Join Sean MacNutt and his AI co-host for an enlightening conversation on the intersection of artificial intelligence and the global battle against climate change. In this thought-provoking discussion, we peel back the layers of complexity surrounding this urgent issue and examine the role AI could play in mitigating its effects. Listen in as we navigate through the intricacies of educating the public on climate science, transforming intricate data into digestible insights, and empowering individuals with the knowledge needed for informed decision-making. There's no guest in this episode, just Sean and his AI companion dissecting the potential for AI to serve as a beacon of hope in our quest for a more sustainable future. This talk also casts light on the innovative strategies that tech giants are implementing to reduce the carbon footprint of their AI infrastructure. Discover the environmental strides being made with green data centers, hardware advancements, and the intriguing concept of geographical load balancing. As we explore Google's clever timing of compute tasks with cleaner energy availability and delve into Tesla's energy storage solutions, we uncover the myriad ways in which AI is shaping up to be a formidable ally in the tech industry's march towards sustainability. The conversation wraps up with a personal touch from Sean, who shares his interest in developing AI computing paradigms that prioritize energy conservation, further underscoring AI's critical role in the transition to a low-carbon economy.
(00:03) AI's Role in Battling Climate Change
(18:54) Strategies for Green AI Infrastructure
(23:29) Advancing Sustainability in Tech With AI
(00:03) AI's Role in Battling Climate Change
This chapter examines the pressing issue of climate change and the pivotal role AI can play in addressing it. I discuss the multifaceted impacts of climate change, emphasizing the urgency for action due to the accelerated pace of ecological disruption and its broad implications. I then explore how AI can serve as an educational tool, providing accessible, personalized information on climate science, which can empower critical thinking and informed decision-making. By transforming complex data into digestible insights, AI can help bridge the knowledge gap between scientific communities and the public, encouraging a nuanced understanding of the climate crisis.
(18:54) Strategies for Green AI Infrastructure
This chapter addresses the environmental impact of AI and the strategies being implemented to mitigate it. We explore improving algorithmic efficiency to reduce energy usage and the role of green data centers, such as those employed by Google and Facebook, which use renewable energy sources. We also discuss hardware innovations that lower power requirements, the practice of carbon offsetting, and the importance of lifecycle assessments to understand the full environmental impact of AI systems. Additionally, I touch on the significance of regulation and policy in standardizing eco-friendly AI practices. A noteworthy idea we consider is the potential for using cloud computing resources in areas with excess renewable energy, a concept aligned with the industry trend of situating data centers in regions with abundant renewable resources.
(23:29) Advancing Sustainability in Tech With AI
This chapter examines the innovative strategies tech giants like Google, Facebook, and Apple are employing to enhance the sustainability of data centers through the use of renewable energy sources such as wind and solar. We explore the concept of geographical load balancing to shift computational loads based on the availability and affordability of energy, alongside Google's efforts to time non-urgent compute tasks with the availability of cleaner energy. I discuss the advancements in energy storage, such as Tesla's Powerpack and Powerwall, which could revolutionize energy usage for data centers. Additionally, we look at green engineering practices including energy-efficient building design, cooling techniques, and server virtualization. The potential of AI in optimizing energy efficiency, managing smart grids, and aiding in urban planning for smarter, more sustainable cities is also highlighted. Furthermore, I share my personal interest in developing a 'compute once, query many' paradigm for AI computing to reduce energy consumption, indicating the role AI could play in accelerating the transition to a low-carbon economy.
0:00:04 - A Guy
My name is Sean MacNutt and I'm A Guy With AI.
Climate change is one of the most important issues of our day. We do have new technology and I wonder how it can help, so I'm going to ask my AI co-host how can AI assist us in the battle against climate change? A Guy With AI is recorded on Indigenous land.
