Hi everyone, following up here with a summary of our workshop!

The AI for Biodiversity Monitoring workshop brought together twenty-five participants to explore uses of machine learning for ecological monitoring. Sponsored by the WILDLABS ‘Boring Fund’, we were able to support travel and lodging for a four-day workshop at the University of Antioquia in Medelín, Colombia. The goal was to bring together ecologists interested in AI tools and data scientists interested in working on AI applications from Colombia and Ecuador.  Participants were selected based on potential impact on their community, their readiness to contribute to the topic, and a broad category of representation, which balanced geographic origin, business versus academic experience, and career progression.

Before the workshop began I developed a website on github that laid out the aims of the workshop and provided a public focal point for uploading information. I made a number of technical videos, covering subjects like VSCODE + CoPilot, both to inform participants, as well as create an atmosphere of early and easy communication. The WhatsApp group, the youtube channel (link) of video introductions, and a steady drumbeat of short tutorial videos were key in establishing expectations for the workshop.

Read "IA para el Monitoreo Ecológico" on

IA para el Monitoreo Ecológico

The workshop material was structured around data collection methods, Day 1) Introduction and Project Organization, Day 2) Camera Traps, Day 3) Bioacoustics, and Day 4) Airborne data. Each day I asked participants to install packages using conda, download code from github, and be active in supporting each other solving small technical problems. The large range of technical experience was key in developing peer support. I toyed with the idea of creating a juypterhub or joint cloud working space, but I am glad that I resisted; it is important for participants to see how to solve package conflicts and the many other myriad installation challenges on 25 different laptops.

We banked some early wins to help ease intimidation and create a good flow to technical training. I started with github and version control because it is broadly applicable, incredibly useful, and satisfying to learn. Using examples from my own work, I focused on github as a way both to contribute to machine learning for biology, as well as receive help. Building from these command line tools, we explored vscode + copilot for automated code completion, and had a lively discussion on how to balance utility of these new features with transparency and comprehension.  

Days two, three and four flew by, with a general theme of existing foundational models, such as BirdNET for bioacoustics, Megadetector for Camera traps, DeepForest for airborne observation. A short presentation each morning was followed by a worked python example making predictions using new data, annotation using label-studio, and model developing with pytorch-lightning. There is a temptation to develop jupyter notebooks that outline perfect code step by step, but I prefer to let participants work through errors and have a live coding strategy.  All materials are in Spanish and updated on the website. I was proud to see the level of joint support among participants, and tried to highlight these contributions to promote autonomy and peer teaching. 

Sprinkled amongst the technical sessions, I had each participant create a two slide talk, and I would randomly select from the group to break up sessions and help stir conversation. I took it as a good sign that I was often quietly pressured by participants to select their talk in our next random draw. While we had general technical goals and each day had one or two main lectures, I tried to be nimble, allowing space for suggestions. In response to feedback, we rerouted an afternoon to discuss biodiversity monitoring goals and data sources. Ironically, the biologists in the room later suggested that we needed to get back to code, and the data scientists said it was great. Weaving between technical and domain expertise requires an openness to change.

Boiling down my takeaways from this effort, I think there are three broad lessons for future workshops.

• The group dynamic is everything. Provide multiple avenues for participants to communicate with each other. We benefited from a smaller group of dedicated participants compared to inviting a larger number.
• Keep the objectives, number of packages, and size of sample datasets to a minimum.
• Foster peer learning and community development. Give time for everyone to speak. Step in aggressively as the arbiter of the schedule in order to allow all participants a space to contribute.

I am grateful to everyone who contributed to this effort both before and during the event to make it a success. Particular thanks goes to Dr. Juan Parra for hosting us at the University of Antioquia, UF staff for booking travel, Dr. Ethan White for his support and mentorship, and Emily Jack-Scott for her feedback on developing course materials. Credit for the ideas behind this workshop goes to Dr. Boris Tinoco, Dr. Sara Beery for her efforts at CV4Ecology and Dr. Juan Sebastian Ulloa. My co-instructors Dr. Jose Ruiz and Santiago Guzman were fantastic, and I’d like to thank ARM through the WILDLABS Boring fund for its generous support.    

Out of curiosity, as we have some projects collecting videos, what would be the best solution for blurring video content?