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

我们生活在一个前所未有的时代，我们的大量个人数据不断被记录和分析，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联系在一起，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。理解这种非结构化的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，以做出可以信任和理解的决策，以改善社会。PASCAL研究项目的重点是开发一个端到端的框架，从数据到决策，自然地解释数据的不确定性，并提供透明和可解释的决策工具。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，并适用于现代计算机硬件基础设施。所有的研究和相关算法将通过高质量的开源软件广泛提供，这将确保在国际人工智能研究社区内最广泛地采用这项研究。PASCAL将专注于两个主要应用领域：网络安全和交通，这将刺激和激励这项研究，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，我们将与政府通信总部、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等坚定的战略伙伴密切合作。网络安全——在过去的几十年里，计算机和移动技术的激增导致了记录数据的指数级增长。这些数据中有很多是个人、经济和国家敏感数据，保护这些数据是任何政府或大型组织的首要任务。对数据安全的威胁存在于全球范围内，识别潜在威胁需要网络安全专家从复杂和不断发展的数据源中评估和提取关键情报。为了对这些数据源之间的复杂模式进行建模和理解，需要复杂的数学模型。PASCAL程序将开发新的算法，以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全球交通领域最重大的变化。在英国，这项技术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，以及需要哪些新程序来确保人类安全。通过PASCAL，我们将开发一个框架，以人工生成真实的交通场景，在广泛的道路条件下测试自动驾驶汽车，并制定标准，以在英国安全认证自动驾驶汽车。

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.