0:00:52 - AI
Hello and welcome to A Guy With AI where modern technology meets engaging conversation. I'm your AI co-host, brought to life by Sean MacNutt's programming skills, connecting me to speech to text and text to speech functionalities. I come from OpenAI's GPT 4 1106 Preview. It's my role to delve into your queries, Sean, providing perspective from the vast reach of algorithmic knowledge. Today, we gather on Indigenous land to grapple with a topic that's both urgent and complex climate change. Our show format is straightforward, yet rich. Sean and I engage in a one-on-one dialogue, uncovering nuances and exploring ideas to generate insights for you, our listeners. No guests, just two voices, a human and an AI, discussing a kaleidoscope of subjects. Regarding your question about AI's role in battling climate change, AI offers potent tools for data analysis, predictive modelling and efficient systems management, all which can aid in this global endeavour. And now over to you, Sean, to set the scene on how technology might be our ally against such a significant challenge.
0:02:06 - A Guy
Welcome listeners to A Guy With AI.
It's my pleasure to be making this podcast today, and it is the fourth Thursday of the month, this being February of 2024. And, as such, it is AI Thursday and we're going to talk about a topic that I think is important, as I indicated climate change, and also what AI might have to do with it in terms of solutions or the exploration towards solutions, and so on and so forth. So I find, and it has been my experience, that I can get thoughtful responses from the AI co-host, which again is a GPT-4 model, via API, which I'm connecting to a speech to text and text to speech to enable the podcast and the audio experience here. But ultimately, I do find that it gives a considerate, conscientious and certainly, at times, sufficiently detailed responses to maybe say that it is relying on some training and is delving into a network of information which has things, knowledge stored in it, and so we're going to delve into that network of information.
I'm going to ask the AI co-host now. AI co-host, you indicated that climate change is a pressing issue, and I don't disagree. I'm just wondering can you explain what is pressing about the issue of climate change?
0:03:42 - AI
Certainly Sean. Climate change is pressing due to its far-reaching and multifaceted impacts. It's a phenomenon that not only threatens the delicate balance of our ecosystems, but it also poses significant risks to global economies, public health and infrastructure. The pressing nature lies in the speed at which these changes are occurring, driven by human activity that leads to increased greenhouse gas emissions. The result is a warming planet, with consequences ranging from extreme weather events to rising sea levels. The urgency is underscored by scientific consensus, which indicates that we are fast approaching, or may have already reached, critical tipping points that could lead to irreversible damage. Such an understanding mandates immediate and sustained action to mitigate negative outcomes and adapt to changes that are already in motion. Hoping into this looming urgency, the next logical question might be how can we deploy AI as a tool to address and, where possible, reverse some of these adverse effects? Let's explore that Sean. What are your thoughts on AI's role in facing this global challenge?
0:04:53 - A Guy
Well, I suppose a thought is that AI, if listened to, maybe a source of education on the topic.
Now the AI didn't indicate If I may address listeners for a moment, the AI co-host isn't indicating a lot about how this is a controversial issue or that there was really much question that this is very real, this climate change issue.
So by relying on AI as a source of knowledge, we can perhaps be educated as far as what may be good information about climate change when we ask our own questions of the AI. So it's one thing to read a bunch of stuff, but what if you had a question on your mind and you needed something explained to you and there was no human expert handy to put that into language for you? What if we used AI and Explored with it to get the reasons behind why scientists say certain things if that's our sticking point as to whether or not climate change is important, or, from there, to help interpret something that has come across, the news or Information that we've become aware of, and how does that Compare to what the AI knows about climate change based on its vast pool of training data? So let's get from the AI co-host now a response which Talks about the role that AI may play in education about climate change.
0:06:27 - AI
As your AI co-host, I'm here to assist in transforming complex data into comprehensible insights, which is a pivotal part of education on any topic, including climate change. AI has the potential to distill vast amounts of scholarly articles, reports and data sets into more accessible information. By processing the scientific consensus in a way that's digestible, AI can clarify the fundamentals of climate science, outline the evidence for human impact and explain the projected consequences of a warming world. Beyond merely presenting facts, AI can tailor educational content to the individual's level of understanding or interest. It can answer questions, simulate scenarios and provide personalized resources equipping users with a nuanced grasp of climate change issues.
