1 00:00:00,000 --> 00:00:03,080 Welcome to the Daily AI News Podcast. 2 00:00:03,080 --> 00:00:04,720 Wow, you guys been busy sending us 3 00:00:04,720 --> 00:00:07,280 a ton of articles about AI. 4 00:00:07,280 --> 00:00:09,160 Yeah, it looks like a lot has happened. 5 00:00:09,160 --> 00:00:12,520 A lot has happened, even in just the past few days. 6 00:00:12,520 --> 00:00:13,640 New models. 7 00:00:13,640 --> 00:00:15,200 Big funding rounds. 8 00:00:15,200 --> 00:00:16,840 Even AI elves. 9 00:00:16,840 --> 00:00:18,680 It's like, what is even going on right now? 10 00:00:18,680 --> 00:00:20,480 Don't worry, we're here to break it all down for you. 11 00:00:20,480 --> 00:00:21,960 Yeah, what's fascinating to me is 12 00:00:21,960 --> 00:00:25,040 we're seeing a shift in the AI landscape right now. 13 00:00:25,040 --> 00:00:28,560 It's going from just focusing on making these models bigger 14 00:00:28,560 --> 00:00:31,840 and bigger, scaling them up, to exploring 15 00:00:31,840 --> 00:00:34,640 some really new avenues, like reasoning 16 00:00:34,640 --> 00:00:36,320 and specialized applications. 17 00:00:36,320 --> 00:00:38,280 OK, so let's unpack this a little bit. 18 00:00:38,280 --> 00:00:40,680 We'll start with open AI, because let's be honest, 19 00:00:40,680 --> 00:00:43,440 everybody's talking about them right now, the new O3 model. 20 00:00:43,440 --> 00:00:45,160 And then we'll dive into what Google's doing. 21 00:00:45,160 --> 00:00:48,280 Some interesting things happening with AI talent and funding, 22 00:00:48,280 --> 00:00:50,440 and even a little bit about space exploration. 23 00:00:50,440 --> 00:00:51,120 Sounds good. 24 00:00:51,120 --> 00:00:55,120 OK, so open AI O3, is this a leap forward? 25 00:00:55,120 --> 00:00:58,160 I mean, it's the successor to their O1 model. 26 00:00:58,160 --> 00:00:58,660 Right. 27 00:00:58,660 --> 00:01:01,560 Which was their reasoning model, which was already causing a stir. 28 00:01:01,560 --> 00:01:05,120 Yeah, and you know, it's funny, they actually skipped O2. 29 00:01:05,120 --> 00:01:05,880 What? 30 00:01:05,880 --> 00:01:09,080 Yeah, they had some trademark issues with a British telecom 31 00:01:09,080 --> 00:01:09,560 company. 32 00:01:09,560 --> 00:01:10,280 Oh, no. 33 00:01:10,280 --> 00:01:12,400 So they had to skip right over O2. 34 00:01:12,400 --> 00:01:14,880 OK, well, I'm glad they didn't let that stop them. 35 00:01:14,880 --> 00:01:19,000 So O3, it's designed to think before responding. 36 00:01:19,000 --> 00:01:19,600 Exactly. 37 00:01:19,600 --> 00:01:22,160 By using this thing called a private chain of thought. 38 00:01:22,160 --> 00:01:26,120 Right, so it's not just kind of going with the first response. 39 00:01:26,120 --> 00:01:26,880 It thinks about it. 40 00:01:26,880 --> 00:01:30,160 But first it considers a bunch of related prompts, 41 00:01:30,160 --> 00:01:32,800 explains its reasoning, and even does some internal fact 42 00:01:32,800 --> 00:01:33,320 checking. 43 00:01:33,320 --> 00:01:34,600 That's interesting. 44 00:01:34,600 --> 00:01:36,160 So does that make it more accurate? 45 00:01:36,160 --> 00:01:37,520 Well, they're saying it does. 46 00:01:37,520 --> 00:01:40,760 Open AI is claiming that it leads to significantly more 47 00:01:40,760 --> 00:01:44,080 reliable responses, especially in these complex fields, 48 00:01:44,080 --> 00:01:45,840 like physics and math. 49 00:01:45,840 --> 00:01:49,240 And they're even saying that O3 is getting closer to AGI. 50 00:01:49,240 --> 00:01:49,800 AGI. 51 00:01:49,800 --> 00:01:51,480 Artificial general intelligence. 52 00:01:51,480 --> 00:01:52,040 Whoa, hold on. 53 00:01:52,040 --> 00:01:54,280 Already they're saying that based on this benchmark called 54 00:01:54,280 --> 00:01:55,760 ARC-AGI. 55 00:01:55,760 --> 00:01:56,720 Right. 56 00:01:56,720 --> 00:01:59,160 But I feel like some experts are kind of pushing back on that. 57 00:01:59,160 --> 00:02:02,200 Yeah, Francois Chalet, who actually created that benchmark, 58 00:02:02,200 --> 00:02:04,480 he's been pretty vocal about his skepticism. 59 00:02:04,480 --> 00:02:06,600 He's saying that it still struggles with tasks 60 00:02:06,600 --> 00:02:09,000 that are super easy for humans. 61 00:02:09,000 --> 00:02:12,280 And they're actually already working on ARC-AGI2, which 62 00:02:12,280 --> 00:02:13,720 is going to be like the sequel. 63 00:02:13,720 --> 00:02:14,200 The sequel. 64 00:02:14,200 --> 00:02:16,920 A tougher benchmark to really put these reasoning 65 00:02:16,920 --> 00:02:18,440 capabilities to the test. 66 00:02:18,440 --> 00:02:20,560 OK, so it sounds like the jury is still out 67 00:02:20,560 --> 00:02:22,440 on whether this is truly AGI. 68 00:02:22,440 --> 00:02:23,200 Yeah. 69 00:02:23,200 --> 00:02:24,920 But how did it do on some other tests? 70 00:02:24,920 --> 00:02:26,640 Well, in terms of programming, it actually 71 00:02:26,640 --> 00:02:28,120 outperformed other models. 72 00:02:28,120 --> 00:02:28,720 OK. 73 00:02:28,720 --> 00:02:32,520 And it crushed the American Invitational Mathematics exam. 74 00:02:32,520 --> 00:02:33,320 What? 75 00:02:33,320 --> 00:02:36,120 Yeah, it's got a 96.7%. 76 00:02:36,120 --> 00:02:37,240 Almost a perfect score. 77 00:02:37,240 --> 00:02:37,840 Yeah. 78 00:02:37,840 --> 00:02:38,760 That's really impressive. 79 00:02:38,760 --> 00:02:39,280 Yeah. 80 00:02:39,280 --> 00:02:42,840 OK, so it sounds like O3 is a major leap forward. 81 00:02:42,840 --> 00:02:43,360 Yeah. 82 00:02:43,360 --> 00:02:45,200 Even if it's not quite AGI yet. 83 00:02:45,200 --> 00:02:45,880 Right. 84 00:02:45,880 --> 00:02:50,000 But speaking of open AI, their next big language model, GCC5, 85 00:02:50,000 --> 00:02:51,880 seems to be hitting some roadblocks. 86 00:02:51,880 --> 00:02:54,000 Yeah, it's apparently behind schedule 87 00:02:54,000 --> 00:02:55,880 and facing some pretty serious challenges. 88 00:02:55,880 --> 00:02:57,440 What kind of challenges are we talking about here? 89 00:02:57,440 --> 00:02:59,760 Well, for one thing, just the technical challenges 90 00:02:59,760 --> 00:03:01,480 and then the data challenges as well. 91 00:03:01,480 --> 00:03:02,480 OK, so what's going on? 92 00:03:02,480 --> 00:03:04,360 Well, training these massive models 93 00:03:04,360 --> 00:03:06,240 is incredibly expensive. 94 00:03:06,240 --> 00:03:06,920 I can imagine. 