Grounding AI in practice: What extension gets wrong, what extension gets right, and what AI can learn
Generative artificial intelligence (gen AI) applications - which produce text, images, code, and other content based on surfacing patterns in vast datasets - promise many benefits for agrifood systems (e.g., reaching more people at less cost, availability around the clock, real-time data).
| Autores principales: | , , |
|---|---|
| Formato: | Blog Post |
| Lenguaje: | Inglés |
| Publicado: |
International Food Policy Research Institute
2025
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/178200 |
Ejemplares similares: Grounding AI in practice: What extension gets wrong, what extension gets right, and what AI can learn
- Asking the right questions: A stakeholder dialogue on generative AI in digital extension
- What responsible AI must get right in fragile water contexts
- Asking the right questions: Grounding generative AI in agricultural advisory practice
- Harnessing AI to scale agricultural extension: Opportunities and emerging pathways
- Beyond the human touch: Can generative AI transform Uganda’s agricultural extension and advisory services?
- Beyond the hype: Centering humans in CGIAR’s genAI research