Recipient Organization
NEW MEXICO STATE UNIVERSITY
1620 STANDLEY DR ACADEMIC RESH A RM 110
LAS CRUCES,NM 88003-1239
Performing Department
(N/A)
Non Technical Summary
The purpose of this Expanding AI Innovation through Capacity Building and Partnerships (ExpandAI) proposal is to contribute to broaden participation in AI research, education, and workforce development through a partnership between New Mexico State University (NMSU) and the AI institute for Next Generation Food Systems (AIFS). This partnership builds on an authentic collaboration, spanning the entire spectrum of foundational and use-inspired AI research innovations, development of educational and training programs, and efforts towards broadening participation in AI for Hispanic students and students from other underrepresented groups. The motivations for this project derive from (1) a shared vision of creating AI innovations to address the pressing needs of resilient food infrastructure through climate-smart agriculture; (2) a desire to contribute to the growth of AI research capacity in a Hispanic Serving Institution; (3) a need to create new research and training opportunities that serve broad and diverse audiences. The project will provide mutually beneficial contributions to NMSU and AIFS; NMSU will expand its AI research capacity and create new educational opportunities to its diverse student population; AIFS will benefit from newly developed AI methodologies and tools which support its existing research thrusts and access a new pool of talent and expertise in broadening participation in AI.
Animal Health Component
0%
Research Effort Categories
Basic
100%
Applied
0%
Developmental
0%
Goals / Objectives
The purpose of this Expanding AI Innovation through Capacity Building and Partnerships (ExpandAI) proposal is to contribute to broaden participation in AI research, education, and workforce development through a partnership between New Mexico State University (NMSU) and the AI institute for Next Generation Food Systems (AIFS). This partnership builds on an authentic collaboration, spanning the entire spectrum of foundational and use-inspired AI research innovations, development of educational and training programs, and efforts towards broadening participation in AI for Hispanic students and students from other underrepresented groups. The motivations for this project derive from (1) a shared vision of creating AI innovations to address the pressing needs of resilient food infrastructure through climate-smart agriculture; (2) a desire to contribute to the growth of AI research capacity in a Hispanic Serving Institution; (3) a need to create new research and training opportunities that serve broad and diverse audiences. The project will provide mutually beneficial contributions to NMSU and AIFS; NMSU will expand its AI research capacity and create new educational opportunities to its diverse student population; AIFS will benefit from newly developed AI methodologies and tools which support its existing research thrusts and access a new pool of talent and expertise in broadening participation in AI.
Project Methods
Development of machine learning-informed methodologies for phenotypic analysis of plants (e.g., pecans, pistachios, cotton, chile) to improve understanding of adaptation possibilities and to improve understanding ofphenotypic correlation to the organisms'genotype.Development of AI-informed climate-smart solutions for resilient crop production systems. This will start with the exploration of methodsfor biotic and abiotic stress detection,using novel tools for monitoring water and nutrients movement andenvironmental drivers. This will be followed by the development of integrated systems to upscale predictions to regional resilience. Finally, this research thrust will explore automated and adaptive land use managementfor resilient crop production systems, relying on the view of a crop production system as a multi-agent system.Development of novel solutions to improve reasoning performance of autonomous agents by integrating new knowledge derived from machine learning and other learning systems (e.g., generative AI systems). This will require exploration of novel neuro-symbolic solutions that integrated symbolic systems (e.g., logic-based frameworks) with statistical systems (e.g., neural networks).Development of novel formalisms for the description of the capabilities of a collective of agents operating in an agent-human environment. The formalism should be grounded in formal semantics to enable the development of provably correct reasoning algorithms (e.g., for planning and diagnosis). The formalism should also be able to capture nuances of operating with humans, e.g., by representing knowledge, beliefs, and attitudes and providing the agent with the ability to reason and reach conclusions in presence of epistemic content.Development of novel algorithms to support various reasoning tasks - including the use of high-performance computing architectures to promote efficiency of solutions.Exploration of novel frameworks to promote explainability of the reasoning algorithms employed by a multi-agent system. The framework should enable the development of a conversation between the agent and the human to reach a consensus in the understanding of the reasoning process.Co-development of curriculum materials exposing AI content in different K-12 disciplines, in collaboration with K-12 teachers..