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

人工智能(AI)中的方法和新技术正在迅速发展、推广和应用于具有挑战性的生物学问题。与此同时，随着新的实验方法、新的数据收集努力以及对生物学的更好理解的发展，可用于人工智能方法的问题的类别和范围正在增长。人们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，一些相关、竞争和重叠的技术和方法也在不断进步。这种丰富的生态系统有利于研究和创新，但也使识别线索和紧迫问题变得更具挑战性。因此，有必要调查目前人工智能方法论和生物学之间的界面前沿，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。该研讨会将邀请从事生物学基本人工智能方法研究的研究人员进行演讲。新出现的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、突出和正式确定。研讨会将促进讨论，并帮助确定这一领域未来研究的议程，有助于启动新的方向和巩固有希望的方向。研讨会将包括大约13次演讲和4次讨论，为期两天。演讲者将根据他们之前的工作和开发回答生物学问题的新的基础人工智能方法的经验来选择。演讲者将被要求集中讨论以下一个或多个与生物学和人类健康有关的基本人工智能主题：(1)人工智能的公平和社会影响；(2)联合学习；(3)生成性深度学习模型；(4)人工智能的可扩展性；(5)人工智能中的隐私和安全；(6)方法优化和自动算法设计；(7)可解释的人工智能和因果性；(8)主动学习和自动化科学；(9)迁移学习；以及(10)在人工智能中融入先验知识。尽管研讨会不可能深入涵盖所有这些基本方面，但我们的目标是尽可能多地涵盖这些方面。演讲者将把这些基本的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。经演讲者允许，演讲将被记录下来并在互联网上分发，参与者将被邀请向一份出版物投稿，该出版物概述了确定的主题、方向和挑战。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.