Abstracts of the invited talks presented at the AAAI Fall Symposium on Discovery Informatics: AI Takes a Science-Centered View on Big Data. Talks include A Data Lifecycle Approach to Discovery Informatics, Generating Biomedical Hypotheses Using Semantic Web Technologies, Socially Intelligent Science, Representing and Reasoning with Experimental and Quasi-Experimental Designs, Bioinformatics Computation of Metabolic Models from Sequenced Genomes, Climate Informatics: Recent Advances and Challenge Problems for Machine Learning in Climate Science, Predictive Modeling of Patient State and Therapy Optimization, Case Studies in Data-Driven Systems: Building Carbon Maps to Finding Neutrinos, Computational Analysis of Complex Human Disorders, and Look at This Gem: Automated Data Prioritization for Scientific Discovery of Exoplanets, Mineral Deposits, and More.

The titles of the five symposia were Discovery Informatics: AI Takes a Science-Centered View on Big Data (FS-13-01); How Should Intelligence Be Abstracted in AI Research: MDPs, Symbolic Representations, Artificial Neural Networks, or --? (FS-13-02); Integrated Cognition (FS-13-03); Semantics for Big Data (FS-13-04); and Social Networks and Social Contagion: Web Analytics and Computational Social Science (FS-13-05). The highlights of each symposium are presented in this report. The symposia included Discovery Informatics: AI Takes a Science-Centered View on Big Data; How Should Intelligence Be Abstracted in AI Research: MDPs, Symbolic Representations, Artificial Neural Networks, or --? Integrated Cognition; Semantics for Big Data; and Social Networks and Social Contagion: Web Analytics and Computational Social Science. The Discovery Informatics symposium provided a forum for understanding the role of AI techniques in improving or innovating scientific processes. Researchers at the How Should Intelligence be Abstracted in AI Research symposium discussed and compared different abstractions of both intelligence and processes that might create it.

Specifically, the assignment of meaningful tags (annotations) to each unique data granule is best achieved through collaborative participation of data providers, curators and end users to augment and validate the results derived from machine learning (data mining) classification algorithms. The annotations provide curation, provenance and semantic (scientifically meaningful) metadata about the data source and the data object being studied. The design and specification of a unique, meaningful, searchable and scientifically impactful set of tags can be achieved through collaborative (human-plus-machine) annotation efforts and through discovery informatics research. These steps will produce a searchable classification and indexing scheme for the curation, classification, discovery, reuse, interoperability, integration and understanding of digital repositories.

Artificial Intelligence researchers have long sought to understand and replicate processes of scientific discovery. This article discusses Discovery Informatics as an emerging area of research that builds on that tradition and applies principles of intelligent computing and information systems to understand, automate, improve, and innovate processes of scientific discovery.

Addressing the ambitious research agendas put forward by many scientific disciplines requires meeting a multitude of challenges in intelligent systems, information sciences, and human-computer interaction. Many aspects of the scientific discovery process are often largely manual and could be automated, improved, or made more efficient. Better interfaces for collaboration, visualization, and understanding would significantly improve scientific practice. Scientific data, publications, and tools could be published in open formats with appropriate semantic descriptions and metadata annotations to improve sharing and dissemination. Opportunities for broader participation in well-defined scientific tasks enable human contributors to provide large amounts of data, annotations, or complex processing results that could not otherwise be obtained. Improvements and innovations across the spectrum of scientific processes and activities will have a profound impact on the rate of scientific discoveries.