1 00:00:00,000 --> 00:00:02,000 Welcome to the Daily AI News Podcast. 2 00:00:02,000 --> 00:00:04,680 We're gonna be diving deep into AI today. 3 00:00:04,680 --> 00:00:05,520 Sounds good. 4 00:00:05,520 --> 00:00:08,360 We've got a range of sources today. 5 00:00:08,360 --> 00:00:11,240 We've got news articles about AI. 6 00:00:11,240 --> 00:00:14,600 We've got some stuff about like the tech industry 7 00:00:14,600 --> 00:00:15,880 and how that relates to AI. 8 00:00:15,880 --> 00:00:16,720 Okay. 9 00:00:16,720 --> 00:00:17,680 And then we've got a research paper 10 00:00:17,680 --> 00:00:22,680 that's all about a benchmark for AI and math. 11 00:00:22,920 --> 00:00:23,840 Interesting. 12 00:00:23,840 --> 00:00:24,680 Yeah, so we're gonna be looking 13 00:00:24,680 --> 00:00:26,160 at a bunch of different stuff. 14 00:00:26,160 --> 00:00:28,040 You know, how is this affecting jobs, 15 00:00:28,040 --> 00:00:30,320 the political landscape? 16 00:00:30,320 --> 00:00:31,160 Yeah. 17 00:00:31,160 --> 00:00:33,200 What are the challenges facing AI right now? 18 00:00:33,200 --> 00:00:36,080 And yeah, where is this all headed? 19 00:00:36,080 --> 00:00:36,920 So. 20 00:00:36,920 --> 00:00:38,720 Yeah, it's really fascinating to see AI 21 00:00:38,720 --> 00:00:41,840 like no longer just this futuristic concept, 22 00:00:41,840 --> 00:00:44,120 but something that's actually like shaping our present. 23 00:00:44,120 --> 00:00:44,960 Yeah. 24 00:00:44,960 --> 00:00:46,080 Yeah, it's already having a huge impact 25 00:00:46,080 --> 00:00:47,320 and we're just at the beginning. 26 00:00:47,320 --> 00:00:48,160 Exactly. 27 00:00:48,160 --> 00:00:51,400 And that's kind of what we're gonna be looking at today. 28 00:00:51,400 --> 00:00:52,520 Oh, not just what's happening, 29 00:00:52,520 --> 00:00:53,800 but what it means for you, right? 30 00:00:53,800 --> 00:00:56,960 Like how could this affect your job, your interests, 31 00:00:56,960 --> 00:00:58,920 maybe how you see what's even possible. 32 00:00:58,920 --> 00:00:59,760 Yeah. 33 00:00:59,760 --> 00:01:00,600 Yeah. 34 00:01:00,600 --> 00:01:04,040 So I guess let's jump right in. 35 00:01:04,040 --> 00:01:06,560 And why don't we start with open AI? 36 00:01:06,560 --> 00:01:09,600 Okay, so open AI, I'm sure you heard of them, 37 00:01:09,600 --> 00:01:11,000 chat GPT and all that. 38 00:01:11,000 --> 00:01:12,440 They're always pushing the boundaries, right? 39 00:01:12,440 --> 00:01:13,680 Like what can AI do? 40 00:01:13,680 --> 00:01:15,400 How far can we take it? 41 00:01:15,400 --> 00:01:18,240 But there's this growing awareness that just, 42 00:01:18,240 --> 00:01:20,480 you know, scaling up, adding more data, 43 00:01:20,480 --> 00:01:22,880 more computing power, it's not enough. 44 00:01:22,880 --> 00:01:24,320 It's like trying to build a tower 45 00:01:24,320 --> 00:01:26,040 by just stacking more bricks. 46 00:01:26,040 --> 00:01:27,960 Eventually the foundation can't hold it. 47 00:01:27,960 --> 00:01:28,800 Right. 48 00:01:28,800 --> 00:01:29,920 So what are they doing differently now? 49 00:01:29,920 --> 00:01:31,960 Well, they're exploring some pretty cool stuff. 50 00:01:31,960 --> 00:01:32,880 Yeah. 51 00:01:32,880 --> 00:01:34,720 Test time compute is one thing. 52 00:01:34,720 --> 00:01:35,560 Okay. 53 00:01:35,560 --> 00:01:37,640 And it's this way of letting models 54 00:01:38,920 --> 00:01:42,440 think through problems, more like humans do, you know. 55 00:01:42,440 --> 00:01:43,280 Okay. 56 00:01:43,280 --> 00:01:45,400 Step by step, instead of just relying on patterns. 57 00:01:45,400 --> 00:01:46,320 Ah, I see. 58 00:01:46,320 --> 00:01:49,400 Like imagine a student using textbooks, calculators, 59 00:01:49,400 --> 00:01:51,760 even the internet to figure something out. 60 00:01:51,760 --> 00:01:53,840 So it's more about reasoning and strategizing. 61 00:01:53,840 --> 00:01:54,680 Exactly. 62 00:01:54,680 --> 00:01:56,120 Other than just pattern recognition. 63 00:01:56,120 --> 00:01:57,120 Exactly, yeah. 64 00:01:57,120 --> 00:01:57,960 Wow. 65 00:01:57,960 --> 00:01:59,720 So that's a big change. 66 00:01:59,720 --> 00:02:01,280 Yeah, that is a pretty big leap. 67 00:02:01,280 --> 00:02:03,200 Like their new 01 model. 68 00:02:03,200 --> 00:02:04,040 Okay. 69 00:02:04,040 --> 00:02:06,320 It's built to tackle more complex things, you know, 70 00:02:06,320 --> 00:02:10,200 like planning, even a bit of experimentation 71 00:02:10,200 --> 00:02:11,840 to find solutions. 72 00:02:11,840 --> 00:02:12,680 Wow. 73 00:02:12,680 --> 00:02:14,120 And this must have some big implications. 74 00:02:14,120 --> 00:02:14,960 Huge, yeah. 75 00:02:14,960 --> 00:02:15,960 For the tech world, right? 76 00:02:15,960 --> 00:02:17,360 Oh, absolutely, yeah. 77 00:02:17,360 --> 00:02:19,800 What kind of chips are needed to even run these things? 78 00:02:19,800 --> 00:02:20,640 Totally. 79 00:02:20,640 --> 00:02:22,080 Think of companies like NVIDIA. 80 00:02:22,080 --> 00:02:22,920 Right. 81 00:02:22,920 --> 00:02:24,240 Their chips are the standard right now. 82 00:02:24,240 --> 00:02:25,080 Mm-hmm. 83 00:02:25,080 --> 00:02:26,280 But this could shake things up. 84 00:02:26,280 --> 00:02:28,200 Yeah, and speaking of AI chips. 85 00:02:28,200 --> 00:02:29,040 Yeah. 86 00:02:29,040 --> 00:02:32,160 Amazon's been making some pretty big moves. 87 00:02:32,160 --> 00:02:33,000 They have, yeah. 88 00:02:33,000 --> 00:02:35,160 They're investing heavily in this area, right? 89 00:02:35,160 --> 00:02:37,080 Through anapurnal labs. 90 00:02:37,080 --> 00:02:41,040 Yeah, their goal is to like challenge and right, 91 00:02:41,040 --> 00:02:43,520 offer something more efficient, more cost effective. 92 00:02:43,520 --> 00:02:44,360 Okay. 93 00:02:44,360 --> 00:02:46,000 They've developed the Tranium II chip 94 00:02:46,000 --> 00:02:49,200 specifically for training these big AI models. 