Chile's Torres Del Paine is famous for its stunning landscapes, but it's also home to a fierce predator: the puma. These majestic creatures feed primarily on guanacos, although the hunting success rate is not very high, especially for female pumas. The photographer followed this female and her two cubs for several days, before witnessing her hunting. Breakthroughs, discoveries, and DIY tips sent six days a week. In Chile's famous Torres Del Paine National Park, a mother puma with her two cubs in tow attacks a guanaco.

He Did PR for Zuckerberg, Musk, and Google. Now He Says He'Only Told Half the Story' Thirty thousand feet in the air, Mark Zuckerberg turned to his speechwriter. The duo were flying in Zuckerberg's jet to the United Nations General Assembly in New York, where the Facebook boss was scheduled to address world leaders. Zuckerberg had a question for his companion. "Wait, what exactly is the UN?" Dex Hunter-Torricke had to hide his surprise. Zuckerberg was, by this point in 2015, the head of a company that was reshaping politics and societies around the world, with 1.5 billion users and counting.

ALS stole this musician's voice. AI let him sing again. Patrick Darling used a music tool from ElevenLabs to perform a song with his former bandmates. There are tears in the audience as Patrick Darling's song begins to play. It's a heartfelt song written for his great-grandfather, whom he never got the chance to meet. But this performance is emotional for another reason: It's Darling's first time on stage with his bandmates since he lost the ability to sing two years ago.

Censorship has been a constant in Ai Weiwei's life. The 68-year-old Chinese dissident, whose activist art has made him among Beijing's most prominent critics, has seen his films, sculptures and other works restricted for their criticisms of China as well as his outspoken advocacy for human rights around the world. Speaking in London ahead of the January 29 launch of his new book On Censorship," he discussed returning to China for the first time in a decade, the impact of AI on freedom of expression, and what he sees as the erosion of free speech in the West. This conversation has been edited and condensed for clarity. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

A poster featuring an image of billionaire Elon Musk, calling for users of his X social media platform to delete their accounts due to the artificial intelligence chatbot Grok's image-creation feature, is seen at a bus stop in London on Tuesday. PARIS - Elon Musk's company xAI has faced global backlash in recent days over sexualized "deepfake" images of women and children created by its Grok chatbot. Here are the essential facts about the scandal, how governments have responded and the company's attempts to cool the controversy. Grok -- Musk's version of the chatbots also offered by OpenAI and other generative AI companies -- has its own account on the X social media network allowing users to interact with it. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Can we learn more from data than existed in the generating process itself? Can new and useful information be constructed from merely applying deterministic transformations to existing data? Can the learnable content in data be evaluated without considering a downstream task? On these questions, Shannon information and Kolmogorov complexity come up nearly empty-handed, in part because they assume observers with unlimited computational capacity and fail to target the useful information content. In this work, we identify and exemplify three seeming paradoxes in information theory: (1) information cannot be increased by deterministic transformations; (2) information is independent of the order of data; (3) likelihood modeling is merely distribution matching. To shed light on the tension between these results and modern practice, and to quantify the value of data, we introduce epiplexity, a formalization of information capturing what computationally bounded observers can learn from data. Epiplexity captures the structural content in data while excluding time-bounded entropy, the random unpredictable content exemplified by pseudorandom number generators and chaotic dynamical systems. With these concepts, we demonstrate how information can be created with computation, how it depends on the ordering of the data, and how likelihood modeling can produce more complex programs than present in the data generating process itself. We also present practical procedures to estimate epiplexity which we show capture differences across data sources, track with downstream performance, and highlight dataset interventions that improve out-of-distribution generalization. In contrast to principles of model selection, epiplexity provides a theoretical foundation for data selection, guiding how to select, generate, or transform data for learning systems.

Pillay is an editorial fellow at TIME. Pillay is an editorial fellow at TIME. Recent AI progress has followed a pattern. Across text, image, audio, and video, once the right technical foundations were discovered, it only took a few years for AI-generated outputs to go from merely passable to indistinguishable from human creation. Although it's early, recent advances suggest that virtual worlds--3D environments you can explore and interact with--could be next.

Study shows leading U.S. universities collaborated with Chinese state-backed AI labs linked to surveillance technology targeting Uyghur Muslims in Xinjiang.

Heatwaves are intensifying worldwide and are among the deadliest weather disasters. The burden falls disproportionately on marginalized populations and the Global South, where under-resourced health systems, exposure to urban heat islands, and the lack of adaptive infrastructure amplify risks. Yet current numerical weather prediction models often fail to capture micro-scale extremes, leaving the most vulnerable excluded from timely early warnings. We present a Graph Neural Network framework for localized, high-resolution temperature forecasting. By leveraging spatial learning and efficient computation, our approach generates forecasts at multiple horizons, up to 48 hours. For Southwestern Ontario, Canada, the model captures temperature patterns with a mean MAE of 1.93$^{\circ}$C across 1-48h forecasts and MAE@48h of 2.93$^{\circ}$C, evaluated using 24h input windows on the largest region. While demonstrated here in a data-rich context, this work lays the foundation for transfer learning approaches that could enable localized, equitable forecasts in data-limited regions of the Global South.

Handling missing data is a central challenge in data-driven analysis. Modern imputation methods not only aim for accurate reconstruction but also differ in how they represent and quantify uncertainty. Yet, the reliability and calibration of these uncertainty estimates remain poorly understood. This paper presents a systematic empirical study of uncertainty in imputation, comparing representative methods from three major families: statistical (MICE, SoftImpute), distribution alignment (OT-Impute), and deep generative (GAIN, MIWAE, TabCSDI). Experiments span multiple datasets, missingness mechanisms (MCAR, MAR, MNAR), and missingness rates. Uncertainty is estimated through three complementary routes: multi-run variability, conditional sampling, and predictive-distribution modeling, and evaluated using calibration curves and the Expected Calibration Error (ECE). Results show that accuracy and calibration are often misaligned: models with high reconstruction accuracy do not necessarily yield reliable uncertainty. We analyze method-specific trade-offs among accuracy, calibration, and runtime, identify stable configurations, and offer guidelines for selecting uncertainty-aware imputers in data cleaning and downstream machine learning pipelines.