A consortium led by Elon Musk said it has offered 97.4bn to buy the nonprofit that controls OpenAI, months after the billionaire sued the artificial intelligence startup to block it from transitioning to a for-profit firm. Musk's bid, revealed on Monday, could ratchet up longstanding tensions with OpenAI CEO Sam Altman over the future of the startup at the heart of a boom in generative AI technology. Altman promptly posted on X: "No thank you but we will buy twitter for 9.74 billion if you want." The two are already embroiled in an ongoing lawsuit. Musk criticised a 500bn OpenAI-led project called Stargate announced with great fanfare at the White House just after United States President Donald Trump returned to office, suggesting the investors involved lacked the funding for the project.

The impact of artificial intelligence on the environment and inequality has dominated the opening exchanges of a global summit in Paris attended by political leaders, tech executives and experts. Emmanuel Macron's AI envoy, Anne Bouverot, opened the two-day gathering at the Grand Palais in the heart of the French capital with a speech referring to the environmental impact of AI, which requires vast amounts of energy and resource to develop and operate. "We know that AI can help mitigate climate change, but we also know that its current trajectory is unsustainable," Bouverot said. Sustainable development of the technology would be on the agenda, she added. The general secretary of the UNI Global Union, Christy Hoffman, warned that without worker involvement in the use of AI, the technology risked increasing inequality.

On Tuesday 4th February 2025, the Children's AI Summit brought together around 150 children from across the UK to share their messages for global leaders, policymakers, and AI developers on what the future of AI should look like. Hosted by the Children and AI team in The Alan Turing Institute's Public Policy Programme and Queen Mary University of London, the event aimed to put children's voices and experiences centre stage by exploring how the technology impacts young people today, and how children can shape its future. As part of the summit, a Children's Manifesto for the Future of AI was developed. This incorporates ideas that were submitted in the run-up to the event, and was refined with the help of summit participants. The Turing's Children and AI team are attending the Paris AI Action Summit this week and will be taking the Children's Manifesto for the Future of AI with them, as well as screening a short film made at the Children's AI Summit.

DeepSeek's new AI model is causing deep consternation from Silicon Valley to Washington. Few would have predicted that a little-known Chinese startup with a couple of hundred homegrown engineers would be able to release a frontier AI model rivaling the capabilities of America's best and biggest tech companies – reportedly at a fraction of the cost and computational power. Experts are hotly debating just how many and which type of chips DeepSeek used and whether the company stockpiled them or circumvented U.S. export controls. But the release and viral adoption of a Chinese AI competitor model has already rattled markets, highlighted the urgent competition for global brainpower, and caused some to ask whether all those billions that U.S. tech companies have spent buying chips and building data centers built a competitive moat or a Maginot line. This moment is game on, not game over.

The artificial intelligence race must be led by "western, liberal, democratic" countries, said the UK technology secretary in a veiled warning over China's role in the contest, before a global AI summit in Paris. Peter Kyle spoke as political leaders and tech company bosses gather in France, and after the emergence of a new Chinese force in AI, DeepSeek, rattled US investors and upended assumptions about Silicon Valley's leadership in the technology. The tech minister told the Guardian he would use the summit to explain why Britain should be at the forefront of developing AI. As well as allowing global leaders and companies to "come together and learn from each other", the summit would give the UK a chance to show why it had the "skills and the scientific pedigree" that were "going to be essential if western, liberal, democratic countries are to remain at the forefront of this critical technology", he said. Kyle added that AI would have an impact on every part of the economy and society, including national security and defence.

This paper introduces a novel six-axis force/torque sensor tailored for compact and lightweight legged robots. Unlike traditional strain gauge-based sensors, the proposed non-contact design employs photocouplers, enhancing resistance to physical impacts and reducing damage risk. This approach simplifies manufacturing, lowers costs, and meets the demands of legged robots by combining small size, light weight, and a wide force measurement range. A methodology for optimizing sensor parameters is also presented, focusing on maximizing sensitivity and minimizing error. Precise modeling and analysis of objective functions enabled the derivation of optimal design parameters. The sensor's performance was validated through extensive testing and integration into quadruped robots, demonstrating alignment with theoretical modeling. The sensor's precise measurement capabilities make it suitable for diverse robotic environments, particularly in analyzing interactions between robot feet and the ground. This innovation addresses existing sensor limitations while contributing to advancements in robotics and sensor technology, paving the way for future applications in robotic systems.

