1 00:00:00,000 --> 00:00:02,880 All right, so you've been digging into AI lately, huh? 2 00:00:02,880 --> 00:00:05,840 All the latest breakthroughs and the big players, 3 00:00:05,840 --> 00:00:07,720 trying to figure out what it all means, right? 4 00:00:07,720 --> 00:00:09,640 It's certainly been hard to ignore. 5 00:00:09,640 --> 00:00:11,440 Well, get ready to go deep 6 00:00:11,440 --> 00:00:14,080 because we've got a fascinating mix to unpack today 7 00:00:14,080 --> 00:00:18,120 from California's AI landscape to, get this, 8 00:00:18,120 --> 00:00:20,680 James Cameron's surprising new role. 9 00:00:20,680 --> 00:00:24,120 And believe it or not, we'll even touch on AI and wine. 10 00:00:24,120 --> 00:00:25,600 Sounds like we've got a lot to cover. 11 00:00:25,600 --> 00:00:26,680 Where should we start? 12 00:00:26,680 --> 00:00:28,840 How about with that AI safety bill in California, 13 00:00:28,840 --> 00:00:30,240 the one the governor just vetoed? 14 00:00:30,240 --> 00:00:33,200 Ah, yes, the one that was supposed to set 15 00:00:33,200 --> 00:00:36,120 some of the first US regulations on AI development. 16 00:00:36,120 --> 00:00:37,240 Yeah, yeah, exactly. 17 00:00:37,240 --> 00:00:38,360 Seemed like a pretty big deal. 18 00:00:38,360 --> 00:00:39,960 It is, or well, it was. 19 00:00:39,960 --> 00:00:40,800 I mean, at first glance, 20 00:00:40,800 --> 00:00:42,440 it just seemed like the same old debate, right? 21 00:00:42,440 --> 00:00:44,680 You mean the whole, are we moving too fast 22 00:00:44,680 --> 00:00:47,440 versus we don't want to stifle innovation thing? 23 00:00:47,440 --> 00:00:50,680 Exactly, but the governor's alternative approach, 24 00:00:50,680 --> 00:00:52,280 that's where things get interesting. 25 00:00:52,280 --> 00:00:55,240 I mean, he's not rejecting regulation outright or anything. 26 00:00:55,240 --> 00:00:56,920 So he's not completely opposed 27 00:00:56,920 --> 00:00:58,520 to the idea of regulating AI. 28 00:00:58,520 --> 00:00:59,720 No, not at all. 29 00:00:59,720 --> 00:01:03,800 But he is calling for a more targeted strategy. 30 00:01:03,800 --> 00:01:07,000 Instead of just making these broad, sweeping regulations, 31 00:01:07,000 --> 00:01:10,400 he's talking about focusing on specific use cases 32 00:01:10,400 --> 00:01:11,440 and risk levels. 33 00:01:11,440 --> 00:01:13,280 So like, I don't know, autonomous vehicles 34 00:01:13,280 --> 00:01:15,680 versus those AI customer service bots, 35 00:01:15,680 --> 00:01:17,600 those are very different things to regulate. 36 00:01:17,600 --> 00:01:20,400 Exactly, the potential harms are just vastly different. 37 00:01:20,400 --> 00:01:21,240 Makes sense. 38 00:01:21,240 --> 00:01:24,000 So less about hitting the brakes on AI development 39 00:01:24,000 --> 00:01:25,800 and more about setting a, what did you call it, 40 00:01:25,800 --> 00:01:27,320 a smarter speed limit. 41 00:01:27,320 --> 00:01:28,560 I like that analogy. 42 00:01:28,560 --> 00:01:30,200 But this whole debate got me thinking, 43 00:01:30,200 --> 00:01:34,320 what are the actual risks of not regulating AI? 44 00:01:34,320 --> 00:01:36,640 I mean, beyond just the economic impact. 45 00:01:36,640 --> 00:01:38,320 Well, that's the question, isn't it? 46 00:01:38,320 --> 00:01:42,320 Like, is it really just about robots taking over the world? 