The Director of a Raunchy 3-Hour Dracula Movie Says AI Is Gross and Slimy. That's Why He Used It Radu Jude is the internet's favorite filmmaker. In 2021, the Romanian writer-director bagged the prestigious Golden Bear at the Berlin International Film Festival for, a black comedy about a school teacher whose career is threatened when a hardcore porno she makes with her husband goes viral. Shot largely on the streets of Bucharest during Covid-19 lockdowns, the film documents the eerie, empty aesthetic of urban centers in the era and captures real citizens snarling and cursing at the camera and at the film's lead actress. His follow-up, 2023's, nailed a different strain of post-Covid alienation. Its heroine, Angela (Ilinca Manolache), toils away 9 to 5 making shady workplace safety videos for a faceless multinational while moonlighting on TikTok, pretending to be a misogynist influencer (modeled after Romania's own model of toxic masculinity, Andrew Tate).

Thousands of tourists warned they'll be'stranded for weeks' in the Caribbean as monster Hurricane Melissa carves path of destruction German activist dubbed'anti-Greta' seeks asylum in US with support of Elon Musk It's an extraordinary power grab that will leave Harry and Meghan quaking... but Diana predicted it all along: MAUREEN CALLAHAN Zohran Mamdani's deep family ties to George Soros revealed: TOM LEONARD unravels years-long web of finances and scheming that leads (wouldn't you guess it!) to Obama Netanyahu orders'powerful strikes in Gaza' and'kills nine' after accusing Hamas of violating ceasefire terms following'faked' return of hostage remains Taylor Swift is HIDING: Insiders spill on secretive behavior at NFL games... and why she's adamant about new life in the shadows Baseball fans go wild for the'most beautiful woman on the planet' singing national anthem at the World Series Sydney Sweeney sparks liberal meltdown with shock appearance on Fox's World Series coverage ...

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In its first foray into business content, the platform has asked lingerie entrepreneur and ex-SuicideGirl Rachael McCrary to teach creators how to monetize their ideas. OnlyFans has tapped the founder of a lingerie company and former nude model to launch business classes on the platform. Rachael McCrary, a longtime lingerie designer and founder of the company Spice Rack, is launching four videos on OnlyFans Wednesday. The videos are quite different from the usual OnlyFans fare. They'll focus on pitching investors, building a brand, and navigating being an entrepreneur as a woman, McCrary tells WIRED.

Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? How much of Europe's oil still comes from Russia? Russia launched 396 attacks on 15 settlements in Ukraine's southern Zaporizhia region, killing one person and injuring three others, Governor Ivan Fedorov said on Tuesday. Russian forces also launched drone attacks, air strikes and artillery shelling across Ukraine's Kherson region, killing one person and wounding six, the head of the Kherson Regional Military Administration, Oleksandr Prokudin, said on Tuesday.

Federal police say they have identified 59 alleged offenders as being in these online networks and have made an unspecified number of arrests. Federal police say they have identified 59 alleged offenders as being in these online networks and have made an unspecified number of arrests. Australian federal police will develop an AI tool to decode gen Z and Alpha slang and emojis in an effort to crackdown on sadistic online exploitation and "crimefluencers". The AFP commissioner, Krissy Barrett, used a speech at the National Press Club on Wednesday to warn of the rise of online crime networks of young boys and men who are targeting vulnerable teen and preteen girls. The newly appointed chief outlined how the perpetrators, who are overwhelmingly from English-speaking backgrounds, were grooming victims and then forcing them to "perform serious acts of violence on themselves, their siblings, others or their pets".

The AI job cuts are here - or are they? Amazon's move this week to slash thousands of corporate jobs fed into a longstanding anxiety: that Artificial Intelligence is starting to replace workers. The tech giant joined a growing list of companies in the US that have pointed to AI technology as a reason behind layoffs. But some question whether AI is fully to blame - and have voiced scepticism that recent high-profile layoffs are a telling sign of the technology's effect on employment. Chegg, the online education firm, cited the new realities of AI as it announced a 45% reduction in workforce on Monday.

Quantifying the uncertainty in the output of a neural network is essential for deployment in scientific or engineering applications where decisions must be made under limited or noisy data. Bayesian neural networks (BNNs) provide a framework for this purpose by constructing a Bayesian posterior distribution over the network parameters. However, the prior, which is of key importance in any Bayesian setting, is rarely meaningful for BNNs. This is because the complexity of the input-to-output map of a BNN makes it difficult to understand how certain distributions enforce any interpretable constraint on the output space. Gaussian processes (GPs), on the other hand, are often preferred in uncertainty quantification tasks due to their interpretability. The drawback is that GPs are limited to small datasets without advanced techniques, which often rely on the covariance kernel having a specific structure. To address these challenges, we introduce a new class of priors for BNNs, called Mercer priors, such that the resulting BNN has samples which approximate that of a specified GP. The method works by defining a prior directly over the network parameters from the Mercer representation of the covariance kernel, and does not rely on the network having a specific structure. In doing so, we can exploit the scalability of BNNs in a meaningful Bayesian way.

Prompting-based conversational query reformulation has emerged as a powerful approach for conversational search, refining ambiguous user queries into standalone search queries. Best-of-N reformulation over the generated candidates via prompting shows impressive potential scaling capability. However, both the previous tuning methods (training time) and adaptation approaches (test time) can not fully unleash their benefits. In this paper, we propose AdaRewriter, a novel framework for query reformulation using an outcome-supervised reward model via test-time adaptation. By training a lightweight reward model with contrastive ranking loss, AdaRewriter selects the most promising reformulation during inference. Notably, it can operate effectively in black-box systems, including commercial LLM APIs. Experiments on five conversational search datasets show that AdaRewriter significantly outperforms the existing methods across most settings, demonstrating the potential of test-time adaptation for conversational query reformulation.

Retrieval-Augmented Generation (RAG) enhances recency and factuality in answers. However, existing evaluations rarely test how well these systems cope with real-world noise, conflicting between internal and external retrieved contexts, or fast-changing facts. We introduce Retrieval-Aware Robustness Evaluation (RARE), a unified framework and large-scale benchmark that jointly stress-tests query and document perturbations over dynamic, time-sensitive corpora. One of the central features of RARE is a knowledge-graph-driven synthesis pipeline (RARE-Get) that automatically extracts single and multi-hop relations from the customized corpus and generates multi-level question sets without manual intervention. Leveraging this pipeline, we construct a dataset (RARE-Set) spanning 527 expert-level time-sensitive finance, economics, and policy documents and 48295 questions whose distribution evolves as the underlying sources change. To quantify resilience, we formalize retrieval-conditioned robustness metrics (RARE-Met) that capture a model's ability to remain correct or recover when queries, documents, or real-world retrieval results are systematically altered. Our findings reveal that RAG systems are unexpectedly sensitive to perturbations. Moreover, they consistently demonstrate lower robustness on multi-hop queries compared to single-hop queries across all domains.