Turing AI Fellowship: Probabilistic Algorithms for Scalable and Computable Approaches to Learning (PASCAL)

图灵人工智能奖学金：可扩展和可计算学习方法的概率算法 (PASCAL)

• 批准号: EP/V022636/1
• 负责人: Christopher Nemeth
• 金额: $ 139.82万
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV022636%2F1
• 关键词: Turing; AI; Fellowship; Probabilistic; Algorithms

We are living in an unprecedented age where vast quantities of our personal data are continually recorded and analysed, for example, our travel patterns, shopping habits and fitness routines. Our daily lives are now tied into this evolving loop of data collection, leading to data-based automated decisions, that can make recommendations and optimise our routines. There is tremendous economic and societal value in understanding this deluge of unstructured disparate data streams. A key challenge in Artificial Intelligence (AI) research is to extract meaningful value from these data sources to make decisions that can be trusted and understood to improve society. The PASCAL research programme is focused on developing an end-to-end framework, from data to decisions, that naturally accounts for data uncertainty and provides transparent and interpretable decision-making tools. The algorithms developed throughout this research project will be generally-applicable in a wide range of application domains and appropriate for modern computer hardware infrastructure. All of the research and associated algorithms will be widely available through high-quality open-source software that will ensure the widest possible uptake of this research within the international AI research community.PASCAL will focus on two primary applications areas: cybersecurity and transportation, which will stimulate and motivate this research and ensure wide-spread impact within these sectors. To drive through the impact and uptake of this research within these sectors, we will work closely with committed strategic partners, GCHQ, the Heilbronn Institute of Mathematical Research, Transport Research Laboratory, the University of Washington and the Alan Turing Institute.Cybersecurity - The proliferation of computers and mobile technology over the last few decades has led to an exponential increase in recorded data. Much of this data is personally, economically and nationally sensitive and protecting it is a key priority for any government or large organisation. Threats to data security exist on a global scale and identifying potential threats requires cybersecurity experts to evaluate and extract critical intelligence from complex and evolving data sources. In order to model and understand the intricate patterns between these data sources requires complex mathematical models. The PASCAL programme will develop new algorithms that maintain the richness of these mathematical models and use them to provide interpretable and transparent decision recommendations. Autonomous vehicles (AV) - The transition to AVs will be the most significant global change in transportation for the past century. The economic benefit and successful implementation of this technology within the UK requires a thorough understanding of the risks posed by driverless vehicles and what new procedures are required to ensure human safety. Through PASCAL, we will develop a framework to artificially-generate realistic traffic scenarios to test AVs under a wide range of road conditions and create criteria to safely accredit AV vehicles in the UK.

我们生活在一个前所未有的时代，我们的大量个人数据被不断记录和分析，例如我们的旅行模式、购物习惯和健身习惯。我们的日常生活现在与这个不断发展的数据收集循环紧密相关，从而导致基于数据的自动化决策，可以提出建议并优化我们的日常工作。理解海量的非结构化不同数据流具有巨大的经济和社会价值。人工智能 (AI) 研究的一个关键挑战是从这些数据源中提取有意义的价值，以做出可信任和理解的决策，从而改善社会。 PASCAL 研究计划专注于开发一个从数据到决策的端到端框架，自然地解释数据的不确定性并提供透明且可解释的决策工具。在整个研究项目中开发的算法将普遍适用于广泛的应用领域，并且适用于现代计算机硬件基础设施。所有研究和相关算法都将通过高质量的开源软件广泛提供，这将确保这项研究在国际人工智能研究界得到最广泛的采用。PASCAL 将专注于两个主要应用领域：网络安全和交通，这将刺激和推动这项研究，并确保在这些领域产生广泛的影响。为了推动这项研究在这些领域的影响和应用，我们将与坚定的战略合作伙伴、GCHQ、海尔布隆数学研究所、交通研究实验室、华盛顿大学和艾伦图灵研究所密切合作。 网络安全 - 过去几十年来计算机和移动技术的激增导致记录数据呈指数级增长。这些数据大部分都对个人、经济和国家敏感，保护这些数据是任何政府或大型组织的首要任务。数据安全威胁在全球范围内存在，识别潜在威胁需要网络安全专家从复杂且不断变化的数据源中评估和提取关键情报。为了建模和理解这些数据源之间的复杂模式，需要复杂的数学模型。 PASCAL 计划将开发新的算法，保持这些数学模型的丰富性，并利用它们提供可解释和透明的决策建议。自动驾驶汽车（AV）——向自动驾驶汽车的过渡将是上个世纪以来全球交通领域最重大的变化。这项技术在英国的经济效益和成功实施需要彻底了解无人驾驶车辆带来的风险以及需要采取哪些新程序来确保人类安全。通过 PASCAL，我们将开发一个框架来人工生成真实的交通场景，以在各种道路条件下测试自动驾驶汽车，并制定在英国安全认证自动驾驶汽车的标准。

项目成果

Transport Elliptical Slice Sampling

• 发表时间: 2022-10
• 期刊: PLOS Computational Biology
• 影响因子: 4.3
• 作者: A. Cabezas;C. Nemeth

Ensemble sampler for infinite-dimensional inverse problems

• DOI: 10.1007/s11222-021-10004-y
• 发表时间: 2020-10
• 期刊: Statistics and Computing
• 影响因子: 2.2
• 作者: Jeremie Coullon;R. Webber

Efficient and generalizable tuning strategies for stochastic gradient MCMC

• DOI: 10.1007/s11222-023-10233-3
• 发表时间: 2021-05
• 期刊: Statistics and Computing
• 影响因子: 2.2
• 作者: Jeremie Coullon;Leah F. South;C. Nemeth

Modelling Insect Populations in Agricultural Landscapes

模拟农业景观中的昆虫种群

• DOI: 10.1007/978-3-031-43098-5_10
• 发表时间: 2023
• 作者: Mimnagh N

Markov chain Monte Carlo for a hyperbolic Bayesian inverse problem in traffic flow modeling

• DOI: 10.1017/dce.2022.3
• 发表时间: 2022-02
• 期刊: Data-Centric Engineering
• 作者: Jeremie Coullon;Y. Pokern