Anne Fehres and Luke Conroy AI4Media Humans Do The Heavy Data Lifting Licenced by CC-BY 4.0 The Royal Statistical Society's AI Task Force has issued a critical mandate via a new paper, AI Regulation Needs Statistics, which demands that statistical principles actively shape global AI governance. The publication escalates the core argument of their foundational work, AI is Statistics . This earlier paper argued that AI is fundamentally statistical, meaning effective and ethical deployment is impossible without statistical literacy. You can watch our expert panel discuss the topic here . A major focus of that work was around the challenges of evaluating AIs, given that they are dynamic systems that continue to evolve once they have been deployed in the real world.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Gubernatorial candidates Steve Hilton, left, and Xavier Becerra spoke Tuesday in Sacramento about their plans if elected to lead California. This is read by an automated voice. Please report any issues or inconsistencies here . See more from the L.A. Times in Google Search.

'I ran because I knew I would die': Russian drones target medics in Ukraine Inna Lytvynenko always puts on her bright orange body armour when she's out on an emergency call. Last June, the paramedic was sent out in Kherson to treat a woman injured in a Russian drone attack, when an FPV drone smashed into her ambulance. Seconds later, she heard a buzzing sound in the sky. Another drone was flying straight towards her, fast. I grabbed my trauma bag and started running, she recalls.

In healthcare settings where patients use LLMs as a medical assistant, LLM performance differs between evaluation and deployment. Closing the gap requires making assumptions explicit, testing which assumptions hold, and updating evaluation protocols accordingly. Healthcare LLM benchmarks are one of the main paradigms by which LLMs are evaluated prior to clinical settings. Benchmarks provide a stable goalpost that allow researchers to iterate quickly and measure progress consistently. However, in high-stakes domains like healthcare, that same abstraction becomes a liability.

Japan's AI gamble: Can technology offset the cost of an ageing society? Beneath the business towers of Tokyo's Otemachi district, a test is taking place to see whether artificial intelligence can help solve one of Japan's biggest economic challenges: a shrinking workforce. Deep underground, Marunouchi Heat Supply Company operates a 30km (18.6-mile) network of heating and cooling pipelines serving offices, commercial buildings and transport facilities in one of Japan's most important business areas. The company is now using AI to manage this complex infrastructure with the goal of moving towards more automated operations by 2027. Developed with the Tokyo-based AI company Preferred Networks, the project uses PlantPilot, an AI system trained on historical operating data and the expertise of experienced engineers.

Pillay is an editorial fellow at TIME. America's healthcare system is notoriously reactive. Could AI shift it from a system that treats illness to one that prevents it? The question framed a panel discussion at the inaugural TIME100 AI Leadership Forum on May 27, which featured Dr. Omar Lateef, the president and CEO of Rush University System for Health; Arianna Huffington, the founder and CEO of Thrive Global; and Neil Lindsay, senior vice president of Amazon Health Services (Amazon One Medical, an Amazon health service, was an event sponsor). The conversation was moderated by TIME senior health correspondent Alice Park.

'I was given a choice - keep my legs or keep my life' - the sepsis patient who lived Farmer Marshall Wylie thought nothing of it when he cut his arm, sorting wood in August 2023. And he thought even less of it when he felt ill over the next 48 hours. But the following week, he said he clinically died due to sepsis, and eventually his legs had to be amputated. Farmers are at particular risk of developing sepsis due to incidents on the farm, but can also be reluctant to seek healthcare. Warning: This article contains some graphic images of hands and feet with sepsis.

Medical Imaging (MI) datasets are fundamental to artificial intelligence in healthcare. The accuracy, robustness, and fairness of diagnostic algorithms depend on the data (and its quality) used to train and evaluate the models. MI datasets used to be proprietary, but have become increasingly available to the public, including on community-contributed platforms (CCPs) like Kaggle or HuggingFace. While open data is important to enhance the redistribution of data's public value, we find that the current CCP governance model fails to uphold the quality needed and recommended practices for sharing, documenting, and evaluating datasets. In this paper, we conduct an analysis of publicly available machine learning datasets on CCPs, discussing datasets' context, and identifying limitations and gaps in the current CCP landscape. We highlight differences between MI and computer vision datasets, particularly in the potentially harmful downstream effects from poor adoption of recommended dataset management practices. We compare the analyzed datasets across several dimensions, including data sharing, data documentation, and maintenance. We find vague licenses, lack of persistent identifiers and storage, duplicates, and missing metadata, with differences between the platforms. Our research contributes to efforts in responsible data curation and AI algorithms for healthcare.

Amid a scandal over nonconsensual sexual images, Musk says his AI chatbot is a force for "truth and beauty." Get your news from a source that's not owned and controlled by oligarchs. In much of the world, Grok and its parent company both appear to be in serious trouble. After Grok, X's AI chatbot, has been used to generate sexualized and violent images of women and children, the social media company has faced a wave of backlash and censure, with new nationwide bans on accessing Grok in place and other consequences on the way. On Monday, the EU threatened to fine X under its broad Digital Services Act if it didn't act "quickly" to fix Grok, in the words of one regulator.

Many reinforcement learning (RL) applications have combinatorial action spaces, where each action is a composition of sub-actions. A standard RL approach ignores this inherent factorization structure, resulting in a potential failure to make meaningful inferences about rarely observed sub-action combinations; this is particularly problematic for offline settings, where data may be limited. In this work, we propose a form of linear Q-function decomposition induced by factored action spaces. We study the theoretical properties of our approach, identifying scenarios where it is guaranteed to lead to zero bias when used to approximate the Q-function. Outside the regimes with theoretical guarantees, we show that our approach can still be useful because it leads to better sample efficiency without necessarily sacrificing policy optimality, allowing us to achieve a better bias-variance trade-off. Across several offline RL problems using simulators and real-world datasets motivated by healthcare, we demonstrate that incorporating factored action spaces into value-based RL can result in better-performing policies. Our approach can help an agent make more accurate inferences within underexplored regions of the state-action space when applying RL to observational datasets.