1 00:00:00,000 --> 00:00:01,800 Okay, you ready for a wild ride? 2 00:00:01,800 --> 00:00:03,240 That's an understatement. 3 00:00:03,240 --> 00:00:05,840 Today, we're diving deep into the world 4 00:00:05,840 --> 00:00:07,880 of artificial intelligence. 5 00:00:07,880 --> 00:00:10,960 And trust me, things are moving faster than you might think. 6 00:00:10,960 --> 00:00:12,640 It's like watching a rocket take off. 7 00:00:12,640 --> 00:00:13,880 I know, right? 8 00:00:13,880 --> 00:00:16,880 AI is advancing at an incredible pace, 9 00:00:16,880 --> 00:00:20,240 and this deep dive is just scratching the surface. 10 00:00:20,240 --> 00:00:21,680 I was listening to my usual 11 00:00:21,680 --> 00:00:24,440 Winemaker's Daily Brief podcast this morning. 12 00:00:24,440 --> 00:00:25,280 Uh-huh. 13 00:00:25,280 --> 00:00:27,240 You know, expecting the usual vineyard updates 14 00:00:27,240 --> 00:00:29,080 and maybe some new cork technology. 15 00:00:29,080 --> 00:00:31,200 You're gonna have to tell me more about that wine podcast. 16 00:00:31,200 --> 00:00:34,720 But no, front and center was a story about open AI, 17 00:00:34,720 --> 00:00:37,160 and let me tell you, it's a real head scratcher. 18 00:00:37,160 --> 00:00:38,680 Okay, what's the scoop? 19 00:00:38,680 --> 00:00:41,240 Apparently, some big names have left the company, 20 00:00:41,240 --> 00:00:43,220 and everyone is wondering what's going on. 21 00:00:43,220 --> 00:00:45,000 It's a big deal because we're talking about 22 00:00:45,000 --> 00:00:46,840 some of the biggest names in AI. 23 00:00:46,840 --> 00:00:49,420 Yeah, I felt a little starstruck reading their bios. 24 00:00:49,420 --> 00:00:54,160 Mir Moradi, the CTO who brought us ChatGPT and Dellie, 25 00:00:54,160 --> 00:00:56,400 Ilya Sitzgever, one of the co-founders, 26 00:00:56,400 --> 00:00:59,200 and even the research VP all out the door 27 00:00:59,200 --> 00:01:00,360 at roughly the same time. 28 00:01:00,360 --> 00:01:02,100 These are the rock stars of AI. 29 00:01:02,100 --> 00:01:03,660 It's like a band breaking up. 30 00:01:03,660 --> 00:01:06,320 And it makes you wonder, why would they leave now? 31 00:01:06,320 --> 00:01:07,440 What's the story here? 32 00:01:07,440 --> 00:01:09,120 That's the million dollar question. 33 00:01:09,120 --> 00:01:13,480 It seems to coincide with rumors of open AI restructuring. 34 00:01:13,480 --> 00:01:16,040 They're moving away from their original nonprofit model, 35 00:01:16,040 --> 00:01:17,280 and it looks like they're heading 36 00:01:17,280 --> 00:01:20,760 toward becoming a for-profit powerhouse. 37 00:01:20,760 --> 00:01:22,920 Wait, $150 billion. 38 00:01:22,920 --> 00:01:26,120 They're even talking about a valuation of $150 billion. 39 00:01:26,120 --> 00:01:29,000 For a company that, as far as I understand, 40 00:01:29,000 --> 00:01:31,540 still hasn't figured out how to make AI 41 00:01:31,540 --> 00:01:33,440 that doesn't occasionally go rogue. 42 00:01:33,440 --> 00:01:35,800 That seems like a lot of faith to put in algorithms. 43 00:01:35,800 --> 00:01:37,040 That's a valid concern. 44 00:01:37,040 --> 00:01:39,640 Some experts are ringing the alarm bells, 45 00:01:39,640 --> 00:01:41,500 saying that these valuations are getting 46 00:01:41,500 --> 00:01:45,080 ahead of the actual progress, and that focusing on profit 47 00:01:45,080 --> 00:01:48,680 could actually hurt responsible development of AI. 48 00:01:48,680 --> 00:01:50,820 So open AI is potentially pivoting 49 00:01:50,820 --> 00:01:52,800 towards a more commercial model, 50 00:01:52,800 --> 00:01:56,200 while the people who championed ethical AI development 51 00:01:56,200 --> 00:01:58,000 are heading for the exits. 