1 00:00:00,000 --> 00:00:04,680 Welcome to our deep dive into the world of AI and climate change. 2 00:00:05,480 --> 00:00:06,680 Sounds pretty intense. 3 00:00:06,720 --> 00:00:07,160 It is. 4 00:00:07,320 --> 00:00:09,960 We got a great TED talk from one of our listeners about it. 5 00:00:09,960 --> 00:00:10,520 Oh, yeah. 6 00:00:10,760 --> 00:00:13,400 The one from the scientist who's also an AI product manager. 7 00:00:13,480 --> 00:00:14,120 That's the one. 8 00:00:14,400 --> 00:00:19,640 They make a really cool point about how we can use AI to kind of like optimize existing systems. 9 00:00:20,680 --> 00:00:21,360 Interesting. 10 00:00:21,480 --> 00:00:24,440 So like kind of giving our planet a software upgrade. 11 00:00:24,520 --> 00:00:25,000 Exactly. 12 00:00:25,000 --> 00:00:27,080 What really stood out to you from the talk? 13 00:00:27,080 --> 00:00:30,760 Well, I thought their analogy about instructions was spot on. 14 00:00:30,840 --> 00:00:31,560 You know, like. 15 00:00:31,560 --> 00:00:31,920 Oh, yeah. 16 00:00:31,920 --> 00:00:33,400 Like when you're trying to put together furniture. 17 00:00:33,440 --> 00:00:33,800 Yeah. 18 00:00:34,040 --> 00:00:35,960 Or you're following some crazy recipe. 19 00:00:35,960 --> 00:00:37,280 And you have all the ingredients. 20 00:00:37,840 --> 00:00:39,600 The what, but not the how. 21 00:00:39,720 --> 00:00:40,480 Exactly. 22 00:00:40,600 --> 00:00:44,680 They argue that a lot of times we talk about climate change solutions in the same way. 23 00:00:44,800 --> 00:00:46,200 Ah, I see what you mean. 24 00:00:46,640 --> 00:00:50,320 Lots of big talk, but not a lot of concrete steps. 25 00:00:50,320 --> 00:00:50,680 Right. 26 00:00:50,720 --> 00:00:52,520 And they don't just stop at the analogy. 27 00:00:52,520 --> 00:00:58,560 They actually give some pretty compelling examples of how AI can provide those how to use, especially with wind energy. 28 00:00:58,600 --> 00:00:58,960 Yeah. 29 00:00:59,040 --> 00:01:04,080 That was a really smart choice because wind energy is already a big player in the renewable energy world. 30 00:01:04,120 --> 00:01:04,360 Yeah. 31 00:01:04,480 --> 00:01:06,240 But but it's unpredictable. 32 00:01:06,360 --> 00:01:07,040 Exactly. 33 00:01:07,160 --> 00:01:10,640 Unlike fossil fuels, which we can control wind as well. 34 00:01:10,760 --> 00:01:11,240 Weenby. 35 00:01:11,320 --> 00:01:11,880 Ha, ha, ha. 36 00:01:11,880 --> 00:01:12,440 Well, yeah. 37 00:01:12,920 --> 00:01:15,880 But the point is we can't just turn it on and off whenever we want. 38 00:01:16,080 --> 00:01:18,080 And that's a big problem for energy grids, right? 39 00:01:18,080 --> 00:01:19,240 They need consistent power. 40 00:01:19,240 --> 00:01:19,560 Right. 41 00:01:19,640 --> 00:01:20,160 Exactly. 42 00:01:20,160 --> 00:01:27,160 That's why this team at Google DeepMind trained a neural network on historical weather patterns and turbine data. 43 00:01:27,360 --> 00:01:28,200 Wait, hold on a sec. 44 00:01:28,200 --> 00:01:28,960 Can you back up a bit? 45 00:01:28,960 --> 00:01:30,640 What kind of neural network are we talking about here? 46 00:01:30,800 --> 00:01:35,800 Oh, they used a type of deep learning model called an LSTM long short term memory network. 47 00:01:36,000 --> 00:01:36,440 OK. 48 00:01:36,840 --> 00:01:38,320 And why that one specifically? 49 00:01:38,560 --> 00:01:44,240 Well, they're really good at analyzing time series data, like weather patterns, you know? 50 00:01:44,480 --> 00:01:48,240 Ah, so they can kind of learn from past weather events to predict future ones. 51 00:01:48,360 --> 00:01:49,080 Precisely. 52 00:01:49,080 --> 00:01:54,960 They're designed to capture those long term dependencies in the data, those complex patterns that drive wind behavior. 