Disney has agreed to invest $1bn (£740m) in OpenAI as part of a deal which will let people use many of its iconic characters in the chatbot ChatGPT and video-generation tool Sora. It is the first major studio to license parts of its catalogue to the tech giant, in a move which could have major implications for the studio's future plans. It means fans will be able to generate and share pictures and videos of more than 200 characters from Disney's franchises, including Pixar, Marvel and Star Wars. The move comes as OpenAI faces mounting questions about how its rapidly advancing tech is used - and as anxiety in Hollywood increases over the impact of AI on the creative industries. According to a blog post announcing the news, the list of eligible characters include those from Disney films Zootopia, Moana and Encanto - as well as characters like Star Wars' Luke Skywalker and Marvel's Deadpool.

'Architects of AI' named Time Magazine's Person of the Year Time Magazine's Person of the Year for 2025 is not a single person. Instead, the magazine has recognised the year's most influential figure as the architects of artificial intelligence (AI). Nvidia boss Jensen Huang, Meta head Mark Zuckerberg, X owner Elon Musk and AI godmother Fei-Fei Li are among those depicted on one of the magazine's two covers. Experts say it highlights how quickly AI, and the firms behind it, are reshaping society. It comes as a boom in the technology, ushered in by OpenAI's launch of ChatGPT in late 2022, continues at pace.

AI has entered the classroom - but is it the solution for overworked teachers? Schools across the UK are trialling the use of deepfake teachers and even employing remote staff to deliver lessons hundreds of miles away from the classroom. It comes as the use of AI is becoming increasingly prevalent in schools. The government says AI has the power to transform education, and improve teacher workload, particularly around admin for teachers. The BBC has spoken to teachers, school leaders and unions who seem divided on what the future of the UK's classrooms should look like.

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Consistency-based methods have emerged as an effective approach to uncertainty quantification (UQ) in large language models. These methods typically rely on several generations obtained via multinomial sampling, measuring their agreement level. However, in short-form QA, multinomial sampling is prone to producing duplicates due to peaked distributions, and its stochasticity introduces considerable variance in uncertainty estimates across runs. We introduce a new family of methods that employ beam search to generate candidates for consistency-based UQ, yielding improved performance and reduced variance compared to multinomial sampling. We also provide a theoretical lower bound on the beam set probability mass under which beam search achieves a smaller error than multinomial sampling. We empirically evaluate our approach on six QA datasets and find that its consistent improvements over multinomial sampling lead to state-of-the-art UQ performance.

Knowledge Distillation (KD) has emerged as a promising technique for model compression but faces critical limitations: (1) sensitivity to hyperparameters requiring extensive manual tuning, (2) capacity gap when distilling from very large teachers to small students, (3) suboptimal coordination in multi-teacher scenarios, and (4) inefficient use of computational resources. We present \textbf{HPM-KD}, a framework that integrates six synergistic components: (i) Adaptive Configuration Manager via meta-learning that eliminates manual hyperparameter tuning, (ii) Progressive Distillation Chain with automatically determined intermediate models, (iii) Attention-Weighted Multi-Teacher Ensemble that learns dynamic per-sample weights, (iv) Meta-Learned Temperature Scheduler that adapts temperature throughout training, (v) Parallel Processing Pipeline with intelligent load balancing, and (vi) Shared Optimization Memory for cross-experiment reuse. Experiments on CIFAR-10, CIFAR-100, and tabular datasets demonstrate that HPM-KD: achieves 10x-15x compression while maintaining 85% accuracy retention, eliminates the need for manual tuning, and reduces training time by 30-40% via parallelization. Ablation studies confirm independent contribution of each component (0.10-0.98 pp). HPM-KD is available as part of the open-source DeepBridge library.

Accurate water quality assessment requires spatially resolved sampling, yet most unmanned surface vehicles (USVs) can collect only a limited number of samples or rely on single-point sensors with poor representativeness. This work presents a solar-powered, fully autonomous USV featuring a novel syringe-based sampling architecture capable of acquiring 72 discrete, contamination-minimized water samples per mission. The vehicle incorporates a ROS 2 autonomy stack with GPS-RTK navigation, LiDAR and stereo-vision obstacle detection, Nav2-based mission planning, and long-range LoRa supervision, enabling dependable execution of sampling routes in unstructured environments. The platform integrates a behavior-tree autonomy architecture adapted from Nav2, enabling mission-level reasoning and perception-aware navigation. A modular 6x12 sampling system, controlled by distributed micro-ROS nodes, provides deterministic actuation, fault isolation, and rapid module replacement, achieving spatial coverage beyond previously reported USV-based samplers. Field trials in Achocalla Lagoon (La Paz, Bolivia) demonstrated 87% waypoint accuracy, stable autonomous navigation, and accurate physicochemical measurements (temperature, pH, conductivity, total dissolved solids) comparable to manually collected references. These results demonstrate that the platform enables reliable high-resolution sampling and autonomous mission execution, providing a scalable solution for aquatic monitoring in remote environments.

Toffee Crisp and Blue Riband can't be called chocolate any more Toffee Crisp and Blue Riband bars can no longer be called chocolate after maker Nestle changed their recipes. To be described as milk chocolate in the UK a product needs to have at least 20% cocoa solids and 20% milk solids, a level each product fell below once a higher amount of cheaper vegetable fat was used. Nestle said its reformulations were needed due to higher input costs but were carefully developed and sensory tested and there were no plans to alter the recipes of other chocolate products. As many ingredient costs, such as cocoa and butter, increased food companies have altered recipes to use less of the expensive ingredients, as well as shrinking serving sizes. Nestle now describes the treats as being encased in a smooth milk chocolate flavour coating rather than being covered in milk chocolate.

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McDonald's pulls AI Christmas ad after backlash McDonald's has taken down a Christmas advert made with Artificial Intelligence (AI) following online backlash. The 45-second advert was produced with generative AI clips and released publicly on McDonald's Netherlands YouTube channel on 6 December. Viewers on social media denounced the use of AI in the film, with one commenter calling it the most god-awful ad I've seen this year . On 9 December McDonald's Netherlands removed the video, adding in a statement to BBC News that the moment served as an important learning as the company explored the effective use of AI. The advert was created for McDonald's by Dutch company TBWA\Neboko and US production company The Sweetshop.