52 00:01:58,000 --> 00:02:00,720 There are some real concerns about how this shift might 53 00:02:00,720 --> 00:02:02,520 affect the future of AI. 54 00:02:02,520 --> 00:02:04,020 It feels like something is shifting. 55 00:02:04,020 --> 00:02:04,720 Absolutely. 56 00:02:04,720 --> 00:02:06,080 And it gets even more interesting 57 00:02:06,080 --> 00:02:10,640 when you consider Sam Altman's vision for an intelligence age. 58 00:02:10,640 --> 00:02:12,080 An intelligence age. 59 00:02:12,080 --> 00:02:14,280 You mean AI assistance becoming as commonplace 60 00:02:14,280 --> 00:02:16,000 as smartphones today? 61 00:02:16,000 --> 00:02:18,880 I'm all for an AI that helps me pick the perfect wine 62 00:02:18,880 --> 00:02:19,920 pairing for dinner. 63 00:02:19,920 --> 00:02:22,160 But a part of me wonders if we're rushing into something 64 00:02:22,160 --> 00:02:23,360 we don't fully understand. 65 00:02:23,360 --> 00:02:24,960 It's definitely a double edged sword. 66 00:02:24,960 --> 00:02:27,560 Altman himself acknowledges the ethical landmines 67 00:02:27,560 --> 00:02:28,640 we need to avoid. 68 00:02:28,640 --> 00:02:32,840 There's bias in algorithms, job displacement, privacy concerns. 69 00:02:32,840 --> 00:02:33,760 It's a big list. 70 00:02:33,760 --> 00:02:35,820 OK, let's unpack this intelligence age 71 00:02:35,820 --> 00:02:36,980 idea a bit further. 72 00:02:36,980 --> 00:02:39,280 What exactly does Altman envision? 73 00:02:39,280 --> 00:02:41,480 He's painting a picture of AI transforming 74 00:02:41,480 --> 00:02:43,200 every aspect of our lives. 75 00:02:43,200 --> 00:02:46,240 Education, health care, even climate change. 76 00:02:46,240 --> 00:02:49,720 He sees AI as a solution to our biggest problems. 77 00:02:49,720 --> 00:02:51,640 It sounds incredible, almost utopian. 78 00:02:51,640 --> 00:02:52,800 That's a big leap of faith. 79 00:02:52,800 --> 00:02:58,040 But what I'm hearing is that his vision hinges on AI evolving 80 00:02:58,040 --> 00:02:59,880 from where it is now, impressive, 81 00:02:59,880 --> 00:03:04,160 but flawed, into something far more sophisticated and, dare 82 00:03:04,160 --> 00:03:05,200 I say, trustworthy. 83 00:03:05,200 --> 00:03:07,080 Open AI, under Altman's leadership, 84 00:03:07,080 --> 00:03:10,200 is chasing this grand vision of the intelligence age 85 00:03:10,200 --> 00:03:13,080 while wrestling with the real world challenges of building AI 86 00:03:13,080 --> 00:03:15,240 that's both powerful and responsible. 87 00:03:15,240 --> 00:03:17,160 It feels like we're standing at the foot of a very tall 88 00:03:17,160 --> 00:03:19,200 mountain, unsure if we have the right gear 89 00:03:19,200 --> 00:03:21,000 or even if the climb is worth the risk. 90 00:03:21,000 --> 00:03:24,520 But before we get lost in the philosophical implications, 91 00:03:24,520 --> 00:03:27,160 I have to ask about something that feels tangible, almost 92 00:03:27,160 --> 00:03:28,920 ripped from a science fiction movie. 93 00:03:28,920 --> 00:03:31,840 Lionsgate and their AI filmmaking venture. 94 00:03:31,840 --> 00:03:32,800 Exactly. 