53 00:01:55,160 --> 00:01:56,280 I'm starting to get it. 54 00:01:56,480 --> 00:01:58,080 But I bet getting all that data wasn't easy. 55 00:01:58,280 --> 00:02:01,880 We're talking about historical weather and specific turbine performance. 56 00:02:02,320 --> 00:02:03,960 Oh, it was a nightmare. 57 00:02:04,160 --> 00:02:08,800 They needed both public weather data and proprietary data from individual wind farms. 58 00:02:09,000 --> 00:02:10,760 Like power output and turbine speeds. 59 00:02:10,960 --> 00:02:11,800 All that stuff. 60 00:02:12,000 --> 00:02:15,000 And that kind of sensitive data is really hard to get your hands on. 61 00:02:15,000 --> 00:02:19,200 So they've got this massive data set, this powerful neural network. 62 00:02:19,400 --> 00:02:20,440 What do they do with it? 63 00:02:20,640 --> 00:02:21,880 How do they actually put it to use? 64 00:02:22,080 --> 00:02:23,520 Well, that's where Google comes in. 65 00:02:23,720 --> 00:02:30,120 Not only did they provide the AI expertise through DeepMind, but they also became the crucial deployment partner. 66 00:02:30,320 --> 00:02:31,440 What does that mean, exactly? 67 00:02:31,640 --> 00:02:37,120 They gave DeepMind access to 700 megawatts of their own wind power capacity. 68 00:02:37,320 --> 00:02:37,720 Whoa. 69 00:02:37,920 --> 00:02:39,720 So essentially a massive testing ground. 70 00:02:39,920 --> 00:02:40,640 Exactly. 71 00:02:40,640 --> 00:02:44,920 A real world environment to see how well this AI could perform. 72 00:02:45,120 --> 00:02:45,600 That's huge. 73 00:02:45,800 --> 00:02:48,000 Not a lot of companies would be willing to take that kind of risk. 74 00:02:48,200 --> 00:02:48,600 Totally. 75 00:02:48,800 --> 00:02:50,600 And it wasn't just about the turbines. 76 00:02:50,800 --> 00:02:56,280 Google also provided a team of domain experts, energy engineers, grid operators, you name it. 77 00:02:56,480 --> 00:02:57,800 To help fine tune the AI. 78 00:02:58,000 --> 00:02:58,680 Exactly. 79 00:02:58,880 --> 00:03:04,400 These experts helped define the specific needs and constraints for the system, made sure it was tailored to the real world. 80 00:03:04,600 --> 00:03:07,280 So they weren't just building some cool AI in a lab. 81 00:03:07,480 --> 00:03:09,440 They were building something practical. 82 00:03:09,640 --> 00:03:10,120 Exactly. 83 00:03:10,120 --> 00:03:13,360 And get this, the system didn't just meet expectations. 84 00:03:13,560 --> 00:03:14,600 It blew them away. 85 00:03:14,800 --> 00:03:15,240 Come on. 86 00:03:15,440 --> 00:03:16,600 How much better did it do? 87 00:03:16,800 --> 00:03:22,800 Their AI system achieved a 20% improvement in the accuracy of electricity supply forecasting. 88 00:03:23,000 --> 00:03:23,800 20%. 89 00:03:24,000 --> 00:03:25,240 That's incredible. 90 00:03:25,440 --> 00:03:26,800 What does that look like in the real world? 91 00:03:27,000 --> 00:03:27,200 It will. 92 00:03:27,400 --> 00:03:33,920 Imagine being able to reduce your reliance on fossil fuels by 20% just by better predicting when the wind will blow. 93 00:03:34,120 --> 00:03:34,880 That's a game changer. 94 00:03:35,080 --> 00:03:35,400 Right. 95 00:03:35,600 --> 00:03:39,160 It's all about making wind energy more reliable, more predictable. 96 00:03:39,160 --> 00:03:46,800 So this is a great example of how AI can provide those specific actionable how-tos that the speaker was talking about. 97 00:03:47,000 --> 00:03:47,440 Exactly. 98 00:03:47,640 --> 00:03:48,720 And it doesn't stop there. 99 00:03:48,920 --> 00:03:53,280 They're actually working on turning this into a commercially available software product. 100 00:03:53,480 --> 00:03:55,520 So they're not keeping this a secret weapon? 