95 00:02:49,200 --> 00:02:50,040 Okay. 96 00:02:50,040 --> 00:02:51,760 And then they've got Inferentia, 97 00:02:51,760 --> 00:02:54,080 that's focused on actually running those models 98 00:02:54,080 --> 00:02:54,920 to generate stuff. 99 00:02:54,920 --> 00:02:57,040 So it's a power play in the AI chip market. 100 00:02:57,040 --> 00:02:57,880 It is, yeah. 101 00:02:57,880 --> 00:02:59,680 And this seems like... 102 00:02:59,680 --> 00:03:00,640 It's a big trend, yeah. 103 00:03:00,640 --> 00:03:01,600 A larger trend. 104 00:03:01,600 --> 00:03:02,440 Totally. 105 00:03:02,440 --> 00:03:05,720 Like we're seeing this with Amazon, Microsoft. 106 00:03:05,720 --> 00:03:08,480 They're all developing their own chips, right? 107 00:03:08,480 --> 00:03:09,720 Yeah, they want more control, 108 00:03:09,720 --> 00:03:12,440 less reliance on outside suppliers. 109 00:03:12,440 --> 00:03:13,280 Yeah. 110 00:03:13,280 --> 00:03:14,120 It's a way to be self-sufficient 111 00:03:14,120 --> 00:03:16,440 in this rapidly changing world. 112 00:03:16,440 --> 00:03:17,720 Exactly, yeah. 113 00:03:17,720 --> 00:03:21,360 Yeah, so let's shift gears a bit. 114 00:03:21,360 --> 00:03:23,600 Move from the tech world to... 115 00:03:23,600 --> 00:03:24,440 Okay. 116 00:03:24,440 --> 00:03:25,280 To politics. 117 00:03:25,280 --> 00:03:26,120 Okay. 118 00:03:26,120 --> 00:03:28,320 There's been a lot of talk about 119 00:03:28,320 --> 00:03:32,800 how President-elect Trump's policies might impact AI, 120 00:03:32,800 --> 00:03:34,440 especially after his last term. 121 00:03:34,440 --> 00:03:35,280 Yeah. 122 00:03:35,280 --> 00:03:37,960 One big topic is President Biden's executive order on AI. 123 00:03:37,960 --> 00:03:38,800 Right. 124 00:03:38,800 --> 00:03:40,800 Which aimed to set up a framework 125 00:03:40,800 --> 00:03:42,440 for managing the risks 126 00:03:42,440 --> 00:03:45,200 and the opportunities that come with AI. 127 00:03:45,200 --> 00:03:46,760 Yeah, across different sectors. 128 00:03:46,760 --> 00:03:49,320 There's a lot of speculation about whether 129 00:03:49,320 --> 00:03:51,720 President-elect Trump will keep that order or not. 130 00:03:51,720 --> 00:03:53,000 Yeah, and there are a lot of opinions 131 00:03:53,000 --> 00:03:53,920 on what that might mean. 132 00:03:53,920 --> 00:03:55,880 A lot of different perspectives, yeah. 133 00:03:55,880 --> 00:04:00,120 Some tech leaders like John Rose, Dell's global CTO, 134 00:04:00,120 --> 00:04:03,040 he believes that that executive order 135 00:04:03,040 --> 00:04:04,280 didn't really change much 136 00:04:04,280 --> 00:04:07,320 in terms of what private companies were already doing 137 00:04:07,320 --> 00:04:08,720 with AI. 138 00:04:08,720 --> 00:04:10,600 But then you have others who disagree. 139 00:04:11,400 --> 00:04:13,680 A vice president at the Information Technology 140 00:04:13,680 --> 00:04:17,280 and Innovation Foundation argues that removing the order 141 00:04:17,280 --> 00:04:19,840 could actually produce bureaucracy 142 00:04:19,840 --> 00:04:22,440 and help get AI products to market faster. 143 00:04:22,440 --> 00:04:22,780 Okay. 144 00:04:22,780 --> 00:04:24,280 And then Vishnarendra, 145 00:04:24,280 --> 00:04:26,640 CIO of Graphic Packaging International, 146 00:04:27,640 --> 00:04:30,720 he stresses the need for companies 147 00:04:30,720 --> 00:04:35,240 to really understand and address bias, exactly. 148 00:04:35,240 --> 00:04:36,520 That work with AI models. 149 00:04:36,520 --> 00:04:39,600 And he thinks organizations need to prioritize 150 00:04:39,600 --> 00:04:41,560 ethical considerations. 151 00:04:41,560 --> 00:04:42,960 Regardless of regulations, yeah. 152 00:04:42,960 --> 00:04:44,600 Yeah, regardless of the rules, so. 153 00:04:44,600 --> 00:04:45,760 It's a complex issue, right? 154 00:04:45,760 --> 00:04:46,240 It is. 155 00:04:46,240 --> 00:04:48,560 Balancing innovation with responsible development. 156 00:04:48,560 --> 00:04:50,800 And then there's the whole Elon Musk situation. 157 00:04:50,800 --> 00:04:52,800 There's this advocacy group called 158 00:04:52,800 --> 00:04:54,880 Americans for Responsible Innovation. 159 00:04:54,880 --> 00:04:55,280 Okay. 160 00:04:55,280 --> 00:04:57,760 And they're actually petitioning for Musk 161 00:04:57,760 --> 00:05:00,480 to be appointed as an advisor on AI. 162 00:05:00,480 --> 00:05:01,040 Interesting. 163 00:05:01,040 --> 00:05:02,160 To the Trump administration. 164 00:05:02,160 --> 00:05:02,560 Wow. 165 00:05:02,560 --> 00:05:04,840 They argue that he's uniquely qualified. 166 00:05:04,840 --> 00:05:05,960 Because of his involvement, yeah. 167 00:05:05,960 --> 00:05:07,840 Because of his deep involvement in the field. 168 00:05:07,840 --> 00:05:08,440 Yeah. 169 00:05:08,440 --> 00:05:10,560 To be a champion for AI safety. 170 00:05:10,560 --> 00:05:11,360 Mm-hmm. 171 00:05:11,360 --> 00:05:14,760 So Musk's potential involvement 172 00:05:14,760 --> 00:05:17,000 definitely adds another layer to this. 173 00:05:17,000 --> 00:05:17,560 Yeah. 174 00:05:17,560 --> 00:05:19,480 Already pretty dynamic landscape, right? 175 00:05:19,480 --> 00:05:22,840 It's interesting, given his complex relationship 176 00:05:22,840 --> 00:05:25,800 with the AI community, he's criticized OpenAI, 177 00:05:25,800 --> 00:05:27,200 a company he co-founded. 178 00:05:27,200 --> 00:05:30,480 But he's also running his own AI venture, XAI. 179 00:05:30,480 --> 00:05:32,840 So how objective would he be? 180 00:05:32,840 --> 00:05:33,340 Yeah. 181 00:05:33,340 --> 00:05:35,120 It'll be interesting to see how that all unfolds. 182 00:05:35,120 --> 00:05:36,560 Definitely. 183 00:05:36,560 --> 00:05:40,440 So continuing on with this whole idea of AI's evolving 184 00:05:40,440 --> 00:05:43,680 influence, let's dive into how it's impacting jobs. 185 00:05:43,680 --> 00:05:44,280 OK. 186 00:05:44,280 --> 00:05:47,960 Specifically, generative AI, stuff like chat GPT. 187 00:05:47,960 --> 00:05:48,360 Right. 188 00:05:48,360 --> 00:05:50,640 How is this changing things for workers? 