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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transcript)


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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
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 Roberts


Yeah, 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 Donti


Yeah. 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 Roberts


Right. 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 Donti


Yeah, 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 Roberts


Right. 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 Donti


Yes, 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 Roberts


Yes. 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 Donti


Yeah. 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 Roberts


Yeah, 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 going to be? Who knows? But they were in
fact enormous, right? And they did, in fact, sort of swamp the
kind of tangible, measurable immediate impacts. So this just to
keep in mind that we are to a large extent, I think, stumbling
around in the dark here, kind of guessing, like, we know
something big is going to happen. Big things are coming, but good
big things? Bad big things. What kind of big things?


To some extent, we're guessing from behind a veil of very little
information. So let's start then with the immediate impacts. And
this is something, when I threw this out on Twitter, this is
something I got a lot of questions about. I think it's in some
ways the easiest question to ask, which is, just as you say, all
these algorithms require a bunch of computing, a bunch of
calculations, which requires a bunch of chips and a bunch of data
centers and a bunch of hardware, basically. And so the first
thing to ask is just, do we know this shift into AI and machine
learning, do we have a good sense of just how much it is
increasing the world's computing load and just sort of exactly
how big the greenhouse gas impacts of that computing load are?
This is a conversation I think people are very familiar with,
vis-a-vis, Bitcoin, right? Like lots of people are asking about
Bitcoin. Is whatever we're getting out of Bitcoin worth the
immense resources we're putting into it, computing wise? Sort of
same question with machine learning and AI. So do we know how to
wrap our head around that do we know how to measure the total
amount of computing devoted to this?


Priya Donti


Yeah, and there are some macro level estimates here but they are
kind of evolving quite a bit over time. So in the kind of latest
numbers at least that I am on top of at a macro level is that in
2020 the total information and communication technology sector
was something like 2% of global greenhouse gas emissions and
machine learning is an unknown fraction of that. And one thing
that was happening is that we were starting to see kind of an
increase and I think exponential increase in the amount of
computational cycles that were being demanded from just various
types of compute that we're doing across society. But hardware
was also getting efficient at a similar rate which kind of kept
these greenhouse gas emissions and energy impacts relatively
constant over a decade or so.


But we're seeing a couple of these trends change. For example,
we're starting to see larger and more energy intensive AI and
machine learning models being developed and we're also
potentially reaching the end of, quote, unquote Moore's Law
improvements that were leading to these hardware efficiencies.
And so it's really important that we get honestly better and more
transparent data on machine learning workloads and sort of the
dynamics and trends of that in order to really understand what
we're dealing with. And this is one of those things where it's,
from a technical perspective, not the hardest in the world to
measure the computational impacts of AI and machine learning. You
sort of know where they're happening or you know what entities
are doing them. And it's a matter of instrumenting some
computational hardware. But for political and organizational
reasons we don't tend to have transparency on that data. It's
also worth noting that hardware is an important part of this
conversation because, of course, data storage and machine
learning algorithms, they all kind of rely on having
computational and storage hardware. And the kind of creation and
disposal and transportation of that hardware has not only kind of
energy impacts but materials impacts and water impacts and all
other sorts of impacts that we really need to be thinking about.


David Roberts


So is it true that Moore's Law is slowing down? I don't know that
I had tuned into this issue, but is it measurably slowing down or
is it a fear it's going to can we see it? I imagine it's not
super clear.


Priya Donti


Yeah. So I'm not a computer systems researcher myself, but I will
say that there has at least been discussion within the community
about are we reaching the end of Moore's Law as we've potentially
run against just physical limits on how small you can make
something.


David Roberts


Right, interesting. Yeah, we're getting down to nano, whatevers.
Now, is it fair to say that the majority of these direct impacts
are about the electricity that is running these things or are the
embedded emissions in the hardware itself that you were just
referring to are they comparably sized? Do we know how those two
compare to one another?


Priya Donti


Yeah. And I will say again, it's a bit of a shifting landscape.
But as of now, I would say that the computational emissions are
higher than the embodied emissions. But this is also shaped by
organizational choices in certain ways. For example, what we see
is that when you have data centers, they are often replacing
their computational infrastructure very quickly in order to make
it so that your computations are more efficient. So you kind of
reduce your computational emissions footprint.


