Dynamically Typed

#30: Climate Change AI at NeurIPS 2019 (Special Edition)

Hey everyone, welcome to Dynamically Typed #30, the first issue of 2020! Since the holidays are usually a bit quiet on productized AI and ML research, I’m dedicating today’s edition of the newsletter to the day-long Climate Change AI (CCAI) workshop at the 33rd annual Neural Information Processing Systems (NeurIPS) conference. The workshop included poster presentations, many spotlight talks, four invited talks, and two panels. I wasn’t able to attend in person, but I’ve watched the recording (on SlidesLive in four parts: 1, 2, 3, 4) and summarized the first spotlight talk and the two panels in this newsletter.

You can find the full program on the CCAI website and see my more detailed notes on Notion. Let me know what you think of this special edition! The next issue of DT will be back in the regular format.

Jeff Dean: Computation + Systems vs Climate Change 🖥

Jeff Dean’s spotlight talk featured work that Google AI is doing on climate-related problems. He first mentioned the work Google is doing to get its own carbon footprint down: in 2012, the company started ramping up its renewable energy purchases, to cover 100% of its use starting in 2017. (They’re purchasing as much renewable power as total power they’re using, but they’re not yet always running on renewables.)

Dean describes Google AI’s general approach to using computation and machine learning to help fight climate change as a three-phase process: Algorithm → System → Climate Application. Specific instantiations of this process include:

Another set of projects is much closer to Google’s core mission: bringing information to the masses. They think about this in terms of leverage for technology and people: “What information do people need to make good decisions? What are the information leverage points? Who should we build tools for?” Some tools they’ve built include the Environmental Insights Explorer which helps municipalities see where building and transport emissions come from, and Project Solar which shows people the rooftop solar potential of their home.

Overall this was a very inspiring talk. You can watch it on SlidesLive or see my more detailed notes on Notion.

Panel 1: Climate Change: A Grand Challenge for ML

The goal of the first panel was to explore the role of the machine learning community in tackling climate problems. Below I’ve summarized (and in some cases transcribed) each question and answer from the panelists. The text is a bit less polished than usual newsletter text, mostly because there was a lot of content during the hour-long discussion. These are the panelists:

Now let’s dive into the panel.

What recent ML advances excite you for climate problems?

Question RE: CCAI’s 97-page paper; I’ve read the paper, now what’s next? How can I get started?

How can we encourage work on climate if the metrics we’re measuring scientists against are e.g. papers published instead of tons of CO2 sequestered?

How can we prevent ML from having climate-negative impacts (e.g. cost of training)?

Question from an architect RE Gomes’ point on combining scientific/prior knowledge with ML models: how can we address this challenge in climate change adaptation?

How do you make sure that AI solutions match the actual problems? If you have a hammer, everything looks like a nail—if you want to have a climate impact, should you really be working on AI?

Should tech unionise?

How do we remain inclusive of people in areas disproportionately affected by climate change in climate discussions? (Own addition: especially because all of us working on it will relatively not be affected by it as much.)

What would you say to the room/community?

Panel 2: Practical Challenges in Applying ML to Climate Change

The goal of the second panel was to dig into the long road from an AI demo to a deployment involving many stakeholders and legacy systems, and what the challenges and opportunities there are. I’ve summarized the panel similarly to the previous one. Here are the panelists:

And here’s my summary of the panel.

What are the biggest bottlenecks in using ML for climate problems?

How do we prioritise projects under constrained funding? How do we avoid ML becoming a distraction?

When and how should decision makers trust ML within established industry like electricity generation?

Dataset maintenance in the long run: you need consistency to keep a model running successfully in production.

Data sharing: how can we ensure that private and public sector data sharing happens enough?

Challenges and opportunities of cross-disciplinary collaboration?

Outside of ML, what can citizens do to encourage councils to start tackling climate change?

And that’s it for panel two! I hope you enjoyed this off-format version of Dynamically Typed.

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