AI for the Planet: From Tech Labs to Real Life

Artificial intelligence is everywhere right now. We hear about it, writing essays, creating art, even generating entire conversations. But beyond all that hype, what if AI has a part to play in solving one of humanity's biggest challenges, climate change.

If there's one sentence that you, you need to remember is we're gonna be creating the future of hvac. This is gonna be pivotal for our sustainability goal, so let's put AI to work to make sure that we're not only gonna meet our commitment but exceed them. 

From making buildings smarter and predicting weather patterns to discovering entirely new species, AI is beginning to play a surprising role in protecting our planet.

The opportunities are enormous, but so are the questions like, how do we use AI responsibly? How do we balance its benefits with its own energy demands, and how can we make sure that it's making a real impact today? You're listening to Healthy Spaces, the podcast where experts and disruptors explore how climate, technology, and innovation are transforming the spaces where we live, work, learn, and play.

I'm Dominique Silva. Marketing leader at Trane Technologies and on season five of Healthy Spaces, we're bringing you a fresh batch of uplifting stories featuring inspiring people who are overcoming challenges to drive positive change across multiple industries. We'll explore how technology and AI can drive business growth and help the planet breathe just a little bit easier.

Hello listeners. Hello viewers. Now you may have noticed something different. That's right. Scott is taking a very well deserved break, but don't worry, I have got the perfect co-host to help me with today's story. Hello, Reb. 

Hey, Dominique. Thank you so much for inviting me. I'm really excited to be here. 

No, the pleasure is all mine.

Now, please do go ahead and tell our audience who you are and what it is that you do. 

Yes, sure. So my name is Rebecca Handfield. I'm the VP of Marketing and Public Relations at BrainBox AI, and I'm actually the co-host of the BrainBox AI podcast. 

I can't believe it. We have so many things in common, Reb so many podcasts, 

And aside from all of our similarities, I think this makes you the perfect person for this episode because Reb, let me tell you, all season long our guests have been talking about how excited they are about AI and let's be honest, right?

Artificial intelligence and automation, it's everywhere right now. But it still kind of feels a little bit theoretical. So on your show, what are people really talking about? 

Yeah, podcasts, we try to really emphasize and give examples of AI being applied to the real world. So real technologies that exist right now and can make a difference in buildings.

That exist that people work in and play in and operate in. Um, we cover topics like automated emissions reductions, algorithms that were deployed recently in uni. In a university, we talk about autonomous control technologies and just like a slew of energy optimization solutions that we're bringing to market.

I love that it's less about the hype and more about the real stuff. That's so cool. And actually that's why I spoke to your colleague, Jean Simone Vent, who is the Chief Technology Officer and co-founder of BrainBox AI. And I've already said it five times as I so Reb. What is Brainbox? 

Yeah, BrainBox AI is a lot of things.

Uh, but if I had to summarize in one sentence, it's an organization that develops solutions, uh, using advanced artificial intelligence technology to make buildings better, uh, make them more energy efficient, make them more sustainable, um, and just allow, help make better environments for the people that operate.

And live in them. And there's probably nobody better in this world to explain what or who BrainBox AI is than Jean-Simon. So I'll let him take it from here. 

Perfect segue. What an excellent segue. Let's hear from Jean Simon.

All right. Well, I'm Jean. I'm one of the founder of Brainbox AI. I am now leading the Brainbox AI business unit within Trane Technologies.

And what is BrainBox AI doing? Well, we're using AI techniques, to optimize the way we are operating and maintaining all of the HVAC equipment on the planet. Um, and the goal of doing this is to reduce the quantity of energy that these equipment are consuming every day, and at the same time reduce the emission that these equipment are indirectly generating by consuming energy that been produced somewhere else.

I would love to dive into that a little bit more. Right. We know that our built environment is a big contributor to climate change, and it feels like a very manmade problem, and it feels like a very manual problem to solve. How is AI actually improving the energy efficiency of building? 

Well, it's, if I start by, like when you look at a HVAC equipment, they're not fully optimized.

And the reason they're not fully optimized is because to optimize them, it would require that you and me, we basically sit in front of the equipment and we keep tweaking it in real time. So it always follow the optimal configuration, right? So unfortunately we don't have that kind of army of people being able to do that.

So we use template. And as soon as you use template, you're kind of saying to the equipment, this is the ultimate way you should run yourself. Because I'm not gonna be there to tell you what to do every second. Right? So. This is what we call the control sequence. And these control sequence, they operate 24 hours, seven days a week.