David Roberts


Right.


Priya Donti


But by doing that, by replacing your hardware so quickly,
especially when your hardware is not actually spent, you're
increasing your embodied emissions. And so I think we're seeing
kind of adds a picture of what the computational emissions are
versus how quickly are we replacing hardware. The kind of
proportion of embodied emissions sort of is increasing if we kind
of believe this fact that the hardware is getting more efficient.


David Roberts


And just in terms of how much to worry about this, about these
impacts in particular, I mean, I guess I'm inclined to just say
most of that comes down to the power sources. A) the power
sources that are running the data centers, or b), the power
sources that are running the factories that are producing the
things. Those power sources are getting cleaner over time. Right.
They're being replaced by renewables over time. And so you can
imagine a not too distant future where this particular family of
impacts, the direct impacts, are fairly low to negligible. So I
guess I'm just inclined to just not worry about that piece of it
much. Is that off? Do you worry more than that about this piece
of it?


Priya Donti


I do worry about it. And this is because if we think about
decarbonization strategies across any energy related sector, the
first order of business is to reduce waste and improve
efficiency. And if every sector feels entitled to its unbounded
growth in energy use, we start to run into various constraints on
the actual "can the grid handle this?" on the
decarbonization-of-the-grid side. So I would say that here this
translates to kind of reduce waste is; if it's not worth running
a particular machine learning algorithm, if the benefit on the
other side isn't worth it, then we shouldn't be doing it.


And then improve efficiency is; for use cases where we've decided
it is worth it, let's make sure to do that in a way that is
reducing energy use as much as possible. And I think this sector,
like every other energy based sector, needs to be thinking about
those primarily in addition to, of course, decarbonizing the
grid.


David Roberts


Right. And there's a lot of runway left to make these things more
efficient, like the computations themselves. Is that mostly a
software thing, a programming thing to make them more efficient?
Or do you mean physical improvements in chips and data centers
and whatnot?


Priya Donti


So there's both stuff that can be done in software and in
hardware. There are kind of physical improvements that are doable
and are being worked on to make hardware more efficient. But also
in terms of the software, there's work that's looking at if you
have a big model, can you somehow actually do something called
pruning or architecture search? Things that allow you to figure
out are there smaller versions of the model that would make
sense. You can also, when actually training your model so getting
it to a state where it's making good predictions. There are
various procedures like hyperparameter tuning that go on, where
you're trying to figure out kind of meta design choices around
how the model is designed.


And there's more and less wasteful ways to do hyperparameter
tuning. We can again pick to not always use the most complex
model if it's not worth the value. So if a kind of much less
energy intensive model gives you 99.9% accuracy and it takes you
1000 times more energy to get to 99.99, that may not be worth it
in every use case. And so really, I think there's a lot that can
be done in there as well.


David Roberts


And it seems like we could also although I don't think we will,
it seems like we could also say as a society that some things are
not worth putting all this effort into. Like maybe if you're
creating a bunch of greenhouse gases and burning a bunch of data
center cycles to sort of improve the performance of a button
position on a particular Amazon page or whatever, maybe we should
just say deal with the current button position. There are
frivolous things that we're throwing enormous resources at
already.


Priya Donti


It's totally true. And I think all of these are driven by the
fact of money speaks. And I think it's unquestionable sort of
where money flows in society.


David Roberts


Okay, well, so those are the computing related sort of direct
physical impacts. The next tier up is what you call immediate
application impacts, which is just what are the things that are
running on machine learning doing now for climate? And I guess
you might say against climate, it's like, oil companies have
access to this stuff too and I imagine are throwing tons of
resources at it. One of the papers you sent me was sort of this
catalog of things that are using machine learning and it's just
already it's so vast that you can't really wrap your head around
it.


It's spread so fast that it's hard to say anything general about
how they're being used. But is there some way of sort of wrapping
our heads around or categorizing what machine learning is being
used for now in this world? In this sort of clean energy climate
world?


Priya Donti


Yeah. So I can give a couple of themes that I think cut across a
lot of the applications that I've seen and these aren't
exhaustive, but hopefully are at least illustrative. So one of
them is machine learning is maybe unsurprisingly being used to
improve predictions and by analyzing past data in order to
provide some kind of foresight. So an example there is the
nonprofit Open Climate Fix in the UK is working with National
Grid ESO to basically create demand and solar power forecasts by
ingesting a combination of historical data, the outputs of
numerical weather prediction models and in the case of solar,
things like videos or images of cloud cover overhead.