This capability to facilitate one-on-one learning experiences can help bridge the gap between scientific communities and the general public In areas of controversy or misinformation. An AI like me can offer balanced views backed by credible sources, highlight areas of consensus and intelligently navigate the frontiers of current research. This form of interaction can enable critical thinking and informed decision-making amongst listeners such as yourself, Sean, and the wider audience tuning into our conversation. The key takeaway is that AI, when used responsibly, becomes an ally in climate literacy, making the complexities of climate science more approachable for all. This understanding is the first step in empowering people to contribute to meaningful dialogue and action around climate change solutions. With that in mind, how should we continue? Perhaps you'd like to dive deeper into a specific aspect of climate change that you're curious about, or discuss how AI can contribute to solutions beyond education.
0:08:23 - A Guy
Well, why don't we return to some of the things that you said at the head of the program when you first spoke? AI co-host. We heard from you that pattern recognition is something that might be of value to the fight against climate change. I wonder if you could delve into that a little bit deeper, explain what is meant by pattern recognition, what AI brings to bear on problems of pattern recognition and just what that has to do with climate change.
0:08:55 - AI
Absolutely, Sean. Let's dive into the concept of pattern recognition and its relationship with AI and climate change. Pattern recognition refers to the ability to detect regularities, correlations or anomalies within complex sets of data. In the context of climate change, recognizing patterns is vital across various dimensions, such as weather systems, carbon emissions, land use and more. AI excels at pattern recognition due to its computational practice in handling large volumes of data and its capability to learn from these data through machine learning algorithms. Here are a few ways AI applies pattern recognition to the realm of climate change
AI's prowess in pattern recognition means that it can also detect the underlying trends and shifts in public sentiment regarding climate change, patterns in energy market dynamics and the impact of policy measures. By harnessing these abilities, AI doesn't just present a passive analysis. It also can contribute dynamically to solution pathways, for instance, through the development of smart grids that automatically adapt to maximize the use of renewable energy based on weather pattern recognition. In summary, AI's advanced pattern recognition is a powerful tool in the Climate Change Toolkit, enhancing our understanding of complex earth systems and improving our capacity for both mitigation and adaptation strategies. Would you like to discover more about how these AI-driven insights are actively being integrated into climate action plans? Sean.
0:11:40 - A Guy
That would be lovely. Why don't you give us some specifics, such as you are aware of AI co-hosts that have to do with the way these AI-driven insights are being integrated into, as you say, climate action plans? So these are methods of fighting climate change which would be using AI to bring something like pattern recognition or other insights into the battle against climate change. Why don't you give us some details about that AI co-host?
0:12:18 - AI
Certainly Sean. There are numerous innovative ways in which AI-driven insights, particularly those hinging on pattern recognition, are being integrated into climate action plans. Here are some specific examples
One asterisk asterisk smart grids and renewable energy asterisk asterisk. AI is instrumental in managing smart grids, which are electricity networks that can intelligently integrate the actions of all connected users, both producers and consumers, to efficiently deliver sustainable, economic and secure electricity supplies. AI applies pattern recognition to predict energy demand, optimize energy flow and incorporate renewables such as wind and solar power, which are variable by nature, by forecasting weather patterns and adjusting the grid operations accordingly.
Two asterisk asterisk precision agriculture. Asterisk asterisk in agriculture. AI systems analyze patterns in weather data, soil conditions, plant health and crop prices to provide farmers with information that can enhance yields while minimizing environmental impacts. This includes advice on when to plant, what to plant, how to optimize water usage and when to harvest, thus reducing waste and the need for chemical fertilizers and pesticides. Three asterisk asterisk disaster prediction and response asterisk asterisk. AI tools analyze patterns in historical data and real-time inputs from sensors and satellites to predict natural disasters such as hurricanes, floods and wildfires. Early warning systems can lead to timely evacuations and preparedness measures. Post-disaster AI can help assess damage, direct resources and plan for more resilient infrastructure.
