About AIFS
The Artificial Intelligence Institute for Next Generation Food Systems, or AIFS, leverages AI to solve the world’s biggest challenges to crop and food production: ensuring a sustainable, nutritious, efficient, and safe food supply while mitigating the impacts of changing conditions.
In order to accelerate critical solutions to these challenges, AIFS will bring artificial intelligence technology to the entire food system from crop breeding and farming to food production and nutrition. The institute will combine the development of the latest breakthroughs in artificial intelligence with preparing the food and agriculture industries to rapidly adopt them and ready the workforce.
Established on October 1, 2020, AIFS brings together more than 40 researchers from six institutions: UC Davis; UC Berkeley; Cornell University; the University of Illinois, Urbana-Champaign; UC Agriculture and Natural Resources; and the U.S. Department of Agriculture's Agricultural Research Service.
The Institute is a leading source of research, development and commercialization of novel AI-based solutions in food and agriculture through a three-pronged strategy of multidisciplinary science, industry engagement and workforce development.
Funding for the institute is provided by the U.S. Department of Agriculture's National Institute of Food and Agriculture as part of a larger initiative led by the U.S. National Science Foundation to establish new artificial intelligence institutes to accelerate research, expand America's workforce and transform society in the decades to come.
The AI Institute for Next Generation Food Systems, or AIFS, aims to meet growing demands in our food supply by increasing efficiencies using AI and bioinformatics spanning the entire system — from growing crops through consumption.
Food System and Supply Chain Resiliency
AI-Driven Food Systems to Improve Human Health
AI Infrastructure Development for Food Systems
AIFS tackles three key challenges by
1. Integrating digital and biological technologies to provide solutions that address the complexity and diversity of food systems.
2. Developing in models and obtaining data sets for AI that are publicly available.
3. Improving the safety, efficiency, and accuracy of food system tasks that requires human involvement and decision-making.