189 00:05:50,640 --> 00:05:53,200 Well, there was this recent study in management science. 190 00:05:53,200 --> 00:05:53,520 OK. 191 00:05:53,520 --> 00:05:57,360 They looked at job postings on a popular freelancing platform. 192 00:05:57,360 --> 00:06:00,240 And what's fascinating is they found a big drop in demand 193 00:06:00,240 --> 00:06:03,480 for jobs that are prone to automation. 194 00:06:03,480 --> 00:06:07,120 Like writing and coding after chat GPT came out. 195 00:06:07,120 --> 00:06:07,600 Oh, wow. 196 00:06:07,600 --> 00:06:11,280 So does this mean AI's coming for everyone's jobs? 197 00:06:11,280 --> 00:06:13,000 Not necessarily. 198 00:06:13,000 --> 00:06:15,760 While demand for those tasks decreased, 199 00:06:15,760 --> 00:06:19,160 the study also found that the complexity of those remaining 200 00:06:19,160 --> 00:06:22,480 jobs and how much employers were willing to pay for them 201 00:06:22,480 --> 00:06:23,480 actually went up. 202 00:06:23,480 --> 00:06:25,800 So it's not about replacing humans. 203 00:06:25,800 --> 00:06:26,200 Right. 204 00:06:26,200 --> 00:06:29,320 But it's about a shift in the types of skills needed. 205 00:06:29,320 --> 00:06:29,840 Exactly. 206 00:06:29,840 --> 00:06:32,080 That's not a doom and gloom scenario. 207 00:06:32,080 --> 00:06:33,160 It's about adaptation. 208 00:06:33,160 --> 00:06:33,560 Yeah. 209 00:06:33,560 --> 00:06:38,560 Workers who can use AI tools to solve more complex problems, 210 00:06:38,560 --> 00:06:39,640 they'll be in high demand. 211 00:06:39,640 --> 00:06:43,240 So it's a call to evolve alongside this technology. 212 00:06:43,240 --> 00:06:43,800 Exactly. 213 00:06:43,800 --> 00:06:44,040 All right. 214 00:06:44,040 --> 00:06:45,440 Now let's shift gears again. 215 00:06:45,440 --> 00:06:45,940 OK. 216 00:06:45,940 --> 00:06:51,400 To a different area where AI is making waves, music, 217 00:06:51,400 --> 00:06:54,360 and artistic legacy. 218 00:06:54,360 --> 00:06:54,680 OK. 219 00:06:54,680 --> 00:06:57,720 There's this pretty amazing story about how Jerry Garcia's 220 00:06:57,720 --> 00:07:00,520 voice is being brought back to life 221 00:07:00,520 --> 00:07:01,680 through artificial intelligence. 222 00:07:01,680 --> 00:07:02,280 That's wild. 223 00:07:02,280 --> 00:07:05,000 So Garcia's a state partnered with 11 Labs. 224 00:07:05,000 --> 00:07:06,360 They're an AI voice company. 225 00:07:06,360 --> 00:07:09,440 And they created a voice model that 226 00:07:09,440 --> 00:07:11,600 can read different texts in his voice. 227 00:07:11,600 --> 00:07:12,120 Wow. 228 00:07:12,120 --> 00:07:13,880 Like books, articles, even fan stories. 229 00:07:13,880 --> 00:07:14,400 No way. 230 00:07:14,400 --> 00:07:16,440 And what's really remarkable is that you could even 231 00:07:16,440 --> 00:07:18,920 hear him speak in different languages. 232 00:07:18,920 --> 00:07:19,400 That's incredible. 233 00:07:19,400 --> 00:07:22,200 It's like blurring the lines between what we thought 234 00:07:22,200 --> 00:07:25,360 was possible and what AI can now do. 235 00:07:25,360 --> 00:07:25,560 Yeah. 236 00:07:25,560 --> 00:07:27,320 It's really a great example of how AI 237 00:07:27,320 --> 00:07:29,920 can be used to preserve and share. 238 00:07:29,920 --> 00:07:30,840 Culture, yeah. 239 00:07:30,840 --> 00:07:31,480 Heritage. 240 00:07:31,480 --> 00:07:31,920 Yeah. 241 00:07:31,920 --> 00:07:33,320 And ways you never even imagined. 242 00:07:33,320 --> 00:07:38,320 And staying on this theme of AI in creative fields, 243 00:07:38,320 --> 00:07:42,000 11 Labs has also been working on AI music generation. 244 00:07:42,000 --> 00:07:42,560 Oh, wow. 245 00:07:42,560 --> 00:07:44,960 So AI composing music? 246 00:07:44,960 --> 00:07:46,760 Yeah, they recently gave a sneak peek 247 00:07:46,760 --> 00:07:48,600 of their music generator. 248 00:07:48,600 --> 00:07:49,400 Interesting. 249 00:07:49,400 --> 00:07:50,800 It's still in its early stages. 250 00:07:50,800 --> 00:07:51,320 OK. 251 00:07:51,320 --> 00:07:55,680 But this really makes you think about the future of music. 252 00:07:55,680 --> 00:07:58,360 Yeah, like could musicians collaborate with AI 253 00:07:58,360 --> 00:08:00,040 to explore new sounds? 254 00:08:00,040 --> 00:08:02,120 It's definitely pushing the boundaries of what 255 00:08:02,120 --> 00:08:03,400 we thought was possible. 256 00:08:03,400 --> 00:08:05,640 So speaking of pushing boundaries, 257 00:08:05,640 --> 00:08:09,000 let's talk about this research paper that's 258 00:08:09,000 --> 00:08:11,160 making waves in the AI world. 259 00:08:11,160 --> 00:08:12,800 It's called Frontier Math. 260 00:08:12,800 --> 00:08:14,040 Frontier Math. 261 00:08:14,040 --> 00:08:16,040 And it's a brand new benchmark designed 262 00:08:16,040 --> 00:08:19,080 to test how well AI can reason mathematically. 263 00:08:19,080 --> 00:08:20,680 So like advanced math? 264 00:08:20,680 --> 00:08:22,520 Yeah, advanced mathematical reasoning. 265 00:08:22,520 --> 00:08:23,360 OK. 266 00:08:23,360 --> 00:08:25,240 Frontier Math is basically a collection 267 00:08:25,240 --> 00:08:27,720 of hundreds of original math problems. 268 00:08:27,720 --> 00:08:29,720 They span different areas of mathematics. 269 00:08:29,720 --> 00:08:33,800 And what makes it really different from other benchmarks 270 00:08:33,800 --> 00:08:36,760 is that even expert mathematicians 271 00:08:36,760 --> 00:08:39,320 might need hours or even days to solve them. 272 00:08:39,320 --> 00:08:42,200 So it's really challenging these AI models, 273 00:08:42,200 --> 00:08:43,720 pushing them to their limits. 274 00:08:43,720 --> 00:08:46,280 And how does AI stack up against this challenge? 275 00:08:46,280 --> 00:08:48,000 Well, even the most advanced models 276 00:08:48,000 --> 00:08:50,720 can only solve a tiny fraction of these problems. 277 00:08:50,720 --> 00:08:51,120 Oh, wow. 278 00:08:51,120 --> 00:08:52,840 Less than 2% to be exact. 