Localizing AI for Agriculture: Success and Challenges

Generative AI tools, such as ChatGPT, Bard, and Claude, have been trained on massive amounts of data. However, much of that data is in the world's most commonly spoken languages, meaning that these models can struggle to understand and communicate in underrepresented languages. Join us for this webinar, where we will hear from several organizations that are working to strengthen the availability and functionality of underrepresented languages and leveraging generative AI to benefit the agriculture sector in low- and middle-income countries. This is a great opportunity to learn more about how to localize AI models and to ask any burning questions you have on your mind.
The session will start with presentations focused on the experiences of each organization, followed by a panel discussion and Q&A. Speakers include:
Rebecca Ryakitimbo, Mozilla Common Voice, who will share her experience helping to build an open language dataset in Kiswahili.
Eliot Jones-Garcia, University of Nottingham, who will talk about an AI tool his team has developed in partnership with Farm Radio International that can analyze and aggregate farmers' calls to radio networks in Bantu languages, with the intention of better tailoring radio shows to farmers
Archana Karanam, Digital Green, who will talk about their development of Farmer.Chat, an AI-assistant for agricultural front-line workers, which has been deployed in local languages in India and Kenya.