NSF Creates Five New Artificial Intelligence Research Institutes
Aug 31, 2020
According to NSF, the five institutes will cover the following topics:
- NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography, led by a team at the University of Oklahoma, Norman, assembles researchers in AI, atmospheric and ocean science, and risk communication to develop user-driven trustworthy AI that addresses pressing concerns in weather, climate, and coastal hazards prediction. With AI certificate programs aimed at workforce skills, the institute is providing the research and training necessary for the future workforce to deliver the advances needed to deal with forecasting and prediction challenges.
- NSF AI Institute for Foundations of Machine Learning, led by a team at the University of Texas, Austin, focuses on major theoretical challenges in AI, including next-generation algorithms for deep learning, neural architecture optimization, and efficient robust statistics. The institute's partners include large industrial technology companies and the city of Austin. Major online coursework and research initiatives will bring current AI tools to thousands of students and professionals across the country.
- NSF AI Institute for Student-AI Teaming, led by a team at the University of Colorado, Boulder, develops groundbreaking AI that helps both students and teachers to work and learn together more effectively, and equitably, while helping educators focus on what they do best: inspiring and teaching students. The vision is to develop engaging "AI partners" that will observe, participate in, and facilitate collaborative STEM learning conversations by interacting naturally through speech, gesture, gaze, and facial expression in real-world classrooms and remote learning settings.
- NSF AI Institute for Molecular Discovery, Synthetic Strategy, and Manufacturing (or the NSF Molecule Maker Lab), led by a team at the University of Illinois at Urbana-Champaign, focuses on development of new AI-enabled tools to accelerate automated chemical synthesis and advance the discovery and manufacture of novel materials and bioactive compounds. The institute also serves as a training ground for the next generation of scientists with combined expertise in AI, chemistry, and bioengineering.
- NSF AI Institute for Artificial Intelligence and Fundamental Interactions, led by a team at the Massachusetts Institute of Technology, incorporates workforce development, digital learning, outreach, and knowledge transfer programs to develop AI methods that integrate the laws of physics as a guiding framework to advance our knowledge — from the smallest building blocks of nature to the largest structures in the universe — and galvanize AI research innovation to broaden societal impacts.
In addition to the five new NSF AI Institutes, USDA is launching two partnering institutes which will receive an additional $40 million over the next five years:
- USDA-NIFA AI Institute for Next Generation Food Systems, led by a team at the University of California, Davis, integrates a holistic view of the food system with AI and bioinformatics to understand biological data and processes, addressing issues of molecular breeding to optimize traits for yield, crop quality, and pest/disease resistance; agricultural production, food processing and distribution, and nutrition. Major emphasis is on inclusive education and outreach approaches to build a diverse, next-generation workforce.
- USDA-NIFA AI Institute for Future Agricultural Resilience, Management, and Sustainability, led by a team at the University of Illinois at Urbana-Champaign, advances AI research in computer vision, machine learning, soft object manipulation and intuitive human-robot interaction to solve major agricultural challenges including labor shortages, efficiency and welfare in animal agriculture, environmental resilience of crops, and the need to safeguard soil health. The institute features a new joint Computer Science + Agriculture degree and global clearinghouse to foster collaboration in AI-driven agriculture research.