47 00:01:42,320 --> 00:01:43,160 Yes. 48 00:01:43,160 --> 00:01:45,320 Because that's a fun thought experiment, 49 00:01:45,320 --> 00:01:47,640 but I have a feeling it's more nuanced than that. 50 00:01:47,640 --> 00:01:48,760 It is, it is. 51 00:01:48,760 --> 00:01:52,160 There are definitely bigger fish to fry. 52 00:01:52,160 --> 00:01:56,120 Unregulated AI could, I mean, well, there's a few things. 53 00:01:56,120 --> 00:01:58,440 Like, for example, we could end up 54 00:01:58,440 --> 00:02:02,240 with systems that just make all of our societal biases worse. 55 00:02:02,240 --> 00:02:03,120 Right, right. 56 00:02:03,120 --> 00:02:05,200 Because if you're not careful about the data you use 57 00:02:05,200 --> 00:02:09,320 to train AI, it's just going to amplify existing inequalities. 58 00:02:09,320 --> 00:02:11,920 Exactly, or even create entirely new ones. 59 00:02:11,920 --> 00:02:13,840 Then there's the fact that these AI systems are 60 00:02:13,840 --> 00:02:15,560 making really important decisions, 61 00:02:15,560 --> 00:02:18,800 but often based on algorithms that nobody really understands. 62 00:02:18,800 --> 00:02:19,920 Like a black box. 63 00:02:19,920 --> 00:02:21,840 We're just supposed to trust that it's working correctly. 64 00:02:21,840 --> 00:02:23,640 And then, of course, there's the whole issue 65 00:02:23,640 --> 00:02:26,000 of AI being used to spread misinformation. 66 00:02:26,000 --> 00:02:27,160 And manipulate people. 67 00:02:27,160 --> 00:02:28,720 Remember that whole deep fake thing 68 00:02:28,720 --> 00:02:30,120 we talked about a while back? 69 00:02:30,120 --> 00:02:31,320 Yeah, that was pretty freaky. 70 00:02:31,320 --> 00:02:33,160 And that's just the tip of the iceberg. 71 00:02:33,160 --> 00:02:35,160 So yeah, I guess those guardrails you mentioned 72 00:02:35,160 --> 00:02:36,640 are looking more and more important. 73 00:02:36,640 --> 00:02:38,840 It's all about finding the right balance, really. 74 00:02:38,840 --> 00:02:40,320 Speaking of finding the right balance, 75 00:02:40,320 --> 00:02:42,640 did you see that article about James Cameron joining 76 00:02:42,640 --> 00:02:44,760 the board of Stability AI? 77 00:02:44,760 --> 00:02:46,880 Oh, yeah, I saw that. 78 00:02:46,880 --> 00:02:49,280 Talk about an interesting turn of events. 79 00:02:49,280 --> 00:02:51,640 I mean, the guy who brought a Skynet 80 00:02:51,640 --> 00:02:54,960 is now involved in shaping the future of generative AI. 81 00:02:54,960 --> 00:02:55,640 I know. 82 00:02:55,640 --> 00:02:57,720 It's kind of wild when you think about it like that. 83 00:02:57,720 --> 00:02:59,640 But honestly, I'm not that surprised. 84 00:02:59,640 --> 00:03:02,520 I mean, Cameron's always been fascinated by technology. 85 00:03:02,520 --> 00:03:03,840 Yeah, I guess you're right. 86 00:03:03,840 --> 00:03:05,080 Both the good and the bad. 87 00:03:05,080 --> 00:03:08,600 So remind me, what is Stability AI known for again? 88 00:03:08,600 --> 00:03:09,840 They do generative AI. 89 00:03:09,840 --> 00:03:13,840 They're all about the kind of AI that can create new content. 90 00:03:13,840 --> 00:03:18,000 Images, music, even scripts, you name it. 91 00:03:18,000 --> 00:03:20,960 And well, I think Cameron sees this as the next big thing 92 00:03:20,960 --> 00:03:21,640 in filmmaking. 