Social media platforms enable the propagation of hateful content across different modalities such as textual, auditory, and visual, necessitating effective detection methods. While recent approaches have shown promise in handling individual modalities, their effectiveness across different modality combinations remains unexplored. This paper presents a systematic analysis of fusion-based approaches for multimodal hate detection, focusing on their performance across video and image-based content. Our comprehensive evaluation reveals significant modality-specific limitations: while simple embedding fusion achieves state-of-the-art performance on video content (HateMM dataset) with a 9.9% points F1-score improvement, it struggles with complex image-text relationships in memes (Hateful Memes dataset). Through detailed ablation studies and error analysis, we demonstrate how current fusion approaches fail to capture nuanced cross-modal interactions, particularly in cases involving benign confounders. Our findings provide crucial insights for developing more robust hate detection systems and highlight the need for modality-specific architectural considerations. The code is available at https://github.com/gak97/Video-vs-Meme-Hate.

The fourth Machine Learning for Health (ML4H) symposium was held in person on December 15th and 16th, 2024, in the traditional, ancestral, and unceded territories of the Musqueam, Squamish, and Tsleil-Waututh Nations in Vancouver, British Columbia, Canada. The symposium included research roundtable sessions to foster discussions between participants and senior researchers on timely and relevant topics for the ML4H community. The organization of the research roundtables at the conference involved 13 senior and 27 junior chairs across 13 tables. Each roundtable session included an invited senior chair (with substantial experience in the field), junior chairs (responsible for facilitating the discussion), and attendees from diverse backgrounds with an interest in the session's topic.

Quantitative Artificial Intelligence (AI) Benchmarks have emerged as fundamental tools for evaluating the performance, capability, and safety of AI models and systems. Currently, they shape the direction of AI development and are playing an increasingly prominent role in regulatory frameworks. As their influence grows, however, so too does concerns about how and with what effects they evaluate highly sensitive topics such as capabilities, including high-impact capabilities, safety and systemic risks. This paper presents an interdisciplinary meta-review of about 100 studies that discuss shortcomings in quantitative benchmarking practices, published in the last 10 years. It brings together many fine-grained issues in the design and application of benchmarks (such as biases in dataset creation, inadequate documentation, data contamination, and failures to distinguish signal from noise) with broader sociotechnical issues (such as an over-focus on evaluating text-based AI models according to one-time testing logic that fails to account for how AI models are increasingly multimodal and interact with humans and other technical systems). Our review also highlights a series of systemic flaws in current benchmarking practices, such as misaligned incentives, construct validity issues, unknown unknowns, and problems with the gaming of benchmark results. Furthermore, it underscores how benchmark practices are fundamentally shaped by cultural, commercial and competitive dynamics that often prioritise state-of-the-art performance at the expense of broader societal concerns. By providing an overview of risks associated with existing benchmarking procedures, we problematise disproportionate trust placed in benchmarks and contribute to ongoing efforts to improve the accountability and relevance of quantitative AI benchmarks within the complexities of real-world scenarios.

Automatically summarizing large text collections is a valuable tool for document research, with applications in journalism, academic research, legal work, and many other fields. In this work, we contrast two classes of systems for large-scale multi-document summarization (MDS): compression and full-text. Compression-based methods use a multi-stage pipeline and often lead to lossy summaries. Full-text methods promise a lossless summary by relying on recent advances in long-context reasoning. To understand their utility on large-scale MDS, we evaluated them on three datasets, each containing approximately one hundred documents per summary. Our experiments cover a diverse set of long-context transformers (Llama-3.1, Command-R, Jamba-1.5-Mini) and compression methods (retrieval-augmented, hierarchical, incremental). Overall, we find that full-text and retrieval methods perform the best in most settings. With further analysis into the salient information retention patterns, we show that compression-based methods show strong promise at intermediate stages, even outperforming full-context. However, they suffer information loss due to their multi-stage pipeline and lack of global context. Our results highlight the need to develop hybrid approaches that combine compression and full-text approaches for optimal performance on large-scale multi-document summarization.