Machine learning can be used as an innovative method to contribute to climate change resiliency. Today on Impact AI, I am joined by the co-founder and CTO of Vibrant Planet, Guy Bayes, to discuss how they are using AI to revitalize forests. Listening in, you’ll hear all about our guest’s background, why he started Vibrant Planet, what the company does, how they apply machine learning to their work, and a breakdown of how they collect the four sets of data they need. We delve into any problem areas they face in their individual and integrated data types before Guy tells us how they cross-validate their models. We even talk about how the teams collaborate, how machine learning and forest knowledge come together, and where he sees the company in the next three to five years. Finally, our guest shares some pearls of wisdom for any leaders of AI-powered startups.
Key Points:
- A warm introduction to today’s guest, Guy Bayes.
- Guy tells us about his background and what led him to create Vibrant Planet.
- What Vibrant Planet does and how it contributes to climate change resiliency.
- How Vibrant Planet applies machine learning to the work.
- A breakdown of the four sets of data they need and how they collect it.
- The challenges they face when it comes to collecting and integrating all their data.
- How Guy makes sure that their models work in different geographic regions.
- Incorporating forest knowledge into data modeling and machine learning development.
- How the Vibrant Planet teams work together and collaborate to achieve their goal.
- What Vibrant Planet does to measure the impact of this technology.
- New AI advancements Guy is particularly excited about for Vibrant Planet.
- Guy shares some advice for leaders of AI-powered startups.
- Where he sees Vibrant Planet’s impact in the next three to five years.
Quotes:
“Getting the forest back into a state that's more able to tolerate fire and more able to produce low-intensity fire rather than high-intensity fire is [Vibrant Planet’s] goal.” — Guy Bayes
“We have – not only super good engineers but also very talented ecological scientists and people that have done physical hands-on forestry for their careers. – This mix of those three personas – work together pretty harmoniously actually because we all share a common goal.” — Guy Bayes
“I don't think you can ever find one person who has all that in their head, but you can find a team that does.” — Guy Bayes
“You will not have an impact without having a combined team that all respects each other and brings different things to the table.” — Guy Bayes
Links:
Resources for Computer Vision Teams:
LinkedIn – Connect with Heather.
Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.
Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.
Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.
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