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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. (PDF transcript)(Active transcript)Text transcript:David RobertsAs 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 DontiThanks for having me on.David RobertsWe 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 DontiYeah, 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 RobertsMost you could say are difficult.Priya DontiExactly. 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 RobertsI 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 DontiRight. 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 RobertsSo 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 DontiIt 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 things like decision trees. There are more complicated methods, like physics-informed machine learning or other methods, that try to just constrain the model in a way such that the goal is you can pull out certain kinds of rules.So, there is that axis of methods, but then there are these other methods, like some of the more complicated deep learning methods you see today, where, agreed, we basically view it. It is a bit of a black box. You don't know exactly why a prediction is being made, and there is some work going on to try to get at this issue and see if there are ways we can understand what the model is doing post hoc. But it's an area of research, I think, one that undergoes a lot of debate. Also, in terms of can you post hoc explain what a deep learning model did?For example, if I, as a person, make a decision and take some kind of action and you, David, ask me, "Hey, why did you do that?" I could probably come up with any number of explanations for you, all of which seem plausible, but those may or may not actually describe how I actually made the decision. So there's a bit of a debate about kind of even if you can try to somehow understand what the deep learning model did, what are the limits of that analysis and interpreting what it actually did and why?David RobertsYeah, there's a lot of things about this whole subject matter that sort of unnerve people. But this is kind of what I think is at the root of it is just that as these things get more complex, you pretty quickly get into an area of kind of trust or faith, almost like our machine masters. They seem to be doing well by us, even though we don't know exactly why. There's just something a little weird about that.Priya DontiYeah. And maybe just one thing I'll add. There are levers here, though, right? In any kind of machine learning pipeline, you have the data, the model, and then the outputs are how you evaluate the outputs. And you do have the ability to kind of quality control or constrain any of those things. So you should know exactly what's going into the data that's going into a model. In order to understand if your model is actually seeing quality things that it's trying to learn from. You can, as I mentioned, constrain your model to be an interpretable model.And then, what some of my work looks at is, you can actually often constrain the output in certain settings. So, if I create a controller for a power grid based on machine learning and it outputs some kind of action, but I know something about the control theoretic constraints that that action should satisfy, there are ways I can actually constrain the output so that it still satisfies various performance criteria that we recognize. So, it isn't sort of a foregone conclusion that AI and machine learning must be this sort of black box, scary thing. But I would say that there is work to be done and kind of intention that goes into making sure that we really understand and are constraining and quality controlling how the whole pipeline goes forward.David RobertsRight. And one other general question. So when people talk about AI these days, I think mostly in the popular imagination, I think mostly what they're talking about is what's called general intelligence. This idea that you could create a program that could find its own data and apply rules and figure things out, basically that has some autonomy, that would be the AI, right, the rules based. Like you give it the rules and then it goes and applies this to the world. Or is there a dispute about how you get to general intelligence? Which of these routes leads you to general intelligence?Priya DontiYeah, so I would say that the distinction between sort of general intelligent AI versus task-specific AI, it's not quite the same as this AI machine learning distinction of rules versus data. It's something different. And it kind of comes down to, when you create an algorithm, there is some objective that you're creating it with in mind. And so, for example, if I am creating a forecasting model of solar power, that's a very specific task. I'm kind of giving very specific data. I'm making a very specific ask when I look at the output of the model. But others are saying, can we somehow imbue a lot of data or a lot of rules and learn some kind of foundational representation that really is capturing a ton of general knowledge that can be kind of tuned or specified in various ways.These are kind of the kinds of works that really are trying to lead towards something more general. And so, yeah, I would say that there's kind of these different threads of work within the machine learning community at the moment.David RobertsRight. And just to be clear, we have not reached general intelligence and no one knows how to do that. And there's a lot of theoretical work going on, a lot of work going on in that. But practically speaking, almost all of the AI or machine learning that is happening today is task based. Right? I mean that's to a first approximation, when we talk about AI and machine learning, that's what we're talking about today.Priya DontiYes, that's right. So, I think that there is some kind of research going on in specific labs that is trying to work on artificial general intelligence. But when we think about the implementation of AI and machine learning across society and what it's really used for in practice, I think it is safe to say that a lot of it is task-based. And even some of the stuff that looks very clever and artificial general intelligence-like, there is genuine debate as to whether that is actually the case. For example, large language models and models like GPT have been called stochastic parrots, which is to say, they're not actually thinking; they are mirroring, parroting in a kind of stochastic way, what they're seeing in their data.And we potentially as people who then read text outputs that seem realistic, we maybe ascribe intelligence to that. But that doesn't necessarily mean there's any thinking actually going on under the hood.David RobertsYes. And then, of course, there's this whole, like, back in the "dark ages", I was in grad school in philosophy and I used to study cognitive science and consciousness and all these sort of theoretical debates around this stuff. There is a sort of debate. There is this sort of idea that all we're doing is what the language models are doing, just on a vast scale. So, there is no sharp line. They're just like, eventually you do that well enough that you are, de facto, deploying intelligence, and the models will eventually, eventually there will be no point in drawing a distinction between what they're doing and true intelligence.But that is well far afield of our subject here today anyway. So we're going to try to wrap our heads around how this all applies to the climate change fight, the clean energy fight. But just as a caveat up front, in one of your papers you write "those impacts that are easiest to measure are likely not those with the largest effects." So just by way of framing the discussion. What do you mean by that?Priya DontiYeah. So when we think about the impacts of AI and machine learning on climate, we need to think about a combination of AI and machine learning's direct carbon footprint through its hardware and computational impacts. The ways in which AI is being used for applications that have quote, unquote immediate impacts on climate change, be those sort of good or bad. But then we also have to think about the broader systemic shifts that AI and machine learning create across society that then may have implications for our ability to move forward on climate goals. And I'm sure we'll get into the specifics of all of those things.But I guess, briefly speaking, these sort of broader systemic shifts that AI and machine learning is going to potentially bring about are extremely hard to quantify, but they'll be large. And so it's important to make sure that as we think holistically about the impact of AI on climate, we do the quantifications in order to guide ourselves. But we also make sure to look at this holistic picture, even for things that we're not able to put so concretely into numbers.David RobertsYeah, I think about going back to, whatever, the beginning of the 19th century and just saying, like, well, what are the systemic impacts of automation go…
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