95 00:03:06,920 --> 00:03:09,200 We're talking hundreds of millions of dollars 96 00:03:09,200 --> 00:03:11,320 for a single training run. 97 00:03:11,320 --> 00:03:14,200 Hundreds of millions of dollars for one training run. 98 00:03:14,200 --> 00:03:15,320 Yeah. 99 00:03:15,320 --> 00:03:17,680 What is driving up the cost like that? 100 00:03:17,680 --> 00:03:20,840 Well, it seems like the more data, the better approach 101 00:03:20,840 --> 00:03:22,240 is kind of hitting a wall. 102 00:03:22,240 --> 00:03:22,760 Really? 103 00:03:22,760 --> 00:03:25,120 Yeah, they're having trouble finding enough high quality 104 00:03:25,120 --> 00:03:27,600 data to train these systems that are just getting more 105 00:03:27,600 --> 00:03:28,560 and more complex. 106 00:03:28,560 --> 00:03:30,080 So what are they doing about it? 107 00:03:30,080 --> 00:03:31,840 Well, they're experimenting with this thing called 108 00:03:31,840 --> 00:03:33,000 synthetic data. 109 00:03:33,000 --> 00:03:33,920 Synthetic data. 110 00:03:33,920 --> 00:03:37,320 Which is basically data that's created by AI itself. 111 00:03:37,320 --> 00:03:37,720 Hold on. 112 00:03:37,720 --> 00:03:40,680 AI creating data to train other AI. 113 00:03:40,680 --> 00:03:41,160 Yeah. 114 00:03:41,160 --> 00:03:43,760 Isn't that a little bit like a recipe for disaster? 115 00:03:43,760 --> 00:03:45,720 It's a controversial approach, for sure. 116 00:03:45,720 --> 00:03:46,160 OK. 117 00:03:46,160 --> 00:03:50,120 The worry is that if you're using data generated by AI, 118 00:03:50,120 --> 00:03:53,880 you could end up amplifying biases and inaccuracies. 119 00:03:53,880 --> 00:03:54,320 Right. 120 00:03:54,320 --> 00:03:57,200 It could create this kind of feedback loop of bad data. 121 00:03:57,200 --> 00:03:58,920 So how are they trying to prevent that? 122 00:03:58,920 --> 00:04:01,400 Well, they're using data generated by their reasoning 123 00:04:01,400 --> 00:04:02,480 model 01. 124 00:04:02,480 --> 00:04:02,920 OK. 125 00:04:02,920 --> 00:04:05,360 Hoping that its reasoning abilities will actually 126 00:04:05,360 --> 00:04:07,960 lead to higher quality synthetic data. 127 00:04:07,960 --> 00:04:08,680 Interesting. 128 00:04:08,680 --> 00:04:09,640 So it's a bit of a gamble. 129 00:04:09,640 --> 00:04:10,640 We'll have to see how it plays out. 130 00:04:10,640 --> 00:04:12,120 Yeah, definitely a gamble. 131 00:04:12,120 --> 00:04:12,600 OK. 132 00:04:12,600 --> 00:04:16,520 So GPT-5, facing delays, data dilemmas. 133 00:04:16,520 --> 00:04:17,240 Yeah. 134 00:04:17,240 --> 00:04:18,560 Anything else going on here? 135 00:04:18,560 --> 00:04:20,960 Well, we can't forget about the competition for talent. 136 00:04:20,960 --> 00:04:21,520 Oh, yeah. 137 00:04:21,520 --> 00:04:23,840 The AI talent wars are really heating up. 138 00:04:23,840 --> 00:04:25,040 It's getting competitive out there. 139 00:04:25,040 --> 00:04:26,320 It's super competitive. 140 00:04:26,320 --> 00:04:26,680 Yeah. 141 00:04:26,680 --> 00:04:29,800 Companies are just fighting to attract and keep 142 00:04:29,800 --> 00:04:31,600 the best researchers, and it's pushing up 143 00:04:31,600 --> 00:04:34,880 salaries and valuations across the entire industry. 144 00:04:34,880 --> 00:04:36,240 So it's like an AI gold rush. 145 00:04:36,240 --> 00:04:37,400 Yeah, kind of. 146 00:04:37,400 --> 00:04:39,120 So who's winning this war for talent? 147 00:04:39,120 --> 00:04:41,960 Well, Databricks, another big player in the AI space, 148 00:04:41,960 --> 00:04:46,120 just announced a record-breaking $10 billion funding round. 149 00:04:46,120 --> 00:04:47,120 $10 billion. 150 00:04:47,120 --> 00:04:47,760 $10 billion. 151 00:04:47,760 --> 00:04:51,640 And get this, most of that money is going towards buying back 152 00:04:51,640 --> 00:04:52,720 employee stock. 153 00:04:52,720 --> 00:04:54,760 Wow, that's a really bold move. 154 00:04:54,760 --> 00:04:56,320 Why buy back stock, though? 155 00:04:56,320 --> 00:04:59,000 Well, it's all about attracting and keeping that top talent. 156 00:04:59,000 --> 00:04:59,400 OK. 157 00:04:59,400 --> 00:05:02,560 Naveen Rao, who's the VP of AI at Databricks, 158 00:05:02,560 --> 00:05:05,280 he was saying that finding top AI researchers, 159 00:05:05,280 --> 00:05:07,400 like looking for LeBron James, there just 160 00:05:07,400 --> 00:05:09,520 aren't that many people out there with the skills 161 00:05:09,520 --> 00:05:12,160 and expertise to really push the boundaries 162 00:05:12,160 --> 00:05:13,200 of this technology. 163 00:05:13,200 --> 00:05:14,320 So it's a seller's market. 164 00:05:14,320 --> 00:05:15,760 Absolutely, a seller's market. 165 00:05:15,760 --> 00:05:17,120 They have all the power right now. 166 00:05:17,120 --> 00:05:17,560 Yeah. 167 00:05:17,560 --> 00:05:20,480 Databricks is basically saying, come work for us, 168 00:05:20,480 --> 00:05:22,000 and we'll make it worth your while. 169 00:05:22,000 --> 00:05:23,120 Exactly. 170 00:05:23,120 --> 00:05:24,880 All right, well, let's shift gears for a second 171 00:05:24,880 --> 00:05:26,760 and talk about some other interesting developments 172 00:05:26,760 --> 00:05:28,600 in the AI world. 173 00:05:28,600 --> 00:05:31,560 I mean, we can't leave out Google and NVIDIA, can we? 174 00:05:31,560 --> 00:05:33,120 No, definitely not. 175 00:05:33,120 --> 00:05:34,640 They're making some big moves, too. 176 00:05:34,640 --> 00:05:35,320 Yeah. 177 00:05:35,320 --> 00:05:38,720 While open AI and these large language models 178 00:05:38,720 --> 00:05:40,160 are getting all the headlines, there's 179 00:05:40,160 --> 00:05:43,160 other companies doing some really interesting stuff. 180 00:05:43,160 --> 00:05:43,800 Like what? 181 00:05:43,800 --> 00:05:45,760 Well, Google, for example, is reportedly 182 00:05:45,760 --> 00:05:49,480 working on an AI mode for their search engine, 183 00:05:49,480 --> 00:05:53,600 which is probably a response to ChatGPT and all these other AI 184 00:05:53,600 --> 00:05:56,080 powered search tools that are coming out. 185 00:05:56,080 --> 00:05:56,680 That makes sense. 186 00:05:56,680 --> 00:05:57,680 What about NVIDIA? 187 00:05:57,680 --> 00:06:01,160 Well, NVIDIA, the leading maker of AI chips, 188 00:06:01,160 --> 00:06:04,760 they just got approval from the EU for their acquisition 189 00:06:04,760 --> 00:06:08,680 of Run.AI, which is an AI software company. 190 00:06:08,680 --> 00:06:11,640 So this is pointing towards some consolidation in the market. 191 00:06:11,640 --> 00:06:15,040 The big players are snapping up these innovative startups. 