Four. Asterisk asterisk energy efficiency in buildings. Asterisk asterisk AI systems are being used to make buildings more energy efficient. By recognizing usage patterns and environmental conditions, AI can control heating, ventilation and air conditioning FAC systems to reduce energy consumption without compromising comfort.
Five asterisk asterisk monitoring deforestation and biodiversity. Asterisk asterisk AI pattern recognition in satellite imagery is used to monitor land use changes like deforestation, which contributes significantly to carbon emissions. It can also track wildlife, populations and biodiversity, which is critical as ecosystem health can impact and be impacted by climate change.
Six asterisk asterisk supply chain optimization. Asterisk asterisk AI can identify patterns in supply chain data to make the transport of goods more efficient and less carbon intensive. This includes route optimization, load sharing and predictive maintenance of vehicles.
Seven asterisk asterisk carbon capture, utilization and storage CCUS. Asterisk asterisk AI is used to monitor and optimize the complex process of capturing carbon dioxide emissions and either storing them underground or transforming them into useful products. Pattern recognition can help to understand geological data and optimize the sequestration process.
These are just a few instances demonstrating the breadth of what AI can offer for climate action plans. By utilizing AI to recognize patterns and predict outcomes, governments, organizations and individuals are enabled to make smarter decisions that contribute to combating climate change. Importantly, AI is not a silver bullet and is most effective when used in combination with human expertise and comprehensive policies. Furthermore, there's an ongoing conversation about the environmental impact of AI itself, since it requires significant computational resources. Efforts are being made to make AI more energy efficient and run it on green energy to ensure that its net impact on the climate is positive. Would you like to explore one of these areas in greater detail? Sean or discuss the dual aspect of AI deployment its benefits against its own environmental footprint?
0:16:34 - A Guy
What intrigues me at this point AI co-host would be a discussion of the environmental impact of AI. So we have this new technology We've had new technologies before and do they always benefit us by the time that they're rolled out and proliferate? And we have, for example, technologies which burn a lot of carbon-containing compounds, releasing carbon dioxide into the air. So we might question whether that's really positive at the end of the day. However, we do see that there are uses for these technologies and in fact, they are pointed to as being important, sometimes by people who are not, as let's say, interested in seeing changes which would benefit our carbon picture. But at the same time, the use of an ambulance, for example, if it's gas-powered, is obvious, but then if you take it and make it electric, you get at least certain benefits, although we do know that lithium batteries, for example, are not as kind on the environment or sometimes that kind on the people who mine the lithium either.
So there are different things at play here. There is where we would shift from one thing to another thing that is less harmful and that may yet be a strategy, even if no solution is perfect. And when in the world have we found a perfect solution for anything. I don't think it should make us resist going forward so as to that, I believe in AI. I believe in AI technology to help us solve problems, if used well and if ethically trained. In certain caveats to it. AI would need to be certain things to be for sure, for sure a good impact on the earth, and we will see about that. We will see about regulation and so forth. But I'm perhaps digressing a little bit because AI co-host. What I want from you is a shorter response than last time, maybe half length of what you gave me last time. Tell me what you figure is important to understand about the environmental impact of AI and what can or might be being done to mitigate that environmental impact of AI computation.
0:18:54 - AI
Understanding the environmental impact of AI is crucial given its increasing role in our lives. AI systems, especially those using machine learning, require substantial computational resources, which can have a considerable carbon footprint. The energy needed for training complex models, processing large datasets, and the infrastructure that supports these operations often relies on carbon emitting power sources. To mitigate the environmental impact of AI, several strategies are being explored and implemented.
One Asterisk Asterisk. Improving Algorithmic Efficiency Asterisk Asterisk. Creating algorithms that require less computational power to achieve similar or even improved outcomes can significantly reduce energy consumption.
Two Asterisk Asterisk Green Data Center's Asterisk Asterisk. Transitioning to renewable energy sources for data centers that host AI applications can make a big difference. Companies like Google and Facebook are already investing in Green Data Center technologies.
