How can AI help with climate change?

In this episode, Priya Donti, executive director of nonprofit Climate Change AI, speaks to how artificial intelligence and machine learning are affecting the fight against climate change.

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Text transcript:

David Roberts

As you might have noticed, the world is in the midst of a massive wave of hype about artificial intelligence (AI) and machine learning (ML) — hype tinged with no small amount of terror.

Here at Volts, though, we’re less worried about theoretical machines that gain sentience and decide to wipe out humanity than we are with the actually existing apocalypse of climate change.

Are AI and ML helping in the climate fight, or hurting? Are they generating substantial greenhouse gas emissions on their own? Are they helping to discover and exploit more fossil fuels? Are they unlocking fantastic capabilities that might one day revolutionize climate models or the electricity grid?

Yes! They are doing all those things. To try to wrap my head around the extent of their current carbon emissions, the ways they are hurting and helping the climate fight, and how policy might channel them in a positive direction, I contact Priya Donti, an assistant professor at MIT and executive director of Climate Change AI, a nonprofit that investigates these very questions.

All right, then, with no further ado, Priya Donti, welcome to Volts. Thank you so much for coming.

Priya Donti

Thanks for having me on.

David Roberts

We are going to discuss the effects of artificial intelligence and machine learning on the climate fight. And I think we're going to, for reasons that will become clear as we talk, kind of like taking on an impossible task here. As we'll see, it's going to be very difficult to sort of wrap our heads around the whole thing. But I think we can make a lot of progress and maybe get clear about sort of some of the directions and some of the applications and get a better sense of how things are going, because this is something I've been sort of meaning to think about and talk about for a while.

I'm excited. But to start, can we just get some definitions out of the way? Because I think people hear a lot of these terms flying around. There's artificial intelligence, AI. There's machine learning, ML, in the business, and then there's just sort of the digitization of everything, and then there's just sort of more powerful computers. Like, if I'm running a climate model and I want to put more variables in there, but I'm constrained by the amount of computing power it would take, computers that have more power and more processing cores or whatever, then I can do that.

So help us understand the distinction between these things, between just sort of more and better and faster computing and something called machine learning and something called artificial intelligence. What do all these things mean?

Priya Donti

Yeah, so I'm going to start with AI: Artificial intelligence. So AI refers to any computational algorithm that can perform a task that we think of as complex so this is things like speech or reasoning or forecasting or something like that. And AI has two kind of main branches. One of them is based on rule-based approaches where you basically write down a set of rules and ask an algorithm to reason over them. So when, for example, Deep Blue beat Gary Kasparov in the game of chess, this was a kind of rule-based scenario where you were able to write down the rules of chess and get an algorithm to understand and reason over what to do given that set of rules. Of course, there are lots of scenarios in the world where it's really difficult to write down a set of rules to capture a task, even though we kind of know how the task goes.

David Roberts

Most you could say are difficult.

Priya Donti

Exactly. And so, one of these things is, like, if I have an image, what does it mean for that image to contain a picture of a cat? I can probably tell you, okay, there's got to be a thing with ears, a head, a tail, but it doesn't capture that always because you can't always see the tail. Like, how does this work? And so, machine learning is a type of AI that basically tries to automatically learn an underlying set of rules based on examples. So, for example, it takes large amounts of data, like, that it can analyze and use to help kind of figure out what the patterns are in that underlying data, and then apply those patterns to other similar scenarios, like classifying other images that the algorithm hasn't yet seen but are similar to what it saw when actually being created.

And yeah, I would say that in terms of what's the distinction between these things and computing, I would say computing is a workhorse behind many of these algorithms. So in order for these algorithms to work, you need fast computers that are able to kind of execute the computations behind the creation of these algorithms. Behind the learning. You also need good data. And with those things together, you can basically create a lot of these more powerful AI and machine learning algorithms that you've seen today.

David Roberts

I see. So in AI, you're kind of telling the computer the rules and then hoping that the computer can use the rules to respond effectively to new data. With machine learning, you're just feeding it an enormous amount of data and it is deriving the rules or patterns from the data.

Priya Donti

Right. And those rules might be derived in a way that either is or is not interpretable. So I may or may not be able to go into the model and actually pull out what the set of rules are. But implicitly, at least in there, there's some set of rules that's being learned based on the data.

David Roberts

So there are so many side paths that I'm going to try not to go down all of them as we go. But I'm sort of curious because one of the fears people are always bringing up is you feed it this enormous amount of data, it derives some rules from it and applies that to new data, but you don't really know what it's doing. And this is something we hear about AI a lot, is sort of relatively quickly the sort of complexity of what's going on and the kind of foreignness of what's going on to our way of thinking, to sort of human reasoning just puts these things out of touch. And pretty quickly we're in a kind of like, well, it seems to be working, so let's keep using it even though we don't know what it's doing.

So I guess my question is, is that a limitation of our knowledge? In other words, is it theoretically possible if we had sort of the time and willpower to dig in and figure out what it's doing? Or is there some reason that in principle it's sort of impossible to know what it's doing? Does that make sense?

Priya Donti

It does, yeah. And I'd say that one thing to kind of step back and note also is that there's a diversity of machine learning methods, some of which are inherently a bit more interpretable than others. So, linear regression, even though people don't think of it as a form of machine learning, it actually is, right? Because you're taking in some data and you're learning parameters that allow you to make some kind of prediction. And linear regression is, abundantly, interpretable. And similarly, you have thi