Conference: NSF-NIH Joint Workshop on Foundational AI in Biology

会议：NSF-NIH 生物学基础人工智能联合研讨会

• 批准号: 2325301
• 负责人: Carleton Kingsford
• 金额: $ 4.97万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2325301&HistoricalAwards=false
• 关键词: Conference; NSF; NIH; Joint; Workshop

Methods and new techniques in artificial intelligence (AI) are rapidly beingdeveloped, extended, and applied to challenging problems in biology. At thesame time, as new experimental methods, new data collection efforts, andgreater understanding are developed in biology, the class and scope of problemsthat are amendable to AI approaches is growing. Extreme interest and rapidprogress in AI has also made the AI research landscape diverse and complex,with a number of related, competing, and overlapping technologies andapproaches being advanced. This rich ecosystem is a boon to research andinnovation, but it also makes identifying threads and pressing problems morechallenging. Thus, there is a need to survey the current frontier of theinterface between AI methodology and biology and to chart future directions andchallenges. We propose a 2-day, online workshop to address that need. Thisworkshop will host invited talks by researchers working on foundational AImethods in biology. New directions and connections between emerging AImethodologies and problems in biology and health will be identified,synthesized, highlighted, and formalized. The workshop will catalyze discussionand help set the agenda for future research in this area, helping to launch newdirections and solidify promising ones.The workshop will consist of approximately 13 talks and 4 discussion sessionsover two days. Speakers will be selected based on their prior work andexperience with developing new foundational AI methodology that answersbiological questions. Speakers will be asked to focus on one or more of thefollowing foundational AI themes as related to biology and human health: (1)Fairness and Social Effects of AI; (2) Federated Learning; (3) Generative DeepLearning Models; (4) Scalability of AI; (5) Privacy and Security in AI; (6)Method Optimization and Automated Algorithm Design; (7) Explainable AI andCausality; (8) Active Learning and Automated Science; (9) Transfer Learning;and (10) Incorporation of Prior Knowledge in AI. Though the workshop cannothope to deeply cover all these foundational aspects, we aim to cover as many aspossible. Speakers will connect these foundational computational techniques toproblems in biology in areas such as genomics, structural biology, drugdevelopment, systems biology, biomedical imaging, neuroscience, and diseaseforecasting. With permission of the speaker, the talks will be recorded anddistributed over the internet, and participants will be invited to contributeto a publication that surveys the identified themes, directions, andchallenges.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人工智能（AI）的方法和新技术正在迅速发展、扩展并应用于生物学中具有挑战性的问题。与此同时，随着新的实验方法、新的数据收集工作以及对生物学的更深入理解的发展，可用于人工智能方法的问题的类别和范围正在增长。对人工智能的极大兴趣和快速发展也使人工智能研究领域变得多样化和复杂，许多相关的，竞争的和重叠的技术和方法正在推进。这个丰富的生态系统对研究和创新来说是一个布恩，但它也使识别线索和紧迫问题更具挑战性。因此，有必要调查人工智能方法学和生物学之间接口的当前前沿，并绘制未来的方向和挑战。我们建议举办一个为期两天的在线研讨会来满足这一需求。这个研讨会将邀请生物学中基础人工智能方法的研究人员进行演讲。新兴人工智能方法与生物学和健康问题之间的新方向和联系将被识别、综合、强调和正式化。研讨会将促进讨论，并帮助设定该领域未来研究的议程，帮助启动新方向并巩固有前途的方向。研讨会将在两天内举行约13场演讲和4场讨论会。演讲者将根据他们之前的工作和开发新的基础AI方法的经验来选择，以回答生物学问题。演讲者将被要求关注以下与生物学和人类健康相关的一个或多个基本AI主题：（1）AI的公平性和社会影响;（2）联邦学习;（3）生成式深度学习模型;（4）AI的可扩展性;（5）AI中的隐私和安全性;（6）方法优化和自动算法设计;（7）可解释的AI和因果关系;（8）主动学习和自动化科学;（9）迁移学习;（10）在人工智能中引入先验知识。虽然研讨会不可能深入涵盖所有这些基础方面，但我们的目标是尽可能多地涵盖。演讲者将把这些基础计算技术与基因组学、结构生物学、药物开发、系统生物学、生物医学成像、神经科学和疾病预测等领域的生物学问题联系起来。 在演讲者的许可下，演讲将被录制并在互联网上发布，参与者将被邀请为一份调查所确定的主题、方向和挑战的出版物投稿。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。