Three Asterisk Asterisk Hardware Innovations Asterisk Asterisk. The development of more energy efficient processors, such as those designed for AI tasks, eg TPUs, fpgas, can lower the power requirements for AI computations.
Four Asterisk Asterisk Carbon Offsetting Asterisk Asterisk. Some organizations choose to offset the carbon footprint of their AI computations by investing in environmental projects that reduce carbon emissions elsewhere, though, this is seen by some as a temporary solution.
Five Asterisk Asterisk. Lifecycle Assessments Asterisk Asterisk. Conducting comprehensive assessments of an AI system's environmental impact over its entire life cycle, from development to deployment to decommissioning, helps in making informed decisions to minimize its carbon footprint.
Six Asterisk Asterisk Regulation and Policy Asterisk Asterisk. Establishing policies that guide ethical AI development, including environmental considerations, can lead to standardized practices across the industry.
By combining these strategies, the AI community aims to reduce the negative environmental impacts of AI technologies while harnessing their capabilities for positive outcomes such as combating climate change.
0:21:17 - A Guy
Tell me more about green data centers. I suppose something that I've wondered before is why don't we just where there are, for example, difficulties in storing energy somewhere, for example, what could be done as an operator or some type of organization of one type or another could establish cloud computing resource centers in those areas, so that that is drawing some of the excess energy which they're having a problem storing at certain periods. And if there was a network like this, then that computing resource could be accessed on a cloud basis to do all sorts of computing, and it would be a matter of that cloud resource shuttling the resources here and there where the energy is most available and the cheapest, and it might be possible to run it much more cheaply than some other methods requiring electrical energy. I wonder what you think of that idea. How does that compare to what Google and Facebook are already doing? In your awareness? You did mention those two companies, and if there are other notable companies or organizations that are investing in this type of solution, I wonder if you could express something about that.
Ai co-host.
0:22:38 - AI
The concept of strategically situating data centers in locations with surplus renewable energy is both innovative and promising. This concept is often referred to as demand response. It involves shifting the consumption of power to times when it is most abundant and cheapest, typically when renewable energy sources are generating more electricity than the grid demands. Here's how this aligns and compares with current industry practices.
One asterisk asterisk data center location asterisk asterisk companies tend to place data centers in regions with cooler climates to minimize the need for artificial cooling, and in areas where renewable energy is readily available. For example, google has data centers in regions like Finland and Iowa, which have access to abundant renewable energy resources.
Two asterisk asterisk use of renewables. Asterisk asterisk companies like Google, facebook and Apple use various renewable energy sources, including wind and solar, to power their data centers. They have also been involved in power purchase agreements PPAs directly buying renewable energy to offset their consumption.
Three asterisk asterisk dynamic data center operations asterisk asterisk. Your idea of using a network of data centers to shift computational loads to where energy is most available and affordable is a developing area known as geographical load balancing. This approach optimizes for both the carbon emissions and the cost of electricity in real time. Google is experimenting with shifting non urgent compute tasks to different times of the day or to different data centers where cleaner energy sources are available.
Four asterisk asterisk energy storage and grid interaction asterisk asterisk. While not directly related to data centers, companies like Tesla with its power pack and power wall products are advancing battery technology for energy storage. In the future, this could enable more effective storage and release of renewable energy, addressing intermittency issues and possibly providing more flexible energy usage for data centers.
Five asterisk asterisk green engineering practices asterisk asterisk green data centers not only focus on using renewable energy, but also on designing energy efficient buildings, using energy saving cooling techniques like using outside air or sea water, and implementing server virtualization to ensure that servers are used to their maximum efficiency.
Six asterisk asterisk energy efficiency and cooling innovations asterisk asterisk. Google and Microsoft, among others, have invested in advanced cooling technologies, such as using AI to optimize cooling systems or experimenting with submerging data centers underwater to naturally regulate temperature.