279 00:08:52,840 --> 00:08:54,440 That's a pretty humbling reality check. 280 00:08:54,440 --> 00:08:55,160 It is, yeah. 281 00:08:55,160 --> 00:08:57,120 It really shows that there's still a big gap. 282 00:08:57,120 --> 00:08:57,880 Huge gap. 283 00:08:57,880 --> 00:08:58,480 They know that. 284 00:08:58,480 --> 00:09:01,560 Between AI and human level mathematical reasoning. 285 00:09:01,560 --> 00:09:02,080 Yeah. 286 00:09:02,080 --> 00:09:03,840 And while AI has come a long way, 287 00:09:03,840 --> 00:09:08,120 this benchmark shows us just how much more there is to explore. 288 00:09:08,120 --> 00:09:09,760 So we're still in the early stages. 289 00:09:09,760 --> 00:09:10,480 Absolutely, yeah. 290 00:09:10,480 --> 00:09:14,000 Of figuring out what AI can really do. 291 00:09:14,000 --> 00:09:15,040 Absolutely, yeah. 292 00:09:15,040 --> 00:09:17,040 So we've journeyed from the inner workings 293 00:09:17,040 --> 00:09:21,720 of these cutting edge AI models to the political forces 294 00:09:21,720 --> 00:09:23,080 shaping its development. 295 00:09:23,080 --> 00:09:26,240 And we've even touched upon its impact on creative fields, 296 00:09:26,240 --> 00:09:27,440 like music. 297 00:09:27,440 --> 00:09:28,880 Music and art. 298 00:09:28,880 --> 00:09:29,960 It's a lot to take in. 299 00:09:29,960 --> 00:09:32,680 But it shows you just how far reaching AI is. 300 00:09:32,680 --> 00:09:33,760 Yeah, yeah. 301 00:09:33,760 --> 00:09:36,000 And it's only going to become more so. 302 00:09:36,000 --> 00:09:37,280 Absolutely, yeah. 303 00:09:37,280 --> 00:09:38,520 So stay tuned for part two. 304 00:09:38,520 --> 00:09:39,880 We'll be back after a short break. 305 00:09:39,880 --> 00:09:40,760 OK, sounds good. 306 00:09:40,760 --> 00:09:46,440 To continue exploring this fascinating and ever-evolving 307 00:09:46,440 --> 00:09:47,920 world of AI. 308 00:09:47,920 --> 00:09:48,760 Looking forward to it. 309 00:09:48,760 --> 00:09:50,040 Yeah. 310 00:09:50,040 --> 00:09:53,240 Welcome back to our exploration of all the latest stuff 311 00:09:53,240 --> 00:09:54,760 happening in the world of AI. 312 00:09:54,760 --> 00:09:59,040 We were just discussing how even the most advanced AI models 313 00:09:59,040 --> 00:10:01,560 are struggling with these complex math problems. 314 00:10:01,560 --> 00:10:05,560 Like, kind of a reality check on where AI is at. 315 00:10:05,560 --> 00:10:09,200 It seems AI is really good at crunching numbers, 316 00:10:09,200 --> 00:10:10,880 finding patterns. 317 00:10:10,880 --> 00:10:14,600 But when it comes to abstract reasoning, 318 00:10:14,600 --> 00:10:17,040 that kind of creative problem solving 319 00:10:17,040 --> 00:10:20,520 that you need for advanced math, it really struggles. 320 00:10:20,520 --> 00:10:21,920 So where do we go from here? 321 00:10:21,920 --> 00:10:23,400 Like, how do we bridge that gap? 322 00:10:23,400 --> 00:10:25,680 That's the big question, right? 323 00:10:25,680 --> 00:10:26,160 Yeah. 324 00:10:26,160 --> 00:10:28,160 Researchers are exploring all sorts of things. 325 00:10:28,160 --> 00:10:31,880 But one thing's become clear, just scaling up, 326 00:10:31,880 --> 00:10:34,880 feeding them more data, more computing power. 327 00:10:34,880 --> 00:10:36,920 It's not going to cut it. 328 00:10:36,920 --> 00:10:37,880 It's not the answer. 329 00:10:37,880 --> 00:10:41,720 So the bigger is better approach has kind of hit a wall. 330 00:10:41,720 --> 00:10:46,160 It's like trying to build a skyscraper 331 00:10:46,160 --> 00:10:48,560 just by stacking more bricks on top of each other. 332 00:10:48,560 --> 00:10:50,480 Eventually, the foundation just crumbles. 333 00:10:50,480 --> 00:10:51,640 We need a new blueprint. 334 00:10:51,640 --> 00:10:52,140 Yeah. 335 00:10:52,140 --> 00:10:54,120 We need a way of thinking about AI. 336 00:10:54,120 --> 00:10:56,480 And maybe we can get some inspiration from how humans 337 00:10:56,480 --> 00:10:57,240 learn, right? 338 00:10:57,240 --> 00:10:57,840 Exactly. 339 00:10:57,840 --> 00:11:01,720 So instead of just training AI on massive data sets 340 00:11:01,720 --> 00:11:03,520 of text and code. 341 00:11:03,520 --> 00:11:07,600 What if we gave them access to the tools that humans use? 342 00:11:07,600 --> 00:11:08,720 Yeah, the resources. 343 00:11:08,720 --> 00:11:09,640 Like calculators. 344 00:11:09,640 --> 00:11:10,400 Textbooks. 345 00:11:10,400 --> 00:11:11,640 Textbooks, even the internet. 346 00:11:11,640 --> 00:11:12,200 The internet. 347 00:11:12,200 --> 00:11:13,160 Wow. 348 00:11:13,160 --> 00:11:16,880 So the idea is to create AI that can learn. 349 00:11:16,880 --> 00:11:18,880 Learn and reason more like a human student. 350 00:11:18,880 --> 00:11:21,000 So they'd be exploring, experimenting. 351 00:11:21,000 --> 00:11:22,080 Digitally exploring. 352 00:11:22,080 --> 00:11:22,720 Yeah. 353 00:11:22,720 --> 00:11:24,120 That sounds so fascinating. 354 00:11:24,120 --> 00:11:24,880 It's pretty cool. 355 00:11:24,880 --> 00:11:28,640 But also incredibly complex. 356 00:11:28,640 --> 00:11:29,560 It is, yeah. 357 00:11:29,560 --> 00:11:31,840 How do you even begin to. 358 00:11:31,840 --> 00:11:32,640 It's a big challenge. 359 00:11:32,640 --> 00:11:35,560 Teach an AI to use a calculator. 360 00:11:35,560 --> 00:11:37,040 Or understand a textbook. 361 00:11:37,040 --> 00:11:38,440 It's a whole combination of things. 362 00:11:38,440 --> 00:11:41,160 Machine learning, natural language processing, 363 00:11:41,160 --> 00:11:44,400 and really understanding how humans learn. 364 00:11:44,400 --> 00:11:47,480 So we're still early days in this approach. 365 00:11:47,480 --> 00:11:48,920 Just scratching the surface. 366 00:11:48,920 --> 00:11:50,840 But it sounds like there's a lot of. 367 00:11:50,840 --> 00:11:51,960 There's a lot of excitement. 368 00:11:51,960 --> 00:11:52,680 Excitement. 369 00:11:52,680 --> 00:11:53,960 Yeah, a lot of optimism. 