Welcome to our deep dive into the world of AI and climate change.
Sounds pretty intense.
It is.
We got a great TED talk from one of our listeners about it.
Oh, yeah.
The one from the scientist who's also an AI product manager.
That's the one.
They make a really cool point about how we can use AI to kind of like optimize existing systems.
Interesting.
So like kind of giving our planet a software upgrade.
Exactly.
What really stood out to you from the talk?
Well, I thought their analogy about instructions was spot on.
You know, like.
Oh, yeah.
Like when you're trying to put together furniture.
Yeah.
Or you're following some crazy recipe.
And you have all the ingredients.
The what, but not the how.
Exactly.
They argue that a lot of times we talk about climate change solutions in the same way.
Ah, I see what you mean.
Lots of big talk, but not a lot of concrete steps.
Right.
And they don't just stop at the analogy.
They actually give some pretty compelling examples of how AI can provide those how to use, especially with wind energy.
Yeah.
That was a really smart choice because wind energy is already a big player in the renewable energy world.
Yeah.
But but it's unpredictable.
Exactly.
Unlike fossil fuels, which we can control wind as well.
Weenby.
Ha, ha, ha.
Well, yeah.
But the point is we can't just turn it on and off whenever we want.
And that's a big problem for energy grids, right?
They need consistent power.
Right.
Exactly.
That's why this team at Google DeepMind trained a neural network on historical weather patterns and turbine data.
Wait, hold on a sec.
Can you back up a bit?
What kind of neural network are we talking about here?
Oh, they used a type of deep learning model called an LSTM long short term memory network.
OK.
And why that one specifically?
Well, they're really good at analyzing time series data, like weather patterns, you know?
Ah, so they can kind of learn from past weather events to predict future ones.
Precisely.
They're designed to capture those long term dependencies in the data, those complex patterns that drive wind behavior.
I'm starting to get it.
But I bet getting all that data wasn't easy.
We're talking about historical weather and specific turbine performance.
Oh, it was a nightmare.
They needed both public weather data and proprietary data from individual wind farms.
Like power output and turbine speeds.
All that stuff.
And that kind of sensitive data is really hard to get your hands on.
So they've got this massive data set, this powerful neural network.
What do they do with it?
How do they actually put it to use?
Well, that's where Google comes in.
Not only did they provide the AI expertise through DeepMind, but they also became the crucial deployment partner.
What does that mean, exactly?
They gave DeepMind access to 700 megawatts of their own wind power capacity.
Whoa.
So essentially a massive testing ground.
Exactly.
A real world environment to see how well this AI could perform.
That's huge.
Not a lot of companies would be willing to take that kind of risk.
Totally.
And it wasn't just about the turbines.
Google also provided a team of domain experts, energy engineers, grid operators, you name it.
To help fine tune the AI.
Exactly.
These experts helped define the specific needs and constraints for the system, made sure it was tailored to the real world.
So they weren't just building some cool AI in a lab.
They were building something practical.
Exactly.
And get this, the system didn't just meet expectations.
It blew them away.
Come on.
How much better did it do?
Their AI system achieved a 20% improvement in the accuracy of electricity supply forecasting.
20%.
That's incredible.
What does that look like in the real world?
It will.
Imagine being able to reduce your reliance on fossil fuels by 20% just by better predicting when the wind will blow.
That's a game changer.
Right.
It's all about making wind energy more reliable, more predictable.
So this is a great example of how AI can provide those specific actionable how-tos that the speaker was talking about.
Exactly.
And it doesn't stop there.
They're actually working on turning this into a commercially available software product.
So they're not keeping this a secret weapon?
Nope.
They're trying to spread the benefits.
Other wind farms can use it to optimize their operations.
That's awesome.
Are there any companies already using it?
Yeah.
A French energy company is one of the first to pilot this technology.
That's fantastic.
It sounds like this could have a real impact on a global scale.
Absolutely.
And that brings up another important point that the speaker made in their talk.
They highlight the work of this small nonprofit in the UK called Open Climate Fix.
Open Climate Fix.
Never heard of them.
Well, they partnered with the UK's National Grid to focus on demand side forecasting.
Demand side forecasting.
What is that exactly?
It's basically predicting how much electricity people will need at different times.
Oh, I see.
So making sure there's enough power to meet demand.
Exactly.
And it's super important with renewable energy because it's so variable.
Makes sense.
How do they use AI for that?
They actually used a different approach than DeepMind.
Instead of an LSTM, they use something called Gaussian processes.
Gaussian processes.
What are those?
It's a statistical method that's really good when you don't have a ton of data.
Which I imagine is often the case with energy consumption patterns.
Right.
It's hard to predict exactly how much energy people will use at any given time.
So did it work?
Were they able to get accurate forecasts?
Yeah, their forecasts were twice as accurate as the UK Grid's previous system.
