Episode 26: Using Artificial Intelligence to Solve Global Environmental Challenges

Hello and welcome back to Tech Forward! On this week’s episode, I spoke with Jennifer Marsman, the Principal Software Engineer of Microsoft’s “AI for Earth” group. Working with the group, she uses data science, machine learning, and artificial intelligence to aid with clean water, agriculture, biodiversity, and climate change. Jennifer has been recognized as one of the “top 100 most influential individuals in artificial intelligence and machine learning” by Onalytica, reaching the #2 slot in 2018. We discussed the work she has led with AI for Earth, the issue of bias, and her insights about building a welcoming culture in the tech sector.

By running a global grant program, developing their own APIs, and funding projects, AI for Earth is, in Jennifer’s words, “a chance to use my passion for machine learning to make a difference.” One such project is FarmBeats, which is interested in sustainable, long-term solutions to maximize yield and, ultimately, reduce world hunger. To do this, they synthesize IoT, drones, machine learning, and cutting edge networking research to create precision agricultural techniques. A combination of smart sensors and aerial imagery — from drones or large helium balloons — can create a moisture map of any given field, and enable precision irrigation. Similarly, precision pesticide application benefits the environment, the farmer’s budget, and the consumer.

Another initiative, Project Premonition, puts mosquitoes to work in order to predict outbreaks of diseases. Mosquitoes collect a large distribution of blood samples, and by analyzing those samples within their genomics pipeline, Project Premonition can determine the animal the blood came from and any diseases it might be carrying to generate a real-time health map of an area. Under the current model, where doctors report local cases of disease to a larger agency such as the Centers for Disease Control, it can take weeks or even a month to identify an outbreak. Using machine learning every step of the way — from placement of the traps, to making sure mosquitoes are trapped with 90% accuracy, to analyzing the data of the blood samples — Project Premonition aims to streamline and accelerate that process.

While machine learning is obviously a powerful tool, there is always the danger of bias. “Any bias in the historical data that you use to make future predictions will be captured in your machine learning algorithm. You have to make sure that your users know how the data was collected. Your model is only as  good as the data you train it with.” Good intentions, such as trying to identify at-risk students and preemptively give them more support, can turn into a self-fulfilling prophecy. This is yet another strong argument in favor of diverse perspectives at all stages of product development.

When it comes to tech’s diversity issues, Jennifer identified two distinct, but related issues: attracting a diverse workforce, and maintaining that diversity. She emphasized the importance of media representation — from computer games with playable female protagonists, to television shows with women in tech roles — to her own personal career journey as well as the journeys of young women today. From her internship days at Ford Motor Company, she has witnessed firsthand how crucial teamwork is: not only to coding, but also to creating a welcoming environment that everyone wants to be a part of.

Jennifer, thank you so much for bringing your passion and insights to the show today. Thank you also to everyone tuning in, leaving reviews, and sharing the show with your friends and colleagues. See you next week!

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