The evolution of machine perception to machine learning and reasoning, and ultimately machine intelligence, has a potential to significantly impact acceleration and advancement of autonomous scientific discovery and the operation of scientific instruments. While machine reasoning will enable intelligent systems to better understand and interact with their physical world, machine intelligence through modeling, simulation and automation, closes the gap between experiments, extreme computing, and scientific discovery. In order to usher in this new era of autonomous science, advances in several areas of artificial intelligence and other disciplines e.g., high-performance computing, data engineering need to come together. Therefore, the goal of this workshop is to bring together researchers from diverse backgrounds to enable extreme scaling of AI for science.
This workshop will address the overarching goal of enabling semi-autonomous and autonomous AI-driven predictive and prescriptive scientific discovery at scale by integrating extreme-scale heterogeneous and reconfigurable computing paradigms, multiscale mathematics, physics-based simulation, data sciences and engineering to address challenges across science, scientific instruments, and security domains e.g., biology, chemistry, and material science. Specific areas of interest include:
● Algorithms: Advance extreme-scale Artificial Intelligence through algorithmic development in the areas of probabilistic reasoning, multimodal representation learning, natural language processing, robotics, decision making, combinatorial optimization and
● Implementation and deployment: Enable scalable Artificial Intelligence through advances in distributed and parallel AI algorithms and tools, heterogeneous and reconfigurable computing platforms and paradigms, Exascale systems, and compilers and system software for extreme-scale AI/ML algorithms.
● Applications: Discuss application use cases in science domains of importance including computational biology, molecular chemistry, material science, epidemiology, energy and physics.
The workshop seeks short and long papers spanning all areas of scaling AI for science, engineering and security domains including but not limited to:
● Parallel and distributed algorithms for machine learning, machine reasoning, and machine intelligence at scale. Specific examples include: probabilistic reasoning, data analytics, knowledge representation learning, multi-modal analysis, natural language processing, robotics, decision making, combinatorial optimization and human-machine interaction
● Software tools, compilers and system software to enable AI for machine perception and reasoning at scale. Specific examples include PyTorch (and Glow), TensorFlow (and XLA), CNTK, TVM, and the MLIR framework.
● Application case studies in all areas of science and engineering such as biology, chemistry, material science, high energy physics, and climate security.