95 00:03:32,800 --> 00:03:35,880 Lionsgate, the studio behind action packed blockbusters 96 00:03:35,880 --> 00:03:39,120 like John Wick, is partnering with an AI startup called 97 00:03:39,120 --> 00:03:42,040 Runway to, get this, help them make movies. 98 00:03:42,040 --> 00:03:43,560 It's fascinating, isn't it? 99 00:03:43,560 --> 00:03:44,040 Hold on. 100 00:03:44,040 --> 00:03:46,720 AI is going to be directing Keanu Reeves next. 101 00:03:46,720 --> 00:03:49,400 Imagine AI generating storyboards, 102 00:03:49,400 --> 00:03:52,200 creating visual effects, even editing footage. 103 00:03:52,200 --> 00:03:54,800 And they're training it on Lionsgate's massive library 104 00:03:54,800 --> 00:03:55,400 of films. 105 00:03:55,400 --> 00:03:58,280 I'm both terrified and strangely excited by this concept. 106 00:03:58,280 --> 00:03:59,080 Mm. 107 00:03:59,080 --> 00:04:02,280 OK, so we've got AI potentially directing Keanu Reeves. 108 00:04:02,280 --> 00:04:05,000 Well, not quite robots demanding top billing just yet. 109 00:04:05,000 --> 00:04:06,560 Part of me is wondering if the robots are going to be 110 00:04:06,560 --> 00:04:08,280 asking for headshots next. 111 00:04:08,280 --> 00:04:09,920 Think of it more as a collaboration. 112 00:04:09,920 --> 00:04:13,080 But seriously, what are the practical implications 113 00:04:13,080 --> 00:04:14,280 of this partnership? 114 00:04:14,280 --> 00:04:19,200 Imagine AI analyzing what makes a John Wick film work visually. 115 00:04:19,200 --> 00:04:21,400 The pacing, the action sequences. 116 00:04:21,400 --> 00:04:21,880 OK. 117 00:04:21,880 --> 00:04:24,680 It could then assist filmmakers in replicating 118 00:04:24,680 --> 00:04:26,960 those elements, potentially saving time and money 119 00:04:26,960 --> 00:04:27,920 in the process. 120 00:04:27,920 --> 00:04:30,320 OK, so faster, potentially cheaper filmmaking. 121 00:04:30,320 --> 00:04:30,920 Right. 122 00:04:30,920 --> 00:04:33,080 But what about the human element? 123 00:04:33,080 --> 00:04:36,680 What about the writers, the editors, the visual effects 124 00:04:36,680 --> 00:04:37,320 artists? 125 00:04:37,320 --> 00:04:37,800 Yeah. 126 00:04:37,800 --> 00:04:40,680 Are they going to be replaced by algorithms? 127 00:04:40,680 --> 00:04:42,120 That's the million dollar question. 128 00:04:42,120 --> 00:04:44,600 And it's already causing a stir in Hollywood. 129 00:04:44,600 --> 00:04:47,560 Some people see AI as a tool to enhance creativity, 130 00:04:47,560 --> 00:04:49,560 while others are worried about job displacement 131 00:04:49,560 --> 00:04:51,880 and what it means for the future of human artistry. 132 00:04:51,880 --> 00:04:52,400 Right. 133 00:04:52,400 --> 00:04:54,560 There are even lawsuits popping up 134 00:04:54,560 --> 00:04:57,120 with artists claiming copyright infringement 135 00:04:57,120 --> 00:05:01,160 because the AI was trained using their work without permission. 136 00:05:01,160 --> 00:05:03,040 So it's the same ethical tightrope 137 00:05:03,040 --> 00:05:06,720 we see with open AI just playing out on the big screen. 138 00:05:06,720 --> 00:05:08,520 It seems to be a recurring theme. 139 00:05:08,520 --> 00:05:11,720 Are we sacrificing artistic integrity and potentially 140 00:05:11,720 --> 00:05:16,120 livelihoods for faster, cheaper, and maybe less inspiring 141 00:05:16,120 --> 00:05:16,720 entertainment? 