93 00:03:21,640 --> 00:03:23,600 The next visual effects revolution. 94 00:03:23,600 --> 00:03:24,120 Right. 95 00:03:24,120 --> 00:03:26,360 I mean, imagine AI that can just generate 96 00:03:26,360 --> 00:03:30,320 entire sets or characters with a level of detail and realism 97 00:03:30,320 --> 00:03:31,440 we've never seen before. 98 00:03:31,440 --> 00:03:32,480 That would be incredible. 99 00:03:32,480 --> 00:03:35,800 But also, wouldn't that come with a whole host 100 00:03:35,800 --> 00:03:36,640 of legal issues? 101 00:03:36,640 --> 00:03:37,440 Oh, absolutely. 102 00:03:37,440 --> 00:03:40,720 Copyright issues, actor's rights, the whole shebang. 103 00:03:40,720 --> 00:03:42,880 So is Cameron walking into a minefield here? 104 00:03:42,880 --> 00:03:43,840 Potentially, yeah. 105 00:03:43,840 --> 00:03:45,340 But that's what makes it so interesting. 106 00:03:45,340 --> 00:03:47,280 He's not just a tech enthusiast. 107 00:03:47,280 --> 00:03:48,560 He's James Cameron. 108 00:03:48,560 --> 00:03:50,160 He's got clout in Hollywood. 109 00:03:50,160 --> 00:03:53,080 Maybe he can be the bridge between the creative world 110 00:03:53,080 --> 00:03:54,160 and the AI world. 111 00:03:54,160 --> 00:03:55,800 I don't know about that, but it's certainly 112 00:03:55,800 --> 00:03:56,920 an interesting thought. 113 00:03:56,920 --> 00:03:58,800 So we've got California trying to figure out 114 00:03:58,800 --> 00:04:02,800 how to regulate AI and James Cameron potentially shaping 115 00:04:02,800 --> 00:04:04,600 how it's used in Hollywood. 116 00:04:04,600 --> 00:04:05,240 What's next? 117 00:04:05,240 --> 00:04:07,200 Well, why don't we bring it down to Earth a bit 118 00:04:07,200 --> 00:04:09,300 and talk about something that's probably 119 00:04:09,300 --> 00:04:11,560 on a lot of people's minds? 120 00:04:11,560 --> 00:04:12,160 Jobs? 121 00:04:12,160 --> 00:04:13,000 OK, yeah. 122 00:04:13,000 --> 00:04:15,880 Because AI is about to upend the workplaces we know it, right? 123 00:04:15,880 --> 00:04:17,040 That's one way to put it. 124 00:04:17,040 --> 00:04:19,460 Especially with all this talk about AI agents becoming 125 00:04:19,460 --> 00:04:22,480 as commonplace as your new coworker. 126 00:04:22,480 --> 00:04:23,680 Exactly. 127 00:04:23,680 --> 00:04:26,320 Remember that Icona Q growth report you sent me? 128 00:04:26,320 --> 00:04:28,840 Oh, yeah, the one that made it sound less like science fiction 129 00:04:28,840 --> 00:04:32,040 and more like next year's HR onboarding process. 130 00:04:32,040 --> 00:04:33,040 Yeah, that's the one. 131 00:04:33,040 --> 00:04:34,360 Well, they had some pretty interesting things 132 00:04:34,360 --> 00:04:35,480 to say about that. 133 00:04:35,480 --> 00:04:37,280 Like what? 134 00:04:37,280 --> 00:04:38,080 Paint me a picture. 135 00:04:38,080 --> 00:04:40,240 What does that future actually look like? 136 00:04:40,240 --> 00:04:42,880 So imagine a world where AI agents are just 137 00:04:42,880 --> 00:04:45,000 handling all the tedious stuff. 138 00:04:45,000 --> 00:04:47,540 You know, data entry, scheduling, even drafting 139 00:04:47,540 --> 00:04:48,600 reports. 140 00:04:48,600 --> 00:04:50,640 And this frees up human employees 141 00:04:50,640 --> 00:04:54,320 to actually focus on things that require, well, being human. 