With the rapid advancement of generative AI, synthetic content across images, videos, and audio has become increasingly realistic, amplifying the risk of misinformation. Existing detection approaches predominantly focus on binary classification while lacking detailed and interpretable explanations of forgeries, which limits their applicability in safety-critical scenarios. Moreover, current methods often treat each modality separately, without a unified benchmark for cross-modal forgery detection and interpretation. T o address these challenges, we introduce METER, a unified, multi-modal benchmark for interpretable forgery detection spanning images, videos, audio, and audio-visual content. Our dataset comprises four tracks, each requiring not only real-vs-fake classification but also evidence-chain-based explanations, including spatio-temporal localization, textual rationales, and forgery type tracing. Compared to prior benchmarks, METER offers broader modality coverage and richer interpretability metrics such as spatial/temporal IoU, multi-class tracing, and evidence consistency. W e further propose a human-aligned, three-stage Chain-of-Thought (CoT) training strategy combining SFT, DPO, and a novel GRPO stage that integrates a human-aligned evaluator with CoT reasoning. W e hope METER will serve as a standardized foundation for advancing gen-eralizable and interpretable forgery detection in the era of generative media.

Simulating stochastic differential equations (SDEs) in bounded domains, presents significant computational challenges due to particle exit phenomena, which requires accurate modeling of interior stochastic dynamics and boundary interactions. Despite the success of machine learning-based methods in learning SDEs, existing learning methods are not applicable to SDEs in bounded domains because they cannot accurately capture the particle exit dynamics. We present a unified hybrid data-driven approach that combines a conditional diffusion model with an exit prediction neural network to capture both interior stochastic dynamics and boundary exit phenomena. Our ML model consists of two major components: a neural network that learns exit probabilities using binary cross-entropy loss with rigorous convergence guarantees, and a training-free diffusion model that generates state transitions for non-exiting particles using closed-form score functions. The two components are integrated through a probabilistic sampling algorithm that determines particle exit at each time step and generates appropriate state transitions. The performance of the proposed approach is demonstrated via three test cases: a one-dimensional simplified problem for theoretical verification, a two-dimensional advection-diffusion problem in a bounded domain, and a three-dimensional problem of interest to magnetically confined fusion plasmas.

During his latest trip to Washington, OpenAI's chief executive, Sam Altman, painted a sweeping vision of an AI-dominated future in which entire job categories disappear, presidents follow ChatGPT's recommendations and hostile nations wield artificial intelligence as a weapon of mass destruction, all while positioning his company as the indispensable architect of humanity's technological destiny. Speaking at the Capital Framework for Large Banks conference at the Federal Reserve board of governors, Altman told the crowd that certain job categories would be completely eliminated by AI advancement. "Some areas, again, I think just like totally, totally gone," he said, singling out customer support roles. "That's a category where I just say, you know what, when you call customer support, you're on target and AI, and that's fine." The OpenAI founder described the transformation of customer service as already complete, telling the Federal Reserve vice-chair for supervision, Michelle Bowman: "Now you call one of these things and AI answers. It can do everything that any customer support agent at that company could do. It does not make mistakes. You call once, the thing just happens, it's done."

OpenAI is seeking capital from new and existing investors, two people familiar with the company's plans tell WIRED. The fundraising effort is part of a 40 billion round announced in March. The round will reopen on Monday, July 28, according to one of the sources, who has direct knowledge of the fundraising effort. The 40 billion round announced earlier this year brought OpenAI's valuation up to 300 billion, making it one of the most highly valued private startups in history. The round was led by Japanese investment conglomerate SoftBank, which committed to contributing 75 percent of the total funding.