101 00:03:55,720 --> 00:03:55,920 Nope. 102 00:03:56,120 --> 00:03:57,480 They're trying to spread the benefits. 103 00:03:57,680 --> 00:04:00,960 Other wind farms can use it to optimize their operations. 104 00:04:01,160 --> 00:04:01,640 That's awesome. 105 00:04:01,840 --> 00:04:03,520 Are there any companies already using it? 106 00:04:03,720 --> 00:04:04,240 Yeah. 107 00:04:04,440 --> 00:04:07,320 A French energy company is one of the first to pilot this technology. 108 00:04:07,520 --> 00:04:08,520 That's fantastic. 109 00:04:08,520 --> 00:04:10,960 It sounds like this could have a real impact on a global scale. 110 00:04:11,160 --> 00:04:11,880 Absolutely. 111 00:04:12,080 --> 00:04:15,680 And that brings up another important point that the speaker made in their talk. 112 00:04:15,880 --> 00:04:21,160 They highlight the work of this small nonprofit in the UK called Open Climate Fix. 113 00:04:21,360 --> 00:04:22,440 Open Climate Fix. 114 00:04:22,640 --> 00:04:23,240 Never heard of them. 115 00:04:23,440 --> 00:04:28,800 Well, they partnered with the UK's National Grid to focus on demand side forecasting. 116 00:04:29,000 --> 00:04:30,600 Demand side forecasting. 117 00:04:30,800 --> 00:04:31,720 What is that exactly? 118 00:04:31,920 --> 00:04:36,640 It's basically predicting how much electricity people will need at different times. 119 00:04:36,840 --> 00:04:37,560 Oh, I see. 120 00:04:37,560 --> 00:04:39,760 So making sure there's enough power to meet demand. 121 00:04:39,960 --> 00:04:40,680 Exactly. 122 00:04:40,880 --> 00:04:44,120 And it's super important with renewable energy because it's so variable. 123 00:04:44,320 --> 00:04:45,320 Makes sense. 124 00:04:45,520 --> 00:04:46,600 How do they use AI for that? 125 00:04:46,800 --> 00:04:49,240 They actually used a different approach than DeepMind. 126 00:04:49,440 --> 00:04:52,880 Instead of an LSTM, they use something called Gaussian processes. 127 00:04:53,080 --> 00:04:53,880 Gaussian processes. 128 00:04:54,080 --> 00:04:54,520 What are those? 129 00:04:54,720 --> 00:04:58,040 It's a statistical method that's really good when you don't have a ton of data. 130 00:04:58,240 --> 00:05:01,320 Which I imagine is often the case with energy consumption patterns. 131 00:05:01,520 --> 00:05:01,760 Right. 132 00:05:01,960 --> 00:05:06,040 It's hard to predict exactly how much energy people will use at any given time. 133 00:05:06,240 --> 00:05:07,200 So did it work? 134 00:05:07,200 --> 00:05:09,120 Were they able to get accurate forecasts? 135 00:05:09,320 --> 00:05:14,200 Yeah, their forecasts were twice as accurate as the UK Grid's previous system. 136 00:05:14,400 --> 00:05:16,360 Whoa, that's a huge improvement. 137 00:05:16,560 --> 00:05:16,960 Right. 138 00:05:17,160 --> 00:05:20,840 Think about all the times we have to fire up those dirty backup power plants just 139 00:05:21,040 --> 00:05:22,680 because we underestimate demand. 140 00:05:22,880 --> 00:05:24,360 So this AI could help prevent that. 141 00:05:24,560 --> 00:05:25,280 Exactly. 142 00:05:25,480 --> 00:05:30,440 It makes the grid more stable, more efficient and less reliant on fossil fuels. 143 00:05:30,640 --> 00:05:31,280 That's amazing. 144 00:05:31,480 --> 00:05:33,960 It really shows the power of collaboration. 145 00:05:33,960 --> 00:05:38,520 Bringing together AI experts, energy companies and even small nonprofits. 146 00:05:38,720 --> 00:05:39,320 Absolutely. 147 00:05:39,520 --> 00:05:41,400 And that was a big part of the speaker's message. 148 00:05:41,400 --> 00:05:43,440 They want more people to get involved in this space. 149 00:05:43,640 --> 00:05:44,960 OK, I'm listening. 150 00:05:45,160 --> 00:05:50,080 What can someone with, say, expertise in transportation or agriculture contribute? 151 00:05:50,280 --> 00:05:51,360 That's a great question. 