142 00:04:54,320 --> 00:04:57,000 So more strategic thinking, creative problem solving. 143 00:04:57,000 --> 00:04:57,560 Exactly. 144 00:04:57,560 --> 00:04:59,240 It's not just about replacing jobs. 145 00:04:59,240 --> 00:05:01,880 It's about using AI to make us better at what we do. 146 00:05:01,880 --> 00:05:03,240 OK, I like that. 147 00:05:03,240 --> 00:05:05,880 So it's less about fearing the robot uprising 148 00:05:05,880 --> 00:05:09,320 and more about figuring out how to work alongside our new AI 149 00:05:09,320 --> 00:05:09,880 colleagues. 150 00:05:09,880 --> 00:05:10,440 Exactly. 151 00:05:10,440 --> 00:05:11,640 It's a collaborative future. 152 00:05:11,640 --> 00:05:14,440 A brave new world of human machine partnerships. 153 00:05:14,440 --> 00:05:17,440 But it's going to require a real shift in mindset. 154 00:05:17,440 --> 00:05:20,520 We need to focus on developing those uniquely human skills 155 00:05:20,520 --> 00:05:24,280 that complement AI, things AI can't replicate. 156 00:05:24,280 --> 00:05:25,200 Like what? 157 00:05:25,200 --> 00:05:26,400 Give me some examples. 158 00:05:26,400 --> 00:05:29,000 Well, adaptability is a big one. 159 00:05:29,000 --> 00:05:31,080 And critical of thinking, of course. 160 00:05:31,080 --> 00:05:36,200 But also emotional intelligence, empathy, communication, 161 00:05:36,200 --> 00:05:39,400 all those things that are crucial in any collaborative 162 00:05:39,400 --> 00:05:40,280 environment. 163 00:05:40,280 --> 00:05:41,720 So it's like we're being challenged 164 00:05:41,720 --> 00:05:45,660 to level up our humanity in the face of these incredibly 165 00:05:45,660 --> 00:05:47,320 powerful AI tools. 166 00:05:47,320 --> 00:05:48,760 I never thought about it like that. 167 00:05:48,760 --> 00:05:50,200 But yeah, that's a good way to put it. 168 00:05:50,200 --> 00:05:53,200 OK, so we've covered the regulatory landscape, 169 00:05:53,200 --> 00:05:56,240 the future of Hollywood, and even the evolving workplace. 170 00:05:56,240 --> 00:05:58,780 But you know, there's this other thing I keep coming back to. 171 00:05:58,780 --> 00:05:59,720 And what's that? 172 00:05:59,720 --> 00:06:02,240 This whole idea of what AI tells us about ourselves. 173 00:06:02,240 --> 00:06:03,000 Oh, interesting. 174 00:06:03,000 --> 00:06:03,640 Like how? 175 00:06:03,640 --> 00:06:05,480 Well, take AI art, for example. 176 00:06:05,480 --> 00:06:07,760 These algorithms are creating some seriously mind blowing 177 00:06:07,760 --> 00:06:08,360 stuff, right? 178 00:06:08,360 --> 00:06:10,680 I've seen some pretty impressive pieces. 179 00:06:10,680 --> 00:06:15,000 But they're trained on data sets of human art, human culture. 180 00:06:15,000 --> 00:06:17,000 Which makes me wonder, are we just 181 00:06:17,000 --> 00:06:20,560 seeing our own biases and limitations reflected back 182 00:06:20,560 --> 00:06:21,640 at us in a new form? 183 00:06:21,640 --> 00:06:24,120 Hmm, you might be onto something there. 184 00:06:24,120 --> 00:06:26,520 It's like AI is holding up a mirror 185 00:06:26,520 --> 00:06:28,680 to our collective consciousness. 186 00:06:28,680 --> 00:06:31,240 Yeah, it's almost like these AI art generators 187 00:06:31,240 --> 00:06:33,840 are revealing patterns and biases that we don't even 188 00:06:33,840 --> 00:06:34,800 realize are there. 