The Pentagon Hopes to Speed Up'Kill Chain' AI Buys With 5-Minute Videos The US government's Tradewinds initiative has made it easier to throw millions of dollars at "nontraditional" defense contractors, including OpenAI, Anthropic, and Google. An under-the-radar procurement program led by the Department of Defense has been granting special statuses to would-be defense contractors that make it easier for the government to quickly purchase their AI offerings, based on product videos that must be no longer than five minutes. It's part of the Pentagon's desperate bid to inject competition in a market the United States military itself destroyed. The initiative, known as Tradewinds, is managed by DOD's Chief Digital and Artificial Intelligence Office (CDAO.) Companies can submit videos describing how their AI products address one of several different "strategic focus areas" that the government periodically updates.

Site users described the appearance of the prompt as'really creepy' and'so disappointing'. Site users described the appearance of the prompt as'really creepy' and'so disappointing'. Users reject site's explanations after detailed instructions for writing response to post on popular forum was published Mumsnet has denied using AI to produce content after detailed instructions to help an AI model write one of its popular "am I being unreasonable" (AIBU) posts appeared on a Mumsnet message board. Users of the long-established forum, which is known for its frank discussions of family tensions, were vexed when the AI prompt appeared in response to a user seeking advice about a family dilemma involving a neurodivergent uncle and a sick child. The user, called AmIAComplete, had asked for "a sanity check" from other users after an awkward social interaction with their brother's family but was met with a prompt titled AIBU Response Schema v.1.0.

Portland mother Allison Roberts sued her children's elementary school after facing repercussions for criticizing an "Intersex-Inclusive Progress" flag.

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'We had to get out of the way': The backlash over delivery robots The first time Chicago resident John Roberts saw a delivery robot trundling down the sidewalk on his street he was impressed. I actually thought they were kind of neat - it felt futuristic, he says. But his attitude started to change when, soon after, he was out for a walk with his family. As another robot approached, they found themselves having to dodge it. To us it felt a little off - the fact that we were on the one strip reserved for walking, and we were having to get out of the way, says Roberts.

Themotivation for this is that these processes will encourage exploration ofπ by simulating continuous velocity paths in between random event times at which the velocity changes.

Bilevel optimization is a central tool in machine learning for high-dimensional hyperparameter tuning. Its applications are vast; for instance, in imaging it can be used for learning data-adaptive regularizers and optimizing forward operators in variational regularization. These problems are large in many ways: a lot of data is usually available to train a large number of parameters, calling for stochastic gradient-based algorithms. However, exact gradients with respect to parameters (so-called hypergradients) are not available, and their precision is usually linearly related to computational cost. Hence, algorithms must solve the problem efficiently without unnecessary precision. The design of such methods is still not fully understood, especially regarding how accuracy requirements and step size schedules affect theoretical guarantees and practical performance. Existing approaches introduce stochasticity at both the upper level (e.g., in sampling or mini-batch estimates) and the lower level (e.g., in solving the inner problem) to improve generalization, but they typically fix the number of lower-level iterations, which conflicts with asymptotic convergence assumptions. In this work, we advance the theory of inexact stochastic bilevel optimization. We prove convergence and establish rates under decaying accuracy and step size schedules, showing that with optimal configurations convergence occurs at an $\mathcal{O}(k^{-1/4})$ rate in expectation. Experiments on image denoising and inpainting with convex ridge regularizers and input-convex networks confirm our analysis: decreasing step sizes improve stability, accuracy scheduling is more critical than step size strategy, and adaptive preconditioning (e.g., Adam) further boosts performance. These results bridge theory and practice, providing convergence guarantees and practical guidance for large-scale imaging problems.

Tucker Carlson's friendly interview with white nationalist Nick Fuentes has led to a major reckoning in the Republican party. Nick Fuentes, a white nationalist known for his deeply antisemitic, racist, and misogynist worldview, just might be tearing the Republican party apart. The schism was triggered last Tuesday when former Fox News host Tucker Carlson released an in-depth interview with Fuentes, the leader of the so-called America First movement who has denied the Holocaust, praised Hitler, and shared deeply misogynistic views. During the interview, Fuentes waxed antisemitic about the threat apparently posed by "organized Jewry" in America, while Carlson slammed figures like senator Ted Cruz and former president George W. Bush as being "Christian Zionists" who have been "seized by this brain virus." Carlson was criticized by, among others, US Ambassador to Israel Mike Huckabee for giving Fuentes a platform, and the argument kicked into overdrive after Kevin Roberts, president of ...

We thank all of the reviewers for their valuable feedback and detailed comments. That is "improvement and justification of any implicit sampler". We know that in practice, even state-of-the-art generative models yield "unrealistic" samples, hence, are biased. (Algorithm 3). Based on our theoretical analysis, we derive different losses for the discriminator (Table 1 in the paper).

A new report warns that the number of US investors in powerful commercial spyware rose sharply in 2024 and names new countries linked to the dangerous technology. The United States has emerged as the largest investor in commercial spyware --a global industry that has enabled the covert surveillance of journalists, human rights defenders, politicians, diplomats, and others, posing grave threats to human rights and national security . In 2024, 20 new US-based spyware investors were identified, bringing the total number of American backers of this technology to 31. This growth has largely outpaced other major investing countries such as Israel, Italy, and the United Kingdom, according to a new report published today by the Atlantic Council. The study surveyed 561 entities across 46 countries between 1992 and 2024, identifying 34 new investors.