18. Bonus Episode: Wadhwani AI
Wadhwani Institute for Artificial Intelligence (Wadhwani AI) has been actively building and deploying AI solutions for underserved populations in developing countries since 2021. As a Google.org recipient, they are using AI to bring the world’s agricultural expertise to every farmer, protecting critical crops and mitigating the threat of hunger for billions of people. On this special episode of Food Lab Talk, Michiel speaks with Aditya Nayan and Soma Dhavala from Wadhwani AI to discuss the importance of understanding the audience and building trust when implementing novel programs, demonstrating value before scaling, and “open sourcing” innovation to open the door for other changemakers.
Wadhwani AI: “We're always inclined to think, okay, what is the specific problem statement that you're solving, how you're solving it and all that. But we're obligated to look at the larger system and see the value. So even in the case of CottonAce, there is as much value in that farmer getting the advisory as there is that the ministry can get a sense that this is the kind of infestation that's happening in these places and I can know that in real time. That's a starting point so that a year, two years down the line, I can create a predictive system which will help me prepare better. That's something that we didn't know in 2019 when we won the Google AI Impact Challenge. We didn't know this part of it. So it's this system thinking that where innovation also has to go and how things that we do maybe on the field, how they plug back in into the larger system is very important.” - Aditya Nayan
01:11 Intro to Wadhwani AI
03:36 How AI can add value in a rural environment
05:22 Why education is critical to adoption of novel technologies
06:19 How smartphones make AI and data accessible
07:17 Proof of concept with CottonAce
09:59 Scaling CottonAce principles to more crops
10:54 Building trust in technology through community engagement
12:35 Considering reliability and safety when developing new technologies
13:12 Open sourcing and “open syncing” for datasets, models, and innovation
15:20 Data is relative - how to determine impact
17:15 Measuring direct impact vs. indirect network effects
19:47 Why systems thinking is important for problem-solving
22:07 Takeaways for changemakers
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*The views expressed by the guests in this podcast don't necessarily represent the host’s views, nor those of his employer.