189 00:06:34,800 --> 00:06:37,180 And it's not like these AI artists are intentionally 190 00:06:37,180 --> 00:06:38,480 trying to be biased. 191 00:06:38,480 --> 00:06:40,480 Right, they're just working with what they've got. 192 00:06:40,480 --> 00:06:42,360 Which is basically a massive data dump 193 00:06:42,360 --> 00:06:44,160 of human history and culture. 194 00:06:44,160 --> 00:06:45,000 Exactly. 195 00:06:45,000 --> 00:06:48,600 So of course, all of our preconceptions and stereotypes 196 00:06:48,600 --> 00:06:49,960 are going to be baked right in. 197 00:06:49,960 --> 00:06:52,560 Think about how we typically see certain professions. 198 00:06:52,560 --> 00:06:55,920 Like doctors being mostly men and nurses being mostly women. 199 00:06:55,920 --> 00:06:57,800 That's a perfect example. 200 00:06:57,800 --> 00:07:00,240 You take an AI model that's been trained on images 201 00:07:00,240 --> 00:07:03,480 that mostly show doctors as men and nurses as women 202 00:07:03,480 --> 00:07:04,640 and what's going to happen. 203 00:07:04,640 --> 00:07:06,960 Even if you try to prompt it differently, 204 00:07:06,960 --> 00:07:08,960 it's probably still going to default to those same old 205 00:07:08,960 --> 00:07:10,280 representations. 206 00:07:10,280 --> 00:07:13,600 It's almost like AI is reinforcing those stereotypes 207 00:07:13,600 --> 00:07:15,680 instead of helping us break free from them. 208 00:07:15,680 --> 00:07:17,920 Which is why that whole conversation about data 209 00:07:17,920 --> 00:07:19,840 governance is so important. 210 00:07:19,840 --> 00:07:22,840 It's not enough to just have tons of data, right? 211 00:07:22,840 --> 00:07:24,960 You have to think critically about where it came from 212 00:07:24,960 --> 00:07:25,760 and what's in it. 213 00:07:25,760 --> 00:07:26,600 Exactly. 214 00:07:26,600 --> 00:07:29,400 And who's making those choices about what data to use? 215 00:07:29,400 --> 00:07:31,360 Because if we're not careful, we'll 216 00:07:31,360 --> 00:07:34,480 end up creating these incredibly powerful AI systems that are 217 00:07:34,480 --> 00:07:36,720 just amplifying our worst impulses. 218 00:07:36,720 --> 00:07:39,640 Like AI could help us overcome our limitations, 219 00:07:39,640 --> 00:07:42,040 but only if we're smart about how we develop it. 220 00:07:42,040 --> 00:07:44,280 Couldn't have said it better myself. 221 00:07:44,280 --> 00:07:47,480 We need to be asking the tough questions right from the start. 222 00:07:47,480 --> 00:07:50,560 What values are we baking into these systems? 223 00:07:50,560 --> 00:07:51,640 Who benefits? 224 00:07:51,640 --> 00:07:53,440 And who might be left behind? 225 00:07:53,440 --> 00:07:56,320 These are big questions, but we can't just ignore them. 226 00:07:56,320 --> 00:08:00,440 It's like we need a whole new set of ethics for the AI age. 227 00:08:00,440 --> 00:08:03,920 It's definitely something we need to figure out and fast. 228 00:08:03,920 --> 00:08:07,680 OK, so we've gone from regulations to Hollywood 229 00:08:07,680 --> 00:08:11,080 to the future of work and now this whole philosophical deep 230 00:08:11,080 --> 00:08:11,600 dive. 231 00:08:11,600 --> 00:08:13,960 Can we switch gears for a second and talk about something 232 00:08:13,960 --> 00:08:15,200 a little more tangible? 