And by basically cleverly combining different data sources and
then using machine learning models to learn correlations between
these, they were able to cut the error of the electricity demand
forecasts in, I believe half —


Oh wow!


by doing that. And there are also applications in the climate
change, adaptation space. So for example, there's a Kenya based
company called Selina Wamucii which is using AI to predict locust
outbreaks which are exacerbated by climate, by basically
combining agricultural data, weather data, satellite data. So the
idea is basically if you have a bunch of different data sources
that are telling you something a bit different about the problem,
machine learning is really good at combining and learning
correlations among these heterogeneous data sources and then kind
of using that to make some kind of forecast in the future. So
that's one theme.


David Roberts


And does that theme also apply to the climate models themselves?
Like, I'm assuming climate modeling in general is going to
benefit from all this stuff.


Priya Donti


Yes. And so there is a lot of work that's looking at not machine
learning as a direct predictor of climate because ultimately
climate involves a shift in what's going to happen. And what
machine learning is good at is you have a data set, you identify
existing patterns and then to the algorithm, those patterns are
the world. So it's going to continue trying to apply the same
patterns. But where machine learning has been used in climate
forecasting is to do things like take these existing physical
models that are really complicated to run and try to approximate
portions of them so that the overall model runs more quickly.


Or take the outputs which are often coarse grained and try to
downscale them or fine grain them based on on the ground data.
Kind of post hoc.


David Roberts


Interesting.


Priya Donti


Yeah.


David Roberts


So just prediction.


Priya Donti


Yes so prediction is one.


David Roberts


Seems like an obvious enough one.


Priya Donti


Yes. The second one I'll talk about is taking large and
unstructured data sources and distilling them into actionable
insights. So this often comes up when thinking about the large
amount of satellite and aerial imagery that's becoming available
as well as the large amount of text documents we have available
on public policies or patents or things like that. So for
example, there's a project called the MAAP Project which is using
satellite imagery to try to give like a real time picture of
deforestation in the Amazon in order to then enable interventions
to actually stop it. And in the public sector, the UN Satellite
Center UNOSAT they use AI and machine learning to analyze
satellite imagery to get high frequency flood reports because
basically you can have a human looking at satellite imagery and
analyzing the extent of flooding, but it's a task that's hard to
do at scale for a human.


And so they use machine learning to actually try to analyze how
is flooding changing and get real time reports that have helped
them improve disaster response actions.


David Roberts


Yeah, in a sense it's just pattern recognition even for data
collections that are so vast and heterogeneous that maybe the
human mind sort of is stymied. The human minds are just pattern
recognition machines too, but we have our wetware limitations so
it just can find patterns in much larger and more heterogeneous
data sets.


Priya Donti


Yeah, I mean, in some cases it's that the patterns are just
really hard for people to grasp. Now, I have to emphasize the
pattern needs to exist. You're not going to find patterns where
they don't exist. But that's one case. But another case is one
where we as humans can grasp them and readily apply them. It's
just that scale is really hard. Kind of labeling a couple of
satellite images to understand flood extent is fine. Labeling
thousands and thousands that you're just going to run out of
human time.


David Roberts


All right, that's two.


Priya Donti


Number three. So the third is machine learning can be used to
optimize complex real world systems in order to improve their
efficiency. So while the kind of last two themes I talked about
with forecasting and distilling data into actionable insights,
it's fundamentally about providing information that ultimately
will go on to inform a decision. But there are places where
machine learning is itself in some sense, making a decision is
automatically optimizing some kind of system. This comes up, for
example, in building automation. So there are companies that are
using AI and machine learning to automatically control heating
and cooling systems. For example, in commercial buildings, based
on sensor data about weather, temperature, and occupancy, we are
trying to leverage that to basically find efficiencies in how the
heating and cooling infrastructure is managed —


David Roberts


You can throw power prices in there.