142 00:05:16,720 --> 00:05:19,640 It's a slippery slope and a debate that's far from over. 143 00:05:19,640 --> 00:05:20,140 Yeah. 144 00:05:20,140 --> 00:05:22,400 What's clear is that AI is no longer 145 00:05:22,400 --> 00:05:24,120 confined to Silicon Valley. 146 00:05:24,120 --> 00:05:24,840 It's true. 147 00:05:24,840 --> 00:05:26,560 It's knocking on Hollywood's door, 148 00:05:26,560 --> 00:05:28,600 and the implications for the future of entertainment 149 00:05:28,600 --> 00:05:29,800 are enormous. 150 00:05:29,800 --> 00:05:32,000 And speaking of enormous implications, 151 00:05:32,000 --> 00:05:34,640 let's shift gears to something else that caught my eye. 152 00:05:34,640 --> 00:05:35,360 OK. 153 00:05:35,360 --> 00:05:37,720 This whole humanities last exam thing. 154 00:05:37,720 --> 00:05:40,200 The humanities last exam, tell me more. 155 00:05:40,200 --> 00:05:42,560 It sounds like the title of a high stakes thriller, 156 00:05:42,560 --> 00:05:45,680 but I'm guessing the reality is a bit less Hollywood 157 00:05:45,680 --> 00:05:47,160 and a lot more nuanced. 158 00:05:47,160 --> 00:05:48,200 Definitely more nuanced. 159 00:05:48,200 --> 00:05:48,700 OK. 160 00:05:48,700 --> 00:05:51,480 Remember, this isn't about AI becoming sentient or turning 161 00:05:51,480 --> 00:05:52,400 against us. 162 00:05:52,400 --> 00:05:54,680 It's about assessing its capabilities 163 00:05:54,680 --> 00:05:56,840 in a rigorous, standardized way. 164 00:05:56,840 --> 00:05:57,400 OK. 165 00:05:57,400 --> 00:05:59,960 Think of it like setting a really high bar 166 00:05:59,960 --> 00:06:02,120 to see what AI can achieve. 167 00:06:02,120 --> 00:06:04,280 OK, so how do you even begin to design 168 00:06:04,280 --> 00:06:08,480 a test for something as complex and rapidly evolving as AI? 169 00:06:08,480 --> 00:06:09,880 It's a challenge for sure. 170 00:06:09,880 --> 00:06:11,920 I mean, are we talking multiple choice questions 171 00:06:11,920 --> 00:06:14,440 or some kind of supercharged Turing test? 172 00:06:14,440 --> 00:06:16,640 It's way more intricate than that. 173 00:06:16,640 --> 00:06:19,280 The team behind humanities last exam 174 00:06:19,280 --> 00:06:21,840 is crowdsourcing at least 1,000 questions 175 00:06:21,840 --> 00:06:25,000 from leading experts across a huge range of disciplines. 176 00:06:25,000 --> 00:06:28,200 So anyone can submit a question for this AI super exam? 177 00:06:28,200 --> 00:06:29,400 Well, not just anyone. 178 00:06:29,400 --> 00:06:30,760 They're looking for questions that 179 00:06:30,760 --> 00:06:33,480 require deep, specialized knowledge 180 00:06:33,480 --> 00:06:34,680 and abstract reasoning. 181 00:06:34,680 --> 00:06:35,320 Oh, wow. 182 00:06:35,320 --> 00:06:39,400 It's like the world's toughest job interview, but for AI? 183 00:06:39,400 --> 00:06:42,280 So if I'm following you, it's not about tricking the AI 184 00:06:42,280 --> 00:06:44,440 or testing its knowledge of trivia. 185 00:06:44,440 --> 00:06:46,080 It's about challenging its ability 186 00:06:46,080 --> 00:06:49,800 to think critically, to problem solve, and maybe even 187 00:06:49,800 --> 00:06:51,640 demonstrate a level of understanding 188 00:06:51,640 --> 00:06:54,920 that goes beyond simply processing information. 189 00:06:54,920 --> 00:06:55,720 You've got it. 190 00:06:55,720 --> 00:06:56,160 Wow. 191 00:06:56,160 --> 00:06:57,760 And to make sure it's a fair test, 192 00:06:57,760 --> 00:07:00,200 they're keeping some of the questions secret. 