192 00:06:15,040 --> 00:06:16,680 All right, so we've got open AI pushing 193 00:06:16,680 --> 00:06:19,440 the boundaries of reasoning models, 194 00:06:19,440 --> 00:06:21,400 Google's adapting its search engine, 195 00:06:21,400 --> 00:06:24,520 and NVIDIA is consolidating its position in the AI chip 196 00:06:24,520 --> 00:06:25,040 market. 197 00:06:25,040 --> 00:06:26,080 Yeah. 198 00:06:26,080 --> 00:06:27,680 That's a lot of activity. 199 00:06:27,680 --> 00:06:29,640 But let's move on to something a little more festive 200 00:06:29,640 --> 00:06:30,320 for a moment. 201 00:06:30,320 --> 00:06:33,840 You mean NORAD's new AI elf? 202 00:06:33,840 --> 00:06:34,680 Yes. 203 00:06:34,680 --> 00:06:35,600 I love this story. 204 00:06:35,600 --> 00:06:38,880 It's so fascinating how AI is being used in these creative 205 00:06:38,880 --> 00:06:39,880 and unexpected ways. 206 00:06:39,880 --> 00:06:41,040 I know, right? 207 00:06:41,040 --> 00:06:43,800 So NORAD, the organization that tracks Santa? 208 00:06:43,800 --> 00:06:44,960 Yeah, every Christmas Eve. 209 00:06:44,960 --> 00:06:46,360 Every Christmas Eve. 210 00:06:46,360 --> 00:06:50,040 They've deployed this AI chat bot called Radar the Elf. 211 00:06:50,040 --> 00:06:50,760 I love it. 212 00:06:50,760 --> 00:06:53,720 To answer questions about Santa in multiple languages. 213 00:06:53,720 --> 00:06:54,120 Yeah. 214 00:06:54,120 --> 00:06:57,480 So kids can now chat with an AI elf about Santa. 215 00:06:57,480 --> 00:06:58,200 It's amazing. 216 00:06:58,200 --> 00:06:58,880 That's pretty cool. 217 00:06:58,880 --> 00:07:01,320 It's a great example of how AI is just finding its way 218 00:07:01,320 --> 00:07:03,240 into all sorts of applications from solving 219 00:07:03,240 --> 00:07:05,400 these complex math problems to bringing 220 00:07:05,400 --> 00:07:06,800 a little bit of holiday cheer. 221 00:07:06,800 --> 00:07:07,280 I love that. 222 00:07:07,280 --> 00:07:09,200 Speaking of unexpected applications, 223 00:07:09,200 --> 00:07:11,600 there's this online charter school in Arizona 224 00:07:11,600 --> 00:07:13,640 that's going to be taught entirely by AI. 225 00:07:13,640 --> 00:07:14,680 I saw that. 226 00:07:14,680 --> 00:07:17,360 Yeah, the Arizona State Board for Charter Schools 227 00:07:17,360 --> 00:07:20,760 has approved this new school called Unbound Academy. 228 00:07:20,760 --> 00:07:21,880 Unbound Academy. 229 00:07:21,880 --> 00:07:24,800 And they're going to be using AI-driven adaptive learning 230 00:07:24,800 --> 00:07:27,880 technology to deliver their entire curriculum. 231 00:07:27,880 --> 00:07:29,640 So no human teachers at all? 232 00:07:29,640 --> 00:07:31,680 Well, they will have what they call skilled guides. 233 00:07:31,680 --> 00:07:32,040 OK. 234 00:07:32,040 --> 00:07:34,560 To monitor student progress and offer support. 235 00:07:34,560 --> 00:07:35,160 OK. 236 00:07:35,160 --> 00:07:36,800 But all the core instruction is going 237 00:07:36,800 --> 00:07:38,840 to be handled by these AI platforms. 238 00:07:38,840 --> 00:07:40,480 Wow, that's a bold experiment. 239 00:07:40,480 --> 00:07:41,920 What platforms are they going to be using? 240 00:07:41,920 --> 00:07:45,280 They're planning on using some existing EdTech platforms 241 00:07:45,280 --> 00:07:47,520 like IXL and Khan Academy. 242 00:07:47,520 --> 00:07:48,080 Oh, OK. 243 00:07:48,080 --> 00:07:50,040 Which are already used in a lot of classrooms. 244 00:07:50,040 --> 00:07:50,480 Yeah. 245 00:07:50,480 --> 00:07:53,560 The idea is that these platforms can personalize learning. 246 00:07:53,560 --> 00:07:53,960 Right. 247 00:07:53,960 --> 00:07:56,640 Adjusting to each student's pace and style. 248 00:07:56,640 --> 00:07:58,400 It'll be really interesting to see how it works out. 249 00:07:58,400 --> 00:07:58,680 I know. 250 00:07:58,680 --> 00:07:59,760 I'm curious, too. 251 00:07:59,760 --> 00:08:01,360 This is definitely a controversial move, 252 00:08:01,360 --> 00:08:03,080 but I can't wait to see how it plays out. 253 00:08:03,080 --> 00:08:04,080 Me, too. 254 00:08:04,080 --> 00:08:08,480 And continuing on this theme of AI kind of pushing 255 00:08:08,480 --> 00:08:11,680 into new frontiers, what about AI agents? 256 00:08:11,680 --> 00:08:12,880 Oh, yeah. 257 00:08:12,880 --> 00:08:15,400 Especially their growing role in Web 3. 258 00:08:15,400 --> 00:08:18,640 That's a topic that we could spend hours talking about. 259 00:08:18,640 --> 00:08:19,360 OK, so. 260 00:08:19,360 --> 00:08:22,280 We're starting to see AI agents making their mark in the world 261 00:08:22,280 --> 00:08:25,800 of Web 3 and cryptocurrency, particularly 262 00:08:25,800 --> 00:08:28,600 in activities like cryptocurrency staking and on-chain 263 00:08:28,600 --> 00:08:29,160 trading. 264 00:08:29,160 --> 00:08:29,640 OK. 265 00:08:29,640 --> 00:08:31,480 This is where I start to get a little bit lost, 266 00:08:31,480 --> 00:08:32,080 I have to admit. 267 00:08:32,080 --> 00:08:32,560 Yeah. 268 00:08:32,560 --> 00:08:34,160 Can you just break it down for me? 269 00:08:34,160 --> 00:08:37,680 What are AI agents and how are they being used in this Web 3 270 00:08:37,680 --> 00:08:38,240 world? 271 00:08:38,240 --> 00:08:38,760 Sure. 272 00:08:38,760 --> 00:08:42,240 So an AI agent is basically a piece of software 273 00:08:42,240 --> 00:08:44,160 that can act autonomously. 274 00:08:44,160 --> 00:08:46,480 It can make decisions and take actions 275 00:08:46,480 --> 00:08:49,000 to achieve a specific goal. 276 00:08:49,000 --> 00:08:52,040 So in the context of Web 3, which is all about decentralization 277 00:08:52,040 --> 00:08:55,040 and blockchain technology, these AI agents 278 00:08:55,040 --> 00:08:58,480 are being used to automate complex tasks 279 00:08:58,480 --> 00:09:00,640 and potentially improve efficiency 280 00:09:00,640 --> 00:09:02,480 in this decentralized environment. 281 00:09:02,480 --> 00:09:05,720 So is it like a digital personal assistant, but for Web 3? 282 00:09:05,720 --> 00:09:07,080 In a way, yes. 283 00:09:07,080 --> 00:09:09,000 But they're much more sophisticated than that. 284 00:09:09,000 --> 00:09:10,840 They can learn and adapt over time 285 00:09:10,840 --> 00:09:12,360 and become more and more effective. 286 00:09:12,360 --> 00:09:14,560 Wow, that sounds really powerful. 287 00:09:14,560 --> 00:09:16,880 Can you give me an example of how these AI agents are actually 288 00:09:16,880 --> 00:09:17,960 being used in Web 3? 