Priya Donti


You can throw power prices in there. Yes. And I think this is
actually a really kind of underrated and underexplored area of
work where there's work using machine learning for demand
response and market trading and there's work using machine
learning for building energy efficiency. But I think actually
there's a lot to be done in kind of bridging those two views. And
so I'm really glad you brought that up, actually.


David Roberts


Well, I can also think of another large complex system that
desperately needs some optimization, which I think you also know
something about one of our shared, shared obsessions, namely the
electricity grid. I'm very curious what is currently being done
with machine learning on the grid?


Priya Donti


Yeah, it's a great question and I will say , so, machine learning
is pretty widely deployed across power grids for forecasting and
situational awareness kinds of tasks. When it comes to
optimization and control, I would say largely a lot of those
applications sit more on the research realm than in the
deployment realm right now. And part of the reason for that is
that I think there's just a big lack of appropriately realistic
data and simulation environments and metrics that actually allow
us to test out and validate research methods in an environment
that is realistic and actually advance their readiness that way.


Because by testing out a research method in an environment that
looks realistic, you then understand how do I need to adjust my
method to make it responsive to the realities of the grid. And
you sort of have that feedback loop and kind of progression of
readiness which I think we're lacking a lot of infrastructure
for. But concretely, where machine learning can play a role there
is when we think about centralized optimization problems. So
things like optimal power flow problems and the stochastic and
robust variance of that, these problems are computationally
intensive to solve. And so sort of similarly to the theme of
improving the runtime of climate models, we can similarly think
about are there parts of the problem we can approximate, or can
we learn quote unquote warm start points?


Or can we even make direct and full approximations to these
centralized optimization models, but in ways that preserve the
physics and hard constraints that we care about? And that's
actually what some of my work looks at. And then also on the kind
of distributed and decentralized control side, we want to
construct controllers that can make decisions based on local
data, maybe plus a limited amount of communication to get some
more centralized data. And this is a place where control theory
is playing a role and AI and machine learning can potentially
also play a role by basically learning complex patterns in the
underlying data and using that to make nuanced control decisions.


David Roberts


When I first thought about AI, machine learning and climate, this
was the very first place my brain went, I guess. No surprise to
any listeners. But the rise of DERs, the rise of distributed
energy resources is just, among other things, an enormous
increase in complexity. You're going from, whatever, a dozen
power plants in your region to potentially thousands, tens of
thousands, hundreds of thousands. And I think I mentioned this
when we talked earlier, but I'm not sure ordinary non-grid nerds
really understand how much of grid operation today is still like
people turning knobs and making phone calls to one another.


It's bizarrely low tech, a lot of it. And so that just seems to
me like an absolutely ripe area for this kind of thing.


Priya Donti


Yeah, I definitely agree. I mean, there's the scale problem you
talked about, there's the speed problem as we deal with increased
variability, and there's actually the physical fidelity problem.
So right now, because on power grids, we find that true physical
representations are really hard to kind of solve computationally.
So you often will use something like DC optimal power flow as an
approximation to the grid physics, rather than something more
realistic like AC optimal power flow. Then what we rely on is we
make a kind of decision a bit ahead of time based on these
approximate physics.


We let that play out, and then we allow real time adjustments on
the grid. Things like automatic generation control take place to
compensate for mispredictions or mischaracterizations of the
physics. And as we have fewer spinning devices on the grid, and
we're starting to see things like faster frequency swings because
we don't have that buffer provided by spinning devices attached
to the grid in the same way, we also lose some of our kind of
buffer in terms of being allowed to be slightly physically off in
terms of our characterization.


David Roberts


So we need to be more precise.


Priya Donti


We need to be more precise.


David Roberts


Yeah, this is the thing about solar power in particular, is just
so digital. It just seems like it lends itself to digital control
and not to this sort of old fashioned kind of inertia and
spinning and all these sort of very physical, very physical
things.


Priya Donti


And I think one way to think of it is I know there's a lot of
folks who are very scared. I mean, we're fundamentally talking
about a safety critical system where if it goes down, it's a real
big issue. And so I think there's a combination of for those
physical constraints that we can kind of write down and really be
certain of, there are ways to start to construct AI and machine
learning methods to fundamentally respect those. And then also, I
mean, it's not unreasonable to think that at certain timescales
that we would possibly have some amount of human in the loop
control.


Sort of in the same way, when you're driving a car, you as a
human are steering it, but you're not dictating every lower level
process that takes place to make the car go.