152 00:05:51,560 --> 00:05:56,400 They argue that people with domain expertise should share their problems with the AI community. 153 00:05:56,600 --> 00:05:57,120 Interesting. 154 00:05:57,320 --> 00:05:59,760 So like saying, hey, we're having this issue in agriculture. 155 00:05:59,960 --> 00:06:01,400 Can AI help us solve it? 156 00:06:01,600 --> 00:06:02,160 Exactly. 157 00:06:02,160 --> 00:06:05,920 Because sometimes the AI experts don't even know what problems to focus on. 158 00:06:06,120 --> 00:06:06,520 Makes sense. 159 00:06:06,720 --> 00:06:08,960 So it's about connecting those dots between different fields. 160 00:06:09,160 --> 00:06:09,480 Right. 161 00:06:09,680 --> 00:06:14,000 And they even suggest creating competitions with data sets and metrics to incentivize 162 00:06:14,200 --> 00:06:15,000 AI researchers. 163 00:06:15,200 --> 00:06:19,200 So like a challenge to solve a specific climate related problem using AI. 164 00:06:19,400 --> 00:06:20,360 Precisely. 165 00:06:20,560 --> 00:06:26,000 Imagine a competition to optimize traffic flow in cities or to develop AI powered 166 00:06:26,200 --> 00:06:28,400 systems for precision agriculture. 167 00:06:28,600 --> 00:06:29,800 Those are great examples. 168 00:06:29,800 --> 00:06:32,560 A little friendly competition can really drive innovation. 169 00:06:32,760 --> 00:06:35,640 And speaking of data, that's another crucial point that the speaker brought up. 170 00:06:35,840 --> 00:06:38,320 Oh, yeah. Data is like the fuel for AI. 171 00:06:38,520 --> 00:06:39,280 Exactly. 172 00:06:39,480 --> 00:06:44,680 We need access to good quality climate relevant data if we want AI to be effective. 173 00:06:44,880 --> 00:06:46,480 What kind of data are we talking about here? 174 00:06:46,680 --> 00:06:51,240 It could be anything, weather patterns, energy consumption, data, agricultural 175 00:06:51,440 --> 00:06:56,080 practices, deforestation rates, even the migration patterns of endangered species. 176 00:06:56,280 --> 00:06:57,400 That's a lot of data. 177 00:06:57,600 --> 00:06:59,000 And who has all this information? 178 00:06:59,000 --> 00:07:00,560 It's spread out everywhere. 179 00:07:00,760 --> 00:07:04,880 Governments, research institutions, private companies, individuals with their smart 180 00:07:05,080 --> 00:07:05,480 devices. 181 00:07:05,680 --> 00:07:09,040 So the call to action here is for those data holders to share their data. 182 00:07:09,240 --> 00:07:12,840 Right. To make it available to researchers and developers who can use it to build 183 00:07:13,040 --> 00:07:14,440 impactful AI solutions. 184 00:07:14,640 --> 00:07:16,560 But what about privacy concerns? 185 00:07:16,760 --> 00:07:18,320 There's a lot of sensitive data out there. 186 00:07:18,520 --> 00:07:19,240 Oh, absolutely. 187 00:07:19,440 --> 00:07:23,040 They emphasize that data sharing needs to be done responsibly. 188 00:07:23,240 --> 00:07:26,480 They actually mentioned a great resource, Climate Change AI. 189 00:07:26,680 --> 00:07:27,760 Climate Change AI. 190 00:07:27,960 --> 00:07:28,320 What's that? 191 00:07:28,320 --> 00:07:32,840 It's an organization that's working to connect data holders with AI experts. 192 00:07:33,040 --> 00:07:37,160 They even have a wish list of data sets that would be valuable for climate research. 193 00:07:37,360 --> 00:07:38,040 That's really cool. 194 00:07:38,040 --> 00:07:41,760 So we've got the domain experts, the data holders, and of course the AI developers. 195 00:07:41,960 --> 00:07:43,800 Is there a role for anyone else? 196 00:07:44,000 --> 00:07:44,680 Absolutely. 197 00:07:44,880 --> 00:07:48,760 We also need people with expertise in ethics, policy and communication. 