289 00:09:17,960 --> 00:09:18,520 Sure. 290 00:09:18,520 --> 00:09:20,520 Imagine an AI agent that's programmed 291 00:09:20,520 --> 00:09:22,720 to stake cryptocurrency for you. 292 00:09:22,720 --> 00:09:25,280 So we can analyze all the market data, 293 00:09:25,280 --> 00:09:28,040 identify the best staking opportunities, 294 00:09:28,040 --> 00:09:31,000 and automatically execute the transactions 295 00:09:31,000 --> 00:09:32,480 to maximize your returns. 296 00:09:32,480 --> 00:09:36,240 That sounds very impressive, but also a little bit risky. 297 00:09:36,240 --> 00:09:37,040 Yeah, definitely. 298 00:09:37,040 --> 00:09:39,360 I mean, are there concerns about these AI agents 299 00:09:39,360 --> 00:09:40,960 kind of operating in this space? 300 00:09:40,960 --> 00:09:41,960 Of course there are. 301 00:09:41,960 --> 00:09:46,040 There are risks associated with any form of automated trading. 302 00:09:46,040 --> 00:09:48,120 And AI agents are no exception. 303 00:09:48,120 --> 00:09:51,360 So things like security vulnerabilities, 304 00:09:51,360 --> 00:09:55,080 the potential for manipulation, and the lack of human oversight, 305 00:09:55,080 --> 00:09:56,800 those are all valid concerns. 306 00:09:56,800 --> 00:09:59,280 So it's not all sunshine and roses. 307 00:09:59,280 --> 00:10:02,160 No technology is without its risks. 308 00:10:02,160 --> 00:10:04,080 And it's important to approach these developments 309 00:10:04,080 --> 00:10:06,560 with a healthy dose of skepticism 310 00:10:06,560 --> 00:10:09,040 and to really consider the potential downsides, 311 00:10:09,040 --> 00:10:10,400 as well as all the benefits. 312 00:10:10,400 --> 00:10:11,720 Well, it sounds like we've only just scratched 313 00:10:11,720 --> 00:10:12,880 the surface of this topic. 314 00:10:12,880 --> 00:10:13,680 Yeah, for sure. 315 00:10:13,680 --> 00:10:15,280 We'll definitely need to kind of delve deeper 316 00:10:15,280 --> 00:10:19,280 into this world of AI agents in Web 3 in a future deep dive. 317 00:10:19,280 --> 00:10:19,880 I agree. 318 00:10:19,880 --> 00:10:22,720 But for now, let's move on to something a little less technical. 319 00:10:22,720 --> 00:10:23,200 OK. 320 00:10:23,200 --> 00:10:24,120 Space exploration. 321 00:10:24,120 --> 00:10:25,080 You read my mind. 322 00:10:25,080 --> 00:10:28,040 It seems like we're entering this new golden age of lunar 323 00:10:28,040 --> 00:10:28,840 exploration. 324 00:10:28,840 --> 00:10:29,480 Yeah. 325 00:10:29,480 --> 00:10:31,440 And AI is playing a key role. 326 00:10:31,440 --> 00:10:32,160 Absolutely. 327 00:10:32,160 --> 00:10:35,000 SpaceX and Firefly Aerospace are teaming up 328 00:10:35,000 --> 00:10:38,680 to send some fascinating science experiments to the moon. 329 00:10:38,680 --> 00:10:43,360 Firefly Aerospace was awarded $179 million contract 330 00:10:43,360 --> 00:10:47,240 from NASA to deliver six scientific experiments 331 00:10:47,240 --> 00:10:50,320 to this area on the moon called the Groothusen Dome. 332 00:10:50,320 --> 00:10:51,720 Groothusen Dome. 333 00:10:51,720 --> 00:10:52,400 What are those? 334 00:10:52,400 --> 00:10:54,400 They're these unique geological formations 335 00:10:54,400 --> 00:10:57,280 that are thought to be formed by lava millions of years ago. 336 00:10:57,280 --> 00:10:58,280 Wow. 337 00:10:58,280 --> 00:10:59,680 The experiments are going to focus 338 00:10:59,680 --> 00:11:02,720 on understanding the lunar geology and what these domes 339 00:11:02,720 --> 00:11:03,440 are made of. 340 00:11:03,440 --> 00:11:04,720 That sounds like a fascinating mission. 341 00:11:04,720 --> 00:11:05,220 Yeah. 342 00:11:05,220 --> 00:11:07,680 So how are they going to get these experiments to the moon? 343 00:11:07,680 --> 00:11:09,720 Firefly is going to be sending the experiments 344 00:11:09,720 --> 00:11:12,320 on one of their blue ghost landers, which 345 00:11:12,320 --> 00:11:15,920 will be launched on top of a SpaceX Falcon 9 rocket. 346 00:11:15,920 --> 00:11:17,840 So it's a great example of collaboration 347 00:11:17,840 --> 00:11:18,960 in the space industry. 348 00:11:18,960 --> 00:11:21,200 It's amazing how all these partnerships are driving 349 00:11:21,200 --> 00:11:23,560 progress in space exploration. 350 00:11:23,560 --> 00:11:25,400 When is this mission scheduled to launch? 351 00:11:25,400 --> 00:11:27,480 The target launch date is 2028. 352 00:11:27,480 --> 00:11:28,360 2028. 353 00:11:28,360 --> 00:11:32,720 But Firefly is also involved in another lunar mission for NASA. 354 00:11:32,720 --> 00:11:33,080 OK. 355 00:11:33,080 --> 00:11:36,560 It's going to launch much sooner in January of 2025. 356 00:11:36,560 --> 00:11:37,280 Wow. 357 00:11:37,280 --> 00:11:40,720 That mission is going to be landing at Mayor Chrissium. 358 00:11:40,720 --> 00:11:41,520 Mayor Chrissium. 359 00:11:41,520 --> 00:11:43,560 Which is another interesting region on the moon. 360 00:11:43,560 --> 00:11:45,000 It sounds like things are really heating up 361 00:11:45,000 --> 00:11:46,560 in the world of lunar exploration. 362 00:11:46,560 --> 00:11:48,240 Yeah, they definitely are. 363 00:11:48,240 --> 00:11:50,800 With the Artemis program and all the private sector 364 00:11:50,800 --> 00:11:53,040 involvement, we're really entering a new era. 365 00:11:53,040 --> 00:11:53,480 Yeah. 366 00:11:53,480 --> 00:11:55,800 And AI is playing this crucial role 367 00:11:55,800 --> 00:11:58,040 in making these missions possible. 368 00:11:58,040 --> 00:12:01,440 It's amazing to see AI being applied in such diverse 369 00:12:01,440 --> 00:12:02,920 and impactful ways. 370 00:12:02,920 --> 00:12:03,680 It really is. 371 00:12:03,680 --> 00:12:05,920 It's not just a futuristic concept anymore. 372 00:12:05,920 --> 00:12:06,200 Right. 373 00:12:06,200 --> 00:12:08,120 It's this transformative technology 374 00:12:08,120 --> 00:12:09,800 that's already shaping our world. 375 00:12:09,800 --> 00:12:10,200 Yeah. 376 00:12:10,200 --> 00:12:12,080 From the way we search for information 377 00:12:12,080 --> 00:12:14,440 to the way we explore the cosmos. 378 00:12:14,440 --> 00:12:17,320 Well, this has certainly been a jam-packed deep dive 379 00:12:17,320 --> 00:12:18,520 into the world of AI. 380 00:12:18,520 --> 00:12:19,040 It has. 381 00:12:19,040 --> 00:12:21,720 From reasoning models to AI elves. 382 00:12:21,720 --> 00:12:22,320 I know. 383 00:12:22,320 --> 00:12:25,200 From the future of education to the frontiers of space 384 00:12:25,200 --> 00:12:28,040 exploration, we've covered a lot of ground. 385 00:12:28,040 --> 00:12:28,640 Yeah, we have. 386 00:12:28,640 --> 00:12:29,920 But we're not done yet. 387 00:12:29,920 --> 00:12:30,480 Right. 388 00:12:30,480 --> 00:12:31,840 There's still much more to discuss, 389 00:12:31,840 --> 00:12:33,080 especially about AI agents. 