The synergies between advances in computing and advances in science open the doors to exciting research agendas in computer science. Scientific questions have motivated computer science research in many areas including distributed sensor networks, high-end computing, distributed systems, scalable databases, statistical and data mining algorithms, computer networks and the web itself. Scientists have now the means to collect and process unprecedented amounts of data to understand phenomena that could not be studied before, from climate change to social networks to phylogenetics.
A new community of Discovery Informatics is emerging to understand the role of information and intelligent systems research in improving and innovating scientific processes in ways that will accelerate discoveries. Although computing has become central to science, there are important hallmarks in the 21st century that remain largely unaddressed and where AI research plays a central role.
First, discovery processes are increasingly complex and broader in scope. They remain largely human driven, and human cognitive limitations have become a bottleneck. New approaches are needed to address this complexity.
Second, data must be connected more closely than ever to the models of the phenomena under study. The current separation of models and data is hurting our ability to test and improve models. We must improve our understanding of how to link data with models of the phenomena under study.
Third, science is an increasingly social endeavor. Recent systems enable citizen volunteers to contribute large amounts of data, annotations, or complex processing results that result in scientific discoveries. We need to design new approaches to harness human abilities in all forms to contribute to science.
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. There are many aspects of the scientific discovery process that our community could help automate, facilitate, or make more efficient through artificial intelligence techniques. For example, although considerable efforts have been directed toward data modeling and integration, these activities continue to demand large investments of scientists’ time and effort. The scientific literature continues to grow and is becoming more and more unmanageable for researchers operating in the most active disciplines. 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. These are just some examples of areas where there are opportunities for artificial intelligent techniques could make a difference. Improvements and innovations across the spectrum of scientific processes and activities will have a profound impact on the rate of scientific discoveries. This symposium provided a forum for researchers interested in understanding the role of AI techniques in improving or innovating scientific processes.
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