370 00:11:53,960 --> 00:11:55,440 Maybe we're on the verge of like. 371 00:11:55,440 --> 00:11:56,800 A major breakthrough. 372 00:11:56,800 --> 00:11:58,160 A major breakthrough. 373 00:11:58,160 --> 00:11:59,840 Yeah, something that could, yeah. 374 00:11:59,840 --> 00:12:02,440 Redefine what AI can do. 375 00:12:02,440 --> 00:12:03,480 Let's shift gears a little bit. 376 00:12:03,480 --> 00:12:05,240 Talk about the economic implications 377 00:12:05,240 --> 00:12:06,400 of all these advancements. 378 00:12:06,400 --> 00:12:08,280 We've talked about jobs. 379 00:12:08,280 --> 00:12:09,960 But what about the bigger picture? 380 00:12:09,960 --> 00:12:11,200 The broader impact, yeah. 381 00:12:11,200 --> 00:12:14,360 How is AI shaping industries? 382 00:12:14,360 --> 00:12:15,520 Well, it's a very. 383 00:12:15,520 --> 00:12:15,920 Yeah. 384 00:12:15,920 --> 00:12:17,760 Dynamic and complex landscapes. 385 00:12:17,760 --> 00:12:20,840 On the one hand, AI has the potential to. 386 00:12:20,840 --> 00:12:21,440 Unlock. 387 00:12:21,440 --> 00:12:23,160 Unlock so much value. 388 00:12:23,160 --> 00:12:24,280 Economic value, yeah. 389 00:12:24,280 --> 00:12:26,040 Automating tasks. 390 00:12:26,040 --> 00:12:27,600 Improving efficiency. 391 00:12:27,600 --> 00:12:28,520 Creating new. 392 00:12:28,520 --> 00:12:30,560 Creating whole new products and services. 393 00:12:30,560 --> 00:12:32,240 Yeah, think of self-driving cars. 394 00:12:32,240 --> 00:12:33,080 Self-driving cars. 395 00:12:33,080 --> 00:12:34,200 Personalized medicine. 396 00:12:34,200 --> 00:12:34,880 Yeah. 397 00:12:34,880 --> 00:12:37,000 AI-powered financial advisors. 398 00:12:37,000 --> 00:12:40,000 These innovations could really revolutionize industries. 399 00:12:40,000 --> 00:12:40,560 Yeah, totally. 400 00:12:40,560 --> 00:12:42,440 Change how we live and work. 401 00:12:42,440 --> 00:12:42,960 Yeah. 402 00:12:42,960 --> 00:12:45,120 But there's a flip side to all this, right? 403 00:12:45,120 --> 00:12:46,280 There's always another side. 404 00:12:46,280 --> 00:12:47,760 What about job displacement? 405 00:12:47,760 --> 00:12:49,320 Yeah, that's a big concern. 406 00:12:49,320 --> 00:12:52,720 Wealth inequality and this concentration of power. 407 00:12:52,720 --> 00:12:53,560 Yeah. 408 00:12:53,560 --> 00:12:55,320 In the hands of a few tech giants. 409 00:12:55,320 --> 00:12:58,000 This is a double-edged sword, right? 410 00:12:58,000 --> 00:13:00,800 We need to harness the power of AI for good. 411 00:13:00,800 --> 00:13:01,280 Yeah. 412 00:13:01,280 --> 00:13:05,200 While also mitigating those potential downsides. 413 00:13:05,200 --> 00:13:06,120 It's a balancing act. 414 00:13:06,120 --> 00:13:06,840 It is, yeah. 415 00:13:06,840 --> 00:13:07,840 It requires a lot of. 416 00:13:07,840 --> 00:13:08,560 Collaboration. 417 00:13:08,560 --> 00:13:08,960 Yeah. 418 00:13:08,960 --> 00:13:09,880 Between governments. 419 00:13:09,880 --> 00:13:10,320 Governments. 420 00:13:10,320 --> 00:13:11,000 Industry leaders. 421 00:13:11,000 --> 00:13:12,160 Industry leaders. 422 00:13:12,160 --> 00:13:13,400 Researchers. 423 00:13:13,400 --> 00:13:13,840 The public. 424 00:13:13,840 --> 00:13:17,120 It's about making sure AI benefits everyone. 425 00:13:17,120 --> 00:13:18,640 Yeah, society's a whole. 426 00:13:18,640 --> 00:13:20,880 Let's focus on a specific industry. 427 00:13:20,880 --> 00:13:21,080 OK. 428 00:13:21,080 --> 00:13:24,040 That's being transformed by AI. 429 00:13:24,040 --> 00:13:24,560 Healthcare. 430 00:13:24,560 --> 00:13:26,280 Healthcare is huge, yeah. 431 00:13:26,280 --> 00:13:29,160 AI has the potential to revolutionize so much. 432 00:13:29,160 --> 00:13:29,600 Absolutely. 433 00:13:29,600 --> 00:13:32,600 From diagnosis and treatment to drug discovery. 434 00:13:32,600 --> 00:13:33,640 Even patient care. 435 00:13:33,640 --> 00:13:34,600 And patient care. 436 00:13:34,600 --> 00:13:34,920 Yeah. 437 00:13:34,920 --> 00:13:36,960 We're already seeing AI being used 438 00:13:36,960 --> 00:13:38,960 to analyze medical images. 439 00:13:38,960 --> 00:13:39,360 Yeah. 440 00:13:39,360 --> 00:13:41,480 Identify patterns in patient data. 441 00:13:41,480 --> 00:13:42,840 Even assist with surgery. 442 00:13:42,840 --> 00:13:43,840 Assisting with surgeries. 443 00:13:43,840 --> 00:13:44,960 That's pretty wild. 444 00:13:44,960 --> 00:13:46,960 And then there's personalized medicine. 445 00:13:46,960 --> 00:13:47,400 Yeah. 446 00:13:47,400 --> 00:13:48,440 Tailoring treatments. 447 00:13:48,440 --> 00:13:50,600 Where AI can help tailor treatments 448 00:13:50,600 --> 00:13:51,960 to individual patients. 449 00:13:51,960 --> 00:13:54,080 Based on their genetics, their lifestyle. 450 00:13:54,080 --> 00:13:55,400 These breakthroughs could really. 451 00:13:55,400 --> 00:13:56,080 Improved outcomes. 452 00:13:56,080 --> 00:13:57,120 Improve outcomes. 453 00:13:57,120 --> 00:13:57,560 Yeah. 454 00:13:57,560 --> 00:13:59,920 Revolutionize how health care is delivered. 455 00:13:59,920 --> 00:14:02,160 But of course, it's not all smooth sailing, right? 456 00:14:02,160 --> 00:14:03,240 There are always challenges. 457 00:14:03,240 --> 00:14:04,200 There are challenges. 458 00:14:04,200 --> 00:14:07,280 Especially in such a complex and sensitive field. 459 00:14:07,280 --> 00:14:11,000 Like how do we ensure these AI systems are accurate? 460 00:14:11,000 --> 00:14:12,120 Accuracy. 461 00:14:12,120 --> 00:14:12,640 It's a key. 462 00:14:12,640 --> 00:14:13,480 Reliable. 463 00:14:13,480 --> 00:14:14,760 Reliability. 464 00:14:14,760 --> 00:14:16,320 What about patient privacy? 465 00:14:16,320 --> 00:14:17,960 That's a huge one. 466 00:14:17,960 --> 00:14:20,920 And then there's the potential for bias in these algorithms. 467 00:14:20,920 --> 00:14:22,440 Bias is a big concern. 468 00:14:22,440 --> 00:14:25,400 So it's not just about building these sophisticated tools. 469 00:14:25,400 --> 00:14:27,040 It's about using them responsibly. 470 00:14:27,040 --> 00:14:28,480 It's about integrating them ethically. 