and join private companies. Some of these companies are household names, like OpenAI and Google, but others are newly formed and just months old, hoping to capitalise on a moment in which mathematics is seen as the secret ingredient with which to improve artificial intelligence - which may in turn transform mathematics itself. "Last May, I was honestly kind of grieving for my scientific identity," says Ken Ono, who in 2025 went on leave from a professorship at the University of Virginia to join Axiom Math, a start-up aiming to build a maths-focused AI. Ono had been asked by a different company, called Epoch AI, to help craft a set of hard-to-solve maths problems that would test AI's problem-solving ability .

Best Power Banks Best Smart Rings Routers vs. Modems Choose the Right Laptop Smart Sprinklers Deals Delivered The company is mapping Alexandria and, soon, Arlington--right across from the power center of Washington, DC. Self-driving cars aren't yet permitted to operate in Virginia. But Alphabet-owned Waymo began transporting its cars to the state last week, a Waymo representative told Virginia officials, to map Arlington and Alexandria, in the northern part of the state. For most autonomous vehicle companies, mapping, or the creation of sensor-aided and ultra-precise digital representations of streets and the features around them, is the first step required to launch a local robotaxi service. Drivers will operate the mapping vehicles for now, Waymo says.

NextEra Energy is seeking to acquire Dominion Energy in an all-stock deal valued at about $67bn, creating a massive power company as the energy needs of artificial intelligence (AI) drive demand higher in the United States. It is one of the biggest proposed mergers so far this year and would create the world's largest regulated electric utility business by market capitalisation, the companies said on Monday. The region has a fast-growing population and the world's biggest data centre hub, which is in Virginia. The deal will enable a swifter build-out of power infrastructure to deliver electricity to data centres proposing to connect to NextEra and Dominion, which total about 130 gigawatts of electricity demand, the companies' executives said. One gigawatt can power about 750,000 homes. The merger builds on NextEra's efforts to tap into surging demand for supplying electricity to data centres developed by Big Tech, largely for training and rolling out AI technologies.

Deep generative models provide flexible frameworks for modeling complex, structured data such as images, videos, 3D objects, and texts. However, when applied to sequences of human skeletons, standard variational autoencoders (VAEs) often allocate substantial capacity to nuisance factors-such as camera orientation, subject scale, viewpoint, and execution speed-rather than the intrinsic geometry of shapes and their motion. We propose the Elastic Shape - Variational Autoencoder (ES-VAE), a geometry-aware generative model for skeletal trajectories that leverages the transported square-root velocity field (TSRVF) representation on Kendall's shape manifold. This representation inherently removes rigid translations, rotations, and global scaling of shapes, and temporal rate variability of sequences, isolating the underlying shape dynamics. The ES-VAE encoder maps skeletal sequences to a low-dimensional latent space incorporating the Riemannian logarithm map, while the decoder reconstructs sequences using the corresponding exponential map. We demonstrate the effectiveness of ES-VAE on two datasets. First, we analyze skeletal gait cycles to predict clinical mobility scores and classify subjects into healthy and post-stroke groups. Second, we evaluate action recognition on the NTU RGB+D dataset. Across both settings, ES-VAE consistently outperforms standard VAEs and a range of sequence modeling baselines, including temporal convolutional networks, transformers, and graph convolutional networks. More broadly, ES-VAE provides a principled framework for learning generative models of longitudinal data on pose shape manifolds, offering improved latent representation and downstream performance compared to existing deep learning approaches.

Before arriving for his high-stakes summit with Chinese leader Xi Jinping, United States President Donald Trump aimed to set expectations high. He said he would urge Xi to "open up" China's economy and announced a delegation of top business executives, including Tesla's Elon Musk, Apple's Tim Cook and Nvidia's Jensen Huang, to accompany him. While Trump and Xi are anticipated to extend the one-year pause in their trade war agreed to in South Korea in October, the expectations are for a stabilisation - not revitalisation - in ties between the world's two largest economies, which are locked in a rivalry that spans everything from trade and artificial intelligence to the status of Taiwan. "It is important to be clear-eyed about the state of relations here," Claire E Reade, a senior counsel at Arnold & Porter who previously worked on China at the Office of the US Trade Representative (USTR), told Al Jazeera. "China does not trust the US, and China wants to beat the US in what it sees as long-term global competition," Reade said.

Variable selection in linear regression models has been a problem since hypothesis testing began. Which variables to include or exclude from a model is not an easy task. Techniques such as Forward, Back ward, Stepwise Regression sequentially add or delete variables from a model. Penalized likelihood methods such as AIC, BIC, etc. seek to choose variables that have a significant contribution to the likelihood. Penalized sum of square methods such as LASSO and Elastic Net have been used to penalize small coefficients to only allow variables with large coefficients in the model. This work introduces an Artificial Intelligence approach to model selection where an ANN is trained to determine the significance of the variables based on OLS estimates. A simulation study shows the accuracy across various sample sizes and variances. Furthermore, a simulation study is conducted to compare the performance of the approach against Forward, Backward, AIC, BIC and LASSO. The approach is illustrated using a dataset from the World Health Organization regarding Life Expectancy. A github link is provided to the pretrained ANN that can handle up to 100 predictor variables, the original WHO dataset and the subset used in this work.

The minimax problems arise throughout machine learning applications, ranging from adversarial training and policy evaluation in reinforcement learning to AUROC maximization. To address the large-scale distributed data challenges across multiple clients with communication-efficient distributed training, federated learning (FL) is gaining popularity. Many optimization algorithms for minimax problems have been developed in the centralized setting (i.e., single-machine). Nonetheless, the algorithm for minimax problems under FL is still underexplored. In this paper, we study a class of federated nonconvex minimax optimization problems. We propose FL algorithms (FedSGDA+ and FedSGDA-M) and reduce existing complexity results for the most common minimax problems. For nonconvex-concave problems, we propose FedSGDA+ and reduce the communication complexity to O(ε 6). Under nonconvex-strongly-concave and nonconvex-PL minimax settings, we prove that FedSGDA-M has the best-known sample complexity of O(κ3N 1ε 3) and the best-known communication complexity of O(κ2ε 2). FedSGDA-M is the first algorithm to match the best sample complexity O(ε 3) achieved by the single-machine method under the nonconvex-strongly-concave setting.

Conditional stochastic optimization has found applications in a wide range of machine learning tasks, such as invariant learning, AUPRC maximization, and meta-learning. As the demand for training models with large-scale distributed data grows in these applications, there is an increasing need for communication-efficient distributed optimization algorithms, such as federated learning algorithms. This paper considers the nonconvex conditional stochastic optimization in federated learning and proposes the first federated conditional stochastic optimization algorithm (FCSG) with a conditional stochastic gradient estimator and a momentumbased algorithm (i.e., FCSG-M). To match the lower bound complexity in the single-machine setting, we design an accelerated algorithm (Acc-FCSG-M) via the variance reduction to achieve the best sample and communication complexity. Compared with the existing optimization analysis for Meta-Learning in FL, federated conditional stochastic optimization considers the sample of tasks.

The DOJ joined Elon Musk's xAI in suing Colorado, alleging a state AI regulation law violates the First and Fourteenth amendments by forcing developers to adopt DEI ideology.