1 00:00:00,000 --> 00:00:02,920 Welcome to the Daily AI News podcast. 2 00:00:02,920 --> 00:00:06,560 Wow, you guys have sent in some wild AI stories this week. 3 00:00:06,560 --> 00:00:09,400 Everything from small language models, 4 00:00:09,400 --> 00:00:13,640 having a moment to AI's role in healthcare, 5 00:00:13,640 --> 00:00:16,200 and even Kanye West is using it 6 00:00:16,200 --> 00:00:18,520 to recreate his daughters in a music video. 7 00:00:18,520 --> 00:00:19,600 Wow. 8 00:00:19,600 --> 00:00:20,600 So buckle up listeners, 9 00:00:20,600 --> 00:00:22,840 because we're diving deep into market trends. 10 00:00:22,840 --> 00:00:25,400 Technological leaps and even some ethical quandaries. 11 00:00:25,400 --> 00:00:26,240 Sounds good. 12 00:00:26,240 --> 00:00:27,080 It's gonna be a fun one. 13 00:00:27,080 --> 00:00:29,280 Yeah, I think what's really fascinating is just how, 14 00:00:29,280 --> 00:00:31,840 AI is just becoming so deeply integrated 15 00:00:31,840 --> 00:00:33,960 into so many different parts of our lives. 16 00:00:33,960 --> 00:00:34,800 Yeah. 17 00:00:34,800 --> 00:00:38,000 You know, from art to finance, 18 00:00:38,000 --> 00:00:40,840 it's just, it really highlights how fast 19 00:00:40,840 --> 00:00:43,320 this tech is building and why we need to be talking about this. 20 00:00:43,320 --> 00:00:44,560 Absolutely, yeah. 21 00:00:44,560 --> 00:00:45,520 Let's jump right in. 22 00:00:45,520 --> 00:00:46,440 Okay. 23 00:00:46,440 --> 00:00:49,760 Let's talk about this buzz around small language models 24 00:00:49,760 --> 00:00:52,320 or SLMs. 25 00:00:52,320 --> 00:00:55,360 We keep hearing that they might be a bigger deal 26 00:00:55,360 --> 00:00:58,240 for businesses than their more famous 27 00:00:58,240 --> 00:01:00,160 siblings, the LLMs. 28 00:01:00,160 --> 00:01:01,000 Right. 29 00:01:01,000 --> 00:01:03,120 Our sources suggest they're faster, 30 00:01:03,120 --> 00:01:05,800 more efficient and potentially easier on the environment. 31 00:01:05,800 --> 00:01:07,560 But what do that actually mean for, 32 00:01:07,560 --> 00:01:09,480 the average person out there? 33 00:01:09,480 --> 00:01:12,120 Well, imagine you can cut your model training time 34 00:01:12,120 --> 00:01:14,600 from say six months. 35 00:01:14,600 --> 00:01:15,440 Okay. 36 00:01:15,440 --> 00:01:16,280 Down to six weeks, right. 37 00:01:16,280 --> 00:01:17,680 Wow, that's a big difference. 38 00:01:17,680 --> 00:01:19,640 That's the kind of speed we're talking about 39 00:01:19,640 --> 00:01:20,960 with these SLMs, you know? 40 00:01:20,960 --> 00:01:22,960 They're trained on smaller data sets. 41 00:01:22,960 --> 00:01:23,800 Okay. 42 00:01:23,800 --> 00:01:25,760 They need less computing power. 43 00:01:25,760 --> 00:01:26,600 Okay. 44 00:01:26,600 --> 00:01:27,880 Much faster to train. 45 00:01:27,880 --> 00:01:29,720 So it's about being more focused. 46 00:01:29,720 --> 00:01:30,720 Exactly. 47 00:01:30,720 --> 00:01:33,440 Like perfect for very specific tasks. 48 00:01:33,440 --> 00:01:34,280 Got it. 49 00:01:34,280 --> 00:01:37,040 Especially for businesses that just don't have the resources 50 00:01:37,040 --> 00:01:40,200 to like wrangle these massive LLMs. 51 00:01:40,200 --> 00:01:42,000 Okay, so it's all about finding the right tool 52 00:01:42,000 --> 00:01:43,040 for the job basically. 53 00:01:43,040 --> 00:01:43,880 Yeah, exactly. 54 00:01:43,880 --> 00:01:48,120 But our sources also say that SLMs might actually be more 55 00:01:48,120 --> 00:01:49,120 secure. 56 00:01:49,120 --> 00:01:49,960 Right. 