Mathematical Foundations of Artificial Intelligence

National Science Foundation (NSF)

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Next deadline: Oct 10, 2024

Later deadlines: Oct 10, 2025, Oct 9, 2026

Grant amount: US $500,000 - US $1,500,000

Fields of work: Mathematics Cognitive Science Computer Science & Engineering Artificial Intelligence (AI)

Applicant type: Nonprofit, College / University, Museum/Library/Zoo

Funding uses: Research

Location of project: Preferred: United States Other eligible locations: Anywhere in the world

Location of residency: United States

Overview:

NOTE: All applications are due by 5:00 PM local time of applicant organization. 

Machine Learning and Artificial Intelligence (AI) are enabling extraordinary scientific breakthroughs in fields ranging from protein folding, natural language processing, drug synthesis, and recommender systems to the discovery of novel engineering materials and products. These achievements lie at the confluence of mathematics, statistics, engineering and computer science, yet a clear explanation of the remarkable power and also the limitations of such AI systems has eluded scientists from all disciplines. Critical foundational gaps remain that, if not properly addressed, will soon limit advances in machine learning, curbing progress in artificial intelligence. It appears increasingly unlikely that these critical gaps can be surmounted with increased computational power and experimentation alone. Deeper mathematical understanding is essential to ensuring that AI can be harnessed to meet the future needs of society and enable broad scientific discovery, while forestalling the unintended consequences of a disruptive technology. The National Science Foundation Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and Social, Behavioral and Economic Sciences (SBE) will jointly sponsor research collaborations consisting of mathematicians, statisticians, computer scientists, engineers, and social and behavioral scientists focused on the mathematical and theoretical foundations of AI.

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This page was last reviewed May 08, 2024 and last updated May 02, 2024