233 00:08:15,200 --> 00:08:16,720 Yeah, what do you have in mind? 234 00:08:16,720 --> 00:08:19,600 How about AI and, wait for it, wine? 235 00:08:19,600 --> 00:08:20,720 AI and wine. 236 00:08:20,720 --> 00:08:21,680 I'm intrigued. 237 00:08:21,680 --> 00:08:22,840 Tell me more. 238 00:08:22,840 --> 00:08:24,480 Well, did you get a chance to look at that article 239 00:08:24,480 --> 00:08:26,040 I sent you about A.Sultana? 240 00:08:26,040 --> 00:08:26,640 I did. 241 00:08:26,640 --> 00:08:29,560 I have to admit, I was a little skeptical at first. 242 00:08:29,560 --> 00:08:32,400 I mean, it seemed like kind of a niche application for AI. 243 00:08:32,400 --> 00:08:34,080 AI for wine snobs. 244 00:08:34,080 --> 00:08:34,960 Something like that. 245 00:08:34,960 --> 00:08:36,880 But the more I read, the more I realized 246 00:08:36,880 --> 00:08:38,680 how much potential it has. 247 00:08:38,680 --> 00:08:40,220 So for those who haven't had a chance 248 00:08:40,220 --> 00:08:43,520 to dive into the article yet, what exactly is A.Sultana? 249 00:08:43,520 --> 00:08:46,840 So basically, it's an AI-powered consultant for the wine 250 00:08:46,840 --> 00:08:47,400 industry. 251 00:08:47,400 --> 00:08:48,880 A digital sommelier. 252 00:08:48,880 --> 00:08:51,480 Kind of, but on a much bigger scale. 253 00:08:51,480 --> 00:08:53,560 They're developing all these tools 254 00:08:53,560 --> 00:08:57,160 that can analyze huge amounts of data about everything 255 00:08:57,160 --> 00:08:59,960 from soil conditions to consumer preferences. 256 00:08:59,960 --> 00:09:01,800 OK, so it's not just about recommending 257 00:09:01,800 --> 00:09:03,160 the perfect bottle of wine. 258 00:09:03,160 --> 00:09:03,640 Right. 259 00:09:03,640 --> 00:09:06,640 I mean, they do that too, but it's much more than that. 260 00:09:06,640 --> 00:09:08,520 Think about a vineyard owner, right? 261 00:09:08,520 --> 00:09:11,600 They can use A.Sultana to get a deep understanding 262 00:09:11,600 --> 00:09:13,920 of their soil, their microclimate, even 263 00:09:13,920 --> 00:09:15,680 the optimal time to harvest. 264 00:09:15,680 --> 00:09:18,360 And all that data helps them produce better wine. 265 00:09:18,360 --> 00:09:19,000 Exactly. 266 00:09:19,000 --> 00:09:21,880 Higher quality, better yields, you name it. 267 00:09:21,880 --> 00:09:23,260 It's like taking the guesswork out 268 00:09:23,260 --> 00:09:25,700 of a process that's been around for centuries. 269 00:09:25,700 --> 00:09:29,360 And it's not just about the quality of the wine itself. 270 00:09:29,360 --> 00:09:32,040 It's about making the entire production process more 271 00:09:32,040 --> 00:09:33,520 efficient and sustainable. 272 00:09:33,520 --> 00:09:34,760 More sustainable how? 273 00:09:34,760 --> 00:09:38,440 I mean, A.Sultana can help winemakers reduce waste, 274 00:09:38,440 --> 00:09:40,800 conserve water, and just generally 275 00:09:40,800 --> 00:09:43,400 make more informed decisions about their environmental 276 00:09:43,400 --> 00:09:44,180 impact. 277 00:09:44,180 --> 00:09:45,160 That's really interesting. 