193 00:07:00,200 --> 00:07:00,720 Really? 194 00:07:00,720 --> 00:07:02,960 That way AI systems can't be trained 195 00:07:02,960 --> 00:07:04,480 on the answers beforehand. 196 00:07:04,480 --> 00:07:05,320 Sneaky. 197 00:07:05,320 --> 00:07:07,240 It's like a pop quiz to see if AI can really 198 00:07:07,240 --> 00:07:08,040 think on its feet. 199 00:07:08,040 --> 00:07:08,560 Exactly. 200 00:07:08,560 --> 00:07:11,560 But what happens if an AI actually aces this exam? 201 00:07:11,560 --> 00:07:13,200 Well, let's not get ahead of ourselves. 202 00:07:13,200 --> 00:07:14,320 And there's their prize. 203 00:07:14,320 --> 00:07:17,000 There is a $500,000 prize pool. 204 00:07:17,000 --> 00:07:17,520 Wow. 205 00:07:17,520 --> 00:07:18,000 OK. 206 00:07:18,000 --> 00:07:20,160 But beyond the money, though, the real reward 207 00:07:20,160 --> 00:07:22,000 is the advancement of AI research. 208 00:07:22,000 --> 00:07:24,600 So it's less about crowning a single AI overlord 209 00:07:24,600 --> 00:07:26,160 and more about pushing the boundaries 210 00:07:26,160 --> 00:07:27,440 of what's possible in the field. 211 00:07:27,440 --> 00:07:28,040 Exactly. 212 00:07:28,040 --> 00:07:30,880 Think of it as a catalyst for innovation. 213 00:07:30,880 --> 00:07:33,840 By creating this incredibly challenging benchmark, 214 00:07:33,840 --> 00:07:35,840 we're not just testing existing AI. 215 00:07:35,840 --> 00:07:37,800 We're also inspiring the development 216 00:07:37,800 --> 00:07:40,520 of even more sophisticated systems in the future. 217 00:07:40,520 --> 00:07:43,960 It's like that old saying, a rising tide lifts all boats. 218 00:07:43,960 --> 00:07:46,680 But in this case, it's a really, really hard exam 219 00:07:46,680 --> 00:07:48,920 pushing the entire field of AI forward. 220 00:07:48,920 --> 00:07:50,120 And here's the thing. 221 00:07:50,120 --> 00:07:51,760 It's not just about the AI. 222 00:07:51,760 --> 00:07:52,320 OK. 223 00:07:52,320 --> 00:07:54,200 This whole endeavor is a testament 224 00:07:54,200 --> 00:07:57,000 to the power and diversity of human intelligence. 225 00:07:57,000 --> 00:07:59,440 It takes brilliant minds from all walks of life 226 00:07:59,440 --> 00:08:01,160 to even come up with these questions, 227 00:08:01,160 --> 00:08:02,240 let alone answer them. 228 00:08:02,240 --> 00:08:03,240 You've got a point there. 229 00:08:03,240 --> 00:08:06,960 It's like we're celebrating human ingenuity at the same time 230 00:08:06,960 --> 00:08:09,280 as we're challenging ourselves to create something that 231 00:08:09,280 --> 00:08:11,240 might one day surpass it. 232 00:08:11,240 --> 00:08:12,840 It's a fascinating paradox. 233 00:08:12,840 --> 00:08:15,520 And it speaks to the very heart of our relationship with AI. 234 00:08:15,520 --> 00:08:17,560 We're both the creators and potentially the students 235 00:08:17,560 --> 00:08:19,200 in this grand experiment. 236 00:08:19,200 --> 00:08:21,000 I love that analogy. 237 00:08:21,000 --> 00:08:23,920 And speaking of being students, I feel like I need a crash 238 00:08:23,920 --> 00:08:28,800 course in just how AI works its magic, 239 00:08:28,800 --> 00:08:31,520 or should I say its algorithms, before we can even 240 00:08:31,520 --> 00:08:33,680 begin to grasp the implications of all this. 241 00:08:33,680 --> 00:08:34,880 An excellent segue. 