ΑΙhub.org
 

New AI research institutes announced in USA


by
27 August 2020



share this:
AIhub | US AI regulation

Yesterday, the National Science Foundation (NSF) and the US Department of Agriculture’s National Institute for Food and Agriculture (USDA-NIFA) announced investment to establish seven new artificial intelligence (AI) research institutes. Each centre will receive roughly $20 million over a five year period. The aim is that these new institutes will serve as hubs in a broader nationwide network.

The seven institutes are:

NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography, led by a team at the University of Oklahoma. The institute plans to develop user-driven trustworthy AI that addresses pressing concerns in weather, climate, and coastal hazards prediction. Find out more from the University of Oklahoma.

NSF AI Institute for Foundations of Machine Learning, led by a team at the University of Texas, Austin. The focus will be on major theoretical challenges in AI, including next-generation algorithms for deep learning, neural architecture optimization, and efficient robust statistics. Find out more from the University of Texas, Austin.

NSF AI Institute for Student-AI Teaming, led by a team at the University of Colorado, Boulder. The aim is to develop AI that helps both students and teachers to work and learn together more effectively and equitably. Find out more from the University of Colorado, Boulder.

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. This institute will focus on development of new AI-enabled tools to accelerate automated chemical synthesis and advance the discovery and manufacture of novel materials and bioactive compounds. Find out more from the University of Illinois at Urbana-Champaign.

NSF AI Institute for Artificial Intelligence and Fundamental Interactions, led by a team at the Massachusetts Institute of Technology. Research here will focus on combining physics and AI research to solve challenging physics problems. Find out more from MIT.

USDA-NIFA AI Institute for Next Generation Food Systems, led by a team at the University of California, Davis. The aim is to integrate food science, AI and bioinformatics to understand biological data and processes, crop issues, agricultural production, food processing and distribution, and nutrition. Find out more from University of California, Davis.

USDA-NIFA AI Institute for Future Agricultural Resilience, Management, and Sustainability, led by a team at the University of Illinois at Urbana-Champaign. The goal is to use machine learning techniques to solve major agricultural challenges such as labour shortages, efficiency and welfare in animal agriculture, environmental resilience of crops, and the need to safeguard soil health. Find out more from the University of Illinois at Urbana-Champaign.

Further reading

News article on the NSF webpage.
USDA’s press release.
VentureBeat article, which also covers the announcement of five new quantum research centres.




Lucy Smith is Senior Managing Editor for AIhub.
Lucy Smith is Senior Managing Editor for AIhub.

            AUAI is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

Maryna Viazovska’s proofs of sphere packing formalized with AI

  27 Apr 2026
Formalization achieved through a collaboration between mathematicians and artificial intelligence tools.

Interview with Deepika Vemuri: interpretability and concept-based learning

  24 Apr 2026
Find out more about Deepika's research bridging the gap between data-driven models and symbolic learning.

As a ‘book scientist’ I work with microscopes, imaging technologies and AI to preserve ancient texts

  23 Apr 2026
Using an array of technologies to recover, understand and preserve many valuable ancient texts.

Sony AI table tennis robot outplays elite human players

  22 Apr 2026
New robot and AI system has beaten professional and elite table tennis players.

Causal models for decision systems: an interview with Matteo Ceriscioli

  21 Apr 2026
How can we integrate causal knowledge into agents or decision systems to make them more reliable?

A model for defect identification in materials

  20 Apr 2026
A new model measures defects that can be leveraged to improve materials’ mechanical strength, heat transfer, and energy-conversion efficiency.

‘Probably’ doesn’t mean the same thing to your AI as it does to you

  17 Apr 2026
Are you sure you and the AI chatbot you’re using are on the same page about probabilities?

Interview with Xinwei Song: strategic interactions in networked multi-agent systems

  16 Apr 2026
Xinwei Song tells us about her research using algorithmic game theory and multi-agent reinforcement learning.



AUAI is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.02 - Association for the Understanding of Artificial Intelligence