471 00:14:28,480 --> 00:14:30,240 Yeah, ethically and responsibly. 472 00:14:30,240 --> 00:14:31,400 It's about considering. 473 00:14:31,400 --> 00:14:32,920 The ethical implication. 474 00:14:32,920 --> 00:14:33,840 The legal. 475 00:14:33,840 --> 00:14:35,080 The social implication. 476 00:14:35,080 --> 00:14:36,360 Plusications of AI. 477 00:14:36,360 --> 00:14:36,960 Yeah. 478 00:14:36,960 --> 00:14:38,360 It's a lot to think about. 479 00:14:38,360 --> 00:14:40,400 Let's turn our attention to another industry. 480 00:14:40,400 --> 00:14:40,840 OK. 481 00:14:40,840 --> 00:14:43,880 That's being profoundly impacted by AI. 482 00:14:43,880 --> 00:14:44,480 OK. 483 00:14:44,480 --> 00:14:45,320 Education. 484 00:14:45,320 --> 00:14:49,720 AI has the potential to personalize learning. 485 00:14:49,720 --> 00:14:51,200 Personalize learning. 486 00:14:51,200 --> 00:14:53,160 Provide individualized feedback. 487 00:14:53,160 --> 00:14:54,920 Imagine AI tutors. 488 00:14:54,920 --> 00:14:55,720 AI tutors, yeah. 489 00:14:55,720 --> 00:14:57,960 Adaptive learning platforms. 490 00:14:57,960 --> 00:15:00,280 Even virtual reality classrooms. 491 00:15:00,280 --> 00:15:01,200 It's pretty amazing. 492 00:15:01,200 --> 00:15:01,680 Yeah. 493 00:15:01,680 --> 00:15:04,000 These innovations could transform how we. 494 00:15:04,000 --> 00:15:05,040 Learn and teach. 495 00:15:05,040 --> 00:15:05,800 Learn and teach. 496 00:15:05,800 --> 00:15:07,640 Make education more accessible. 497 00:15:07,640 --> 00:15:08,720 Make it more engaging. 498 00:15:08,720 --> 00:15:09,680 More engaging. 499 00:15:09,680 --> 00:15:11,640 But just like with health care. 500 00:15:11,640 --> 00:15:13,520 There are concerns, right? 501 00:15:13,520 --> 00:15:14,080 There are. 502 00:15:14,080 --> 00:15:16,000 Especially about fairness and accessibility. 503 00:15:16,000 --> 00:15:16,320 Yeah. 504 00:15:16,320 --> 00:15:19,680 Like what if these AI tools are only available to. 505 00:15:19,680 --> 00:15:20,240 Right. 506 00:15:20,240 --> 00:15:21,080 Wealthy students. 507 00:15:21,080 --> 00:15:22,400 It could widen the achievement. 508 00:15:22,400 --> 00:15:23,160 Exactly. 509 00:15:23,160 --> 00:15:24,440 That's a valid concern. 510 00:15:24,440 --> 00:15:25,320 We need to make sure. 511 00:15:25,320 --> 00:15:27,560 That AI is used to create. 512 00:15:27,560 --> 00:15:28,960 Then AI is used. 513 00:15:28,960 --> 00:15:29,680 More equitable. 514 00:15:29,680 --> 00:15:32,960 To create more equitable and accessible opportunities. 515 00:15:32,960 --> 00:15:33,960 For all students. 516 00:15:33,960 --> 00:15:34,640 All students. 517 00:15:34,640 --> 00:15:35,120 Yeah. 518 00:15:35,120 --> 00:15:36,400 Regardless of their background. 519 00:15:36,400 --> 00:15:38,280 So it's not just about the technology itself. 520 00:15:38,280 --> 00:15:39,480 It's about how it's used. 521 00:15:39,480 --> 00:15:41,040 It's about how it's implemented. 522 00:15:41,040 --> 00:15:42,000 And who has access. 523 00:15:42,000 --> 00:15:42,880 And who has access. 524 00:15:42,880 --> 00:15:43,280 Yeah. 525 00:15:43,280 --> 00:15:45,760 The goal is to leverage AI. 526 00:15:45,760 --> 00:15:46,480 To create. 527 00:15:46,480 --> 00:15:47,160 More inclusive. 528 00:15:47,160 --> 00:15:48,200 A more inclusive. 529 00:15:48,200 --> 00:15:48,960 And effective. 530 00:15:48,960 --> 00:15:51,120 An effective education system. 531 00:15:51,120 --> 00:15:52,040 For everyone. 532 00:15:52,040 --> 00:15:53,800 Let's step back for a moment. 533 00:15:53,800 --> 00:15:55,920 And think about the big picture. 534 00:15:55,920 --> 00:15:59,160 The broader societal implications of all this. 535 00:15:59,160 --> 00:16:00,520 The societal implications. 536 00:16:00,520 --> 00:16:02,760 As AI becomes more and more pervasive. 537 00:16:02,760 --> 00:16:04,280 Mm-hmm. 538 00:16:04,280 --> 00:16:06,120 It really makes us question. 539 00:16:06,120 --> 00:16:07,360 What it means to be human. 540 00:16:07,360 --> 00:16:08,320 What it means to be human. 541 00:16:08,320 --> 00:16:08,480 Yeah. 542 00:16:08,480 --> 00:16:09,880 Like what is intelligence. 543 00:16:09,880 --> 00:16:10,160 Right. 544 00:16:10,160 --> 00:16:11,200 What is consciousness. 545 00:16:11,200 --> 00:16:11,920 Consciousness. 546 00:16:11,920 --> 00:16:13,560 What is even the nature of reality. 547 00:16:13,560 --> 00:16:14,120 Yeah. 548 00:16:14,120 --> 00:16:15,600 These are big questions. 549 00:16:15,600 --> 00:16:18,040 Will AI surpass human intelligence. 550 00:16:18,040 --> 00:16:20,640 Will it take over our jobs. 551 00:16:20,640 --> 00:16:23,920 Will it change the fabric of our relationships. 552 00:16:23,920 --> 00:16:24,880 Our communities. 553 00:16:24,880 --> 00:16:26,120 These are profound questions. 554 00:16:26,120 --> 00:16:26,320 Yeah. 555 00:16:26,320 --> 00:16:28,080 Profound and complex. 556 00:16:28,080 --> 00:16:29,240 And there are no easy answers. 557 00:16:29,240 --> 00:16:30,400 There are no easy answers. 558 00:16:30,400 --> 00:16:31,880 One thing is certain. 559 00:16:31,880 --> 00:16:33,000 We need to talk about it. 560 00:16:33,000 --> 00:16:34,040 We need to have this conversation. 561 00:16:34,040 --> 00:16:35,440 We need to have a dialogue. 562 00:16:35,440 --> 00:16:37,400 About the future of AI. 563 00:16:37,400 --> 00:16:39,080 About its role in society. 564 00:16:39,080 --> 00:16:40,120 And its role in society. 565 00:16:40,120 --> 00:16:40,920 Yeah. 566 00:16:40,920 --> 00:16:43,000 We need to think critically. 567 00:16:43,000 --> 00:16:44,640 About the ethical implication. 568 00:16:44,640 --> 00:16:45,400 About the ethical. 569 00:16:45,400 --> 00:16:46,960 The social implication. 570 00:16:46,960 --> 00:16:48,120 See, economic implications. 571 00:16:48,120 --> 00:16:49,480 Economic implications. 572 00:16:49,480 --> 00:16:51,200 And we need to work together. 573 00:16:51,200 --> 00:16:51,720 To make sure. 574 00:16:51,720 --> 00:16:55,720 To make sure AI is used to create a better future. 