David Roberts


Yeah, the car analogy getting slightly off course again. But the
car analogy raises something that I've been thinking about, which
is some of the dangers of automation coming from machine learning
and AI. And I think the car example works really well. So it's
generally pretty safe for a human being to be 100% in charge of
the car. And I can imagine a level of AI and sensing and et
cetera, and infrastructure sympathetic infrastructure makes it
such that 100% automated control is safe. But what doesn't seem
safe to me is the sort of quasi semi-automation where the car can
drive itself most of the time, but then you need a human out of
nowhere, possibly quite suddenly. And it's just we humans are not
really made for that, to sit there not doing anything for hours
on end and then be ready at any second to jump in. And I wonder
if there's an analogy to other systems in that is there that gap
between no automation and full automation where there's weird
automation-human interactions that are kind of sketchy. Is that
analogy broadly applicable or is it just a car thing?


Priya Donti


No, I mean, I think it is broadly applicable and it's a
combination of what is the correct level of sort of human
automation-interaction both at the level of an individual
component but also you're often thinking of multiple components
interacting with each other that may have different trade offs.
So in cars, that is, if you have a mixture of autonomous,
semi-autonomous and fully human controlled cars on the road in
grids, you can imagine, of course right, multiple grids. I mean,
it's a physical system, but there are different sort of
governance and jurisdiction related things such that we're doing
different things on different parts of the system. And so how do
those interact with each other becomes a super important
question.


David Roberts


Yeah, and it's one thing in a car, it's another thing if you're
driving a grid. As you say, the cost of mistakes is much higher.
But I interrupted your list, I think. Was there a fourth?


Priya Donti


Yeah, I had a last theme that I wanted to talk about. Yeah, so
the last one is machine learning for accelerating the discovery
of next-generation clean technologies. We've talked so far about
machine learning for operational systems, but of course, as we're
trying to transition systems, how do we come up with that better
battery for frequency regulation on the grid or for your electric
vehicles or how do you come up with a better carbon dioxide
sSorbent for sequestration related applications, things like
that, or electrofuels. So what machine learning has been used to
do is analyze the outcomes of past experiments in order to
suggest which experiments to try next, with the goal of cutting
down the number of design and experimental cycles that are needed
to get to that next better material or clean technology.


David Roberts


Right. Yeah, I hear a lot about this, and this always seems
enormously positive to me. And I thought, isn't it also in
addition to just suggesting experiments, isn't it also a thing
that they can sort of run the experiments virtually? Sort of do
the materials science experiments virtually, so you don't have to
do the physical experiment at all?


Priya Donti


Yeah. So you can do some amount of physical virtual simulation
rather in order to understand what the performance
characteristics of a particular material are. But virtual
simulations are not perfect. And so ultimately you do sort of
need to synthesize at some point. Right? You need to synthesize
or create the thing and test it out in the physical world.


David Roberts


At least you could narrow down the number of physical experiments
you need.


Priya Donti


That's exactly right. That's exactly right. And so the goal is
really to in this case, it's again, not that you're sort of
letting a machine learning algorithm itself sort of dictate
exactly what experiments you do at all times, right. There is
sort of human scientific knowledge that's really coming into
play. On the other side, to look at the output and say, that
seems reasonable, that seems like something I'm going to try
versus this might not be worth the millions of dollars it takes
me to synthesize this thing. So it's sort of an interaction
between the computational insight and sort of the human judgment
on the other side.


David Roberts


This is a big thing in pharmaceuticals too, right? Like drug
development. Is there a clear sort of a success story in that
particular application? Like, is there a materials advance where
the company that did it was like, look what we did with AI. Can
we point to something yet?


Priya Donti


Yeah. So a group of us wrote this report for the Global
Partnership on AI, which provides recommendations to policymakers
on how they can align the use of AI with climate action. And as a
part of that, we actually highlighted a couple of real-world use
cases where we are seeing kind of on the ground successes. And so
actually, some of the examples I've talked through today are from
there. But in this category, one of the successful ones that we
highlighted in that report, it's a startup called Aionics, which
is a Stanford spin out. And what they do is they work with
battery manufacturers across different sectors, so across energy
and transport in order to help them kind of speed up their
process of battery design, where of course, the properties of
your ideal battery vary based on your use case.


