AI for Collective Intelligence (AI4CI)

人工智能促进集体智慧 (AI4CI)

• 批准号: EP/Y028392/1
• 负责人: Seth Bullock
• 金额: $ 1309.15万
• 依托单位: University of Bristol
• 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%2FY028392%2F1
• 关键词: AI; Collective; Intelligence; AI4CI

AI and Machine Learning often address challenges that are relatively monolithic in nature: determine the safest route for an autonomous car; translate a document from English to French; analyse a medical image to detect a cancer; answer questions about a difficult topic. These kinds of challenge are very important and worthwhile targets for AI research. However, an alternative set of challenges exist that are more *collective* in nature and that unfold in *real time*: - help minimise the impact of a pandemic sweeping through a population of people by informing the coordination of local and national testing, social distancing and vaccination interventions; - predict and then monitor the extent and severity of an extreme weather event using multiple real-time physical and social data streams; - anticipate and prevent a stock market crash caused by the interactions between many automated trading agents each following its own trading algorithm; - derive city-wide patterns of changing mobility from high-frequency time series data and use these patterns to drive city planning decisions that maximise liveability and sustainability in the future city;- assist populations of people with type 2 diabetes to avoid acute episodes and hospitalisation by identifying patterns in their pooled disease trajectories while preserving their privacy and anonymity. Developing AI systems for these types of problem presents unique challenges: extracting reliable and informative patterns from multiple overlapping and interacting data streams; identifying and controlling for inherent biases within the data; determining the local interventions that can allow smart agents to influence collective systems in a positive way; developing privacy preserving machine learning and advancing ethical best practices for collective AI; embedding novel machine learning and AI in portals, devices and tools that can be used transparently and successfully by different types of user.The AI for Collective Intelligence (AI4CI) Hub will address these challenges for AI in the context of critically important real world use cases (cities, pandemics, health care, environment and finance) working with key stakeholder partners from each sector.In addition to significantly advancing applied AI research for collective intelligence, the AI4CI Hub will also work to build *community* in this research area, linking together academic research groups across the UK with each other and with key industry, government and public sector organisations, and to build *capability* by developing and releasing open access training materials, tools, demonstrator systems and best practice guidance, and by supporting the career development of early and mid-career researchers both within academia and beyond.The AI for Collective Intelligence Hub will be a centre of gravity for a nation-wide research effort applying new AI to collective systems.

人工智能和机器学习经常解决本质上相对单层的挑战：确定自动驾驶汽车的最安全途径；将文档从英语翻译成法语；分析医疗图像以检测癌症；回答有关一个困难话题的问题。这些挑战是AI研究的非常重要且值得的目标。但是，存在一系列替代挑战，它们本质上是 *集体 *，并且在 *实时 *中展开。 - 预测，然后使用多个实时的物理和社交数据流来监视极端天气事件的程度和严重性； - 预期并防止因其自己的交易算法而导致的许多自动交易代理之间的相互作用引起的股票市场崩溃； - 从高频时间序列数据中改变活动能力的模式，并使用这些模式来推动城市规划决策，从而最大程度地提高未来城市的宜居性和可持续性；为这些类型的问题开发AI系统提出了独特的挑战：从多个重叠和交互数据流中提取可靠和信息丰富的模式；识别和控制数据中固有的偏见；确定可以使智能代理以积极影响智能代理影响集体系统的本地干预措施；开发隐私保护机器学习并推进集体AI的道德最佳实践； embedding novel machine learning and AI in portals, devices and tools that can be used transparently and successfully by different types of user.The AI for Collective Intelligence (AI4CI) Hub will address these challenges for AI in the context of critically important real world use cases (cities, pandemics, health care, environment and finance) working with key stakeholder partners from each sector.In addition to significantly advancing applied AI research for collective intelligence, the AI4CI Hub will also work to build *community* in this research area, linking together academic research groups across the UK with each other and with key industry, government and public sector organisations, and to build *capability* by developing and releasing open access training materials, tools, demonstrator systems and best practice guidance, and by supporting the career development of early and mid-career researchers both within academia and beyond.The AI for Collective Intelligence Hub will be a centre of全国研究工作的重力，将新AI应用于集体系统。