Whoa, that's a huge improvement.
Right.
Think about all the times we have to fire up those dirty backup power plants just
because we underestimate demand.
So this AI could help prevent that.
Exactly.
It makes the grid more stable, more efficient and less reliant on fossil fuels.
That's amazing.
It really shows the power of collaboration.
Bringing together AI experts, energy companies and even small nonprofits.
Absolutely.
And that was a big part of the speaker's message.
They want more people to get involved in this space.
OK, I'm listening.
What can someone with, say, expertise in transportation or agriculture contribute?
That's a great question.
They argue that people with domain expertise should share their problems with the AI community.
Interesting.
So like saying, hey, we're having this issue in agriculture.
Can AI help us solve it?
Exactly.
Because sometimes the AI experts don't even know what problems to focus on.
Makes sense.
So it's about connecting those dots between different fields.
Right.
And they even suggest creating competitions with data sets and metrics to incentivize
AI researchers.
So like a challenge to solve a specific climate related problem using AI.
Precisely.
Imagine a competition to optimize traffic flow in cities or to develop AI powered
systems for precision agriculture.
Those are great examples.
A little friendly competition can really drive innovation.
And speaking of data, that's another crucial point that the speaker brought up.
Oh, yeah. Data is like the fuel for AI.
Exactly.
We need access to good quality climate relevant data if we want AI to be effective.
What kind of data are we talking about here?
It could be anything, weather patterns, energy consumption, data, agricultural
practices, deforestation rates, even the migration patterns of endangered species.
That's a lot of data.
And who has all this information?
It's spread out everywhere.
Governments, research institutions, private companies, individuals with their smart
devices.
So the call to action here is for those data holders to share their data.
Right. To make it available to researchers and developers who can use it to build
impactful AI solutions.
But what about privacy concerns?
There's a lot of sensitive data out there.
Oh, absolutely.
They emphasize that data sharing needs to be done responsibly.
They actually mentioned a great resource, Climate Change AI.
Climate Change AI.
What's that?
It's an organization that's working to connect data holders with AI experts.
They even have a wish list of data sets that would be valuable for climate research.
That's really cool.
So we've got the domain experts, the data holders, and of course the AI developers.
Is there a role for anyone else?
Absolutely.
We also need people with expertise in ethics, policy and communication.
Ah, I see.
Because it's not just about building the technology.
It's about using it responsibly.
Exactly.
We need to think about the potential unintended consequences of AI,
the possibility of bias and make sure these solutions benefit everyone.
It's a lot to consider.
It sounds like we need a truly multidisciplinary approach to tackle this.
And that's exactly what the speaker argued for.
They said we need scientists, engineers, policymakers, ethicists, communicators,
everyone working together.
This is all very inspiring.
But I also want to be realistic.
AI is a powerful tool, but it's not a magic bullet, right?
You're right.
AI isn't going to solve every problem.
But it can be a game changer if we use it wisely.
So what are some of the limitations of AI?
What are the potential pitfalls we need to watch out for?
Well, for starters, there's the issue of AI's carbon footprint.
Ah, yeah.
Training those massive AI models takes a lot of energy.
It does.
And a lot of that energy still comes from fossil fuels.
So there's a bit of irony there.
We're using a technology that contributes to climate change to try to fight
climate change.
Right. But it's important to remember that AI's footprint will shrink as we
transition to a cleaner energy grid.
So it's not a deal breaker.
We just need to be mindful of it.
Exactly. And there are also people working on developing more energy
efficient AI algorithms and hardware.
So there's hope for the future.
What other limitations are there?
Well, another big one is bias.
AI models are only as good as the data they're trained on.
Right. So if the data is biased, the AI will be biased too.
Exactly. And that can lead to all sorts of problems,
especially when it comes to things like fairness and justice.
So it's really important to have diverse and representative data sets.
Absolutely. And we need experts who can check for bias and make sure the AI is
being used ethically.
It's a lot to think about.
It's not as simple as just throwing AI at every problem.
You're right.
It takes careful planning and consideration.
But I'm still optimistic about the potential of AI to make a real difference.
Me too.
This conversation has given me a lot to think about.
It's easy to get caught up in the doom and gloom,
but there are so many reasons for hope.
I agree. We have the tools and the ingenuity to solve this.
It's just a matter of putting those pieces together.
You know, it's really stuck with me throughout this deep dive.
It's that idea of optimization,
making existing systems better instead of starting from scratch.
I think that's a really smart approach.
It feels more attainable, more realistic.
And AI can play a huge role in that, right?
Helping us find those areas for improvement and make them happen.
Exactly. Think about all the systems we have in place.
Transportation, energy, agriculture, manufacturing.
We can make them all more efficient, more sustainable.
Give me some examples.
How could we optimize those systems?