David Roberts


Right.


Priya Donti


And they use a combination of machine learning and some physical
knowledge to do this analysis. And per their reporting, they've
been able to cut down design times by a factor of ten for some of
their customers.


David Roberts


Super interesting.


Priya Donti


I think there's a lot of potentially very impressive gains in
that area.


David Roberts


Yeah. I mean, to return to my theme, how do you even begin to
predict where that's going to go? I mean, the mind boggles on
some, on some level. So in terms of these immediate application
impacts, you listed four sort of broad themes, all positive
examples. I'm assuming carbon intensive industries are also —


Priya Donti


Very much seeing the power of AI.


David Roberts


Yeah. Are there prominent sort of examples where AI is being used
to find or burn more fossil fuels?


Priya Donti


Definitely. So AI is being used in large amounts by the oil and
gas industry to facilitate their operations. So things like
advanced subsurface modeling to kind of facilitate exploration,
the optimization of drilling and pipelines in ways that try to
improve extraction and transportation and also, I mean,
marketing, right. To increase sales. And so there's a lot of
applications here. And there was a report called Oil in the Cloud
by Greenpeace that came out a few years ago.


David Roberts


Yes, I recall.


Priya Donti


Yeah. And that one estimated that AI was going to generate
hundreds of billions of dollars in value for the oil and gas
sector by kind of the middle of this decade. And that is
substantial.


David Roberts


Yeah. And I believe their point was like Google is out there
claiming to be a champion of clean energy and decarbonization and
et cetera, et cetera, and it is providing these technologies that
are turbocharging the fossil fuel industry. Seems odd.


Priya Donti


Yeah. And there's genuine debate, which I do happen to fall on a
particular side of, but there's genuine debate about sort of
whose responsibility the resultant emissions are. But I guess
what I will say is every entity is very — the tech sector, the
oil and gas sector, they're very eager to claim that every set of
emissions is scope three emissions that are not within their
direct control. And given the urgency of hitting climate change
related goals, if anything, we shouldn't be so worried about,
well, we need to make sure that — this sector is responsible and
this isn't — by all means, double count it. Make multiple
entities responsible for any packet of emissions and just make
sure something happens.


David Roberts


Yeah. What's the danger of double counting? We might reduce
emissions, accidentally reduce emissions too much.


Priya Donti


Yeah.


David Roberts


So here's an unanswerable question for you then. When you look
out over the landscape of these immediate application impacts,
sort of the way AI and machine learning is being used today, is
there any way to sort of net things out and say, oh, it's good
for climate or bad for climate, or is this just sort of like this
is just making everybody who does everything slightly more
powerful? You know what I mean?


Priya Donti


Yeah. AI is an accelerator of the systems in which it's used. And
this is not an original quote, it's a quote from many other
people much smarter than I am. But what that means is that we
need to look at what are the societal incentives around kind of
who gets to leverage technologies like this and what kinds of
processes does it mean it's likely accelerating as a result. For
example, is there more money in oil and gas than in renewables?
Right. That picture is shifting. But I mean, as long as that's
the macro level case, you're going to see AI deployed where there
is more money to spend for the use of AI.


And so, yeah, I would say that in some sense, the kind of obvious
answer would be net, like, the impact is not good for climate. I
mean, and this is aligned with the fact that we as a society are
needing to work pretty hard to hit our climate change related
goals —


David Roberts


Just because society isn't good for climate right now.


Priya Donti


Exactly. But I think importantly, as we think about both the
broader climate fight and the role of AI within it, these are
shapable. Right. So I think that in some sense, the macro level
question of is AI good or bad for climate? Often leads to maybe
the wrong implied downstream action of should we do or not do AI?
Which I think unfortunately, or fortunately at this point is a
bit of a foregone conclusion. And instead we need to really be
thinking about how do we shape these developments on a macro
level to be aligned with climate action.


And that's not to say that certain applications shouldn't go
forward. Like, I think that's a very valid thing to say. A
particular application is one where we should not be applying AI,
but on a macro level, it's really about kind of steering both
thinking about where we should and shouldn't use it and then how
we should use it where we should.


