As a recent Computing Community Consortium (CCC) white paper noted, scientific progress in many disciplines is increasingly enabled by our ability to examine natural phenomena through the computational lens, and our ability to acquire, share, integrate and analyze disparate types of data. The emergence of “big data”, instead of making the scientific method obsolete as some have suggested, underscores challenges in the development of algorithmic or information processing abstractions of various aspects of the scientific methods and processes; the development of cognitive tools that complement and extend human intellect, in the form of computational artifacts (representations, processes, protocols, workflows, software) to partner with humans on all aspects of science (e.g., mapping the state of knowledge in a discipline and identifying gaps, formulating and prioritizing questions; designing, prioritizing and executing experiments; drawing inferences and constructing explanations and hypotheses from the literature, databases, knowledge bases, expressing and reasoning with scientific arguments of variable certainty and provenance; synthesizing findings from disparate observational and experimental studies; formulating new questions, in a closed-loop fashion); Integration of the resulting cognitive tools into collaborative human-machine systems and infrastructure to advance science, including tools for documentation, replication and communication of scientific studies, collaboration, team formation (incentivizing participants, decomposing tasks, combining results, engaging participants with different levels of expertise and abilities), communication (across disciplinary boundaries and across levels of abstraction), tracking scientific progress and impact.

The AAAI Fall Symposium on Accelerating Science: A Grand Challenge for AI (co-sponsored by AAAI and the CRA Computing Community Consortium) aimed to bring together researchers in relevant areas of artificial intelligence (e.g., machine learning, causal inference, knowledge representation and inference, planning, decision making, human computer interaction, distributed problem solving, natural language processing, multi-agent systems, semantic web, information integration, scientific workflows), high performance data and computing infrastructures and services, and selected application areas (e.g., life sciences, learning sciences, health sciences, social sciences, food energy and water nexus) to discuss progress on, and articulate a research agenda aimed at addressing, the AI grand challenge of accelerating science.

This was one of several symposia in the AAAI 2016 Fall Symposium series. Learn more about the symposia here and register for the Symposium series here.

You can watch video recording of the symposium here. Learn more about the CCC’s work on AI by visiting the AI task force page.