Is AI a Silver Bullet for our Climate Problems?
Is AI a Silver Bullet for our Climate Problems?
by Alp Kucukelbir, Chief Scientist of Fero Labs, Adjunct Professor of Computer Science at Columbia University, and Entrepreneurship Lead at ClimateChange.ai.
You have probably heard the expression: “the best way to learn is to teach.” Nobel prize-winning physicist Richard Feynman famously advocated and used this technique. As an academic and entrepreneur seeking to leverage my skills in computer science for the greater good, I wanted to understand where AI can have an impact on solving climate problems — arguably one of the greatest challenges of our time. So, I decided to follow in Feynman’s footsteps — I developed a brand-new course at Columbia University that explores the intersection of Machine Learning and Climate. Here is what I learned (and taught) in the process.
AI is over-represented in certain climate problems
As I was putting together the syllabus for my course, I realized that AI is currently focused on a few climate niches: solving problems using mostly text and image data. I found disproportionate numbers of researchers working with satellite imagery to make weather predictions. In contrast, fewer academics appear to be solving problems in the energy sector, agriculture, and manufacturing. Why is that?
It comes down to two things: data availability and knowledge scarcity.
Text and image data are easy to gather and work with. Many datasets can be directly scraped off the internet. But these represent only a small subset of climate problems. Governments, companies, and non-profits can raise awareness of the need for broader solutions by incentivizing the gathering and sharing of more data. For example, at ClimateChange.AI, a non-profit based in the US, we are running a grants program to foster the development of impactful datasets to spur AI research in climate problems.
Knowledge tends to silo itself. Energy experts rarely have convenient ways to interact with AI experts. So, AI researchers gravitate to solving problems they can easily understand — a satellite image is much simpler to understand than the complexity of energy generation and distribution. Efforts to bridge this gap are already making a difference. Columbia University recently launched a brand-new Climate School aimed at trans-disciplinary research. These kinds of initiatives can bring communities together and enable effective problem-solving.
Takeaway: Expect many AI solutions to be biased towards easily accessible data. We must encourage the development of new AI methods to solve problems in a broader space of climate problems. Increasing data availability and breaking down knowledge barriers will help.
AI must quantify its confidence
In my class, we quickly realized that for AI to have an impact in solving climate problems, it must measure and communicate its confidence in its recommendations.
Many AI systems focus on solving problems where the cost of making a mistake is negligible. Take ChatGPT, which blurts its answers out with full confidence — regardless of whether or not they are correct. Or a media streaming service, which flatly suggests the next movie you should watch — a recommendation you can simply ignore at no cost.
In climate applications, the cost of making a mistake is rarely negligible. Consider biodiversity loss, recently discussed at the 2022 World Economic Forum. We can use AI to study and forecast biodiversity trends and act to minimize biodiversity loss — a wrong action may impart decades of (potentially irreversible) harm. Take heavy manufacturing, energy, and agriculture. AI can help decarbonize these sectors, which collectively make up around 75% of global emissions, by simulating production changes that minimize resource usage and waste. But, a single mistake on a batch of production can cost hundreds of thousands of dollars, thousands of tons of CO2e, and raise personnel safety risks — sometimes all at once.
This allows AI to empower humans to trade benefits with risks. For a specific batch of production, an AI system may hesitantly recommend a change. A human expert can then weigh the benefits of this recommendation with the risk of making a mistake — doing precisely the type of thing that humans are good at: exercising judgment.
Takeaway: You should be suspicious of AI that does not quantify its own confidence. Such AI methods are likely built on the principle of “mistakes are cheap.” Mistakes in climate problems are rarely cheap.
Back to the classroom
By the end of the semester, my students had explored myriad problems, ranging from how to optimally place wind turbines in a field, to forecasting climate policy effectiveness under multiple climate scenarios. The most successful student projects embraced new datasets and AI methods that provide confidence, pivoting away from black-box AI algorithms early in their projects.
The landscape is changing in our favor; projects like Climate Trace are introducing increasingly ambitious datasets into the public sphere. I am eager to see what projects my students decide to work on next semester — and watch the next generation bring an expanded perspective and greater toolbox to address our climate challenges.
✍️ The Draw-down
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✨ Highlights
🌊 Propeller is now accepting applications for the next cohort of their Ocean MBA, an intense startup bootcamp for ocean-climate entrepreneurs. May 17-19 at MIT Sloan. More info and application here.
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Ah this is awesome. As a data engineer that’s passionate about climate, this intersection is one that I’m very excited about! Thanks for sharing.
The link for the Career Transitions Meetup is broken - is there another?