242 00:08:34,880 --> 00:08:36,800 Because to understand the potential 243 00:08:36,800 --> 00:08:40,760 and the limitations of AI, we need to peel back the layers 244 00:08:40,760 --> 00:08:42,800 and see what's really going on under the hood. 245 00:08:42,800 --> 00:08:43,280 All right. 246 00:08:43,280 --> 00:08:46,280 So we've talked about the what of AI, 247 00:08:46,280 --> 00:08:49,520 the groundbreaking applications, the ethical questions swirling 248 00:08:49,520 --> 00:08:52,880 around, and even the idea of AI taking tests. 249 00:08:52,880 --> 00:08:54,400 But what about the how? 250 00:08:54,400 --> 00:08:57,200 What's actually going on inside those silicon brains? 251 00:08:57,200 --> 00:08:59,840 At its heart, a lot of the AI we're talking about 252 00:08:59,840 --> 00:09:01,640 relies on something called machine learning. 253 00:09:01,640 --> 00:09:02,280 Machine learning. 254 00:09:02,280 --> 00:09:04,560 It sounds so, well, mechanical. 255 00:09:04,560 --> 00:09:06,520 Is it really as simple as feeding a computer 256 00:09:06,520 --> 00:09:08,280 a bunch of data and letting it loose? 257 00:09:08,280 --> 00:09:12,480 It's simpler in a way, but also way more complex than that. 258 00:09:12,480 --> 00:09:15,480 Imagine teaching a child to recognize a cat. 259 00:09:15,480 --> 00:09:18,080 You wouldn't just say, it's a furry animal with four 260 00:09:18,080 --> 00:09:19,400 legs and a tail. 261 00:09:19,400 --> 00:09:21,960 You'd show them pictures of cats, tons of them, 262 00:09:21,960 --> 00:09:24,120 in different positions with different fur. 263 00:09:24,120 --> 00:09:25,800 To help them learn what a cat looks like. 264 00:09:25,800 --> 00:09:26,800 Exactly. 265 00:09:26,800 --> 00:09:29,440 Machine learning works in a similar way. 266 00:09:29,440 --> 00:09:34,440 We feed algorithms huge amounts of data images, text, code, 267 00:09:34,440 --> 00:09:37,600 and they learn to spot patterns, make predictions, even 268 00:09:37,600 --> 00:09:39,880 generate new content based on what they've learned. 269 00:09:39,880 --> 00:09:44,840 So when we talk about chat GPT writing poetry or deli 270 00:09:44,840 --> 00:09:46,760 creating those mind blowing images, 271 00:09:46,760 --> 00:09:48,640 it's not actually thinking like we do. 272 00:09:48,640 --> 00:09:49,600 No, not really. 273 00:09:49,600 --> 00:09:53,240 It's more like it's learned to mimic human creativity 274 00:09:53,240 --> 00:09:56,000 by analyzing a mountain of existing work. 275 00:09:56,000 --> 00:09:57,360 That's a great way to put it. 276 00:09:57,360 --> 00:09:59,600 It's pattern recognition on a massive scale. 277 00:09:59,600 --> 00:10:03,200 But instead of numbers, it's manipulating language, images, 278 00:10:03,200 --> 00:10:04,800 even musical notes. 279 00:10:04,800 --> 00:10:07,240 OK, that makes a certain kind of sense, 280 00:10:07,240 --> 00:10:08,680 even if it is mind boggling. 281 00:10:08,680 --> 00:10:10,520 It is pretty wild when you think about it. 282 00:10:10,520 --> 00:10:14,200 But if it's all just patterns and algorithms, 283 00:10:14,200 --> 00:10:15,760 where do things like bias come in? 284 00:10:15,760 --> 00:10:17,480 That's the key point. 285 00:10:17,480 --> 00:10:19,600 AI doesn't come from nowhere. 286 00:10:19,600 --> 00:10:21,680 It's shaped by the data it learns from. 287 00:10:21,680 --> 00:10:25,720 And that data often reflects our own biases, our blind spots, 288 00:10:25,720 --> 00:10:27,840 even our best and worst impulses. 