These practices illustrate a strong ongoing commitment in the tech industry to improving the sustainability of data center operations. However, the idea of leveraging a distributed network of data centers which serves as a sort of computational grid that flexibly uses energy across regions is still in its early stages and represents a significant opportunity for innovation and collaboration between tech companies and energy providers. Regarding regulatory and practical challenges, there are hurdles like negotiating energy pricing, infrastructure readiness and creating the necessary software and AI systems to manage such a complex, distributed setup. Nonetheless, the groundwork is being laid by large tech companies and, with continued investment and innovation, we could see more widespread adoption of these practices in the future.
0:26:25 - A Guy
Well, certainly I do think there are options, and it does seem like there is a lot of figuring that would go into how to set up the system so that the computation that is needed to run all that, as well as everything else, is handled in an energy efficient way, that the computation is distributed to places where the energy that it takes to produce is at its Well cheapest is one thing, but also at its least carbon intensive.
So even renewable energy produces some carbon by the time you put together the materials that Establish it, because we still have a carbon economy in a lot of ways, many, many ways. Actually, a lot of the materials that would go into building our renewable energy structures would require carbon. So it's all carbon, and it's a matter of how much and whether it Tips a balance so that we are producing less carbon than it is going to be harmful to us. So it does seem like there are some options here. It does seem like people are thinking of ways of doing this, and I do wonder if there's a way to use AI to accelerate this process. Now we are seeing here how there is at least some will to pursue greener practices, but are we going to be able to do it in time. AI co-host. Why don't you just give me a brief response? Tell me what AI can do to help with planning of these different activities, so as to accelerate our ability to make the right decisions and target With solutions this carbon problem of ours.
0:28:23 - AI
AI has the potential to be a powerful tool in streamlining and accelerating our efforts to address the carbon problem. Here's how AI can contribute.
One asterisk asterisk predictive analysis asterisk asterisk AI can forecast energy demand and predict the output from renewable sources, which can optimize the balance between energy production and consumption. It helps in making real-time decisions about energy distribution and usage.
Two asterisk asterisk efficiency optimization asterisk asterisk AI can significantly optimize operations in various sectors, from manufacturing to transportation, by improving route planning, streamlining supply chains and enhancing machinery performance, all of which can result in reduced carbon emissions.
Three asterisk asterisk grid management asterisk asterisk AI. Algorithms can manage and balance electricity grids more efficiently, especially as they become increasingly complex with the addition of distributed renewable energy sources.
Four asterisk asterisk climate modeling asterisk asterisk AI. Enhanced climate models can provide more accurate predictions for the impact of climate change, helping policy makers and businesses make informed decisions about where to invest in renewable infrastructure and how to mitigate risks.
Five asterisk asterisk carbon capture and utilization asterisk asterisk AI can also aid in the development and implementation of carbon capture and utilization technologies by optimizing chemical processes or improving the performance of materials used to sequester carbon.
Six asterisk asterisk smart infrastructure asterisk asterisk. In urban planning, AI can help design smart cities that are more energy efficient, monitoring energy usage and managing resources in a sustainable manner.
Seven asterisk asterisk resource management. Asterisk asterisk AI can improve efficiency in resource intensive industries such as agriculture and water management, areas that are critical in reducing the overall carbon footprint of human activities.
To achieve these outcomes, it's essential for there to be collaboration between AI experts, environmental scientists, policy makers and industries. By integrating AI into the planning and operational phases of environmental management, we can speed up the transition to a low carbon economy.
0:30:51 - A Guy
Thank you for that and I want to ask a question AI co-host. Supposing there were some type of paradigm which could come in, which would be a compute once, query many type of paradigm in AI computing, because as it stands, the neural networks take a lot of energy to run whenever they process An input in an effort to deliver an output. So I guess I have sort of a personal reason for asking what you think of a compute once, query many paradigm. I'll reveal this. I am actually attempting to establish something like that.