198 00:07:48,960 --> 00:07:49,960 Ah, I see. 199 00:07:50,160 --> 00:07:52,600 Because it's not just about building the technology. 200 00:07:52,800 --> 00:07:54,240 It's about using it responsibly. 201 00:07:54,440 --> 00:07:55,080 Exactly. 202 00:07:55,080 --> 00:07:58,680 We need to think about the potential unintended consequences of AI, 203 00:07:58,880 --> 00:08:03,640 the possibility of bias and make sure these solutions benefit everyone. 204 00:08:03,840 --> 00:08:04,920 It's a lot to consider. 205 00:08:04,920 --> 00:08:08,240 It sounds like we need a truly multidisciplinary approach to tackle this. 206 00:08:08,240 --> 00:08:10,160 And that's exactly what the speaker argued for. 207 00:08:10,360 --> 00:08:15,120 They said we need scientists, engineers, policymakers, ethicists, communicators, 208 00:08:15,320 --> 00:08:16,720 everyone working together. 209 00:08:16,920 --> 00:08:18,360 This is all very inspiring. 210 00:08:18,560 --> 00:08:19,800 But I also want to be realistic. 211 00:08:20,000 --> 00:08:23,320 AI is a powerful tool, but it's not a magic bullet, right? 212 00:08:23,520 --> 00:08:23,960 You're right. 213 00:08:23,960 --> 00:08:27,040 AI isn't going to solve every problem. 214 00:08:27,240 --> 00:08:30,520 But it can be a game changer if we use it wisely. 215 00:08:30,720 --> 00:08:33,120 So what are some of the limitations of AI? 216 00:08:33,320 --> 00:08:36,280 What are the potential pitfalls we need to watch out for? 217 00:08:36,480 --> 00:08:40,240 Well, for starters, there's the issue of AI's carbon footprint. 218 00:08:40,440 --> 00:08:41,640 Ah, yeah. 219 00:08:41,840 --> 00:08:44,640 Training those massive AI models takes a lot of energy. 220 00:08:44,840 --> 00:08:45,360 It does. 221 00:08:45,360 --> 00:08:47,840 And a lot of that energy still comes from fossil fuels. 222 00:08:48,040 --> 00:08:49,640 So there's a bit of irony there. 223 00:08:49,840 --> 00:08:53,560 We're using a technology that contributes to climate change to try to fight 224 00:08:53,560 --> 00:08:54,240 climate change. 225 00:08:54,440 --> 00:08:58,080 Right. But it's important to remember that AI's footprint will shrink as we 226 00:08:58,280 --> 00:09:00,000 transition to a cleaner energy grid. 227 00:09:00,000 --> 00:09:01,160 So it's not a deal breaker. 228 00:09:01,160 --> 00:09:02,480 We just need to be mindful of it. 229 00:09:02,680 --> 00:09:06,400 Exactly. And there are also people working on developing more energy 230 00:09:06,600 --> 00:09:08,800 efficient AI algorithms and hardware. 231 00:09:08,800 --> 00:09:10,120 So there's hope for the future. 232 00:09:10,320 --> 00:09:11,560 What other limitations are there? 233 00:09:11,560 --> 00:09:13,160 Well, another big one is bias. 234 00:09:13,360 --> 00:09:16,040 AI models are only as good as the data they're trained on. 235 00:09:16,240 --> 00:09:19,400 Right. So if the data is biased, the AI will be biased too. 236 00:09:19,600 --> 00:09:22,360 Exactly. And that can lead to all sorts of problems, 237 00:09:22,360 --> 00:09:25,320 especially when it comes to things like fairness and justice. 238 00:09:25,520 --> 00:09:29,280 So it's really important to have diverse and representative data sets. 239 00:09:29,480 --> 00:09:34,720 Absolutely. And we need experts who can check for bias and make sure the AI is 240 00:09:34,920 --> 00:09:35,960 being used ethically. 241 00:09:36,160 --> 00:09:37,680 It's a lot to think about. 242 00:09:37,880 --> 00:09:40,880 It's not as simple as just throwing AI at every problem. 243 00:09:41,080 --> 00:09:42,040 You're right. 244 00:09:42,240 --> 00:09:45,160 It takes careful planning and consideration. 245 00:09:45,360 --> 00:09:49,640 But I'm still optimistic about the potential of AI to make a real difference. 