Attention:The NSF Public Access Repository (PAR) system and access will be unavailable from 10:00 PM ET on Thursday, July 16 until 12:00 AM ET on Friday 17 due to maintenance. We apologize for the inconvenience.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: The AIFS Institute: Building a better food system through AI
Abstract Our food system is complex, multifaceted, and in need of an upgrade. Population growth, climate change, and socioeconomic disparities are some of the challenges that create a systemic threat to its sustainability and capacity to address the needs of an evolving planet. The mission of the AI Institute of Next Generation Food Systems (AIFS) is to leverage the latest advances in AI to help create a more sustainable, efficient, nutritious, safe, and resilient food system. Instead of using AI in isolation, AIFS views it as the connective tissue that can bring together interconnected solutions from farm to fork. From guiding molecular breeding and building autonomous robots for precision agriculture, to predicting pathogen outbreaks and recommending personalized diets, AIFS projects aspire to pave the way for infrastructure and systems that empower practitioners to build the food system of the next generation. Workforce education, outreach, and ethical considerations related to the emergence of AI solutions in this sector are an integral part of AIFS with several collaborative activities aiming to foster an open dialogue and bringing closer students, trainees, teachers, producers, farmers, workers, policy makers, and other professionals.  more » « less
Award ID(s):
1934568
PAR ID:
10555981
Author(s) / Creator(s):
; ; ; ; ; ; ;
Publisher / Repository:
John Wiley & Sons, Inc.
Date Published:
Journal Name:
AI Magazine
Volume:
45
Issue:
1
ISSN:
0738-4602
Page Range / eLocation ID:
89 to 93
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; (, JMIR Formative Research)
    BackgroundChronic diseases such as heart disease, stroke, diabetes, and hypertension are major global health challenges. Healthy eating can help people with chronic diseases manage their condition and prevent complications. However, making healthy meal plans is not easy, as it requires the consideration of various factors such as health concerns, nutritional requirements, tastes, economic status, and time limits. Therefore, there is a need for effective, affordable, and personalized meal planning that can assist people in choosing food that suits their individual needs and preferences. ObjectiveThis study aimed to design an artificial intelligence (AI)–powered meal planner that can generate personalized healthy meal plans based on the user’s specific health conditions, personal preferences, and status. MethodsWe proposed a system that integrates semantic reasoning, fuzzy logic, heuristic search, and multicriteria analysis to produce flexible, optimized meal plans based on the user’s health concerns, nutrition needs, as well as food restrictions or constraints, along with other personal preferences. Specifically, we constructed an ontology-based knowledge base to model knowledge about food and nutrition. We defined semantic rules to represent dietary guidelines for different health concerns and built a fuzzy membership of food nutrition based on the experience of experts to handle vague and uncertain nutritional data. We applied a semantic rule-based filtering mechanism to filter out food that violate mandatory health guidelines and constraints, such as allergies and religion. We designed a novel, heuristic search method that identifies the best meals among several candidates and evaluates them based on their fuzzy nutritional score. To select nutritious meals that also satisfy the user’s other preferences, we proposed a multicriteria decision-making approach. ResultsWe implemented a mobile app prototype system and evaluated its effectiveness through a use case study and user study. The results showed that the system generated healthy and personalized meal plans that considered the user’s health concerns, optimized nutrition values, respected dietary restrictions and constraints, and met the user’s preferences. The users were generally satisfied with the system and its features. ConclusionsWe designed an AI-powered meal planner that helps people create healthy and personalized meal plans based on their health conditions, preferences, and status. Our system uses multiple techniques to create optimized meal plans that consider multiple factors that affect food choice. Our evaluation tests confirmed the usability and feasibility of the proposed system. However, some limitations such as the lack of dynamic and real-time updates should be addressed in future studies. This study contributes to the development of AI-powered personalized meal planning systems that can support people’s health and nutrition goals. 
    more » « less
  2. (, npj Science of Food)
    Abstract By 2050, feeding nearly 10 billion people will require transformative changes to ensure nutritious, sustainable food for all. Our current food system is inefficient and unsustainable. Traditional attempts to transform the global food system are too slow to drive innovation at scale. Here we explore the potential of artificial intelligence to reshape the future of food. We review the state of the art in food development, discuss the data needed to define a new food product, and highlight seven challenges where AI can help us design nutritious, delicious, and sustainable foods for all. By leveraging AI to democratize food innovation, we can accelerate the transition to resilient global food systems that meet the urgent challenges of food security, climate change, and planetary health. 
    more » « less
  3. ; ; ; ; ; ; ; ; ; ; et al (, Environmental Research: Food Systems)
    Abstract Achieving large-scale, transformative climate change adaptations in agriculture while mitigating further climate impacts and supporting sustainable and equitable rural livelihoods is a grand challenge for society. Transformation of the agri-food system is necessary and inevitable, but the extent to which transformation can be intentionally guided toward desirable states remains unclear. We argue that, instead of targeting leverage points (LPs) in isolation, coordinated interventions at multiple LPs and their interactions are necessary to create a broader system transformation toward more adaptive futures. Using the southeastern United States of America as a case study, we conceptualize a way of doing transformation research in agri-food systems that integrates multiple theoretical and practical perspectives of how transformative pathways can be constructed from ‘chains’ of interacting LPs. We outline several principles for transformative research, the core of which are participatory, transdisciplinary, and convergence research methods needed for articulating a shared vision. These principles embrace an action-oriented approach to research in which the act of assembling diverse networks of researchers, stakeholders, and community partners itself can activate community- and regional-level LPs to scale up changes. Finally, we present tangible examples of specific LPs and their interactions targeted by agri-food system interventions currently underway or planned. This work offers an ‘anticipatory’ vision for agri-food system transformation research that recognizes the need to normatively create an enabling environment to build momentum toward shared visions of secure, equitable, and sustainable regional agri-food systems. 
    more » « less
  4. ; ; ; ; ; ; ; ; ; ; et al (, AI Magazine)
    Abstract The Institute for Student‐AI Teaming (iSAT) addresses the foundational question:how to promote deep conceptual learning via rich socio‐collaborative learning experiences for all students?—a question that is ripe for AI‐based facilitation and has the potential to transform classrooms. We advance research in speech, computer vision, human‐agent teaming, computer‐supported collaborative learning, expansive co‐design, and the science of broadening participation to design and study next generation AI technologies (called AI Partners) embedded in student collaborative learning teams in coordination with teachers. Our institute ascribes to theoretical perspectives that aim to create a normative environment of widespread engagement through responsible design of technology, curriculum, and pedagogy in partnership with K–12 educators, racially diverse students, parents, and other community members. 
    more » « less
  5. ; ; ; ; ; ; ; (, New Phytologist)
    Summary Use of synthetic microbial communities (SynComs) is a promising approach that harnesses nature‐based solutions to support soil fertility and food security, mitigate climate change impacts, and restore terrestrial ecosystems. Several microbial products are in the market, and many others are at different stages of development and commercialization. Yet, we are still far from being able to fully harness the potential and successful applications of such biotechnological tools. The limited field efficiency and efficacy of SynComs have significantly constrained commercial opportunities, resulting in market growth falling below expectations. To overcome these challenges and manage expectations, it is critical to address current limitations, failures, and potential environmental consequences of SynComs. In this Viewpoint, we explore how using multiple eco‐evolutionary theories can inform SynCom design and success. We further discuss the current status of SynComs and identify the next steps needed to develop and deploy the next generation of tools to boost their ability to support multiple ecosystem services, including food security and environmental sustainability. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Text Encoded Conversion
  • XML
  • Save / Share this Record
  • Send to Email