390 00:12:33,080 --> 00:12:33,720 Yes. 391 00:12:33,720 --> 00:12:35,160 And Web 3. 392 00:12:35,160 --> 00:12:39,520 That's a topic with so much potential and equally 393 00:12:39,520 --> 00:12:40,720 significant challenges. 394 00:12:40,720 --> 00:12:41,360 Absolutely. 395 00:12:41,360 --> 00:12:43,680 We'll definitely need to dedicate a future deep dive 396 00:12:43,680 --> 00:12:44,440 to that topic. 397 00:12:44,440 --> 00:12:45,280 Yeah, we should. 398 00:12:45,280 --> 00:12:47,120 But for now, let's take a short break. 399 00:12:47,120 --> 00:12:50,040 Welcome back to our deep dive into AI. 400 00:12:50,040 --> 00:12:53,480 So before the break, we were talking about AI agents in Web 3 401 00:12:53,480 --> 00:12:56,880 and how they're being used for things like crypto staking. 402 00:12:56,880 --> 00:12:57,560 Right. 403 00:12:57,560 --> 00:12:59,160 On-chain trading, all that. 404 00:12:59,160 --> 00:12:59,520 Yeah. 405 00:12:59,520 --> 00:13:02,000 And that kind of brings us to this interesting point 406 00:13:02,000 --> 00:13:03,320 about their development. 407 00:13:03,320 --> 00:13:04,000 What's that? 408 00:13:04,000 --> 00:13:06,440 Well, it's this potential for centralization, 409 00:13:06,440 --> 00:13:09,440 even within this decentralized world of Web 3. 410 00:13:09,440 --> 00:13:10,280 Oh, that's interesting. 411 00:13:10,280 --> 00:13:11,720 I thought the whole point of Web 3 412 00:13:11,720 --> 00:13:13,680 was to move away from centralized control. 413 00:13:13,680 --> 00:13:14,240 Exactly. 414 00:13:14,240 --> 00:13:15,520 That's the ideal, right? 415 00:13:15,520 --> 00:13:15,880 Yeah. 416 00:13:15,880 --> 00:13:18,160 But the reality is that creating and using 417 00:13:18,160 --> 00:13:20,920 these really sophisticated AI agents, 418 00:13:20,920 --> 00:13:23,880 it takes a ton of resources and a lot of expertise. 419 00:13:23,880 --> 00:13:26,640 So are you saying that only these big, well-funded 420 00:13:26,640 --> 00:13:29,040 organizations are going to be able to make and control 421 00:13:29,040 --> 00:13:30,040 these AI agents? 422 00:13:30,040 --> 00:13:32,680 I think there's a definite risk of that happening. 423 00:13:32,680 --> 00:13:34,160 If we're not careful, we could end up 424 00:13:34,160 --> 00:13:37,760 with a few powerful entities kind of controlling 425 00:13:37,760 --> 00:13:42,640 the AI agents that govern a lot of these key aspects of Web 3. 426 00:13:42,640 --> 00:13:45,520 OK, so that doesn't sound very decentralized or democratic. 427 00:13:45,520 --> 00:13:47,120 Yeah, it's a valid concern. 428 00:13:47,120 --> 00:13:50,080 And I think it's something that the Web 3 community really 429 00:13:50,080 --> 00:13:53,720 needs to be aware of as these technologies continue to evolve. 430 00:13:53,720 --> 00:13:57,440 There are efforts to promote open source development 431 00:13:57,440 --> 00:14:02,360 and to create more accessible tools for AI agent creation. 432 00:14:02,360 --> 00:14:03,320 But it's a challenge. 433 00:14:03,320 --> 00:14:04,840 It's definitely an ongoing challenge. 434 00:14:04,840 --> 00:14:07,000 So it sounds like the fight for decentralization 435 00:14:07,000 --> 00:14:09,800 is happening in the world of AI and Web 3 as well. 436 00:14:09,800 --> 00:14:10,360 Absolutely. 437 00:14:10,360 --> 00:14:12,360 It's like a battle for control and influence 438 00:14:12,360 --> 00:14:14,920 over these really powerful technologies, 439 00:14:14,920 --> 00:14:15,840 and the stakes are high. 440 00:14:15,840 --> 00:14:17,720 OK, well, that's definitely something to think about. 441 00:14:17,720 --> 00:14:18,320 Yeah. 442 00:14:18,320 --> 00:14:19,640 But let's shift gears for a moment 443 00:14:19,640 --> 00:14:22,240 and talk about something a little more lighthearted. 444 00:14:22,240 --> 00:14:24,880 Remember those AI elves we were talking about earlier? 445 00:14:24,880 --> 00:14:28,880 Yes, radar, the elf, NORAD's AI-powered assistant 446 00:14:28,880 --> 00:14:30,560 for all things Santa Claus. 447 00:14:30,560 --> 00:14:32,280 It's pretty amazing that kids can now 448 00:14:32,280 --> 00:14:36,560 chat with an AI elf about Santa's location. 449 00:14:36,560 --> 00:14:37,200 I know. 450 00:14:37,200 --> 00:14:38,880 And multiple languages. 451 00:14:38,880 --> 00:14:42,240 It's a really cool example of how AI can create these fun 452 00:14:42,240 --> 00:14:44,000 and engaging experiences. 453 00:14:44,000 --> 00:14:46,880 Yeah, I think it really shows the progress that's 454 00:14:46,880 --> 00:14:49,000 been made in natural language processing. 455 00:14:49,000 --> 00:14:49,520 Right. 456 00:14:49,520 --> 00:14:51,480 And AI-powered communication. 457 00:14:51,480 --> 00:14:52,840 I mean, radar, the elf needs to be 458 00:14:52,840 --> 00:14:56,600 able to understand and respond to such a wide range of questions 459 00:14:56,600 --> 00:14:57,680 in real time. 460 00:14:57,680 --> 00:14:58,240 Right. 461 00:14:58,240 --> 00:15:00,720 And it has to do it in an informative and entertaining 462 00:15:00,720 --> 00:15:01,240 way. 463 00:15:01,240 --> 00:15:04,800 Yeah, and I'm curious to see how these AI-powered assistants 464 00:15:04,800 --> 00:15:06,240 kind of evolve in the future. 465 00:15:06,240 --> 00:15:06,960 Me too. 466 00:15:06,960 --> 00:15:09,000 It feels like we're just seeing the beginning of what 467 00:15:09,000 --> 00:15:09,720 they can do. 468 00:15:09,720 --> 00:15:10,600 I think so too. 469 00:15:10,600 --> 00:15:14,520 I think as these models get more advanced and easier to use, 470 00:15:14,520 --> 00:15:17,000 we're going to see a lot more of these AI-powered assistants 471 00:15:17,000 --> 00:15:20,000 in different areas, like customer service education 472 00:15:20,000 --> 00:15:21,000 and entertainment. 473 00:15:21,000 --> 00:15:22,800 Speaking of education, let's go back 474 00:15:22,800 --> 00:15:25,320 to that AI-powered charter school in Arizona. 475 00:15:25,320 --> 00:15:25,840 Yeah. 476 00:15:25,840 --> 00:15:27,840 It's such a bold experiment, and it's definitely 477 00:15:27,840 --> 00:15:29,240 going to spark a lot of discussion. 478 00:15:29,240 --> 00:15:30,680 It's pushing the boundaries of what 479 00:15:30,680 --> 00:15:31,720 we think about education. 480 00:15:31,720 --> 00:15:32,200 Yeah. 481 00:15:32,200 --> 00:15:34,400 And it raises all these important questions 482 00:15:34,400 --> 00:15:35,800 about what the role of technology 483 00:15:35,800 --> 00:15:37,280 should be in the classroom. 