UK financial regulator partners with Nvidia in AI 'sandbox'

A NVIDIA logo is displayed on a building in Taipei, Taiwan April 16, 2025. REUTERS/Ann Wang/File Photo
LONDON -Financial firms in Britain will be able to test artificial intelligence tools later this year in a regulatory "sandbox" launched on Monday by the country's financial watchdog, part of a broader government strategy to support innovation and economic growth.
The Financial Conduct Authority (FCA) has partnered with U.S. chipmaker Nvidia to provide access to advanced computing power and bespoke AI software through what it calls a "Supercharged Sandbox."
A sandbox refers to a controlled environment where companies can test new ideas such as products, services or technologies.
The programme is intended to help firms in the early stages of exploring AI, offering access to technical expertise, better datasets and regulatory support, the FCA said. It is open to all financial services companies experimenting with AI.
"This collaboration will help those that want to test AI ideas but who lack the capabilities to do so," Jessica Rusu, the FCA's chief data, information and intelligence officer, said. "We'll help firms harness AI to benefit our markets and consumers, while supporting economic growth."
Finance minister Rachel Reeves has urged Britain's regulators to remove barriers to economic growth, describing it as an "absolute top priority" for the government.
In April, she said she was pleased with how the FCA and the Prudential Regulation Authority, part of the Bank of England, were responding to her call to cut red tape.
Nvidia said the initiative would allow firms to explore AI-powered innovations in a secure environment, using its accelerated computing platform.
"AI is fundamentally reshaping the financial sector," said Jochen Papenbrock, EMEA head of financial technology at Nvidia, citing improvements in data analysis, automation and risk management.
He added that the sandbox will provide firms with a "secure environment to explore AI innovations using Nvidia's full-stack accelerated computing platform, supporting industry-wide growth and efficiency."
The testing is set to begin in October.
(Reporting by Sam Tabahriti; Editing by David Holmes)

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PolyU develops novel multi-modal agent to facilitate long video understanding by AI, accelerating development of generative AI-assisted video analysis

Malay Mail

8 hours ago

Malay Mail

PolyU develops novel multi-modal agent to facilitate long video understanding by AI, accelerating development of generative AI-assisted video analysis