David Roberts


Which is the same set of questions that face us on everything
else too. Right. On any technology or doing anything, really. In
a sense, the effects of these immediate effects are downstream of
just sort of larger forces and will change as those larger forces
change.


Priya Donti


Yeah, and the reason to think about them in an AI specific
context is the same reason we think about sector specific
policies when we look at climate action. There are in principle
macro level policies that should just address everything, right?
Like if you deal with the emissions and the pricing, sure,
technically all of the underlying incentives should follow. But
in practice, we find that sector specific policies that are
really cognizant of the bottlenecks and trends in a given sector
are helpful. And so this is the same thing with AI understanding
who the players are, what the levers are, and how we can come up
with more targeted policy and organizational strategies. To
actually address those is ideally additive to thinking about it
on just a macro level.


David Roberts


Right, well, I want to talk about policy, but just real quick
before we get there, this third level of impacts is system level
impacts, which are just going to — I barely even know how to talk
about them. There's going to be sort of emergent large systemic
shifts that arise out of the changes that these things bring. Are
there examples of systemic impacts that could help us wrap our
mind around what we mean by them and is there anything general to
say about them other than they're probably going to happen?


Priya Donti


Yeah, I mean, I would say that there are some that are a little
more in that, "Uh, they're probably going to happen" and others
that are more shapeable. So things like machine learning is a key
driver behind advertising and increased consumption, not just
because of advertising, but because of on demand delivery and all
of these things that AI and machine learning creates which often
increase emissions, but not always in ways that make us happier.
Right. Which again, like emissions increases. I think there's
this thing about well, but if there's a benefit on the other
side. But there isn't always, and largely, it's obviously a big
question across society, is increased consumption making us
happier?


And AI is certainly driving that. In addition, AI is changing not
just how we consume goods, but also information. So different
people, when googling something will get a different answer. And
on social media, also the targeting of posts, the generation of
misinformation, but also the detection of misinformation. So I
think there are some complex ways in which AI actually interacts
with this, both in terms of having the capability to serve better
information, but likewise be able to serve worse information as a
result. And then there are things like the use of AI for
autonomous vehicles where it's unclear what the impacts will look
like, but they are potentially very shapeable.


Where if AI and autonomous vehicles are developed in a way that
facilitates private and fossil fueled transportation, that has
very different implications for the transport sector than if
you're facilitating kind of multimodal and public transportation,
right. Making it easier for people to connect between different
modes of transit. And that's not a foregone conclusion, the
direction we go in. And so I think there's actually a lot we can
do to kind of shape the directions these technologies take in
these settings.


David Roberts


Before we move on. There's just one other thought that occurred
to me, is the use of these algorithms in trading in people day
trading stocks, they're down to like one millisecond whatever
trades. Now, I've read a lot of people a lot smarter than me
write about this, and their conclusion is just like, no one needs
this. No one is benefiting. The market is not benefiting from
this. This does nothing but allow people skimming off the middle
to skim more off the middle. So there's an application of
algorithms and machine learning where we could just say, no, just
don't. Just stop doing that.


Priya Donti


Yeah, I think my tagline for people working on financial markets
is; energy markets are way more interesting because you have both
your financial system and your underlying physical system. I know
there's a lot to be done there to facilitate renewables
integration. Come join us.


David Roberts


Yeah, there's a reality on the other side of all our numbers
instead of just this weird sandbox that you're all just playing
pretend in. Okay, by way of wrapping up, then let's talk about
policies. So in your paper where you are making policy
recommendations, some of the policies are just sort of obvious.
You price carbon emissions, right? And then that produces a more
or less universal force, pushing down carbon emissions and things
like that. You offer tax incentives for greenhouse gas
reductions. Just general good climate policy, you recommend a lot
of that and all that stuff would be great, of course.


But are there more sort of AI specific policy directions we
should be thinking about?


Priya Donti


Definitely. So when it comes to facilitating the use of AI for
climate action, what we want to think about is creating the right
enabling data digital infrastructure, kind of targeting research
funding in particular ways, enabling deployment pipelines. So I
talked about this kind of research to deployment infrastructure
that's needed in power grids and also capacity building. I mean,
I think that both in terms of people who have the skills to
actually implement all or parts of AI and machine learning
workflows, but also people who have the ability to run
organizations or govern systems where AI and machine learning
will play a role.


