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:
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Yoshua Bengio (University of Montréal, Mila, Turning Award): happy with the interest from the field but now we have to get our hands dirty. Working on: visualization of impact for education; materials science (longer term).
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Andrew Ng (Stanford): working on: methane prediction; understanding geospatial imagery.
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Carla Gomes (Cornell): founder of computational sustainability field; solar fuel materials discovery; takeaway: we can have both social impact and fundamental research.
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Lester Mackey (Microsoft Research, Stanford): sub-seasonal forecasting at the border of climate and weather w/ predictions 6ish weeks out to help water management etc. deal with climate impact.
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Jeff Dean (Google AI)
Now let’s dive into the panel.
What recent ML advances excite you for climate problems?
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Ng: improvements in analysing small datasets (common in satellite imagery e.g. only 100s of examples for things like wind farm detection) using things like unsupervised learning
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Gomes: combination of ML + simulations + physics-based models (e.g. thermodynamic formulas). Big challenge for ML in science: how to incorporate these things.
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Bengio: unsupervised learning (e.g. optimizing processes like energy consumption)
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Dean: excited about huge-scale heavily multi-scale models if we can share data and training between tasks.
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Mackey: also applying old techniques.
Question RE: CCAI’s 97-page paper; I’ve read the paper, now what’s next? How can I get started?
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Mackey: get involved in (e.g. Kaggle) challenges in e.g. sub-seasonal climate prediction. Great way to get to know the data and challenges.
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Bengio: you have to reach out to experts in the fields you’re interested in; you need to read more than you expect; need to engage in discussions. Also connect to other interested people. We also need some humility so that we don’t reinvent the wheel badly.
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Dean: collaborate! Best projects I’ve worked on had a small group of people with very different expertise.
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Gomes: connect with experts.
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Ng: “our job is not done when we publish a paper. Our job is done when we have an impact.” Sometimes we’re very good at the ML bit, but the practical deployment bits are also very hard (change management). One problem: our models are often not robust to the real world. We need to get better at going beyond a result in a Jupyter notebook to a deployment.
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?
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Bengio: change your objective function. Look at global impact beyond paper publication. As academics in ML, it seems we’ve lost track of contributing to science and instead keep pushing to get a paper published at the next conference two months from now. We have to step back.
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Ng: academic system incentivises papers published; but we should push things like tenure applications more towards real-world impact.
How can we prevent ML from having climate-negative impacts (e.g. cost of training)?
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Bengio: public shaming. Also: when reviewing papers: we should integrate societal impact as an evaluation metric.
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Ng: ethical question. Good ethical statements are going around (e.g. OECD, Google Microsoft), but two things are missing: (1) not actionable; medical field for example has right checks and balances (doctors have a responsibility to the patient, not to their hospital); (2) due process for making decisions. Wants an ethics code written by and for the AI community.
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Gomes: we usually optimise for a single objective; we should also consider other criteria and look at impact.
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?
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Gomes: this is a good question and big challenge. We need to develop new technology that is better at incorporating prior knowledge.
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?
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Bengio: each of us should ask the question: what can I do? If I know about AI, I should use that. Just AI won’t solve climate, but that’s OK: each of us should contribute their share of what we’re best at.
Should tech unionise?
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Bengio: some general principles seem to be relevant and useful.
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Ng: Tech-lash—in tech bubbles, we don’t feel this, but outside of them there is a distrust in tech that we need to solve. Not sure if unions is the best route. Linking back to codes of ethics.
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.)
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Dean: question for ML in general—people building AI systems should represent the people who will be affected by AI.
What would you say to the room/community?
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Bengio: we should question the systems around us.
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Ng: importance of community: genuinely celebrate the successes of others; when someone asks for a favour, just give it to them; don’t get salty about someone not citing you; having allies (each other) at our backs will help us. “If you can wake up in the morning really happy that someone else had a great result, that’s a really good place to be in.”
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Gomes: agrees; has a lot of respect for people working on problems in this field; she now worries more about the world than when she was working on abstract problem (but that’s good).
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Mackey: it’s everybody’s problem and the only way we’ll get through this is together. Take some time to think about how you can help others and the planet and not just your career.
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Dean: we’re bringing a lot of disciplines and regions of the world together here; we need to work together and celebrate successes; also don’t get discouraged if something doesn’t work.