Every year, the newsroom produces a list of some of the most promising climate tech firms on the planet. It's an exercise that we hope brings positive attention to companies working to decarbonize major sectors of the economy, whether by spinning up new, cleaner sources of energy or reinventing how we produce foods and distribute goods. Though the political and funding landscape has shifted dramatically in the US since last year, nothing has altered the urgency of the climate dangers the world now faces--we need to rapidly curb greenhouse gas emissions to avoid the most catastrophic impacts of climate change. This project highlights the firms making progress toward that end. Check out the third annual edition of the list, and learn more about why we selected these companies . It's a foregone conclusion that the world will not meet the goals for limiting emissions and global warming laid out in the 2015 Paris Agreement.

Meta's leaked internal document reveals AI chatbot guidelines that strictly forbid sexual roleplay with minors amid FTC investigation into child safety.

Illinois Governor JB Pritzker says Trump to deploy 400 National Guard troops from Texas to'invade' liberal states Mark Sanchez's alleged victim's family breaks silence as grim photos emerge after violent attack Trump reveals plan to'restore the American Dream' by unlocking 2 million lots for new homes He slaughtered her son in cold blood. But mom of Idaho murder victim reveals why she is happy NEVER knowing Bryan Kohberger's motive I ran America's infamous Supermax prison. Hell awaits 37 killers Joe Biden refused to execute... but a notorious villain did beat the system Trump's immigration guru Stephen Miller gets called'the face of evil' by his own cousin Charlize Theron ignores former co-star Johnny Depp while greeting Bernard Arnault and Brigitte Macron as she shuns his'slow return to the spotlight' at Dior PFW after court case South Carolina judge's $1.5M beachfront home burned to the ground in possible arson attack just weeks after brutal decision against Trump administration Meghan Markle savaged for'utterly bewildering' Instagram video as she's driven past Diana crash tunnel with feet on the seat... and royal expert suggests Harry could take a dim view A near-death experience left Eric Dane in tears, forced to confront his terminal diagnosis but would it stop his wife from abandoning him? 'Dirty hippies' lose 25-year battle to save their homes after their community was deemed a 12-acre bio-hazard It was a dream vacation. But then my vision went and I could barely breathe.

Bernie Sanders warns that artificial intelligence and robotics could replace nearly 100 million American jobs over the next decade, including 40% of nurses and 89% of fast food workers.

OpenAI's Sora 2 app allows users to make AI-generated videos based on a text prompt. OpenAI's Sora 2 app allows users to make AI-generated videos based on a text prompt. Company behind the AI video app says it will work with rights holders to'block characters from Sora at their request' Mon 6 Oct 2025 00.10 EDTLast modified on Mon 6 Oct 2025 00.11 EDT Sora 2, a video generator powered by artificial intelligence, was launched last week on an invite-only basis. The app allows users to generate short videos based on a text prompt. Varun Shetty, OpenAI's head of media partnerships, said: "We'll work with rights holders to block characters from Sora at their request and respond to takedown requests."

OpenAI CEO Sam Altman speaks in Washington in July. OpenAI signed a multiyear partnership Monday with chipmaker Advanced Micro Devices as the ChatGPT-maker continues an investment spree to secure massive amounts of computing power for rolling out generative artificial intelligence. The companies announced the plan to develop AI data centers that the chipmaker said would bring in tens of billions of dollars in new revenue over the next five years. AMD's share price surged 35% when markets opened on news of the agreement that would see the company deliver six gigawatts worth of chips to the ChatGPT-maker. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Meghan is ridiculed for her'Zoolander' walk for cameras in Paris and boasting about her'return to the shows after 10 years' (when she was on the Z-list) Mark Sanchez's alleged victim's family breaks silence as grim photos emerge after violent attack Trump reveals plan to'restore the American Dream' by unlocking 2 million lots for new homes Mystery of the Schuylkill County notes explodes as fears mount over plague of creepy messages: 'What else could they do?' My son made a horrifying accusation about me in therapy... it's destroyed our relationship: DEAR JANE Trump's immigration guru Stephen Miller gets called'the face of evil' by his own cousin Illinois Governor JB Pritzker says Trump to deploy 400 National Guard troops from Texas to'invade' liberal states US billionaire retail estate tycoon is ordered to sell off his'exceptional' £36million London mansion in bitter divorce battle with ex-wife South Carolina judge's $1.5M beachfront home burned to the ground in possible arson ...

The rapid growth of online video content, especially on short video platforms, has created a growing demand for efficient video editing techniques that can condense long-form videos into concise and engaging clips. Existing automatic editing methods predominantly rely on textual cues from ASR transcripts and end-to-end segment selection, often neglecting the rich visual context and leading to incoherent outputs. In this paper, we propose a human-inspired automatic video editing framework (HIVE) that leverages multimodal narrative understanding to address these limitations. Our approach incorporates character extraction, dialogue analysis, and narrative summarization through multimodal large language models, enabling a holistic understanding of the video content. To further enhance coherence, we apply scene-level segmentation and decompose the editing process into three subtasks: highlight detection, opening/ending selection, and pruning of irrelevant content. To facilitate research in this area, we introduce DramaAD, a novel benchmark dataset comprising over 800 short drama episodes and 500 professionally edited advertisement clips. Experimental results demonstrate that our framework consistently outperforms existing baselines across both general and advertisement-oriented editing tasks, significantly narrowing the quality gap between automatic and human-edited videos.

While language models (LMs) offer great potential for conversational recommender systems (CRSs), the paucity of public CRS data makes fine-tuning LMs for CRSs challenging. In response, LMs as user simulators qua data generators can be used to train LM-based CRSs, but often lack behavioral consistency, generating utterance sequences inconsistent with those of any real user. To address this, we develop a methodology for generating natural dialogues that are consistent with a user's underlying state using behavior simulators together with LM-prompting. We illustrate our approach by generating a large, open-source CRS data set with both preference elicitation and example critiquing. Rater evaluation on some of these dialogues shows them to exhibit considerable consistency, factuality and naturalness.

The growth of highly advanced Large Language Models (LLMs) constitutes a huge dual-use problem, making it necessary to create dependable AI-generated text detection systems. Modern detectors are notoriously vulnerable to adversarial attacks, with paraphrasing standing out as an effective evasion technique that foils statistical detection. This paper presents a comparative study of adversarial robustness, first by quantifying the limitations of standard adversarial training and then by introducing a novel, significantly more resilient detection framework: Perturbation-Invariant Feature Engineering (PIFE), a framework that enhances detection by first transforming input text into a standardized form using a multi-stage normalization pipeline, it then quantifies the transformation's magnitude using metrics like Levenshtein distance and semantic similarity, feeding these signals directly to the classifier. We evaluate both a conventionally hardened Transformer and our PIFE-augmented model against a hierarchical taxonomy of character-, word-, and sentence-level attacks. Our findings first confirm that conventional adversarial training, while resilient to syntactic noise, fails against semantic attacks, an effect we term "semantic evasion threshold", where its True Positive Rate at a strict 1% False Positive Rate plummets to 48.8%. In stark contrast, our PIFE model, which explicitly engineers features from the discrepancy between a text and its canonical form, overcomes this limitation. It maintains a remarkable 82.6% TPR under the same conditions, effectively neutralizing the most sophisticated semantic attacks. This superior performance demonstrates that explicitly modeling perturbation artifacts, rather than merely training on them, is a more promising path toward achieving genuine robustness in the adversarial arms race.