289 00:10:27,840 --> 00:10:30,400 It's like if you train a language model on all 290 00:10:30,400 --> 00:10:35,240 of Twitter, it might pick up some less than desirable habits. 291 00:10:35,240 --> 00:10:36,360 Precisely. 292 00:10:36,360 --> 00:10:38,600 And that's why the responsible development of AI 293 00:10:38,600 --> 00:10:39,680 is so important. 294 00:10:39,680 --> 00:10:41,840 It's not just about making sure the algorithms work. 295 00:10:41,840 --> 00:10:44,920 It's about making sure that the data itself is representative, 296 00:10:44,920 --> 00:10:49,520 inclusive, and as free from harmful biases as possible. 297 00:10:49,520 --> 00:10:52,040 It's like they say garbage in, garbage out. 298 00:10:52,040 --> 00:10:54,000 But with the potential for that garbage 299 00:10:54,000 --> 00:10:56,640 to have a much wider and more damaging impact. 300 00:10:56,640 --> 00:10:57,520 Exactly. 301 00:10:57,520 --> 00:11:00,760 As we stand here on the edge of this intelligence age, 302 00:11:00,760 --> 00:11:04,320 it's not just about being amazed by the technology. 303 00:11:04,320 --> 00:11:06,560 It's about asking tough questions 304 00:11:06,560 --> 00:11:08,920 about what kind of future we're building with AI 305 00:11:08,920 --> 00:11:11,120 and how we can make sure it benefits everyone. 306 00:11:11,120 --> 00:11:12,280 I couldn't agree more. 307 00:11:12,280 --> 00:11:13,640 We're at a crossroads. 308 00:11:13,640 --> 00:11:14,280 It feels like it. 309 00:11:14,280 --> 00:11:16,920 And the decisions we make today will determine whether AI 310 00:11:16,920 --> 00:11:19,320 becomes a force for good or just ends up 311 00:11:19,320 --> 00:11:22,200 making existing inequalities even worse. 312 00:11:22,200 --> 00:11:23,680 A lot to think about. 313 00:11:23,680 --> 00:11:25,680 But that's what we do here on the deep dive, right? 314 00:11:25,680 --> 00:11:26,320 That's right. 315 00:11:26,320 --> 00:11:28,960 We explore these fascinating complex topics 316 00:11:28,960 --> 00:11:32,000 and hopefully leave you with more questions and answers. 317 00:11:32,000 --> 00:11:33,920 And maybe even more importantly, to keep 318 00:11:33,920 --> 00:11:36,080 asking the right questions. 319 00:11:36,080 --> 00:11:37,240 Absolutely. 320 00:11:37,240 --> 00:11:39,520 So to our listeners, if this deep dive 321 00:11:39,520 --> 00:11:44,120 into the world of AI has sparked your curiosity, keep digging. 322 00:11:44,120 --> 00:11:46,800 There's a whole universe of information out there. 323 00:11:46,800 --> 00:11:48,520 And who knows, maybe you'll be the one 324 00:11:48,520 --> 00:11:50,240 to make the next big breakthrough, 325 00:11:50,240 --> 00:11:51,720 write the next chapter in the story 326 00:11:51,720 --> 00:11:53,040 of artificial intelligence. 327 00:11:53,040 --> 00:11:55,160 Or even help write the rules for how 328 00:11:55,160 --> 00:11:57,800 this powerful technology shapes our future. 329 00:11:57,800 --> 00:11:58,560 Beautifully said. 330 00:11:58,560 --> 00:11:59,160 Thank you. 331 00:11:59,160 --> 00:12:02,000 On that note, we'll leave you to ponder the possibilities. 332 00:12:02,000 --> 00:12:02,880 Until next time. 333 00:12:02,880 --> 00:12:03,680 Until next time. 334 00:12:03,680 --> 00:12:05,760 Keep exploring, keep questioning, 335 00:12:05,760 --> 00:12:25,520 and keep diving deep.