Well, in transportation, we could use AI to optimize traffic flow,
reduce congestion and emissions.
That would be amazing, especially in cities where traffic is a nightmare.
Or in agriculture, we could use AI to help farmers make better decisions
about irrigation, fertilization and pest control.
So it's about making those practices more precise, less wasteful.
Exactly. And that precision extends to other areas too.
Like manufacturing, we can use AI to optimize production processes,
reduce waste and even design more energy efficient products.
It feels like we're at a turning point where AI is no longer just a futuristic
concept, but a real tool we can use to build a better future.
I agree. And it's not just up to big companies or governments to make these changes.
We can all contribute.
OK, so let's talk about that.
What can we do as individuals to help?
Well, we can support companies that are using AI for good.
So like voting with our wallets.
Exactly. We can also advocate for policies that promote the responsible
development of AI for climate action.
So making our voices heard and engaging with our elected officials.
And of course, we can educate ourselves about all of this.
The more we understand, the more we can do.
I love that.
It's about turning knowledge into action.
This deep dive has given me a lot to think about.
That's great to hear.
And remember, this conversation doesn't end here.
Keep exploring. Keep asking questions.
Keep pushing for change.
Couldn't have said it better myself.
Thanks for joining me on this deep dive.
It's been really eye opening.
The pleasure was all mine.
And to everyone listening, remember, the future is in our hands.
Let's use it wisely.
Well, for starters, there's the issue of AI's own carbon footprint.
Oh, yeah. Training those massive AI models takes a lot of energy.
It does. And a lot of that energy still comes from fossil fuels.
So there's a bit of irony there.
We're using a technology that contributes to climate change to try to fight climate change.
Right. But it's important to remember that AI's footprint will shrink as we
transition to a cleaner energy grid.
So it's not a deal breaker.
We just need to be mindful of it.
Exactly. And there are also people working on developing more energy
efficient algorithms and hardware.
So there's hope for the future.
What other limitations are there?
Well, another big one is bias AI models are only as good as the data they're trained on.
Right. So if the data is biased, the AI will be biased too.
Exactly. And that can lead to all sorts of problems,
especially when it comes to things like fairness and justice.
So it's really important to have diverse and representative data sets.
Absolutely. And we need experts who can check for bias and make sure the AI is being used ethically.
It's a lot to think about.
It's not as simple as just throwing AI at every problem.
You're right. It takes careful planning and consideration.
But I'm still optimistic about the potential of AI to make a real difference.
Me too.
This conversation has given me a lot to think about.
It's easy to get caught up in the doom and gloom, but there are so many reasons for hope.
I agree. We have the tools and the ingenuity to solve this.
It's just a matter of putting those pieces together.
You know, it's really stuck with me throughout this deep dive.
It's that idea of optimization,
making existing systems better instead of starting from scratch.
I think that's a really smart approach.
It feels more attainable, more realistic.
And AI can play a huge role in helping us find those areas for improvement and make them happen.
Exactly. Think about all the systems we have in place.
Transportation, energy, agriculture, manufacturing.
We can make them all more efficient, more sustainable.
Give me some examples.
How could we optimize those systems?
Well, in transportation, we could use AI to optimize traffic flow.
Reduce congestion and emissions.
That would be amazing, especially in cities where traffic is a nightmare.
Or in agriculture, we could use AI to help farmers make better decisions about
irrigation, fertilization and pest control.
So it's about making those practices more precise, less wasteful.
Exactly. And that precision extends to other areas too, like manufacturing.
We can use AI to optimize production processes, reduce waste,
and even design more energy efficient products.
It feels like we're at a turning point where AI is no longer just a
futuristic concept, but a real tool we can use to build a better future.
I agree.
And it's not just up to big companies or governments to make these changes.
We can all contribute.
OK, so let's talk about that.
What can we do as individuals to help?
Well, we can support companies that are using AI for good.
So like voting with our wallets.
Exactly. We can also advocate for policies that promote the responsible
development of AI for climate action.
So making our voices heard and engaging with our elected officials.
And of course, we can educate ourselves about all of this.
The more we understand, the more we can do.
I love that.
It's about turning knowledge into action.
This deep dive has given me a lot to think about.
What about you? What are your final thoughts?
I think this TED talk gave us a lot to think about.
We dove deep into wind energy and saw how AI can make a real difference.
And it's not just about wind.
It's about applying this kind of thinking to all sorts of systems.
So yeah, I'm feeling optimistic.
Me too.
It's been really eye opening to see how AI can be a force for good in the fight
against climate change.
And it's not just about the technology itself.
It's about collaboration, innovation and a willingness to challenge the status quo.
Couldn't have said it better myself.
It's been a pleasure exploring this with you.
Likewise.
And everyone listening, thanks for joining us on this deep dive into AI and climate change.
Let's keep the conversation going and work together to build a more sustainable future.