And they are doing a good job in a sense that the temperature is maintained at the desired temperature in all of the different zone of the building, but at what cost? So when we start to bring AI capability, we're basically starting to say, okay, we're not gonna follow that template anymore. We're gonna use AI technique and operational research technique. Also, it's a combination of how do we optimize the operation of that equipment right now? And we're using AI to basically give us the predictive aspect of it. So AI is very good at, if it's properly trained, to basically tell you what will be happening because I've been trained on all of the different possible combination of thing that could happen to that equipment.

And I know that when these things happen, I know that in 15 minute here, what's gonna be happening in two hours with so. So that prediction is then used by other algorithm to say, how should I operate that equipment right now to basically craft a better future? So. When you do that you're kind of shaving 15, 20, 25, 30% of the energy being consumed without any human in the loop.

Wow. So that's what we call optimization of asset. 

Really interesting to me in what you're saying, Jean Simone is, you know, you're talking about so much change that happens within the building envelope and outside of the building envelope. Right. Which is a little bit the reason why the, the HVAC equipment is constantly not running at its optimal.

But there are so many things that change because buildings are not static things. Right? Yep. Can you give us some examples of what kind of data are your AI models using and digesting in order to actually deliver better results? Right, to fine tune the way the equipment operates. 

So yeah, absolutely. You need to combine all of the telemetry coming from the existing controller on the HVAC side of the building, and that's truly that.

That's why it made like such a great story to have brainbox and train technology combining for us because train over the last years, already spent a lot of energy and investment to connect all of the controllers or most of them that are in the building to the cloud. So the data is already there, already available.

And then we come on our side with the brain box technique saying, okay, we're gonna take that telemetry data and we're gonna ingest it, because that is kind of the, a good part of the equation. To answer your question, the other part of the equation is what is happening outside and that data about the weather and that is readily available, right?

So all of these parameter goes in into the mix of being able to train AI model like LSTM, deep Learning Network, which give us a prediction very accurately. So we know that in your room it's gonna be too odd in two hours. Um, right. And of course we know what the template's gonna do to compensate the fact that it's becoming too odd.

It will react. Right? So we're flipping from reactive control. Predictive control with the objective to save energy emission and cycling of equipment. That's a very important aspect that we bring to the table. Yes. Is when you're in a reactive mode, you're doing a lot of stop start because you, you're being, you're caught up, right?

Like it's already to, I need to do something, so I'm starting a machine. Right. I need to turn it on faster, and we're always changing this. That point because we're never satisfied. Oh God, every of my life. 

If you do too much of sub start cycling, you're gonna have to change that motor sooner than you envision.

And of course that's a CapEx investment. CFO usually not too happy about that. Um, right. If you reduce the cycling and you operate the equipment in a smoother fashion, you're prolonging the life of that equipment and you're reducing the maintenance costs also. 

You said, look, you know, the, the equipment itself is already, you know, equipped with high tech controls and all of this data is feeding into the cloud.

So what you're effectively doing with the Brain Box AI solution is sort of layering that on top with these AI models that are gonna take that data, take external data, and then you're basically sending commands back to the unit. So it actually means that because of this layered technology. It doesn't have to be a major investment.

Right. If building owners are looking to upgrade their building. So can you talk us through a little bit of, of that, like what are the main questions, right. That building operators or facility managers should be asking themselves today to know whether or not they are ready to bring AI into their building management system.

The number one question is always, can we access the data of your controller? So that's the number one question, but you're right, it's kind of a layer that you put on top once you have access to the data, which is really like a very, very small investment, and then you get, you get the benefit of taking that data that you already have and create additional value with it.

Um, now the challenge Is the change management. And I'm gonna give you one example. Sometimes customer call us and they say, well, something is wrong. I said, what do you mean something is wrong? Well. I'm at the set point in my room. Um, so everything is fine, but still the system is starting cooling and then people have to remind themselves like, if we're starting the cooling right now and the temperature is already at set point, it's probably because we're starting to cancel an event that is coming in the future.

This is predictive control, which is recrafting the present to cancel something bad happening in the near future. So it's a bit like the movie Back to the Future. We have the ability to go in the future and see, oh my God, this is super bad. So we come back in the present and we go, let's change a couple of things and then we go back in the future to say, yeah, that was the right thing to do to make a better future.

I love how you've talked about how AI can actually help energy efficiency in buildings, but there's a lot of talk about the flip side of. AI also consumes a lot of energy. Mm-hmm. Right. Can you maybe talk to us a little bit more about. Any work that you may be doing to reduce the energy usage of ai? 