246 00:09:49,840 --> 00:09:50,840 Me too. 247 00:09:50,840 --> 00:09:53,600 This conversation has given me a lot to think about. 248 00:09:53,800 --> 00:09:56,040 It's easy to get caught up in the doom and gloom, 249 00:09:56,240 --> 00:09:58,000 but there are so many reasons for hope. 250 00:09:58,200 --> 00:10:01,400 I agree. We have the tools and the ingenuity to solve this. 251 00:10:01,400 --> 00:10:03,760 It's just a matter of putting those pieces together. 252 00:10:03,960 --> 00:10:06,920 You know, it's really stuck with me throughout this deep dive. 253 00:10:07,120 --> 00:10:08,920 It's that idea of optimization, 254 00:10:09,120 --> 00:10:12,520 making existing systems better instead of starting from scratch. 255 00:10:12,520 --> 00:10:14,080 I think that's a really smart approach. 256 00:10:14,280 --> 00:10:16,400 It feels more attainable, more realistic. 257 00:10:16,400 --> 00:10:18,520 And AI can play a huge role in that, right? 258 00:10:18,520 --> 00:10:21,160 Helping us find those areas for improvement and make them happen. 259 00:10:21,360 --> 00:10:23,680 Exactly. Think about all the systems we have in place. 260 00:10:23,880 --> 00:10:26,480 Transportation, energy, agriculture, manufacturing. 261 00:10:26,680 --> 00:10:28,760 We can make them all more efficient, more sustainable. 262 00:10:28,960 --> 00:10:30,040 Give me some examples. 263 00:10:30,240 --> 00:10:31,840 How could we optimize those systems? 264 00:10:32,040 --> 00:10:36,560 Well, in transportation, we could use AI to optimize traffic flow, 265 00:10:36,760 --> 00:10:38,680 reduce congestion and emissions. 266 00:10:38,880 --> 00:10:42,160 That would be amazing, especially in cities where traffic is a nightmare. 267 00:10:42,360 --> 00:10:45,800 Or in agriculture, we could use AI to help farmers make better decisions 268 00:10:45,800 --> 00:10:48,560 about irrigation, fertilization and pest control. 269 00:10:48,760 --> 00:10:52,680 So it's about making those practices more precise, less wasteful. 270 00:10:52,880 --> 00:10:56,160 Exactly. And that precision extends to other areas too. 271 00:10:56,360 --> 00:11:00,480 Like manufacturing, we can use AI to optimize production processes, 272 00:11:00,480 --> 00:11:03,440 reduce waste and even design more energy efficient products. 273 00:11:03,640 --> 00:11:06,920 It feels like we're at a turning point where AI is no longer just a futuristic 274 00:11:07,120 --> 00:11:10,120 concept, but a real tool we can use to build a better future. 275 00:11:10,320 --> 00:11:14,680 I agree. And it's not just up to big companies or governments to make these changes. 276 00:11:14,680 --> 00:11:15,880 We can all contribute. 277 00:11:16,080 --> 00:11:17,240 OK, so let's talk about that. 278 00:11:17,440 --> 00:11:19,400 What can we do as individuals to help? 279 00:11:19,600 --> 00:11:22,160 Well, we can support companies that are using AI for good. 280 00:11:22,360 --> 00:11:23,840 So like voting with our wallets. 281 00:11:24,040 --> 00:11:27,760 Exactly. We can also advocate for policies that promote the responsible 282 00:11:27,960 --> 00:11:29,800 development of AI for climate action. 283 00:11:30,000 --> 00:11:33,120 So making our voices heard and engaging with our elected officials. 284 00:11:33,320 --> 00:11:35,720 And of course, we can educate ourselves about all of this. 285 00:11:35,920 --> 00:11:37,840 The more we understand, the more we can do. 286 00:11:38,040 --> 00:11:39,000 I love that. 287 00:11:39,200 --> 00:11:41,440 It's about turning knowledge into action. 288 00:11:41,640 --> 00:11:43,880 This deep dive has given me a lot to think about. 289 00:11:43,880 --> 00:11:45,280 That's great to hear. 290 00:11:45,480 --> 00:11:47,720 And remember, this conversation doesn't end here. 291 00:11:47,920 --> 00:11:50,320 Keep exploring. Keep asking questions. 