ProbAI: A Hub for the Mathematical and Computational Foundations of Probabilistic AI

ProbAI：概率人工智能的数学和计算基础中心

• 批准号: EP/Y028783/1
• 负责人: Paul Fearnhead
• 金额: $ 1092.86万
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY028783%2F1
• 关键词: ProbAI; Hub; Mathematical; Computational; Foundations

Probabilistic AI involves the embedding of probability models, probabilistic reasoning and measures of uncertainty within AI methods. The ProbAI hub will develop a world leading, diverse and UK-wide research programme in probabilistic AI, that will develop the next generation of mathematically-rigorous, scalable and uncertainty-aware AI algorithms. It will have far-reaching impact across many aspects of AI, including:(1) The sudden and rapid growth of AI systems has led to a new impetus for businesses, governments and creators of AI tools to understand and convey the inherent uncertainties in their systems. A probabilistic approach to AI provides a framework to represent and manipulate uncertainty about models and predictions and already plays a central role in scientific data analysis, robotics and cognitive science. The consequential impact arising from from such developments has the potential to be wide-ranging and substantial: from utilising a probabilistic approach for effective resource allocation (healthcare), prioritisation of actions (infrastructure planning), pattern recognition (cyber security) and the development of robust strategies to mitigate risks (finance).(2) It is possible to gain important theoretical insights into AI models and algorithms through studying their, often probabilistic, limiting behaviour in different asymptotic scenarios. Such results can help with understanding why AI methods work, and how best to choose appropriate architectures - with the potential to substantially reduce the computational cost and carbon footprint of AI.(3) Recent breakthroughs in generative models are based on simulating stochastic processes. There is huge potential to both use these ideas to help develop efficient and scalable probabilistic AI methods more generally; and also to improve and extend current generative models. The latter may lead to more computationally efficient and robust methods, to generative models that use different stochastic processes and are suitable for different types of data, or to novel approaches that can give a level of certainty to the output of a generative model. (4) Models from AI are increasingly being used as emulators. For example, fitting a deep neural network to realisations of a complex computer model for the weather, can lead to more efficient approaches to forecasting the weather. However, in most applications for such methods to be used reliably requires that the emulators report a measure of uncertainty -- so the user can know when the output can be trusted. Also, building on recent generalisations of Bayes updates gives new approaches to incorporate known physical constraints and other structure into these neural network emulators, leading to more robust methods that generalise better outside the training sampler and that have fewer parameters and are easier to fit.Developing these new, practical, general-purpose probabilistic AI methods requires overcoming substantial challenges, and at their heart many of these challenges are mathematical. The hub will unify a fragmented community with interests in Probabilistic AI and bring together UK researchers across the breadth of Applied Mathematics, Computer Science, Probability and Statistics. The hub will promote the area of probabilistic AI widely, encouraging and facilitating cross-disciplinary mathematics research in AI, and has substantial flexibility to fund the involvement of researchers from across the breadth of the UK during its lifetime.ProbAI will draw on and benefit from the well-established world-leading strength in areas relevant to probabilistic AI across different areas of Mathematics and Computer Science, with the aim of making the UK the world-leader in probabilistic AI.

ProbabilityAI涉及在AI方法中嵌入概率模型，概率推理和不确定性度量。ProbAI中心将开发一个世界领先的，多样化的，英国范围内的概率AI研究计划，该计划将开发下一代严格，可扩展和不确定性感知的AI算法。它将对人工智能的许多方面产生深远的影响，包括：（1）人工智能系统的突然和快速增长为企业，政府和人工智能工具的创造者带来了新的动力，以理解和传达其系统中固有的不确定性。人工智能的概率方法提供了一个框架来表示和操纵模型和预测的不确定性，并且已经在科学数据分析，机器人和认知科学中发挥了核心作用。这些发展所产生的后续影响可能是广泛和实质性的：从利用概率方法进行有效的资源分配（医疗保健），优先行动（基础设施规划），模式识别（网络安全）以及制定稳健的战略来减轻风险（金融）。(2)通过研究人工智能模型和算法在不同渐近场景下的极限行为（通常是概率性的），可以获得对它们的重要理论见解。这些结果可以帮助理解为什么人工智能方法有效，以及如何最好地选择合适的架构-有可能大幅降低人工智能的计算成本和碳足迹。(3)生成模型的最新突破是基于模拟随机过程。利用这些想法来帮助更普遍地开发高效和可扩展的概率AI方法，以及改进和扩展当前的生成模型，都有巨大的潜力。后者可能会导致更有效的计算和强大的方法，生成模型，使用不同的随机过程，并适合于不同类型的数据，或新的方法，可以给一定程度的确定性生成模型的输出。(4)人工智能的模型越来越多地被用作仿真器。例如，将深度神经网络拟合到复杂的天气计算机模型的实现中，可以带来更有效的天气预测方法。然而，在大多数应用程序中，要想可靠地使用这种方法，需要仿真器报告一个不确定性的度量--这样用户就可以知道什么时候输出是可信的。此外，基于最近对贝叶斯更新的概括，提供了新的方法来将已知的物理约束和其他结构纳入这些神经网络仿真器，从而产生更强大的方法，这些方法在训练采样器之外更好地概括，参数更少，更容易拟合。开发这些新的，实用的，通用的概率AI方法需要克服重大挑战，这些挑战的核心是数学。该中心将统一一个分散的社区，对概率论AI感兴趣，并将应用数学，计算机科学，概率论和统计学领域的英国研究人员聚集在一起。该中心将广泛推广概率人工智能领域，鼓励和促进人工智能领域的跨学科数学研究，并具有相当大的灵活性，在其生命周期内资助来自英国各地的研究人员参与。ProbAI将借鉴并受益于数学和计算机科学不同领域的概率人工智能相关领域的成熟世界领先优势，目的是让英国成为概率人工智能的世界领导者。