AI for Science
Rick Stevens
Professor
Professor Rick Stevens is internationally known for work in high-performance computing, collaboration and visualization technology, and for building computational tools and web infrastructures to support large-scale genome and metagenome analysis for basic science and infectious disease research. A current focus is the national initiatives for Exascale computing and Artificial Intelligence (AI). He is the Associate Laboratory Director at Argonne National Laboratory, and a Professor of Computer Science at the University of Chicago. In addition, he is the principle investigator of the NIH-NIAID funded PATRIC Bioinformatics Resource Center, the Exascale Computing Project (ECP) Exascale Deep Learning and Simulation Enabled Precision Medicine for Cancer project, and the predicitive models pilot of the DOE-NCI funded Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) project. Over the past twenty years, he and his colleagues have developed the SEED, RAST, MG-RAST, and ModelSEED genome analysis and bacterial modeling servers that have been used by tens of thousands of users to annotate and analyze more than 250,000 microbial genomes and metagenomic samples.
In this talk, I will describe an emerging initiative at Argonne National Laboratory to advance the concept of Artificial Intelligence (AI) aimed at addressing challenge problems in science. We call this initiative “AI for Science”.
The basic concept is threefold:
- (1) to identify those scientific problems where existing AI and machine learning methods can have an immediate impact (and organize teams and efforts to realize that impact);
- (2) identify areas of where new AI methods are needed to meet the unique needs of science research (frame the problems, develop test cases, and outline work needed to make progress); and
- (3) to develop the means to automate scientific experiments, observations, and data generation to accelerate the overall scientific enterprise.
Science offers plenty of hard problems to motivate and drive AI research, from complex multimodal data analysis to integration of symbolic and data intensive methods, to coupling large-scale simulation and machine learning to drive improved training to control and accelerate simulations. A major sub-theme is the idea of working toward the automation of scientific discovery through integration of machine learning (active learning and reinforcement learning) with simulation and automated high-throughput experimental laboratories. I will provide some examples of projects underway and layout a set of long-term driver problems.