OpenAI's latest app encourages users to generate a personal digital avatar and scroll AI-generated videos of themselves and their friends. On Tuesday, OpenAI released an AI video app called Sora . The platform is powered by OpenAI's latest video generation model, Sora 2, and revolves around a TikTok-like For You page of user-generated clips. This is the first product release from OpenAI that adds AI-generated sounds to videos. For now, it's available only on iOS and requires an invite code to join.

A Harvard Business School study shows that several AI companions use various tricks to keep a conversation from ending. Before you close this browser tab, just know that you risk missing out on some very important information. If you want to understand the subtle hold that artificial intelligence has over you, then please, keep reading. That was, perhaps, a bit manipulative. But it is just the kind of trick that some AI companions, which are designed to act as a friend or a partner, use to discourage users from breaking off a conversation.

Robert Griffin III involved in'scary' car crash with wife and kids as shocking photos emerge Shroud of Turin mystery deepens as surgeon spots hidden detail that points to Jesus' resurrection I was so happy after trying a trendy new cosmetic procedure. But 10 years later I suffered a devastating side effect... the doctor had lied I'm no longer sleeping with my husband - and never will again, says MOLLY RYDDELL. I love him, but counted down the moments until he climaxed. Then I couldn't bear it any more and the truth spilled out... so many women feel the same The'middle-class kinks' saving marriages: Wives reveal the eight buzzy sex trends that revived their lagging libidos - including the fantasy husbands are secretly obsessed with I'm a woman with autism... here are the signs you might be masking, even from yourself Lori Loughlin's husband Mossimo Giannulli seen with mystery brunette in tiny skirt day after shock split Body count from Houston's bayous rises as serial killer whispers grip city and residents are told: 'Be vigilant' Realtor with expensive ex-wife arrested over shocking $11.6m claims about how he was funding Palm Beach lifestyle Trump dollar coin design released by Treasury... and it's inspired by the most iconic political photo of the century I've loved Taylor Swift for years. Mystery deepens over Hulk Hogan's death as his widow faces fresh anguish Warning as pasta salad is recalled due to risk of'fatal infections' Plan to pump 45,000 gallons of RADIOACTIVE water into New York's Hudson River A controversial plan to release 45,000 gallons of radioactive water into the Hudson River has been approved in court.

This gear for your home, your office, and the great outdoors treads gently on the planet without sacrificing design, comfort, or usability. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. When you buy something new--a new piece of apparel, some home decor, a set of speakers for your desk--you're making several decisions at once about what your needs are and how the purchase is going to meet them. One thing that you're hopefully thinking about more these days is what your purchase is doing to meet the needs of the environment--or more accurately, how it's already affecting it.

The EPA Is Ending Greenhouse Gas Data Collection. Who Will Step Up to Fill the Gap? With the agency no longer collecting emissions data from polluting companies, attention is turning to whether climate NGOs have the tools--and legal right--to fulfill this EPA function. The Environmental Protection Agency announced earlier this month that it would stop making polluting companies report their greenhouse gas emissions to it, eliminating a crucial tool the US uses to track emissions and form climate policy. Climate NGOs say their work could help plug some of the data gap, but they and other experts fear the EPA's work can't be fully matched. "I don't think this system can be fully replaced," says Joseph Goffman, the former assistant administrator at the EPA's Office of Air and Radiation.

The energy and climate change minister, Chris Bowen, right, and the assistant minister for climate change, Josh Wilson, discuss the National Climate Risk Assessment. The energy and climate change minister, Chris Bowen, right, and the assistant minister for climate change, Josh Wilson, discuss the National Climate Risk Assessment. News Corp embraces fantasy genre by turning climate crisis into'laughable' science fiction On the front page of the Daily Telegraph, Australia's first comprehensive assessment of the risks from climate change became "SCIENCE FICTION". In other leading stories, wind turbines became a frightening obstacle for firefighting planes and solar panels were a source of mountains of landfill waste. Some might say there's a pattern there that would not be out of character with News Corporation's more than occasional animosity towards climate change science and renewable energy.