I think having just that base level of literacy in terms of what
you're dealing with becomes super important in sort of allowing
there to be a lot of ground up innovation where people now are
equipped with knowledge of their particular context and these
tools and can make things happen as a result. So I think there's
a lot that can be done and those all sound like very general
levers, but of course there are specifics in there like how
should research funding look? I mean, it should not be that
climate funding is diverted to becoming AI plus climate funding
only. It shouldn't be a narrowing of scope.


It should be things like making sure you have AI expert
evaluators in climate calls so that they can understand when
something's being submitted that makes sense. And it's about
shaping AI calls to have climate focuses. So there's some
subtleties there, but basically a lot of things that are needed
to enable the use of AI for climate action.


David Roberts


And it also occurs to me that there's tons of things you could
think of where AI and machine learning would improve outcomes
that won't necessarily make anybody money or might even by
increasing public provision or reducing demand for some services,
cost people money, like might reduce the net amount of money to
be made. And that seems like a place where government policy
could help nudge research funding and activity into those areas.


Priya Donti


Absolutely trying to identify those quote unquote public interest
technologies and channeling funding towards them. Exactly. And of
course we talked about the kind of negative impacts of AI on
climate and these should absolutely be accounted for as well. So
when it comes to the computational and hardware footprint, we
talked earlier about how it's just really hard to understand
what's going on because you don't have transparency on what the
computational energy impacts look like, even though you know in
principle how to measure them because there aren't reporting
incentives or requirements or things like that. And when it comes
to hardware impacts, we can get a sense of embodied emissions.


But I mean, measurements on water and materials are really hard,
just kind of putting in place at minimum reporting frameworks and
standards so that those who want to report voluntarily know what
that means. But I think more importantly, putting in kind of more
mandatory reporting frameworks for some of these things so we can
figure out what the dynamics and trends are and what it makes
sense to do next.


David Roberts


Right, final issue. But this is something that several people
flagged to me that they wanted to hear about is we've recently, I
think, seen some articles about the enormous amount of human
labor that is behind these AI things. And of course, the world
being the way it is, it's often poor people, it's often exploited
people, a lot of people that aren't treated well, aren't paid
well. So once again we find ourselves with this sort of shiny new
thing in the west and you scratch down a few levels and you find
blood and tears from poor people behind it.


Is there any sort of like climate or energy specific way of
thinking about that or is that just a general concern and do you
have any thoughts about sort of like what to do about that?


Priya Donti


Yeah, I mean it is a general concern and I would say that some of
this also comes from machine learning being right now
predominantly developed in contexts that have certain assumptions
associated with them, like large scale internet data that is able
to be scraped and maintained by entities in the west. Whereas in
many settings in the climate realm, for example, you don't have
data that's that large nor do you have the capability to maintain
it. But then when you make the assumption that large data and
larger models are sort of the way to progress AI and machine
learning, which is an implicit it is an assumption that is
created by virtue of who it is who's doing it. Now then you also
create all these human costs, all these hidden costs that are
really important to take into account.


And so I think really what has to happen is that and this is sort
of along this point of also what can we do at a policy level to
sort of align the use of AI with broader climate goals: I think
we really need to think about what it means to develop AI in a
way that is actually serving the needs of people around the
world, which doesn't always mean biggest data AI. There are other
ways to do AI and where the applications are ones where we're
also picking in ways that drive the development of AI in these
directions. So if you think about the development of AI for power
grids, you're going to think about robustness and safety critical
aspects differently than if you're looking at other areas.


And that's going to shape how AI itself moves forward and what
other domains it immediately has benefits for. And so this just
integration of climate and equity considerations more deeply into
AI strategies in a way that should then inform funding programs
and incentive schemes and the creation of infrastructure and all
of that is going to be really important.


David Roberts


Thank you so much for this. This is really helpful for me to wrap
my head around all this. And it just highlights again the fact
that I emphasize over and over on this pod, which is it really
seems like we are on the cusp of a wild wild time to be alive, to
put it as bluntly as possible. Like, we're going to see some
crazy stuff in our lifetime. Thank you for helping get our heads
around, at least, how that's shaping up so far, so Priya Donti,
thank you so much for coming and sharing.


Priya Donti


Thanks so much.


David Roberts


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