Discovery Informatics: AI Takes a Science-Centered View on Big Data
AAAI Technical Report FS-13-01
Discovery informatics focuses on intelligent systems aimed at accelerating discovery, particularly in science but also from any data-rich domain. It is a generalization of scientific informatics work (for example, medical-, bio-, eco-, or geoinformatics) that seeks to apply principles of intelligent computing and information systems in order to understand, automate, improve, and innovate any aspects of discovery processes. A range of AI research is directly relevant including process representation and workflows; intelligent interfaces; causal reasoning; machine learning; knowledge representation and engineering; semantic web; advanced visualization toolkits and social computing. e application of AI approaches to assist in scientific discovery is an open ended knowledge-driven challenge with a very high potential impact. is is especially true in this era of big data, which provides the theme of this symposium.
is symposium provides a forum for researchers interested in understanding the role of AI techniques in improving or innovating scientific processes. Specific topics of discussion include, but are not limited to the following:
• What are the broad AI challenges in scientific discovery?
• How can we support the way scientists approach big data?
• How do we get to big data from smaller (or darker) data sets through automated or assisted integration and aggregation?
• What integrated AI capabilities are needed to tackle big data in science?
• How can we improve our understanding of science and discovery processes and the role of AI in the context of those processes?
• How can we capture science processes and open them to scientists in other disciplines and the broader public?
• Can AI be eﬀective in facilitating insights and looking for knowledge gaps using big data?
Gully APC Burns (University of Southern California)
Yolanda Gil (University of Southern California)
Yan Liu (University of Southern California)
Natalia Villanueva-Rosales (University of Texas at El Paso) vii