Austin, Texas – The National Science Foundation Artificial Intelligence Institute, based at the University of Texas at Austin, receives ongoing funding for research that improves the accuracy and reliability of AI models and leads to the development and improvement of new drugs in clinical diagnostics.
Research on the NSF AI Instation for Machange Learning (IFML) supports the next generation of artificial intelligence and is important for developing more accurate AI systems, from the mathematics of diffusion models to algorithms that improve the speed and accuracy of magnetic resonance imaging.
“UT Austin is a research powerhouse focused on preparing students who will flourish in an AI-driven future,” said David Vanden Match, interim executive vice president and provost at UT, to resume his dean of the University of Natural Sciences on August 1. Learning affects almost every field of science and technology. ”
Updated funding will enable IFML to address key challenges related to training best practices and large-scale models of fine-tuning, deep network robustness and interpretability, and domain adaptation in areas such as protein engineering and AI for health. It also enables IFML to support new postdocs and graduate students, expand workforce development efforts, and meet future demands for a highly skilled AI workforce based on the newly launched Masters of Science program at UT.
“Machine learning is the engine that powers AI applications across industries around the world, but it is often difficult to use on its own,” says Adam Klivans, professor of computer science at the University of Texas Austin University. “At IFML, we are working on open source development so that everyone can apply new models and algorithms. This openness directly leads to a wide range of impacts across multiple fields.”
Less than 30 NSF-led National Artificial Intelligence Research is undertaken across the US, two based in UT: IFML and NSF-Simons AI Institute for Cosmic Origins.
IFML is made up of UT researchers. University of Washington; Wichita State University; Microsoft Research; Stanford University; Santa Fe Institute; University of California, Los Angeles. University of California, Berkeley. California Institute of Technology. Boston College; University of Nevada, Reno.
Since its founding, IFML has been at the forefront of generative AI research and has contributed to the advancement of OpenClip and DataComp, tools that improve the way AI systems understand images and text together. This ongoing investment from NSF totals $20 million over five years, and IFML is built on an impressive track record, allowing it to further strengthen its position as a global leader in basic machine learning.