The practical use of future climate projections from global circulation models (GCMs) is often limited by their coarse spatial resolution, requiring downscaling to generate high-resolution data. Regional climate models (RCMs) provide this refinement, but are computationally expensive. To address this issue, machine learning models can learn the downscaling function, mapping coarse GCM outputs to high-resolution fields. Among these, generative approaches aim to capture the full conditional distribution of RCM data given coarse-scale GCM data, which is characterized by large variability and thus challenging to model accurately. We introduce EnScale, a generative machine learning framework that emulates the full GCM-to-RCM map by training on multiple pairs of GCM and corresponding RCM data. It first adjusts large-scale mismatches between GCM and coarsened RCM data, followed by a super-resolution step to generate high-resolution fields. Both steps employ generative models optimized with the energy score, a proper scoring rule. Compared to state-of-the-art ML downscaling approaches, our setup reduces computational cost by about one order of magnitude. EnScale jointly emulates multiple variables -- temperature, precipitation, solar radiation, and wind -- spatially consistent over an area in Central Europe. In addition, we propose a variant EnScale-t that enables temporally consistent downscaling. We establish a comprehensive evaluation framework across various categories including calibration, spatial structure, extremes, and multivariate dependencies. Comparison with diverse benchmarks demonstrates EnScale's strong performance and computational efficiency. EnScale offers a promising approach for accurate and temporally consistent RCM emulation.

We introduce a formal active learning methodology for guiding the placement of Lagrangian observers to infer time-dependent vector fields -- a key task in oceanography, marine science, and ocean engineering -- using a physics-informed spatio-temporal Gaussian process surrogate model. The majority of existing placement campaigns either follow standard `space-filling' designs or relatively ad-hoc expert opinions. A key challenge to applying principled active learning in this setting is that Lagrangian observers are continuously advected through the vector field, so they make measurements at different locations and times. It is, therefore, important to consider the likely future trajectories of placed observers to account for the utility of candidate placement locations. To this end, we present BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories. We observe noticeable benefits of BALLAST-aided sequential observer placement strategies on both synthetic and high-fidelity ocean current models.

While previous AI Scientist systems can generate novel findings, they often lack the focus to produce scientifically valuable contributions that address pressing human-defined challenges. We introduce DeepScientist, a system designed to overcome this by conducting goal-oriented, fully autonomous scientific discovery over month-long timelines. It formalizes discovery as a Bayesian Optimization problem, operationalized through a hierarchical evaluation process consisting of "hypothesize, verify, and analyze". Leveraging a cumulative Findings Memory, this loop intelligently balances the exploration of novel hypotheses with exploitation, selectively promoting the most promising findings to higher-fidelity levels of validation. Consuming over 20,000 GPU hours, the system generated about 5,000 unique scientific ideas and experimentally validated approximately 1100 of them, ultimately surpassing human-designed state-of-the-art (SOT A) methods on three frontier AI tasks by 183.7%, 1.9%, and 7.9%. This work provides the first large-scale evidence of an AI achieving discoveries that progressively surpass human SOT A on scientific tasks, producing valuable findings that genuinely push the frontier of scientific discovery.Figure 1: Comparison of research progress timelines for AI text detection on the RAID (Dugan et al., 2024). The right panel shows that DeepScientist achieves progress in two weeks that is comparable to three years of human research (Su et al.; Bao et al., a;b; Hu et al., 2023) (left panel). All zero-shot methods, including the system-generated T -Detect, TDT, and P A-Detect, uniformly adopt Falcon-7B (Almazrouei et al., 2023) as the base model. Additionally, all methods produced by DeepScientist demonstrate higher throughput than the previous SOT A method, Binoculars (Hans et al., 2024). 1 Scientific discovery is inherently a process of continuous exploration and trial-and-error, where vast amounts of time and effort are invested to push the boundaries of human knowledge forward by a small step. This principle of persistent, incremental advancement is visible across the history of technology. For example, the decades-long optimization of semiconductor manufacturing has seen the feature size of transistors systematically reduced from micrometers to single-digit nanometers (Moore, 1965). Similarly, the efficiency of photovoltaic cells has been continuously advanced over half a century, with myriad material and architectural innovations pushing conversion rates from nascent single-digit percentages ever closer to their theoretical limits (Green, 1993). These historical trajectories underscore a process where human scientists engage in decades of goal-directed, iterative work to advance the SoT A artifacts continuously. Recently, the emergence of Large Language Models (LLMs) has propelled automated scientific discovery, where LLM-based AI Scientist systems take the lead in exploration (Xie et al., 2025b).