575 00:16:55,720 --> 00:16:56,480 For everyone. 576 00:16:56,480 --> 00:16:57,480 For everyone. 577 00:16:57,480 --> 00:16:57,880 Yeah. 578 00:16:57,880 --> 00:16:59,120 A better future for all. 579 00:16:59,120 --> 00:17:00,560 It's a daunting challenge. 580 00:17:00,560 --> 00:17:01,080 It is. 581 00:17:01,080 --> 00:17:02,680 But it's also exciting. 582 00:17:02,680 --> 00:17:03,080 Yeah. 583 00:17:03,080 --> 00:17:03,320 It's it's right. 584 00:17:03,320 --> 00:17:04,400 Demands innovation. 585 00:17:04,400 --> 00:17:05,120 Innovation. 586 00:17:05,120 --> 00:17:06,120 Responsibility. 587 00:17:06,120 --> 00:17:06,760 Ambition. 588 00:17:06,760 --> 00:17:08,040 And humility. 589 00:17:08,040 --> 00:17:09,360 Humility, yeah. 590 00:17:09,360 --> 00:17:12,280 As we navigate this uncharted territory. 591 00:17:12,280 --> 00:17:13,080 Mm-hmm. 592 00:17:13,080 --> 00:17:14,120 It's important to remember. 593 00:17:14,120 --> 00:17:14,720 It's at the future. 594 00:17:14,720 --> 00:17:17,960 That the future of AI is not predetermined. 595 00:17:17,960 --> 00:17:19,560 It's not set in stone. 596 00:17:19,560 --> 00:17:21,600 It's something we are shaping. 597 00:17:21,600 --> 00:17:22,520 We are shaping it. 598 00:17:22,520 --> 00:17:24,320 Through our choices. 599 00:17:24,320 --> 00:17:25,360 Through our actions. 600 00:17:25,360 --> 00:17:27,480 So let's choose wisely. 601 00:17:27,480 --> 00:17:28,520 Let's use this power. 602 00:17:28,520 --> 00:17:29,600 Let's harness this power. 603 00:17:29,600 --> 00:17:30,840 To build a better world. 604 00:17:30,840 --> 00:17:33,520 To build a better world for everyone. 605 00:17:33,520 --> 00:17:34,880 A more just world. 606 00:17:34,880 --> 00:17:35,720 More just. 607 00:17:35,720 --> 00:17:36,560 More equitable. 608 00:17:36,560 --> 00:17:37,480 More sustainable. 609 00:17:37,480 --> 00:17:38,720 And sustainable. 610 00:17:38,720 --> 00:17:39,880 For all. 611 00:17:39,880 --> 00:17:40,960 We'll pause here. 612 00:17:40,960 --> 00:17:41,600 OK. 613 00:17:41,600 --> 00:17:46,960 And continue our exploration of this ever-evolving world of AI 614 00:17:46,960 --> 00:17:49,680 in the final part of our deep dive. 615 00:17:49,680 --> 00:17:51,160 Sounds good. 616 00:17:51,160 --> 00:17:56,880 Welcome back to the final part of our deep dive into this. 617 00:17:56,880 --> 00:17:58,520 Yeah, this amazing world of AI. 618 00:17:58,520 --> 00:17:59,160 It is, yeah. 619 00:17:59,160 --> 00:18:00,200 We've covered so much. 620 00:18:00,200 --> 00:18:01,520 From the technical stuff. 621 00:18:01,520 --> 00:18:01,960 Yeah. 622 00:18:01,960 --> 00:18:04,080 To how it's impacting different industries. 623 00:18:04,080 --> 00:18:04,360 Yeah. 624 00:18:04,360 --> 00:18:04,800 And even like. 625 00:18:04,800 --> 00:18:06,400 The big societal questions. 626 00:18:06,400 --> 00:18:08,600 The societal questions it raises. 627 00:18:08,600 --> 00:18:12,280 And we've talked about both the amazing possibilities. 628 00:18:12,280 --> 00:18:13,440 Yeah, the potential pitfalls. 629 00:18:13,440 --> 00:18:15,000 Potential pitfalls as well. 630 00:18:15,000 --> 00:18:18,360 But there's this one crucial aspect 631 00:18:18,360 --> 00:18:19,800 we haven't really dug into yet. 632 00:18:19,800 --> 00:18:20,840 Ethics. 633 00:18:20,840 --> 00:18:21,320 Ethics. 634 00:18:21,320 --> 00:18:22,040 Exactly. 635 00:18:22,040 --> 00:18:23,200 It's so important. 636 00:18:23,200 --> 00:18:25,680 As these AI systems get more powerful. 637 00:18:25,680 --> 00:18:26,640 More autonomous. 638 00:18:26,640 --> 00:18:27,320 More autonomous. 639 00:18:27,320 --> 00:18:29,720 We have to make sure that they reflect our values. 640 00:18:29,720 --> 00:18:30,760 Yeah, that they're not. 641 00:18:30,760 --> 00:18:33,520 And that they don't perpetuate harmful biases 642 00:18:33,520 --> 00:18:35,080 or discriminatory practices. 643 00:18:35,080 --> 00:18:37,600 So it's not just about making sure. 644 00:18:37,600 --> 00:18:39,040 Because this AI is smart. 645 00:18:39,040 --> 00:18:42,400 That it's used to create a fairer society. 646 00:18:42,400 --> 00:18:44,520 It's about making sure it's ethically sound. 647 00:18:44,520 --> 00:18:45,240 Ethically sound. 648 00:18:45,240 --> 00:18:46,600 Aligned with human values. 649 00:18:46,600 --> 00:18:47,000 Yeah. 650 00:18:47,000 --> 00:18:49,240 And that requires a real shift in thinking. 651 00:18:49,240 --> 00:18:50,680 Not just for AI developers. 652 00:18:50,680 --> 00:18:51,640 Not just developers. 653 00:18:51,640 --> 00:18:54,120 But for everyone. 654 00:18:54,120 --> 00:18:54,400 Right. 655 00:18:54,400 --> 00:18:55,560 Across society. 656 00:18:55,560 --> 00:18:58,360 We need to see ethics as part of the design. 657 00:18:58,360 --> 00:18:59,400 Part of the process. 658 00:18:59,400 --> 00:19:00,720 Part of the development process. 659 00:19:00,720 --> 00:19:01,680 From the beginning. 660 00:19:01,680 --> 00:19:03,040 Yeah, it can't be an afterthought. 661 00:19:03,040 --> 00:19:04,000 Exactly. 662 00:19:04,000 --> 00:19:04,240 Yeah. 663 00:19:04,240 --> 00:19:06,720 So it's not just about building AI that's smart. 664 00:19:06,720 --> 00:19:09,200 It's about building AI that's good. 665 00:19:09,200 --> 00:19:09,960 Yeah, that's a good. 666 00:19:09,960 --> 00:19:12,280 AI that's aligned with our values. 667 00:19:12,280 --> 00:19:14,680 And that means asking some tough questions. 668 00:19:14,680 --> 00:19:16,920 Yeah, what are the consequences? 669 00:19:16,920 --> 00:19:18,040 What are the intended? 670 00:19:18,040 --> 00:19:19,880 Yeah, intended and unintended. 671 00:19:19,880 --> 00:19:21,680 Unintended consequences. 672 00:19:21,680 --> 00:19:22,520 We don't need quences. 673 00:19:22,520 --> 00:19:25,200 We need frameworks for accountability. 674 00:19:25,200 --> 00:19:26,760 Accountability, transparency. 675 00:19:26,760 --> 00:19:27,640 We're on transparency. 