Experimental AI models from Google DeepMind and OpenAI have achieved a gold-level performance in the International Mathematical Olympiad (IMO) for the first time. The companies are hailing the moment as an important milestone for AIs that might one day solve hard scientific or mathematical problems, but mathematicians are more cautious because details of the models' results and how they work haven't been made public. The IMO, one of the world's most prestigious competitions for young mathematicians, has long been seen by AI researchers as a litmus test for mathematical reasoning that AI systems tend to struggle with. After last year's competition held in Bath, UK, Google DeepMindannounced that AI systems it had developed, called AlphaProof and AlphaGeometry, had together achieved a silver medal-level performance, but its entries weren't graded by the competition's official markers. Before this year's contest, which was held in Queensland, Australia, companies including Google, Huawei and TikTok-owner ByteDance, as well as academic researchers, approached the organisers to ask whether they could have their AI models' performance officially graded, says Gregor Dolinar, the IMO's president.

Ministers are facing calls for greater transparency about public data that may be shared with the US tech company OpenAI after the government signed a wide-ranging agreement with the 300m ( 222m) company that critics compared to letting a fox into a henhouse. Chi Onwurah, the chair of the House of Commons select committee on science, innovation and technology, warned that Monday's sweeping memorandum of understanding between OpenAI's chief executive, Sam Altman, and the technology secretary, Peter Kyle, was "very thin on detail" and called for guarantees that public data would remain in the UK and clarity about how much of it OpenAI would have access to. The deal paves the way for the Silicon Valley firm behind ChatGPT to explore deploying advanced AI technology in areas including justice, defence and security, and education. It includes OpenAI and the government "partnering to develop safeguards that protect the public and uphold democratic values". Kyle said he wanted Britain to be "front and centre when it comes to developing and deploying AI" and "this can't be achieved without companies like OpenAI".

A recent, hours-long fire at a data center used by Elon Musk's X may have begun after an electrical or mechanical issue in a power system, according to an official fire investigation. WIRED was the first to report on the blaze, which occurred on May 22 in Hillsboro, Oregon. Data center giant Digital Realty operates the 13-acre site, and multiple people familiar with the matter previously told WIRED that the Musk-run social platform X has servers there. Data center fires are rare, with about two dozen well-known incidents over the past decade across thousands of facilities globally, according to various researchers. But growing demand for generative AI technology--which relies on large clusters of advanced computers--is stretching the size and power needs of data centers.

Most people's browser tabs are filled with unread news articles. Mine are filled with AI agents and ghost clicks. I have four instances of OpenAI's ChatGPT Agent--the generative AI tool released last week, which can run searches and perform tasks on the web--already open with each running in its own tab. I've given these first four agents relatively simple jobs based on ChatGPT's suggestions. One is clicking around to find a birthday gift on the Target website, and another is generating a pitch deck about robotic dogs.

The text of the memorandum of understanding says the UK and OpenAI will "improve understanding of capabilities and security risks, and to mitigate those risks". It also says that the UK and OpenAI may develop an "information sharing programme", adding that they will "develop safeguards that protect the public and uphold democratic values". OpenAI chief executive Sam Altman said the plan would "deliver prosperity for all". "AI is a core technology for nation building that will transform economies and deliver growth," he added. The deal comes as the UK government looks for ways to improve the UK's stagnant economy, which is forecast to have grown at 0.1% to 0.2% for the April to June period.

Recent advances in artificial intelligence (AI) - particularly generative AI - present new opportunities to accelerate, or even automate, epidemiological research. Unlike disciplines based on physical experimentation, a sizable fraction of Epidemiology relies on secondary data analysis and thus is well-suited for such augmentation. Yet, it remains unclear which specific tasks can benefit from AI interventions or where roadblocks exist. Awareness of current AI capabilities is also mixed. Here, we map the landscape of epidemiological tasks using existing datasets - from literature review to data access, analysis, writing up, and dissemination - and identify where existing AI tools offer efficiency gains. While AI can increase productivity in some areas such as coding and administrative tasks, its utility is constrained by limitations of existing AI models (e.g. hallucinations in literature reviews) and human systems (e.g. barriers to accessing datasets). Through examples of AI-generated epidemiological outputs, including fully AI-generated papers, we demonstrate that recently developed agentic systems can now design and execute epidemiological analysis, albeit to varied quality (see https://github.com/edlowther/automated-epidemiology). Epidemiologists have new opportunities to empirically test and benchmark AI systems; realising the potential of AI will require two-way engagement between epidemiologists and engineers.