Mathematical Foundations of Artificial Intelligence NSF: In the last decade, we have seen phenomenal breakthroughs in the field of technologies. And, if two fields can be credited for that, they have to be machine learning and artificial intelligence. They are closely related and have led major advancements in domains like natural language processing, protein folding, drug synthesis, recommender systems, discovery of engineering materials, etc. When you think about it, the source of achievements can be found in the confluence of conventional fields like statistics, mathematics, computer science, and engineering. Though AI and ML have made giant strides, there are still foundational gaps that remain.
Deeply understanding the mathematics behind AI is essential. Mathematical Foundations of Artificial Intelligence (MFAI) sponsored by NSF directorates supports research focused on harnessing the true power of AI. Essentially, it dives deep on comprehending the limitations, capabilities, and evolving features of AI methods. It also aims to develop practical and mathematically-sound principles that can help in design and analysis of AI algorithms. Let us briefly discuss the main aspects of the program in this blog.
Goals of Mathematical Foundations of Artificial Intelligence NSF
Teams for research, i.e., Principal Investigator (PI) teams must have requisite skillsets in the fields of statistics, engineering, computer science, and mathematics, etc. They must have sufficient knowledge about all the tools and methods relevant to the project. Their priority must be to explore the science behind AI. They must study the theoretical and mathematical basics in detail. At the same time, it is important to explore the application areas where mathematical improvements can lead to discovery of new capabilities. It is important to also keep an eye on the improvements in empirical observations if and when they occur. Specific objectives of the program are relayed in the following points:
- Mathematical comprehension, grounded in basics, about the factors that underline the capabilities and drawbacks of the AI systems.
- Research and analysis of foundational models, federated learning, generative models, statistical learning, and other algorithms when they occur.
- Creating analysis and design principles based on mathematics for the present and future generations of AI.
- Implementing robust approaches for model characterization and verification of machine learning algorithms.
- Carrying out research that enables general-purpose AI algorithms and systems that are translational, reliable, and robust.
- Ensuring collaboration between empirical and theoretical researchers to promote sharing of knowledge.
- Facilitate new collaborations among researchers and computer scientists from a broad community of several disciplines, institutions, etc.
Opportunities of Mathematical Foundations of Artificial Intelligence NSF
The program offers several opportunities to the researchers that are mentioned in the points given below:
- Researchers get opportunities to improve the mathematical basics of modern AI and drive changes in the modern world.
- PI teams can improve their understanding of concepts like methodology and statistical inference, symbolic and formal logic, complexity theory, topology, representation theory, algebraic geometry, etc.
- Researchers can also understand and advance analysis approaches like partial differential equations, approximation theory, mean field theory, and optimization theory.
- An opportunity to contribute toward interpretability, generalizability, and scalability of Artificial Intelligence
- Candidates can expect awards such as a continuing grant or standard grant. The anticipated number of awards is currently 15. The size of the award might vary and is dependent on the availability of funds, scale of the project, and merit of the proposal. However, as per the website, the anticipated funding amount for new awards is currently $8,500,000.
Grant Opportunity of Mathematical Foundations of Artificial Intelligence NSF
Participants can be awarded with grants that range from $500-$1,500,000. It might be subject to the quality of proposal, Grant General Conditions, NSF CA-FTC, budget of the project, NSW Proposal and Award policies, etc. For more information on the policies, you may refer to NSF PAPPG. Only authorized participants are eligible for the grants such as research laboratories, educational institutes, independent museums, professional societies related to research or education, institutions of higher education (IHEs) etc. For detailed information on Grant opportunities like eligibility, cost sharing or matching requirement, award ceiling, award floor, etc., check out grants.gov website. You can also apply for the grant there if you are eligible. However, be careful with the closing date of applications which you can find on the website.
Merit Review Criteria of Mathematical Foundations of Artificial Intelligence NSF
NSF assesses all the proposals through its well-defined merit review criteria. In some cases, there might be special objectives and activities of the program. In such situations, NSF might adopt additional criteria to evaluate such proposals. For more information on additional criteria, you can follow the PAPPG link given above. The two main criteria utilized for evaluation of proposals are discussed below:
- Broader Impacts: The criterion for broader impacts covers aspects that measures the potential of the research to benefit the society. It measures the contribution that the research might have toward achievement of a certain societal outcome.
- Intellectual Merit: As the name suggests, using this criterion, the reviewers evaluate the potential of the research in terms of advancing the knowledge.
While evaluating the proposals, basically reviewers focus on what proposers aim to achieve and why they want to achieve it. They also assess the specific strategies that proposers have in mind and the overall outcome should they achieve success in their endeavor. In proposal evaluation, reviewers pay attention to both technical details of the proposal as well as the project’s potential to make a broader impact.
Conclusion
As AI is all set to drive the technological advancement of the world, continuous research, innovation, and research collaborations is essential. NSF has stepped forward with a program that aims to bring together a broad community of researchers, advance capabilities of AI, minimize limitations, and harness its true potential. The program provides handsome grants and rewards to the best teams and provides opportunities to further mathematical foundations of AI and spark innovation. The blog focuses on Mathematical Foundations of Artificial Intelligence NSF and explains objectives, opportunities, grant, and merit review criteria.
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