676 00:19:27,640 --> 00:19:28,080 Yeah. 677 00:19:28,080 --> 00:19:30,440 How do these systems make decisions? 678 00:19:30,440 --> 00:19:32,960 How do we hold them responsible for their actions? 679 00:19:32,960 --> 00:19:34,000 It's a big challenge. 680 00:19:34,000 --> 00:19:35,600 But we have to figure it out. 681 00:19:35,600 --> 00:19:37,080 And it's going to take a lot of collaboration. 682 00:19:37,080 --> 00:19:40,000 Collaboration between ethicists. 683 00:19:40,000 --> 00:19:40,920 Ethicists, yeah. 684 00:19:40,920 --> 00:19:42,160 Social scientists. 685 00:19:42,160 --> 00:19:43,000 Piggle experts. 686 00:19:43,000 --> 00:19:45,520 Policy makers, developers. 687 00:19:45,520 --> 00:19:46,720 It's a complex issue. 688 00:19:46,720 --> 00:19:47,120 Yeah. 689 00:19:47,120 --> 00:19:48,200 But it's an important one. 690 00:19:48,200 --> 00:19:50,560 If we want AI to benefit everyone. 691 00:19:50,560 --> 00:19:52,240 If we want to build that better future. 692 00:19:52,240 --> 00:19:54,200 So where is all this headed? 693 00:19:54,200 --> 00:19:56,840 What does the future hold for AI? 694 00:19:56,840 --> 00:19:58,120 It's hard to say for sure. 695 00:19:58,120 --> 00:19:58,920 It is, yeah. 696 00:19:58,920 --> 00:20:00,280 But one thing's certain. 697 00:20:00,280 --> 00:20:00,680 Yeah. 698 00:20:00,680 --> 00:20:01,960 AI is here to stay. 699 00:20:01,960 --> 00:20:03,520 It's going to continue to evolve. 700 00:20:03,520 --> 00:20:04,240 Exponentially. 701 00:20:04,240 --> 00:20:05,840 It's going to transform industries. 702 00:20:05,840 --> 00:20:07,040 Through shape society. 703 00:20:07,040 --> 00:20:08,880 It's going to challenge our understanding. 704 00:20:08,880 --> 00:20:10,280 Or that means to be human. 705 00:20:10,280 --> 00:20:12,880 Some experts believe we're on the verge of. 706 00:20:12,880 --> 00:20:13,760 The singularity. 707 00:20:13,760 --> 00:20:14,640 A singularity. 708 00:20:14,640 --> 00:20:14,920 Yeah. 709 00:20:14,920 --> 00:20:17,680 Where AI surpasses human intelligence. 710 00:20:17,680 --> 00:20:21,200 And it could lead to a whole cascade of. 711 00:20:21,200 --> 00:20:22,480 Unpredictable changes. 712 00:20:22,480 --> 00:20:23,120 Unpredictable. 713 00:20:23,120 --> 00:20:24,400 Irreversible changes. 714 00:20:24,400 --> 00:20:25,480 Irreversible changes. 715 00:20:25,480 --> 00:20:25,960 Yeah. 716 00:20:25,960 --> 00:20:27,400 It's a big idea. 717 00:20:27,400 --> 00:20:28,000 It is. 718 00:20:28,000 --> 00:20:29,520 And others are more cautious. 719 00:20:29,520 --> 00:20:29,880 Yeah. 720 00:20:29,880 --> 00:20:32,720 They say AI will keep advancing. 721 00:20:32,720 --> 00:20:33,920 What? 722 00:20:33,920 --> 00:20:35,520 But it's not an existential threat. 723 00:20:35,520 --> 00:20:36,680 Not a threat to humanity. 724 00:20:36,680 --> 00:20:37,920 But no matter what you believe. 725 00:20:37,920 --> 00:20:38,920 You need to talk about it. 726 00:20:38,920 --> 00:20:40,480 We need to keep having this conversation. 727 00:20:40,480 --> 00:20:41,840 We need to think critically. 728 00:20:41,840 --> 00:20:42,320 Yeah. 729 00:20:42,320 --> 00:20:43,280 About the ethical. 730 00:20:43,280 --> 00:20:43,920 The social. 731 00:20:43,920 --> 00:20:44,400 Social. 732 00:20:44,400 --> 00:20:45,000 Economic. 733 00:20:45,000 --> 00:20:45,560 Applications. 734 00:20:45,560 --> 00:20:47,040 Of this technology. 735 00:20:47,040 --> 00:20:49,640 And we need to work together. 736 00:20:49,640 --> 00:20:50,720 To make sure. 737 00:20:50,720 --> 00:20:52,440 To make sure AI is used. 738 00:20:52,440 --> 00:20:53,200 For good. 739 00:20:53,200 --> 00:20:53,880 For good. 740 00:20:53,880 --> 00:20:55,320 To create a better future. 741 00:20:55,320 --> 00:20:57,360 To create a better future for everyone. 742 00:20:57,360 --> 00:20:58,000 It's a challenge. 743 00:20:58,000 --> 00:20:58,960 It's a big challenge. 744 00:20:58,960 --> 00:21:00,240 But it's an exciting one. 745 00:21:00,240 --> 00:21:02,360 And as we navigate all of this. 746 00:21:02,360 --> 00:21:03,640 It's important to remember. 747 00:21:03,640 --> 00:21:07,440 It's important to remember that the future of AI. 748 00:21:07,440 --> 00:21:08,920 It's not predetermined. 749 00:21:08,920 --> 00:21:10,320 It's not set in stone. 750 00:21:10,320 --> 00:21:10,760 Yeah. 751 00:21:10,760 --> 00:21:12,000 We are shaping it. 752 00:21:12,000 --> 00:21:12,760 We're shaping it. 753 00:21:12,760 --> 00:21:13,880 With our choices. 754 00:21:13,880 --> 00:21:14,960 With our actions. 755 00:21:14,960 --> 00:21:16,800 So let's choose wisely. 756 00:21:16,800 --> 00:21:17,840 Let's use this power. 757 00:21:17,840 --> 00:21:19,200 Let's harness this power. 758 00:21:19,200 --> 00:21:20,640 To build a better world. 759 00:21:20,640 --> 00:21:22,080 To build a better world. 760 00:21:22,080 --> 00:21:22,880 For everyone. 761 00:21:22,880 --> 00:21:24,080 More just. 762 00:21:24,080 --> 00:21:24,840 More equitable. 763 00:21:24,840 --> 00:21:25,680 More equitable. 764 00:21:25,680 --> 00:21:26,480 More sustainable. 765 00:21:26,480 --> 00:21:27,400 More sustainable. 766 00:21:27,400 --> 00:21:28,880 For all. 767 00:21:28,880 --> 00:21:32,040 Well, we've reached the end of our deep dive. 768 00:21:32,040 --> 00:21:33,320 The end of our journey. 769 00:21:33,320 --> 00:21:34,600 Into the world of AI. 770 00:21:34,600 --> 00:21:35,880 It's been quite a journey. 771 00:21:35,880 --> 00:21:37,800 We hope you found it insightful. 772 00:21:37,800 --> 00:21:38,640 Thought provoking. 773 00:21:38,640 --> 00:21:39,440 Thought provoking. 774 00:21:39,440 --> 00:21:41,320 And that you have a better understanding of. 775 00:21:41,320 --> 00:21:44,560 Yeah, better understanding of this incredible, rapidly 776 00:21:44,560 --> 00:21:45,960 evolving field. 777 00:21:45,960 --> 00:21:48,720 Thanks for listening to the Daily AI News Podcast. 778 00:21:48,720 --> 00:21:50,600 And stay tuned for more. 779 00:21:50,600 --> 00:22:06,640 We'll see you next time.