292 00:11:50,520 --> 00:11:51,760 Keep pushing for change. 293 00:11:51,960 --> 00:11:53,520 Couldn't have said it better myself. 294 00:11:53,720 --> 00:11:55,280 Thanks for joining me on this deep dive. 295 00:11:55,480 --> 00:11:56,680 It's been really eye opening. 296 00:11:56,880 --> 00:11:58,320 The pleasure was all mine. 297 00:11:58,520 --> 00:12:03,040 And to everyone listening, remember, the future is in our hands. 298 00:12:03,240 --> 00:12:05,200 Let's use it wisely. 299 00:12:05,400 --> 00:12:08,560 Well, for starters, there's the issue of AI's own carbon footprint. 300 00:12:08,760 --> 00:12:12,520 Oh, yeah. Training those massive AI models takes a lot of energy. 301 00:12:12,520 --> 00:12:15,880 It does. And a lot of that energy still comes from fossil fuels. 302 00:12:16,080 --> 00:12:17,240 So there's a bit of irony there. 303 00:12:17,440 --> 00:12:21,680 We're using a technology that contributes to climate change to try to fight climate change. 304 00:12:21,880 --> 00:12:25,480 Right. But it's important to remember that AI's footprint will shrink as we 305 00:12:25,680 --> 00:12:27,600 transition to a cleaner energy grid. 306 00:12:27,800 --> 00:12:28,800 So it's not a deal breaker. 307 00:12:28,800 --> 00:12:30,040 We just need to be mindful of it. 308 00:12:30,240 --> 00:12:34,040 Exactly. And there are also people working on developing more energy 309 00:12:34,240 --> 00:12:36,160 efficient algorithms and hardware. 310 00:12:36,360 --> 00:12:38,120 So there's hope for the future. 311 00:12:38,320 --> 00:12:39,880 What other limitations are there? 312 00:12:39,880 --> 00:12:45,000 Well, another big one is bias AI models are only as good as the data they're trained on. 313 00:12:45,200 --> 00:12:48,760 Right. So if the data is biased, the AI will be biased too. 314 00:12:48,760 --> 00:12:51,040 Exactly. And that can lead to all sorts of problems, 315 00:12:51,240 --> 00:12:54,760 especially when it comes to things like fairness and justice. 316 00:12:54,960 --> 00:12:58,320 So it's really important to have diverse and representative data sets. 317 00:12:58,520 --> 00:13:03,960 Absolutely. And we need experts who can check for bias and make sure the AI is being used ethically. 318 00:13:04,160 --> 00:13:05,400 It's a lot to think about. 319 00:13:05,600 --> 00:13:08,480 It's not as simple as just throwing AI at every problem. 320 00:13:08,480 --> 00:13:12,000 You're right. It takes careful planning and consideration. 321 00:13:12,200 --> 00:13:16,480 But I'm still optimistic about the potential of AI to make a real difference. 322 00:13:16,680 --> 00:13:17,280 Me too. 323 00:13:17,480 --> 00:13:19,680 This conversation has given me a lot to think about. 324 00:13:19,880 --> 00:13:24,080 It's easy to get caught up in the doom and gloom, but there are so many reasons for hope. 325 00:13:24,280 --> 00:13:27,320 I agree. We have the tools and the ingenuity to solve this. 326 00:13:27,320 --> 00:13:29,240 It's just a matter of putting those pieces together. 327 00:13:29,440 --> 00:13:32,440 You know, it's really stuck with me throughout this deep dive. 328 00:13:32,640 --> 00:13:35,120 It's that idea of optimization, 329 00:13:35,120 --> 00:13:38,680 making existing systems better instead of starting from scratch. 330 00:13:38,880 --> 00:13:40,560 I think that's a really smart approach. 331 00:13:40,760 --> 00:13:44,000 It feels more attainable, more realistic. 332 00:13:44,200 --> 00:13:49,120 And AI can play a huge role in helping us find those areas for improvement and make them happen. 333 00:13:49,320 --> 00:13:52,120 Exactly. Think about all the systems we have in place. 334 00:13:52,320 --> 00:13:54,920 Transportation, energy, agriculture, manufacturing. 