A research team led by Prof. Changwen Chen, Interim Dean of the PolyU Faculty of Computer and Mathematical Sciences and Chair Professor of Visual Computing, has developed a novel video-language agent VideoMind that allows AI models to perform long video reasoning and question-answering tasks by emulating humans' way of thinking. The VideoMind framework incorporates an innovative Chain-of-LoRA strategy to reduce the demand for computational resources and power, advancing the application of generative AI in video analysis. HONG KONG SAR - Media OutReach Newswire - 10 June 2025 - While Artificial Intelligence (AI) technology is evolving rapidly, AI models still struggle with understanding long videos. A research team from The Hong Kong Polytechnic University (PolyU) has developed a novel video-language agent, VideoMind, that enables AI models to perform long video reasoning and question-answering tasks by emulating humans' way of thinking. The VideoMind framework incorporates an innovative Chain-of-Low-Rank Adaptation (LoRA) strategy to reduce the demand for computational resources and power, advancing the application of generative AI in video analysis. The findings have been submitted to the world-leading AI especially those longer than 15 minutes, carry information that unfolds over time, such as the sequence of events, causality, coherence and scene transitions. To understand the video content, AI models therefore need not only to identify the objects present, but also take into account how they change throughout the video. As visuals in videos occupy a large number of tokens, video understanding requires vast amounts of computing capacity and memory, making it difficult for AI models to process long Changwen CHEN, Interim Dean of the PolyU Faculty of Computer and Mathematical Sciences and Chair Professor of Visual Computing, and his team have achieved a breakthrough in research on long video reasoning by AI. In designing VideoMind, they made reference to a human-like process of video understanding, and introduced a role-based workflow. The four roles included in the framework are: the Planner, to coordinate all other roles for each query; the Grounder, to localise and retrieve relevant moments; the Verifier, to validate the information accuracy of the retrieved moments and select the most reliable one; and the Answerer, to generate the query-aware answer. This progressive approach to video understanding helps address the challenge of temporal-grounded reasoning that most AI models core innovation of the VideoMind framework lies in its adoption of a Chain-of-LoRA strategy. LoRA is a finetuning technique emerged in recent years. It adapts AI models for specific uses without performing full-parameter retraining. The innovative chain-of-LoRA strategy pioneered by the team involves applying four lightweight LoRA adapters in a unified model, each of which is designed for calling a specific role. With this strategy, the model can dynamically activate role-specific LoRA adapters during inference via self-calling to seamlessly switch among these roles, eliminating the need and cost of deploying multiple models while enhancing the efficiency and flexibility of the single is open source on GitHub and Huggingface. Details of the experiments conducted to evaluate its effectiveness in temporal-grounded video understanding across 14 diverse benchmarks are also available. Comparing VideoMind with some state-of-the-art AI models, including GPT-4o and Gemini 1.5 Pro, the researchers found that the grounding accuracy of VideoMind outperformed all competitors in challenging tasks involving videos with an average duration of 27 minutes. Notably, the team included two versions of VideoMind in the experiments: one with a smaller, 2 billion (2B) parameter model, and another with a bigger, 7 billion (7B) parameter model. The results showed that, even at the 2B size, VideoMind still yielded performance comparable with many of the other 7B size Chen said, "Humans switch among different thinking modes when understanding videos: breaking down tasks, identifying relevant moments, revisiting these to confirm details and synthesising their observations into coherent answers. The process is very efficient with the human brain using only about 25 watts of power, which is about a million times lower than that of a supercomputer with equivalent computing power. Inspired by this, we designed the role-based workflow that allows AI to understand videos like human, while leveraging the chain-of-LoRA strategy to minimise the need for computing power and memory in this process."AI is at the core of global technological development. The advancement of AI models is however constrained by insufficient computing power and excessive power consumption. Built upon a unified, open-source model Qwen2-VL and augmented with additional optimisation tools, the VideoMind framework has lowered the technological cost and the threshold for deployment, offering a feasible solution to the bottleneck of reducing power consumption in AI Chen added, "VideoMind not only overcomes the performance limitations of AI models in video processing, but also serves as a modular, scalable and interpretable multimodal reasoning framework. We envision that it will expand the application of generative AI to various areas, such as intelligent surveillance, sports and entertainment video analysis, video search engines and more."Hashtag: #PolyU #AI #LLMs #VideoAnalysis #IntelligentSurveillance #VideoSearch The issuer is solely responsible for the content of this announcement.

Digital Ministry outlines three key initiatives to strengthen Malaysia's AI ecosystem

The Sun

9 hours ago

The Sun

Digital Ministry outlines three key initiatives to strengthen Malaysia's AI ecosystem

JOHOR BAHRU: The Ministry of Digital has outlined three key initiatives aimed at strengthening Malaysia's artificial intelligence (AI) ecosystem, in a move to position the country as a regional hub for AI innovation by 2030. Deputy Digital Minister Datuk Wilson Ugak Kumbong, said these initiatives include the National Artificial Intelligence (AI) Roadmap (2016–2030), AI Code of Ethics, and the establishment of a dedicated AI Centre of Excellence. Speaking at the Artificial Intelligence and Robotics Festival 2025 (AirFest 2025) at Universiti Teknologi Malaysia (UTM), Ugak said the AI Roadmap, originally developed under the Ministry of Science, Technology and Innovation, serves as a strategic framework to guide AI adoption and innovation in key economic sectors. 'This roadmap outlines comprehensive policies and strategies to drive economic growth and strengthen national competitiveness. Our goal is to position Malaysia as a leading AI hub in Southeast Asia by 2030,' he said. He said that the AI Code of Ethics will serve as a critical guide to ensure responsible and ethical use of AI technologies, especially in sectors such as healthcare, transportation, agriculture, education, public services and SMEs. Meanwhile, he also said that the third initiative, AI Centre of Excellence, aims to accelerate AI integration nationwide. The centre will focus on refining implementation strategies to maximise the impact of AI on both the economy and society. This festival brings together researchers, industry leaders, and policymakers to explore AI's transformative potential through forums, exhibitions, competitions and training sessions.

Malaysia unveils three key initiatives to boost AI ecosystem