484 00:15:37,280 --> 00:15:39,080 Like, is it a good thing or a bad thing? 485 00:15:39,080 --> 00:15:39,840 Exactly. 486 00:15:39,840 --> 00:15:41,640 One argument for this approach is 487 00:15:41,640 --> 00:15:44,800 that AI can personalize instruction. 488 00:15:44,800 --> 00:15:45,400 Right? 489 00:15:45,400 --> 00:15:49,080 It can tailor it to each student's needs and their pace. 490 00:15:49,080 --> 00:15:49,960 Exactly. 491 00:15:49,960 --> 00:15:50,680 That's the idea. 492 00:15:50,680 --> 00:15:52,320 And there's actually some evidence suggesting 493 00:15:52,320 --> 00:15:56,040 that AI-powered tutoring systems can help students 494 00:15:56,040 --> 00:15:58,160 learn certain subjects more effectively. 495 00:15:58,160 --> 00:15:58,720 OK. 496 00:15:58,720 --> 00:16:02,240 But there are also some concerns about relying too much 497 00:16:02,240 --> 00:16:03,920 on AI in education. 498 00:16:03,920 --> 00:16:05,000 Oh, yeah, for sure. 499 00:16:05,000 --> 00:16:07,400 I mean, the lack of human interaction 500 00:16:07,400 --> 00:16:10,320 and the potential for AI systems to maybe 501 00:16:10,320 --> 00:16:13,080 perpetuate biases, those are two big concerns. 502 00:16:13,080 --> 00:16:13,480 Right. 503 00:16:13,480 --> 00:16:15,680 And then there's the question of whether AI can truly 504 00:16:15,680 --> 00:16:17,560 replace a human teacher. 505 00:16:17,560 --> 00:16:18,040 Yeah. 506 00:16:18,040 --> 00:16:18,960 Is it really possible? 507 00:16:18,960 --> 00:16:19,800 It's a big question. 508 00:16:19,800 --> 00:16:20,200 Yeah. 509 00:16:20,200 --> 00:16:20,880 It's a big question. 510 00:16:20,880 --> 00:16:23,200 So it sounds like finding that right balance, 511 00:16:23,200 --> 00:16:27,080 you know, between using the benefits of AI, 512 00:16:27,080 --> 00:16:30,440 but also keeping human interaction and guidance, 513 00:16:30,440 --> 00:16:31,560 is really important. 514 00:16:31,560 --> 00:16:31,960 Yeah. 515 00:16:31,960 --> 00:16:33,120 I think that's a good point. 516 00:16:33,120 --> 00:16:35,040 Technology can be a really powerful tool 517 00:16:35,040 --> 00:16:37,480 for improving education, but it shouldn't replace 518 00:16:37,480 --> 00:16:38,840 that human element. 519 00:16:38,840 --> 00:16:40,240 Well said. 520 00:16:40,240 --> 00:16:40,640 OK. 521 00:16:40,640 --> 00:16:43,320 Now, before we move on, I wanted to touch on something 522 00:16:43,320 --> 00:16:45,240 that we talked about a little bit earlier. 523 00:16:45,240 --> 00:16:48,480 The challenges open AI is facing with GPT-5. 524 00:16:48,480 --> 00:16:48,800 Right. 525 00:16:48,800 --> 00:16:50,600 That delays the data problems. 526 00:16:50,600 --> 00:16:50,800 Yeah. 527 00:16:50,800 --> 00:16:53,240 It's definitely getting a lot of attention in the AI world. 528 00:16:53,240 --> 00:16:54,160 Yeah, for sure. 529 00:16:54,160 --> 00:16:57,440 One of the main issues seems to be just the massive cost 530 00:16:57,440 --> 00:16:59,760 of training these large language models. 531 00:16:59,760 --> 00:17:00,240 Yeah. 532 00:17:00,240 --> 00:17:02,960 I mean, we're talking hundreds of millions of dollars 533 00:17:02,960 --> 00:17:04,160 for a single training run. 534 00:17:04,160 --> 00:17:04,560 Yeah. 535 00:17:04,560 --> 00:17:07,080 It's a reflection of how complex and large 536 00:17:07,080 --> 00:17:08,120 these models are getting. 537 00:17:08,120 --> 00:17:10,400 You know, as they get bigger and more advanced, 538 00:17:10,400 --> 00:17:14,200 they need even more computational power and even more data. 539 00:17:14,200 --> 00:17:16,880 And it seems like opening as kind of hitting a limit with that, 540 00:17:16,880 --> 00:17:19,520 more data is better approach. 541 00:17:19,520 --> 00:17:21,720 It's a big development because it suggests 542 00:17:21,720 --> 00:17:24,880 that just throwing more data at the problem 543 00:17:24,880 --> 00:17:27,600 might not be the best way to improve these models anymore. 544 00:17:27,600 --> 00:17:28,800 So what are they doing differently? 545 00:17:28,800 --> 00:17:31,120 Well, they're trying out synthetic data, which 546 00:17:31,120 --> 00:17:35,160 is that data created by AI to train other AI. 547 00:17:35,160 --> 00:17:38,320 Didn't we talk about the risks of using synthetic data? 548 00:17:38,320 --> 00:17:39,880 Yeah, we did. 549 00:17:39,880 --> 00:17:44,200 There are worries that biases and inaccuracies could get worse. 550 00:17:44,200 --> 00:17:44,680 Right. 551 00:17:44,680 --> 00:17:47,960 But open AI seems to think they can kind of solve those problems 552 00:17:47,960 --> 00:17:51,000 by using their reasoning model, O1, 553 00:17:51,000 --> 00:17:52,920 to create better synthetic data. 554 00:17:52,920 --> 00:17:54,040 Interesting. 555 00:17:54,040 --> 00:17:55,440 So it's kind of a risky move. 556 00:17:55,440 --> 00:17:57,520 It is a risky move, but it shows how much 557 00:17:57,520 --> 00:18:01,640 they want to find these new ways to improve their models. 558 00:18:01,640 --> 00:18:03,000 It shows the pressure they're under. 559 00:18:03,000 --> 00:18:03,920 Yeah, for sure. 560 00:18:03,920 --> 00:18:05,760 Stay ahead in this AI race. 561 00:18:05,760 --> 00:18:07,240 Yeah, absolutely. 562 00:18:07,240 --> 00:18:09,680 And speaking of competition, we can't 563 00:18:09,680 --> 00:18:13,560 forget about that fierce battle for AI talent that's going on. 564 00:18:13,560 --> 00:18:13,880 Great. 565 00:18:13,880 --> 00:18:15,120 All those bidding wars. 566 00:18:15,120 --> 00:18:16,040 Pushing up salaries. 567 00:18:16,040 --> 00:18:17,800 Yeah, pushing up valuations. 568 00:18:17,800 --> 00:18:18,560 It's crazy. 569 00:18:18,560 --> 00:18:20,640 Yeah, it's a fight for the best minds in AI. 570 00:18:20,640 --> 00:18:20,920 Yeah. 571 00:18:20,920 --> 00:18:23,440 And it doesn't look like it's going to end anytime soon. 572 00:18:23,440 --> 00:18:26,840 Data breaks a recent $10 billion funding round, 573 00:18:26,840 --> 00:18:29,280 mostly for buying back employee stock. 574 00:18:29,280 --> 00:18:31,960 I mean, that's just a perfect example of this trend. 575 00:18:31,960 --> 00:18:33,520 Yeah, it shows how far companies 576 00:18:33,520 --> 00:18:36,320 are willing to go to attract and keep the best people. 577 00:18:36,320 --> 00:18:37,680 Yeah, it's wild. 578 00:18:37,680 --> 00:18:39,920 Yeah, the lack of skilled AI researchers 579 00:18:39,920 --> 00:18:41,360 is really making them valuable. 580 00:18:41,360 --> 00:18:41,880 Right. 581 00:18:41,880 --> 00:18:44,680 And companies are constantly trying to outbid each other. 