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Tackling Climate Change with Machine Learning

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This is an engaging and fact-filled podcast. We focused on green machine learning, discussing how data science can be used to fight climate change and what you can do in your own life and with your own skills to help the planet.

About Vince Petaccio II
Vince Petaccio II is a data scientist at Amazon Web Services, where he develops machine learning tools to identify and mitigate fraud activity. Previously, he worked as a data scientist at untapt, where he produced deep-learning natural language tools to help job candidates find fulfilling careers while avoiding systemic bias. Prior to this work, he mobilized realtime neural data analysis in brain and spine surgery in premier medical centers in and around New York City and Philadelphia, collaborating with surgeons to protect patients and to improve surgical outcomes. Vince is a passionate advocate for action on climate change: he has worked with Citizens’ Climate Lobby since 2016 to advance politically neutral climate policy and to build the political will for climate action and lends his technical skills to projects seeking to mitigate the climate crisis with novel solutions. Vince holds biomedical engineering degrees from Drexel University and a computer science degree from Georgia Tech.
Vince is very learned in climate change and how data science can be effective on that front. He has been interested in climate change since kindergarten where they celebrated Earth Day for an entire week. This taught him to be observant of his natural surroundings and how humans shared a space with the denizens of the natural world. In high school, a park in his hometown was flooded by a broken damn. After school, he took up a data science job at untapt and realized he could use those skills to protect the climate.
Vince volunteers as a lobbyist for the Citizen’s Climate Lobby, lobbying for policies around pricing carbon equitably. They build relationships with community leaders and elected officials to best advocate for the policy. The idea is heavy emitters will be charged on a growing floor of carbon fees. So it becomes economically essential for emitters to lower their emissions. The political aspect is just one part of this battle. Even data science alone is not capable, as Vince points out, subject matter experts and the participation of everyone is crucial.
Examples of this collaboration of efforts include optimizing smart buildings for energy efficiency which combines economic value, data science, and subject matter experts working in synergy. Supply chain optimization is another example, to get as much delivery payload on the shortest possible route. Precise agriculture is yet another—producing enough food to feed people around the world through automation and scalability, which we see in the continued progress of vertical farming. Social impact is also hugely important. Data science can be used on this front, helping to flag and filter misinformation campaigns. Then there is of course climate modeling to help predict what a climate will look like in the future.
Vince points out individual action is also hugely important, and sometimes overshadowed by the need for global and social solutions. Data science can help individuals make better consumption choices, for example at grocery stores where the environmental footprint of food can be known to consumers easily. It may surprise consumers how much certain crops can cost the economy, such as almonds, an extremely thirsty crop that has been implicated in draughts in California. You can make an impact by collaborating across domains—network and connect with others interested in tackling this problem using their own skills and experiences.
Are there risks of relying too much on technology to fix the problem? Absolutely. Climate change disproportionately affects vulnerable communities. So we need to be vigilant at how our technology perpetuates existing abuses of marginalized groups. We need to have a big impact, but we need to have the right one. Make sure the tools aren’t too complex for simple problems. Ultimately, we should avoid using large hammers for small nails, or on the reverse, having too simple an understanding of something more complex. Machine learning is only a tool and we are responsible for the ways we use it.
This article has been published from the source link without modifications to the text. Only the headline has been changed.
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