278 00:09:45,160 --> 00:09:46,660 And I also remember reading something 279 00:09:46,660 --> 00:09:50,480 about how A.Sultana can create those personalized product 280 00:09:50,480 --> 00:09:52,120 recommendations for customers. 281 00:09:52,120 --> 00:09:52,640 Oh, yeah. 282 00:09:52,640 --> 00:09:54,000 That's one of the coolest parts. 283 00:09:54,000 --> 00:09:55,960 Imagine walking into a wine shop, 284 00:09:55,960 --> 00:09:59,040 and instead of being overwhelmed by a million different bottles, 285 00:09:59,040 --> 00:10:01,600 you get matched with the perfect wine for your taste. 286 00:10:01,600 --> 00:10:03,680 No more pretending you know what you're talking about in the wine 287 00:10:03,680 --> 00:10:04,180 aisle. 288 00:10:04,180 --> 00:10:04,800 I like it. 289 00:10:04,800 --> 00:10:08,280 It's all about using AI to make wine more accessible 290 00:10:08,280 --> 00:10:10,560 and less intimidating for everyone. 291 00:10:10,560 --> 00:10:13,040 So is this the future of wine? 292 00:10:13,040 --> 00:10:15,280 I think it has the potential to be. 293 00:10:15,280 --> 00:10:16,920 And it's not just wine either. 294 00:10:16,920 --> 00:10:20,480 We're seeing AI being used in all sorts of niche industries 295 00:10:20,480 --> 00:10:21,280 these days. 296 00:10:21,280 --> 00:10:22,180 Like what? 297 00:10:22,180 --> 00:10:23,320 Give me another example. 298 00:10:23,320 --> 00:10:25,840 Well, there's AI-powered tools for everything 299 00:10:25,840 --> 00:10:30,640 from fashion design to financial forecasting to drug discovery. 300 00:10:30,640 --> 00:10:33,500 You name it, there's probably an AI application for it. 301 00:10:33,500 --> 00:10:36,080 It's incredible how quickly this technology is spreading. 302 00:10:36,080 --> 00:10:37,620 It's not just something that's happening 303 00:10:37,620 --> 00:10:38,880 in Silicon Valley anymore. 304 00:10:38,880 --> 00:10:39,680 Exactly. 305 00:10:39,680 --> 00:10:40,600 It's everywhere. 306 00:10:40,600 --> 00:10:43,020 And I think that's what makes this whole moment so exciting. 307 00:10:43,020 --> 00:10:44,520 We're only just beginning to scratch 308 00:10:44,520 --> 00:10:46,680 the surface of what AI can do. 309 00:10:46,680 --> 00:10:49,480 OK, so we've talked about the risks of AI, 310 00:10:49,480 --> 00:10:52,320 but it sounds like there's a lot of potential upside too. 311 00:10:52,320 --> 00:10:53,000 Absolutely. 312 00:10:53,000 --> 00:10:55,800 Like any powerful tool, it all comes down 313 00:10:55,800 --> 00:10:57,600 to how we choose to use it. 314 00:10:57,600 --> 00:11:00,640 It really does feel like we're on the cusp of something huge, 315 00:11:00,640 --> 00:11:01,480 doesn't it? 316 00:11:01,480 --> 00:11:04,560 Like we've talked about all the potential pitfalls, 317 00:11:04,560 --> 00:11:08,360 but it's clear that AI also has the power 318 00:11:08,360 --> 00:11:11,520 to make our lives easier, more efficient, maybe even 319 00:11:11,520 --> 00:11:13,360 more meaningful in some ways. 320 00:11:13,360 --> 00:11:14,560 I think so too. 321 00:11:14,560 --> 00:11:17,840 But as you said, it all comes down to how we use it. 322 00:11:17,840 --> 00:11:19,120 It's in our hands now. 323 00:11:19,120 --> 00:11:20,560 It's kind of a scary thought, right? 324 00:11:20,560 --> 00:11:22,240 It is a bit daunting, yeah. 325 00:11:22,240 --> 00:11:26,600 Like we're holding the keys to this incredibly powerful engine, 326 00:11:26,600 --> 00:11:29,080 but do we really know where we're going? 