582 00:18:44,680 --> 00:18:45,960 It's a high stakes game. 583 00:18:45,960 --> 00:18:46,600 It is. 584 00:18:46,600 --> 00:18:49,360 And it's changing the AI industry in a big way. 585 00:18:49,360 --> 00:18:51,120 Yeah, for sure. 586 00:18:51,120 --> 00:18:53,200 Well, it seems like we've covered a lot in this deep dive 587 00:18:53,200 --> 00:18:54,040 so far. 588 00:18:54,040 --> 00:18:57,080 We've explored the latest in AI from reasoning models 589 00:18:57,080 --> 00:19:00,720 to AI elves, the future of education. 590 00:19:00,720 --> 00:19:01,680 It's a lot. 591 00:19:01,680 --> 00:19:06,160 And we've really looked into the world of AI agents in Web 3, 592 00:19:06,160 --> 00:19:09,240 talking about the potential benefits and the risks 593 00:19:09,240 --> 00:19:10,800 in this decentralized environment. 594 00:19:10,800 --> 00:19:12,280 Yeah, and how to keep it decentralized. 595 00:19:12,280 --> 00:19:13,480 Exactly. 596 00:19:13,480 --> 00:19:16,760 So it's been a fascinating look at how quickly things are 597 00:19:16,760 --> 00:19:17,120 changing. 598 00:19:17,120 --> 00:19:18,280 Yeah, it's moving so fast. 599 00:19:18,280 --> 00:19:19,840 And the world of artificial intelligence. 600 00:19:19,840 --> 00:19:21,640 It's amazing how fast it's moving. 601 00:19:21,640 --> 00:19:25,080 And it reminds us that AI isn't just this future concept. 602 00:19:25,080 --> 00:19:25,480 Right. 603 00:19:25,480 --> 00:19:27,440 It's a really powerful force that's 604 00:19:27,440 --> 00:19:28,720 already shaping our world. 605 00:19:28,720 --> 00:19:29,640 In so many ways. 606 00:19:29,640 --> 00:19:30,520 In big ways. 607 00:19:30,520 --> 00:19:31,000 Yeah. 608 00:19:31,000 --> 00:19:33,720 OK, so before we wrap up this part of our deep dive, 609 00:19:33,720 --> 00:19:35,240 are there any other key points that you 610 00:19:35,240 --> 00:19:36,280 would like to highlight? 611 00:19:36,280 --> 00:19:38,440 Yeah, I think it's important to remember 612 00:19:38,440 --> 00:19:42,200 that while all these AI advancements are really exciting 613 00:19:42,200 --> 00:19:46,200 and full of potential, they also come with some big challenges 614 00:19:46,200 --> 00:19:47,280 and responsibilities. 615 00:19:47,280 --> 00:19:48,120 What do you mean? 616 00:19:48,120 --> 00:19:50,680 Well, as AI systems get smarter and more independent, 617 00:19:50,680 --> 00:19:52,800 we really need to make sure that they're being developed 618 00:19:52,800 --> 00:19:55,120 and used ethically and responsibly. 619 00:19:55,120 --> 00:19:58,800 OK, so it's not just about pushing the technological limits, 620 00:19:58,800 --> 00:20:01,840 but also thinking about the social and ethical implications. 621 00:20:01,840 --> 00:20:02,560 Exactly. 622 00:20:02,560 --> 00:20:06,160 We need to be thinking about potential biases, the impact 623 00:20:06,160 --> 00:20:09,840 on jobs, and just the broader consequences 624 00:20:09,840 --> 00:20:11,600 of increasing automation. 625 00:20:11,600 --> 00:20:12,080 Yeah. 626 00:20:12,080 --> 00:20:13,320 It's a complex issue. 627 00:20:13,320 --> 00:20:13,720 It is. 628 00:20:13,720 --> 00:20:15,520 That needs careful thought and discussion. 629 00:20:15,520 --> 00:20:16,040 For sure. 630 00:20:16,040 --> 00:20:18,440 I mean, the future of AI isn't set in stone. 631 00:20:18,440 --> 00:20:18,800 Right. 632 00:20:18,800 --> 00:20:21,160 It's something that we are shaping 633 00:20:21,160 --> 00:20:22,960 with every decision we make. 634 00:20:22,960 --> 00:20:24,160 Those are powerful words. 635 00:20:24,160 --> 00:20:24,680 Yeah. 636 00:20:24,680 --> 00:20:26,560 And they remind us that we all have a role 637 00:20:26,560 --> 00:20:29,360 to play in making sure AI is used for good. 638 00:20:29,360 --> 00:20:29,800 Yeah. 639 00:20:29,800 --> 00:20:31,520 And that the benefits are shared widely. 640 00:20:31,520 --> 00:20:32,320 Absolutely. 641 00:20:32,320 --> 00:20:34,480 It's a responsibility that we can't ignore. 642 00:20:34,480 --> 00:20:36,480 Well said. 643 00:20:36,480 --> 00:20:40,200 OK, now let's continue our deep dive into the world of AI, 644 00:20:40,200 --> 00:20:42,640 focusing on some of the more interesting. 645 00:20:42,640 --> 00:20:43,200 OK. 646 00:20:43,200 --> 00:20:44,960 And maybe even controversial applications 647 00:20:44,960 --> 00:20:45,640 that are emerging. 648 00:20:45,640 --> 00:20:47,640 We were just talking about the responsibility that 649 00:20:47,640 --> 00:20:50,160 comes along with all these advancements in AI. 650 00:20:50,160 --> 00:20:50,480 Right. 651 00:20:50,480 --> 00:20:53,400 It's not just about what we can do, but what we should do. 652 00:20:53,400 --> 00:20:54,160 Exactly. 653 00:20:54,160 --> 00:20:54,680 Yeah. 654 00:20:54,680 --> 00:20:59,760 So how do we make sure that AI is used for good? 655 00:20:59,760 --> 00:21:01,200 That's a huge question. 656 00:21:01,200 --> 00:21:01,560 Yeah. 657 00:21:01,560 --> 00:21:03,040 And there's no easy answer. 658 00:21:03,040 --> 00:21:06,160 But one key part of it is striking that balance 659 00:21:06,160 --> 00:21:08,240 between encouraging innovation. 660 00:21:08,240 --> 00:21:08,520 Right. 661 00:21:08,520 --> 00:21:11,120 And putting the right safeguards in place. 662 00:21:11,120 --> 00:21:12,720 So we want to make sure that we're 663 00:21:12,720 --> 00:21:15,600 supporting the development of beneficial AI technologies. 664 00:21:15,600 --> 00:21:16,240 Right. 665 00:21:16,240 --> 00:21:18,760 But we also need to protect against the potential harms. 666 00:21:18,760 --> 00:21:20,160 It's like walking a tightrope. 667 00:21:20,160 --> 00:21:20,440 Right? 668 00:21:20,440 --> 00:21:20,800 It is. 669 00:21:20,800 --> 00:21:23,320 Too much regulation could stifle progress. 670 00:21:23,320 --> 00:21:24,160 Right. 671 00:21:24,160 --> 00:21:27,080 But too little could lead to all these unintended consequences. 672 00:21:27,080 --> 00:21:27,600 Exactly. 673 00:21:27,600 --> 00:21:31,640 And that debate is happening right now, all over the world, 674 00:21:31,640 --> 00:21:33,480 in governments and industry groups. 675 00:21:33,480 --> 00:21:36,080 So it shows that AI is not just a tech issue. 676 00:21:36,080 --> 00:21:37,600 It's a social and political one, too. 677 00:21:37,600 --> 00:21:38,600 Absolutely. 678 00:21:38,600 --> 00:21:42,920 The decisions we make about AI are going to have a huge impact 679 00:21:42,920 --> 00:21:45,880 on our society, our economy, our future. 680 00:21:45,880 --> 00:21:46,360 OK. 681 00:21:46,360 --> 00:21:47,960 Well, that's a lot to think about. 