The long-awaited V4 is more efficient and a win for Chinese chipmakers. On Friday, Chinese AI firm DeepSeek released a preview of V4, its long-awaited new flagship model. Notably, the model can process much longer prompts than its last generation, thanks to a new design that helps it handle large amounts of text more efficiently. Like DeepSeek's previous models, V4 is open source, meaning it is available for anyone to download, use, and modify. V4 marks DeepSeek's most significant release since R1, the reasoning model it launched in January 2025. R1, which was trained on limited computing resources, stunned the global AI industry with its strong performance and efficiency, turning DeepSeek from a little-known research team into China's best-known AI company almost overnight.

Direct policy search serves as one of the workhorses in modern reinforcement learning (RL), and its applications in continuous control tasks have recently attracted increasing attention. In this work, we investigate the convergence theory of policy gradient (PG) methods for learning the linear risk-sensitive and robust controller. In particular, we develop PG methods that can be implemented in a derivative-free fashion by sampling system trajectories, and establish both global convergence and sample complexity results in the solutions of two fundamental settings in risk-sensitive and robust control: the finite-horizon linear exponential quadratic Gaussian, and the finite-horizon linear-quadratic disturbance attenuation problems. As a by-product, our results also provide the first sample complexity for the global convergence of PG methods on solving zero-sum linear-quadratic dynamic games, a nonconvex-nonconcave minimax optimization problem that serves as a baseline setting in multi-agent reinforcement learning (MARL) with continuous spaces. One feature of our algorithms is that during the learning phase, a certain level of robustness/risk-sensitivity of the controller is preserved, which we termed as the implicit regularization property, and is an essential requirement in safety-critical control systems.

In second-order optimization, a potential bottleneck can be computing the Hessian matrix of the optimized function at every iteration. Randomized sketching has emerged as a powerful technique for constructing estimates of the Hessian which can be used to perform approximate Newton steps. This involves multiplication by a random sketching matrix, which introduces a trade-off between the computational cost of sketching and the convergence rate of the optimization algorithm. A theoretically desirable but practically much too expensive choice is to use a dense Gaussian sketching matrix, which produces unbiased estimates of the exact Newton step and which offers strong problem-independent convergence guarantees. We show that the Gaussian sketching matrix can be drastically sparsified, significantly reducing the computational cost of sketching, without substantially affecting its convergence properties. This approach, called Newton-LESS, is based on a recently introduced sketching technique: LEverage Score Sparsified (LESS) embeddings. We prove that Newton-LESS enjoys nearly the same problem-independent local convergence rate as Gaussian embeddings, not just up to constant factors but even down to lower order terms, for a large class of optimization tasks. In particular, this leads to a new state-of-the-art convergence result for an iterative least squares solver. Finally, we extend LESS embeddings to include uniformly sparsified random sign matrices which can be implemented efficiently and which perform well in numerical experiments.

His Supporters Are Already Looking for the Next Target. "Grok has thoughts on who to look at next." Acting Attorney General Todd Blanche and FBI Director Kash Patel hold a press conference on their prosecution of the Southern Poverty Law Center on April 21, 2026. Get your news from a source that's not owned and controlled by oligarchs. The Justice Department this week announced criminal charges against the Southern Poverty Law Center, alleging that the longtime civil rights watchdog had defrauded its own donors by secretly paying large sums of money to informants within various hate groups.

Owner of US tech giant reveals breach of one of world's most powerful AI models. Reports of unauthorised access to one of the most powerful Artificial Intelligence models yet developed have emerged. Nothing malicious, say the owners - but it has intensified focus on such technology falling into the wrong hands. So, how is AI being controlled globally? Will complex EU loan deal intensify conflict?

The Department of Justice has announced that it's intervening on the behalf of xAI in the company's recent lawsuit against the state of Colorado. The law is set to go into effect in June, and the DOJ is now asking a Colorado District Court to declare it unconstitutional. In xAI's original argument, Colorado Bill SB24-205 violated the company's First Amendment rights by forcing its developers to change how they create AI products and compelling them to align their products with Colorado's views on diversity and discrimination. The DOJ acknowledges those concerns in its complaint, but specifically focuses its argument on the idea that the law violates the Equal Protection Clause of the Fourteenth Amendment. According to the DOJ, because the law relies on demographics and statistical disparities as evidence of discrimination, it will essentially require developers to distort an AI system's outputs and discriminate based on race, sex, religion and other protected characteristics, a violation of the Fourteenth Amendment.

More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The Artemis II crew saw first-hand how water behaves a bit differently in zero-G. Breakthroughs, discoveries, and DIY tips sent six days a week. While space exploration is serious and sometimes dangerous scientific work, that does not mean that there is no room for fun. Something as mundane as a little ball of water can be supremely entertaining.