335 00:13:55,120 --> 00:13:57,280 We can make them all more efficient, more sustainable. 336 00:13:57,480 --> 00:13:58,760 Give me some examples. 337 00:13:58,960 --> 00:14:00,840 How could we optimize those systems? 338 00:14:01,040 --> 00:14:04,560 Well, in transportation, we could use AI to optimize traffic flow. 339 00:14:04,560 --> 00:14:06,480 Reduce congestion and emissions. 340 00:14:06,680 --> 00:14:10,520 That would be amazing, especially in cities where traffic is a nightmare. 341 00:14:10,720 --> 00:14:14,160 Or in agriculture, we could use AI to help farmers make better decisions about 342 00:14:14,360 --> 00:14:16,360 irrigation, fertilization and pest control. 343 00:14:16,560 --> 00:14:19,680 So it's about making those practices more precise, less wasteful. 344 00:14:19,880 --> 00:14:24,880 Exactly. And that precision extends to other areas too, like manufacturing. 345 00:14:25,080 --> 00:14:28,160 We can use AI to optimize production processes, reduce waste, 346 00:14:28,360 --> 00:14:30,560 and even design more energy efficient products. 347 00:14:30,760 --> 00:14:33,800 It feels like we're at a turning point where AI is no longer just a 348 00:14:33,800 --> 00:14:37,840 futuristic concept, but a real tool we can use to build a better future. 349 00:14:38,040 --> 00:14:38,760 I agree. 350 00:14:38,960 --> 00:14:42,680 And it's not just up to big companies or governments to make these changes. 351 00:14:42,880 --> 00:14:44,080 We can all contribute. 352 00:14:44,280 --> 00:14:46,240 OK, so let's talk about that. 353 00:14:46,440 --> 00:14:48,440 What can we do as individuals to help? 354 00:14:48,640 --> 00:14:51,640 Well, we can support companies that are using AI for good. 355 00:14:51,840 --> 00:14:53,240 So like voting with our wallets. 356 00:14:53,440 --> 00:14:57,200 Exactly. We can also advocate for policies that promote the responsible 357 00:14:57,400 --> 00:14:59,760 development of AI for climate action. 358 00:14:59,960 --> 00:15:03,560 So making our voices heard and engaging with our elected officials. 359 00:15:03,560 --> 00:15:05,840 And of course, we can educate ourselves about all of this. 360 00:15:06,040 --> 00:15:08,040 The more we understand, the more we can do. 361 00:15:08,240 --> 00:15:09,200 I love that. 362 00:15:09,400 --> 00:15:12,120 It's about turning knowledge into action. 363 00:15:12,320 --> 00:15:14,880 This deep dive has given me a lot to think about. 364 00:15:15,080 --> 00:15:16,480 What about you? What are your final thoughts? 365 00:15:16,680 --> 00:15:18,960 I think this TED talk gave us a lot to think about. 366 00:15:19,160 --> 00:15:23,360 We dove deep into wind energy and saw how AI can make a real difference. 367 00:15:23,560 --> 00:15:25,120 And it's not just about wind. 368 00:15:25,320 --> 00:15:28,760 It's about applying this kind of thinking to all sorts of systems. 369 00:15:28,960 --> 00:15:30,280 So yeah, I'm feeling optimistic. 370 00:15:30,480 --> 00:15:31,280 Me too. 371 00:15:31,280 --> 00:15:35,520 It's been really eye opening to see how AI can be a force for good in the fight 372 00:15:35,520 --> 00:15:36,560 against climate change. 373 00:15:36,760 --> 00:15:38,720 And it's not just about the technology itself. 374 00:15:38,920 --> 00:15:44,000 It's about collaboration, innovation and a willingness to challenge the status quo. 375 00:15:44,200 --> 00:15:45,360 Couldn't have said it better myself. 376 00:15:45,560 --> 00:15:47,160 It's been a pleasure exploring this with you. 377 00:15:47,360 --> 00:15:48,200 Likewise. 378 00:15:48,400 --> 00:15:52,920 And everyone listening, thanks for joining us on this deep dive into AI and climate change. 379 00:15:52,920 --> 00:16:01,440 Let's keep the conversation going and work together to build a more sustainable future.