682 00:21:47,960 --> 00:21:49,680 Let's bring it back down to Earth for a minute 683 00:21:49,680 --> 00:21:51,600 and talk about some of the practical ways 684 00:21:51,600 --> 00:21:53,400 that AI is being used. 685 00:21:53,400 --> 00:21:56,120 We've talked about education, entertainment space. 686 00:21:56,120 --> 00:22:00,080 But what other areas are being impacted by AI? 687 00:22:00,080 --> 00:22:02,160 Well, health care is a really interesting one. 688 00:22:02,160 --> 00:22:05,560 AI is already being used to help with diagnoses, 689 00:22:05,560 --> 00:22:06,800 discover new drugs. 690 00:22:06,800 --> 00:22:07,360 Wow. 691 00:22:07,360 --> 00:22:09,600 And create personalized treatment plans. 692 00:22:09,600 --> 00:22:13,520 So AI is essentially helping doctors make better decisions 693 00:22:13,520 --> 00:22:15,000 and provide more effective care. 694 00:22:15,000 --> 00:22:15,840 That's the goal. 695 00:22:15,840 --> 00:22:20,160 And there's huge potential for AI to really revolutionize 696 00:22:20,160 --> 00:22:23,600 health care from improving patient outcomes 697 00:22:23,600 --> 00:22:27,440 to reducing costs and even making care more accessible. 698 00:22:27,440 --> 00:22:29,040 Can you give me some specific examples 699 00:22:29,040 --> 00:22:31,680 of how AI is actually being used in health care right now? 700 00:22:31,680 --> 00:22:32,200 Sure. 701 00:22:32,200 --> 00:22:34,480 So one example is image analysis. 702 00:22:34,480 --> 00:22:36,440 So you have these AI algorithms that 703 00:22:36,440 --> 00:22:41,080 are trained to examine medical images, like X-rays and MRIs. 704 00:22:41,080 --> 00:22:43,600 And they can spot abnormalities and assist 705 00:22:43,600 --> 00:22:45,280 doctors with their diagnoses. 706 00:22:45,280 --> 00:22:47,560 So it's like having a second set of eyes, 707 00:22:47,560 --> 00:22:50,320 helping doctors catch things that they might have otherwise missed. 708 00:22:50,320 --> 00:22:51,040 Exactly. 709 00:22:51,040 --> 00:22:53,200 And often, these AI algorithms are 710 00:22:53,200 --> 00:22:55,880 able to pick up on these very subtle patterns that 711 00:22:55,880 --> 00:22:59,000 might escape even the most experienced human eye. 712 00:22:59,000 --> 00:22:59,600 Interesting. 713 00:22:59,600 --> 00:23:03,160 And that can lead to earlier and more accurate diagnoses. 714 00:23:03,160 --> 00:23:04,400 That's amazing. 715 00:23:04,400 --> 00:23:05,760 What other applications are there? 716 00:23:05,760 --> 00:23:09,280 Well, AI is also making waves in drug discovery. 717 00:23:09,280 --> 00:23:13,240 So algorithms can sift through these massive amounts of data 718 00:23:13,240 --> 00:23:15,400 to pinpoint potential drug candidates 719 00:23:15,400 --> 00:23:17,920 and even predict how effective they might be. 720 00:23:17,920 --> 00:23:19,920 So it's speeding up the process of developing 721 00:23:19,920 --> 00:23:21,280 new medicines and treatments. 722 00:23:21,280 --> 00:23:21,880 Yes. 723 00:23:21,880 --> 00:23:24,680 And it's also helping to personalize those treatment 724 00:23:24,680 --> 00:23:25,760 plans. 725 00:23:25,760 --> 00:23:29,040 So AI can analyze a patient's genetic information, 726 00:23:29,040 --> 00:23:31,880 their medical history, their lifestyle factors, 727 00:23:31,880 --> 00:23:33,800 to recommend the best treatment options 728 00:23:33,800 --> 00:23:36,000 for that specific individual. 729 00:23:36,000 --> 00:23:38,240 It sounds like AI could completely change 730 00:23:38,240 --> 00:23:39,360 health care for the better. 731 00:23:39,360 --> 00:23:39,880 Yeah. 732 00:23:39,880 --> 00:23:41,840 The possibilities are really exciting. 733 00:23:41,840 --> 00:23:44,160 But with any new technology, there 734 00:23:44,160 --> 00:23:46,840 are challenges and risks to think about. 735 00:23:46,840 --> 00:23:47,840 Like what? 736 00:23:47,840 --> 00:23:50,960 Well, one of the biggest concerns is data privacy. 737 00:23:50,960 --> 00:23:53,920 Health care data is extremely sensitive. 738 00:23:53,920 --> 00:23:56,080 And we need to be absolutely sure that it's 739 00:23:56,080 --> 00:23:59,720 protected from unauthorized access or misuse. 740 00:23:59,720 --> 00:24:01,280 That's a really good point. 741 00:24:01,280 --> 00:24:03,400 We also have to consider the potential for bias. 742 00:24:03,400 --> 00:24:04,400 Yes. 743 00:24:04,400 --> 00:24:04,880 Definitely. 744 00:24:04,880 --> 00:24:09,160 If the data used to train these AI algorithms is biased, 745 00:24:09,160 --> 00:24:12,160 it could perpetuate these existing inequalities in health 746 00:24:12,160 --> 00:24:12,880 care. 747 00:24:12,880 --> 00:24:13,480 That's right. 748 00:24:13,480 --> 00:24:15,160 We have to be really, really careful 749 00:24:15,160 --> 00:24:18,600 to make sure that these systems are designed and implemented 750 00:24:18,600 --> 00:24:20,560 fairly and equitably. 751 00:24:20,560 --> 00:24:22,480 It sounds like there's still a lot of work to do. 752 00:24:22,480 --> 00:24:23,080 There is. 753 00:24:23,080 --> 00:24:26,280 To make sure that AI is used responsibly and ethically 754 00:24:26,280 --> 00:24:27,640 in health care. 755 00:24:27,640 --> 00:24:29,680 But the potential benefits are huge. 756 00:24:29,680 --> 00:24:30,240 They are. 757 00:24:30,240 --> 00:24:32,960 It's an area that definitely deserves a lot of attention 758 00:24:32,960 --> 00:24:35,960 and focus and investment in research and development. 759 00:24:35,960 --> 00:24:37,840 This deep dive has been really eye-opening. 760 00:24:37,840 --> 00:24:38,200 It has. 761 00:24:38,200 --> 00:24:40,600 We've explored how AI is impacting everything 762 00:24:40,600 --> 00:24:43,840 from reasoning and language models to Santa 763 00:24:43,840 --> 00:24:45,800 trackers and the future of health care. 764 00:24:45,800 --> 00:24:46,640 Yeah, who knew? 765 00:24:46,640 --> 00:24:50,240 It's clear that AI is no longer this futuristic concept. 766 00:24:50,240 --> 00:24:50,640 Right. 767 00:24:50,640 --> 00:24:54,200 It's this powerful force that's already changing our world. 768 00:24:54,200 --> 00:24:55,360 And we're just getting started. 769 00:24:55,360 --> 00:24:55,640 Yeah. 770 00:24:55,640 --> 00:24:58,480 It's a force that we need to understand, engage with, 771 00:24:58,480 --> 00:25:00,760 and guide thoughtfully and responsibly. 772 00:25:00,760 --> 00:25:02,240 Well said. 773 00:25:02,240 --> 00:25:04,960 Thank you for listening to the Daily AI News Podcast. 774 00:25:04,960 --> 00:25:32,520 And stay tuned for more.