327 00:11:29,080 --> 00:11:31,120 That's the million dollar question, isn't it? 328 00:11:31,120 --> 00:11:32,440 And speaking of questions, I feel 329 00:11:32,440 --> 00:11:34,680 like this whole deep dive has just raised more questions 330 00:11:34,680 --> 00:11:35,600 than it's answered. 331 00:11:35,600 --> 00:11:37,680 That's usually how it goes with these things, right? 332 00:11:37,680 --> 00:11:40,000 The more you learn, the more you realize you don't know. 333 00:11:40,000 --> 00:11:40,520 Exactly. 334 00:11:40,520 --> 00:11:42,160 But I guess that's the beauty of it too, right? 335 00:11:42,160 --> 00:11:43,320 It keeps us on our toes. 336 00:11:43,320 --> 00:11:44,120 Definitely. 337 00:11:44,120 --> 00:11:46,560 And it forces us to really think critically 338 00:11:46,560 --> 00:11:48,360 about the choices we're making. 339 00:11:48,360 --> 00:11:51,280 Because the decisions we make today about AI 340 00:11:51,280 --> 00:11:54,720 are going to have ripple effects for generations to come. 341 00:11:54,720 --> 00:11:56,360 There's a lot at stake here. 342 00:11:56,360 --> 00:11:57,480 No doubt about it. 343 00:11:57,480 --> 00:11:58,800 But I'm optimistic. 344 00:11:58,800 --> 00:11:59,560 You are. 345 00:11:59,560 --> 00:12:00,360 Why is that? 346 00:12:00,360 --> 00:12:02,360 Because I think these conversations we're having 347 00:12:02,360 --> 00:12:03,440 are a good sign. 348 00:12:03,440 --> 00:12:04,680 You mean the fact that we're even 349 00:12:04,680 --> 00:12:05,960 talking about these issues. 350 00:12:05,960 --> 00:12:06,520 Exactly. 351 00:12:06,520 --> 00:12:08,160 We're starting to ask the right questions. 352 00:12:08,160 --> 00:12:08,320 Yeah. 353 00:12:08,320 --> 00:12:09,320 And that's the first step. 354 00:12:09,320 --> 00:12:09,880 To what? 355 00:12:09,880 --> 00:12:10,960 What's the next step? 356 00:12:10,960 --> 00:12:13,520 Well, to actually figuring out how 357 00:12:13,520 --> 00:12:17,720 to build a future where AI benefits everyone, not just 358 00:12:17,720 --> 00:12:18,920 a select few. 359 00:12:18,920 --> 00:12:21,880 It's a tall order, but someone's got to do it. 360 00:12:21,880 --> 00:12:23,840 And I think we're up to the challenge. 361 00:12:23,840 --> 00:12:24,680 I hope you're right. 362 00:12:24,680 --> 00:12:25,600 Me too. 363 00:12:25,600 --> 00:12:28,720 Well, on that note, I think it's time to wrap things up. 364 00:12:28,720 --> 00:12:29,640 Sounds good. 365 00:12:29,640 --> 00:12:32,000 But before we go, I want to thank you again 366 00:12:32,000 --> 00:12:34,880 for joining me on this deep dive into the world of AI. 367 00:12:34,880 --> 00:12:36,440 It's been a pleasure, as always. 368 00:12:36,440 --> 00:12:38,560 And to our listeners, thank you for tuning in. 369 00:12:38,560 --> 00:12:40,600 We hope you learned something new today. 370 00:12:40,600 --> 00:12:42,960 And maybe even had your mind blown a little bit. 371 00:12:42,960 --> 00:12:44,240 That's what we strive for. 372 00:12:44,240 --> 00:12:48,000 Until next time, keep exploring, keep questioning, 373 00:12:48,000 --> 00:12:50,960 and most importantly, keep the conversation going. 374 00:12:50,960 --> 00:13:02,960 We'll see you next time.