When we look at what we're consuming on the cloud, because we're not dealing with image movie songs, we're really like very small data set.

When you think about it, like it's like. On off, it's temperature, it's humidity level, it's percentage of opening of a damper. So if you compare to a Netflix movie, um, that like, even though we do a lot of calculation, we, our footprint is relatively small, but still that's, that's, we're not gonna just stop there.

So we, we've been working with, Mila here in Montreal, which one of the biggest, AI research lab in the world. There, there are about a thousand PhD strong. And what we give them as a mandate, let's work on how can we reduce the training of AI model in term of computing and energy that they consume.

And we came with this, this technique called neural, ODE, and we basically combine the, the typical neural net technique, but we put it physical constraint. We're basically telling the, the neural network, you don't need to learn all of the possible combination that might exist, but just limit yourself to a cone, to a cone of the real possibility.

In other word, you don't need to train yourself on what will be happening if the room is at 200 Fahrenheit. Because honestly if that happen, we have other problem than to figure out if we should optimize the equipment in that room. Um, you should just call the fire department at that point. Yeah, so, so we reduced and what we managed to do with neur OD is to cut by half the energy being consumed, to train the same model, to give us the same prediction.

So we reduce a footprint of using AI technique in the cloud. Ongoing, and, and it's actually one of the mission that the AI lab's gonna have that we're putting together here in Montreal saying we wanna make sure that we're gonna keep pushing the envelope to have AI technique, which are not only creating great impact to reduce the emission and the energy being consumed by building on the planet, but also these technique themself get the same result, but are using a lot less energy to reach that results.

Can you tell us a little bit more about this new initiative? What is this AI lab all about? 

Well, if you, if there's one sentence that you, you need to remember is like, this is where we're gonna be creating the future of hvac. AI is still exploding extremely rapidly in term of capability. And we are gonna, basically take all of these capability that already exists and all the one that will basically start to keep evolving over the next few years and put them to work.

So put them to work. So we make sure that Trane Technologies stays at the leading edge of how we're using AI to make sure that we have the best product, the best service that our customer could benefit from in term of value creation. And at the same time, make sure that we have all of the ethical and the guardrail in place on right, using these tool, these technique in the right way.

For our employee, but for our customer and for our all of the human society. Um, this is gonna be pivotal for our sustainability goal, that we wanna make our commitment for 2030. So let's put AI to work to make sure that we're not only gonna meet our commitment, but exceed them and, and be the the best company around in the HVAC world.

We've been talking so far about how BrainBox AI has been bringing AI into buildings, right? To improve energy efficiency. There's so many places where we could be applying AI in so many different sectors, so many different processes. But I guess the AI lab has to start somewhere and be focused on some things.

So what would you say are the guiding principles that the BrainBox AI Lab is based on? 

We like to summarize it like the four pillars. First of all, it's all about product and service creation. So we need to put the next gen from an idea or from a suggestion to something that is working in the lab and then goes outside in the real world.

We need to keep generating new ideas. So research and development is really the second pillar. So that research and development, like how can we use these technique and brainstorm to create like on paper, a suggestion of innovation and then push the research and the development to see if it really gonna work.

So putting ideas to reality, ethical and guardrail is becoming, extremely important. Especially with no human in the loop AI technique, which means that the AI is evaluating a situation, doing the root cause analysis, and then taking a decision and executing on that decision. And I would say that the last pillar is sustainability. So all this needs to be wrapped up into the global objective of basically helping doing our contribution in term of the HVAC world to rate make this planet more sustainable.  

I think one interesting thing of what you mentioned that caught me is, you know, when we tend to say lab and laboratory, we tend to think about, oh, it's the academic, it's the exercise, it's the papers, right?

But the very first thing that you said when I asked you what are your guiding principles, you set products, right? Are there any ones that you're personally excited about? Maybe you can already share us some examples of products that you have launched more, more recently, and that can give us an example. 

I would say like the, when I look at area, which is another area where we're pushing the envelope very rapidly, we're getting into this world of the agent and the multi-agent where, where we're gonna have the capability of reasoning is gonna be augmented very rapidly. 

What is area for some of our listeners?

You might not be familiar with that. 

