Mathematical Foundations of Artificial Intelligence

The summary for the Mathematical Foundations of Artificial Intelligence grant is detailed below. This summary states who is eligible for the grant, how much grant money will be awarded, current and past deadlines, Catalog of Federal Domestic Assistance (CFDA) numbers, and a sampling of similar government grants. Verify the accuracy of the data FederalGrants.com provides by visiting the webpage noted in the Link to Full Announcement section or by contacting the appropriate person listed as the Grant Announcement Contact. If any section is incomplete, please visit the website for the National Science Foundation, which is the U.S. government agency offering this grant.
Mathematical Foundations of Artificial Intelligence: 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. Research activities should focus on the most challenging mathematical and theoretical questions aimed at understanding the capabilities, limitations, and emerging properties of AI methods as well as the development of novel, and mathematically grounded, design and analysis principles for the current and next generation of AI approaches. Specific research goals include: establishing a fundamental mathematical understanding of thefactors determining the capabilities and limitations of current and emerging generations of AI systems, including, but not limited to, foundation models, generative models, deep learning, statistical learning, federated learning, and other evolving paradigms; the development of mathematically grounded design and analysis principles for the current and next generations of AI systems; rigorous approaches for characterizing and validating machine learning algorithms and their predictions; research enabling provably reliable, translational, general-purpose AI systems and algorithms; encouragement of new collaborations across this interdisciplinary research community and from diverse institutions. The overall goal is to establish innovative and principled design and analysis approaches for AI technology using creative yet theoretically grounded mathematical and statistical frameworks, yielding explainable and interpretable models that can enable sustainable, socially responsible, and trustworthy AI.
Federal Grant Title: Mathematical Foundations of Artificial Intelligence
Federal Agency Name: National Science Foundation (NSF)
Grant Categories: Science and Technology and other Research and Development
Type of Opportunity: Discretionary
Funding Opportunity Number: 24-569
Type of Funding: Grant
CFDA Numbers: 47.041, 47.049, 47.070, 47.075
CFDA Descriptions: Information not provided
Current Application Deadline: October 10th, 2024
Original Application Deadline: October 10th, 2024
Posted Date: May 2nd, 2024
Creation Date: May 2nd, 2024
Archive Date: November 8th, 2026
Total Program Funding: $8,500,000
Maximum Federal Grant Award: $1,500,000
Minimum Federal Grant Award: $500,000
Expected Number of Awards:
Cost Sharing or Matching: No
Last Updated: May 2nd, 2024
Applicants Eligible for this Grant
Others (see text field entitled "Additional Information on Eligibility" for clarification.)
Additional Information on Eligibility
*Who May Submit Proposals: Proposals may only be submitted by the following: -Non-profit, non-academic organizations: Independent museums, observatories, research laboratories, professional societies and similar organizations located in the U.S. that are directly associated with educational or research activities. - <span>Institutions of Higher Education (IHEs) - Two- and four-year IHEs (including community colleges) accredited in, and having a campus located in the US, acting on behalf of their faculty members.</span> *Who May Serve as PI: <div class="OutlineElement Ltr SCXW177155816 BCX0"> <p class="Paragraph SCXW177155816 BCX0"><span class="TrackChangeTextInsertion TrackedChange SCXW177155816 BCX0"><span class="TextRun SCXW177155816 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW177155816 BCX0">As of the date the proposal is </span><span class="NormalTextRun SCXW177155816 BCX0">submitted</span><span class="NormalTextRun SCXW177155816 BCX0">, any PI, co-PI, or senior/key personnel must hold either:</span></span></span><span class="EOP TrackedChange SCXW177155816 BCX0" data-ccp-props="{"></span> <ul> <li><span class="TrackChangeTextInsertion TrackedChange SCXW177155816 BCX0"><span class="TextRun SCXW177155816 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none">a tenured or tenure-track position, </span></span><span class="TrackChangeTextInsertion TrackedChange SCXW177155816 BCX0"><span class="TextRun SCXW177155816 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none">or</span></span><span class="EOP TrackedChange SCXW177155816 BCX0" data-ccp-props="{"></span></li> <li><span class="TrackChangeTextInsertion TrackedChange SCXW177155816 BCX0"><span class="TextRun SCXW177155816 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none">a primary, full-time, paid appointment in a research or teaching position</span></span></li> </ul> <span class="TrackChangeTextInsertion TrackedChange SCXW177155816 BCX0"><span class="TextRun SCXW177155816 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW177155816 BCX0">at a US-based campus of an organization eligible to </span><span class="NormalTextRun SCXW177155816 BCX0">submit</span><span class="NormalTextRun SCXW177155816 BCX0"> to this solicitation (see above), with exceptions granted for family or medical leave, as </span><span class="NormalTextRun SCXW177155816 BCX0">determined</span><span class="NormalTextRun SCXW177155816 BCX0"> by the </span><span class="NormalTextRun SCXW177155816 BCX0">submitting</span><span class="NormalTextRun SCXW177155816 BCX0"> organization. Individuals with </span></span></span><span class="TrackChangeTextInsertion TrackedChange SCXW177155816 BCX0"><span class="TextRun SCXW177155816 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none">primary</span></span><span class="TrackChangeTextInsertion TrackedChange SCXW177155816 BCX0"><span class="TextRun SCXW177155816 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"> appointments at for-profit non-academic organizations or at overseas branch campuses of U.S. i
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NSF Publication 24-569
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