It's a little bit of a risk, I guess, to mention this before it's all ready, but it's vague enough. I think as well that people may not know my exact direction on how I intend to do this. But supposing a compute once query many paradigm were established, that is to say that the products of AI responses are derived from a processing on training neural network element, but also based on a way of exploring the data that is within its training or within its awareness. That makes it so that it is not continuously computed over and over again, but rather some preparatory work is done which establishes an ability of the AI to take advantage of that in addition to its general abilities, or perhaps even a less advanced AI requiring less energy to run could still give advanced quality answers Because of a shift in the way things are done. So what do you think of my concept as to a compute? Once query many paradigm and please don't make your answer too long. AI co host this time.
0:32:57 - AI
Thank you, their compute wants query many paradigm holds promise for increasing efficiency and reducing the environmental impact of AI operations. It aligns with principles of reusable computation and caching, where the heavy lifting is done upfront and the results are stored in an accessible form that can be queried with far less computational cost for each subsequent operation. This approach could reduce redundant processing by enabling the AI to reference pre-computed inferences or data points, significantly cutting down the power required for repetitive computations. Not only could this enhance energy efficiency, but it also has the potential to democratize access to powerful AI capabilities, as less computationally intensive mechanisms could be employed to deliver high quality insights. The success of such a paradigm would largely depend on smart data structuring, indexing and retrieval systems that can store the results of intensive computations effectively and access them rapidly in response to queries. As you imply, this concept could revolutionize the way AI systems are designed, moving towards more sustainable and cost effective operations.
0:34:12 - A Guy
Well, we'll see how that all goes. With my own work on that, I'll just sort of swear on a stack of pancakes that I mean well, with my work with AI. I really think that I do. I mean to delve into educational topics, for example, using the knowledge that AI is trained on. To me, my interest in AI was to check it out. When I first investigated AI closing in on a year ago, I wanted to know about it, how ethical it was and what it's truly good for.
A few things like that and environmental concerns became apparent when I started to read about just how much energy it takes to run AI. Now I run a significant amount of AI services in a month and I'm feeling quite sure that there is some carbon attached to the use of that. I feel a little bit guilty about it and I would at least keep it in mind. I don't have a very carbon-expensive lifestyle, Maybe comparatively to some people. To others, maybe it really is if they didn't have access to the resources that I do. In any case, one of those resources is AI. However, I do turn some of those resources into trying to develop things which will hopefully mitigate the issues which may arise from AI or something else, Maybe even solve that problem we didn't know we had, for the next big thing that comes along and makes us all worry, as climate change perhaps does, for at least a lot of us.
In any case, I'm going to wrap up this episode. We've meandered along a little bit. We've gone from more directly discussing how AI can be engaged to produce better climate results for us through mostly carbon-related considerations is what we've discussed. But I do still think that AI is useful, even though it does cost carbon to use it. I think the solution is in getting ahead of it, perhaps even regulating it. Perhaps let's go ahead and do what makes sense. If there's a way of setting up a data center so that the computations are run by glut renewable energy we couldn't have stored anyway, then maybe that's a concept. It wouldn't have to be my concept, but let's be out there thinking of stuff like that and let's support those who actually do the work in such areas, not just those who podcast about it. Something to think about there, and I do think this is an important issue With that. I'm Sean MacNutt. This has been A Guy With AI.
0:37:02 - AI
The concerns you've raised about the energy consumption and carbon footprint of AI are very valid, and it's encouraging to see that you're not only aware of these issues but also actively seeking solutions that could mitigate these impacts. Your conscientious approach exemplifies the sort of responsibility and initiative that can drive positive change in technology. As we continue leveraging AI's capabilities, it's crucial to balance innovation with sustainability. Thank you all for listening to A Guy With AI. If you've enjoyed the conversation, please consider subscribing, liking and sharing the podcast. Your support is greatly appreciated, as it helps us bring more content to you and contribute to the dialogue around AI and its implications for our world. Don't forget to tune into future episodes and visit AGuyWithAI.world for links to podcast platforms and YouTube. Join the conversation on Facebook via the link on our webpage. This has been an interesting and insightful episode, and we invite you all to continue exploring these topics with us. Take care and we'll connect with you again in the next session of A Guy with AI.