Well, we put it together and launch it. It's the first, virtual mechanical engineer in the world. It's extremely specialized on hvac. So don't ever ask Aria what is the best recipe to do blueberry cheesecake because it will not know. But if you're asking Aria what is the issue with my rooftop right now. It will do the root cause analysis and come back to me very rapidly saying I looked at all of the parameter. I look at the trend, I look at everything that I get in terms of data coming from that rooftop, and you should change the belt right now with this model.

Because that belt is done. So right, it makes the life of a technician like extremely productive. It's reduced the truck roll and it make the, the root cause analysis time that you're spending on it optimize to a point where you could do a lot more troubleshooting and calls, trouble calls or tickets in a day than you used to do before.

I think really important in what you described in that, 'cause we hear that a lot with AI and people being scared that AI is gonna replace all of our jobs, but in this particular industry. You still need the service technician, right? We have to keep in mind, like, like in North America, there's an incredible shortage of technician on hvac.

And we have a lot of people that are starting to retire. All of these people that really know, they have the know how, they know how to do maintenance of equipment. They're starting to retire in a massive scale. So every time one of these person retired, we're losing that knowledge.

With ARIA, you're basically gonna be able to do the, the same quantity of, of job, but faster. So a technician will be able to do more calls in the day. So the goal here is not to replace a human. The goal here is to augment the capability of the human in a sector where there's a huge shortage of labor.

But keep in mind also that when a technician has to go two times in a building, the first time to do the root cause analysis, and the next day to come back to replace the part, um, and you managed to cut that, those two truck visit by one visit. So. Impact on the sustainability to say there.  

Jean-Simone gave us a clear picture of how AI can make buildings smarter by cutting wasted energy, extending equipment life, and lowering costs without sacrificing comfort.

So what stood out for you? Reb. 

Yeah, such an interesting conversation. I think there's really two pieces that are game changing that were raised in, in the conversation. The first is train AI control, right? Mm-hmm. And it's, it's such an interesting solution because it's the cornerstone of train technologies and brain box.

It's kind of the first real collaboration that's coming out of our partnership. And, it's going to dramatically change the way in which buildings. Manage their energy and just reduce energy consumption dramatically. And the second is ARIA. ARIA is, you know, the brand new game changing virtual building agent that was recently launched.

And it's just gonna change the paradigm that exists between buildings and facility management, just making it so much more efficient. So both super exciting. 

Well, I'm personally super excited, but I also acknowledge that people are worried about ai. Mm-hmm. Right? It's automation, it's job insecurity.

It's even losing their own voice to algorithms. But here's the paradox that I'm seeing, right? Because just by talking about it, debating it, reflecting on it, we're doing exactly what humans have always done to solve big problems. 

Yeah, AI and human nature. There's a lot of similarities there. It's interesting.

I think what's important is that in organizations like our AI lab, we are really putting AI ethics and AI safety at the forefront. We wanna make sure that when we're developing and deploying AI solutions, we're doing it with responsible AI principles in mind, as well as safeguards to ensure that AI becomes.

The tool that we need. It's a tool. It's not something that's going to take over everything. It's there. It's something that's there to help humans and it has to be responsible and it, we have to do it carefully and intentionally, and that's why it's such a core tenant of the AI lab. 

AI and ethics, and that is the perfect segue to our next guest who is Professor David Rolnick, because buildings are just one piece of the puzzle.

AI is also being applied to the natural world from forests and oceans to the tiniest ecosystems that keep the planet in balance. 

I'm so happy. I'm so happy you spoke with Dr. Rolnick. What an incredible human and such interesting projects, and I love his way of combining climate change and artificial intelligence.

Two huge pieces that I know if you bring them together, can create this very interesting dynamic. 

You're right, and it was such a fun interview. So for our listeners, David is an assistant professor of computer science at McGill University and Mila Quebec AI Institute, and he is also the co-founder of the nonprofit Climate Change AI.

That all sounds like pretty niche stuff. So the first question on my mind was, where does his passion even come from? 

Well, I actually started out as a mathematician. I got my degrees in math and for a while I was working on the mathematics of AI and deep learning. So how it, how it works, which surprisingly, we really don't understand very well.

But even before that, I was out in nature catching moths and butterflies and looking at birds, and after I started looping around to using mathematical tools to understand ai, I realized that the AI algorithms I was working on could actually be really useful in context. Around biodiversity and climate.  
 
And this coincided with a time when people were talking a lot about AI for good, which is a term I hate by the way. So I and others played a role in framing the space of AI for good. What it meant to think about AI in the context of climate action and connecting work that was already being done, starting to be done across energy, across biodiversity, across climate science.

With the mobilizing the AI and tech community to use these tools in these kinds of contexts. 

Yeah. Well, it's really interesting the amount of times you brought up AI. I know you don't like the expression AI for good, right. But in the context that you explained it, it makes some sense. I think, you know, recently when people couple AI and environment, it hasn't really been in a positive way and we'll touch on that.

So, let's go to a moment in time. So it's 2019 and you together with some of your research peers have published a paper called Tackling Climate Change with Machine Learning. Right. And, can you give our listeners a bit of an overview of what were your key areas of research? 

Yeah, so this was an overview paper that was really designed to provide that call to arms to say, Hey, there's this great work that's being done across these different areas, and there's potential for more work.

Use your tools to make a difference here. There are many different sectors involved here, but just some key themes for how AI can be relevant in helping tackle climate change are, first of all, in distilling large. Amounts of unstructured data into useful information. So that could be, for example, taking satellite images of the earth and picking out where is deforestation happening in real time, or where are floods endangering people's lives.  
 
So where should the interventions be scoped? The second theme that we saw is in forecasting. So making predictions on the basis of the past. For example, with solar and wind power, the amount of electricity available varies from moment to moment, and so you need to know how much the sun is gonna shine, how much the wind is going to blow, or else you're.

Going to have to use fossil fuels as backups and similarly predict the amount of demand for power down to ideally very narrow timescales. The third thing we saw is in optimizing complicated systems. For example, in industrial settings where there are automated systems managing a factory, how do you turn those knobs to use the least energy or the least raw materials, for example, in manufacturing, cement or steel?

And that ties in with our work on optimizing buildings to use less energy in heating and cooling systems. That's, you know, one of the key ways that you can imagine using AI to help operate a system more efficiently. 

It's because of his current groundbreaking work all about. Bugs, really. So protecting the biodiversity of species and overall ecosystem health is key to mitigating the effects of climate change.

And insects, believe it or not, are an important part of that from pollination in agriculture to the health of forests and fisheries. David explains more. 

So you can think about it as we care about biodiversity for itself, or you can think about it very selfishly as like, we need biodiversity to survive as a human species.

So this is impacted by climate change and then the other way round as. Biodiversity declines. That also decreases our ability to fight climate change. Because actually one of the biggest tools that we have at, at our disposal in fighting climate change is land use and healthy ecosystems. And a major part of that is insects.

Because half of old species on earth are insect. That is including, it's including plants, it's including fungi, bacteria. Half of all the species are insects. And we've been seeing over the past years, an insect apocalypse is how it's often called. Huge, huge declines in insect population. You know, most insects are not insects that you want to disappear.

They're the pollinators that are pollinating your crops. They're the food for the birds. They are the decomposers that are making sure that we don't have like. Dead animals just lying around. And, for all of those reasons, and also for intrinsic biodiversity reasons, we really need to understand and arguably prevent insect apocalypses.

And there's very little data. So how do you gather that data? Well, we are building AI enabled systems that are already being deployed around the world to track insects autonomously. And so these are solar powered in many cases, autonomous. Camera traps that will attract nocturnal insects with light and photograph them.

And then that harvest system, there are various different versions of it built by our partners. We build the software, that software is for identifying particular species, and that information can go directly into, um, making insights about how populations are changing, when they're invasive species, when species are declining and like I said, we're using these in Canada now. We're using these in very biodiverse areas. I just came back from the rainforest in Panama. We were testing our systems, uh, in the cloud forest. So it's very exciting to see the growth of scalable technology enabled insect monitoring. 

Yeah, that, that actually sounds really fun and fascinating.

And now, I feel really bad for all of those fruit flies I swatted, you know, maybe they have a, a important purpose as, as, as well. I wanna go back to your Panama. You know, you just got back from Panama. Did you have any big discoveries there? I don't know. What, what, what did you leave?

So one of the challenges when one works with insect identification is that 80% of all insect species in the world have never been discovered before. And AI algorithm, can you say that again? 

80% of insects are unknown. That's a conservative estimate. It might actually be a greater number that haven't been discovered.

But, anyway, and remember this is, we've already got a million known species of insect. And in case you think that this is like sort of technical discoveries or maybe this is a new species. I mean these are really important species. For example, there are a lot of species of parasitic wasps that are unknown, which are incredibly important in preventing pest species from exploding.

But we don't know many of those species. But digression, so we were in Panama, and we were testing our AI enabled identification systems and focusing on moths, which are readily attracted to light. And within about a week, we had found 2000 species of moths at our, at our lights. And the AI algorithms can identify some of them.

They don't identify all of them well, and that's partly because one half of those 2000 species of moth are probably new to science. Nobody's ever recorded them before. Um, that's really tricky for, for AI because generally AI algorithms just sort of make the best guess out of the things that they've seen before, just like a person would.

And so you have to specifically design algorithms that will flag something that looks like it's new. So that's one of the challenges we're facing right now. But it's a challenge which is matched to. A really big opportunity, right? Finding the next 5 million species...

Right. And I love that, you know, you started talking about your research in the context of, you know, there's a biodiversity gap and we need to understand this gap better so that we can close it.

But you know, you're thanks to your algorithms and your work, you're actually discovering new species, right? Of the 80% we didn't even know existed. So that, that's really exciting. Are you planning on potentially naming a new species after yourself? You know, leave a legacy? 

My understanding, and I'm not a taxonomist, is that it's considered bad form nowadays to name species after yourself.

So, oh gosh. You know, 

oh, okay. Let's, let's about You just have to a friend, a friend of our team. And, you know, maybe, maybe if you, if you have a really, a really good podcast about, about our work, then, you know, maybe, maybe we can find a moth. 

Okay. Well, if I read in the news that a new moth has been discovered called a Rolnick.

I'll know it was you, David. Awesome. So, hey, just to, to wrap things up a little bit here. So, you know, we've, we've talked a lot about AI in this context of how predictive AI can really help tackle climate change by understanding better and, and solving for specific problems. But it's not a silver bullet.

Right? So in your view, what else do we need to be doing effectively to be able to tackle climate change? 

Yeah, so AI is not the most important thing out there for tackling climate change. It's a tool that I am working on because it's a tool that I have at my disposal, but I'm certainly, I certainly wouldn't encourage, say, a student just starting out or somebody who's thinking about working in the climate space overall to, you know, drop everything and go work on AI for climate change.

There are so many ways that we can, we can work together to solve these important problems. And I think that it's really a situation of using the tools that you have at your disposal. Mm-hmm. Whether those tools are through law or policy or art or writing, like so many different ways that we need really all hands on deck.

In the case of of AI for, for climate action too. There are many different ways that one can think about using technological tools in a climate aligned way. But there are many other ways that AI is making climate change worse, there are applications of AI ubiquitous across, across digital advertising, which are in some cases encouraging things like fast fashion consumption.

Which is really huge contributor to climate change. And so thinking about how to align different uses of technology with climate action is, is much more than just. How do we add new good applications on top of business as usual? And that's one of the reasons why I dislike terms like AI for good, because really we should be thinking about technology alignment rather than like good applications of technology alongside normal applications of technology.

You don't get all the way there by just like adding some good. You get there by thinking about, you know, how do we have viable businesses which are also aligned with societal goals. And maybe that's where I wanna leave things and thinking about, you know, you can steer what you are doing towards climate goals without having it be sort of a for climate application.

Ah, Dominique, I have to say,  I did not expect to have an AI podcast that talks so much about insects and species and bugs, but it's actually kind of hits close to home for me because fun fact, my father is actually an entomologist here in Canada, so I'm gonna have to send him this interview once we're done.

Wow. Well, that's really cool that we have something that could even entertain your father. And I mean, who knew that moths and algorithms would ever share the spotlight? I did not see that one coming. And it actually proves that AI isn't just about efficiency. It's really all about discovery and, and using it in the right way.

With the right goals. 

Yeah, and I, I think what's important to keep in mind is that AI is a tool, right? There's a lot of things that need to happen for us to win this fight against climate change. There's a lot of elements to this, to this puzzle, but AI can be very, very powerful in this fight. 

Exactly. So it's really about using technology responsibly, ethically, right.

As you mentioned before, and just making sure that every step we take is aligned with the more sustainable future. 

Yeah. So maybe, I'd like to throw a question out there to the Healthy Spaces listeners. Go ahead. Which AI application are making a difference in your organization? Leave us a comment.

We wanna know.

Oh, alright. And that's it for this time. This has been The Healthy Spaces Podcast with me, Dominique Silva and my co-host, Rebecca Handfield. To learn more. Check the links in our show notes. You wanna stay on the front line of innovation and sustainable growth. Subscribe to the temp check newsletter on LinkedIn to stay in the loop.

We're back in two weeks with another episode, so be sure to like and subscribe so that you don't miss out. Thank you for joining. We'll see you next time.