57 00:01:49,960 --> 00:01:53,200 Because they use less data, making them, you know. 58 00:01:53,200 --> 00:01:54,120 Less of a target, yeah. 59 00:01:54,120 --> 00:01:56,320 Less of a target, exactly. 60 00:01:56,320 --> 00:02:00,520 For cyber attacks, that's a big win. 61 00:02:00,520 --> 00:02:03,720 In a world where data breaches are becoming all too common. 62 00:02:03,720 --> 00:02:04,560 It really is. 63 00:02:04,560 --> 00:02:07,320 And you know, since they can be deployed locally 64 00:02:07,320 --> 00:02:10,720 a lot of times, businesses have like much more control 65 00:02:10,720 --> 00:02:14,120 over their data, which is a huge advantage 66 00:02:14,120 --> 00:02:16,480 for compliance and for privacy. 67 00:02:16,480 --> 00:02:18,720 So SLMs are making some serious waves, it seems. 68 00:02:18,720 --> 00:02:19,920 Yeah, absolutely. 69 00:02:19,920 --> 00:02:22,320 We've got Microsoft jumping into the game 70 00:02:22,320 --> 00:02:24,920 with their Phi3 models, which are apparently. 71 00:02:24,920 --> 00:02:25,760 Yeah, their Phi3 model. 72 00:02:25,760 --> 00:02:29,480 It's great for content creation and chatbots. 73 00:02:29,480 --> 00:02:33,080 Then there's Mistral going the open source route, 74 00:02:33,080 --> 00:02:35,920 letting developers customize their models. 75 00:02:35,920 --> 00:02:38,360 It seems like a whole new ecosystem is springing up. 76 00:02:38,360 --> 00:02:40,760 It really does feel like there's this like shift 77 00:02:40,760 --> 00:02:43,800 in the generative AI market happening, right? 78 00:02:43,800 --> 00:02:47,880 Like instead of just chasing bigger, more complex models, 79 00:02:47,880 --> 00:02:52,880 like companies are realizing the value of tailored solutions. 80 00:02:53,520 --> 00:02:54,360 Yeah, yeah. 81 00:02:54,360 --> 00:02:55,480 That are like, you know. 82 00:02:55,480 --> 00:02:56,320 Right. 83 00:02:56,320 --> 00:02:58,080 Efficient and cost effective and secure. 84 00:02:58,080 --> 00:02:59,360 And they do what you needed to do, right? 85 00:02:59,360 --> 00:03:00,200 Exactly. 86 00:03:00,200 --> 00:03:01,040 That's the key. 87 00:03:01,040 --> 00:03:01,880 Yeah. 88 00:03:01,880 --> 00:03:03,520 Speaking of tailored solutions, 89 00:03:03,520 --> 00:03:06,000 Apple researchers are doing some really fascinating work 90 00:03:06,000 --> 00:03:09,240 on optimizing SLM training. 91 00:03:09,240 --> 00:03:11,720 They're looking at how different hardware setups, 92 00:03:11,720 --> 00:03:16,200 batch sizes, even things like, you know, crash attention, 93 00:03:16,200 --> 00:03:18,240 impact efficiency and cost. 94 00:03:18,240 --> 00:03:21,560 And they're finding that sometimes the most expensive 95 00:03:21,560 --> 00:03:25,320 cutting edge hardware isn't always the best answer. 96 00:03:25,320 --> 00:03:30,320 It's about finding that sweet spot between performance 97 00:03:30,320 --> 00:03:31,720 and cost effectiveness. 98 00:03:31,720 --> 00:03:33,600 And you know, what's really exciting here. 99 00:03:33,600 --> 00:03:34,440 What's that? 100 00:03:34,440 --> 00:03:37,480 Is that it really feels like there's this focus 101 00:03:37,480 --> 00:03:41,800 on making AI accessible to more people. 102 00:03:41,800 --> 00:03:43,080 Yeah, making it more democratic. 103 00:03:43,080 --> 00:03:43,920 Exactly. 104 00:03:43,920 --> 00:03:44,760 It's great. 105 00:03:44,760 --> 00:03:48,520 It's like, not everyone needs a supercomputer 106 00:03:48,520 --> 00:03:53,200 to take advantage of the power of AI. 107 00:03:53,200 --> 00:03:56,120 And Apple's researchers really, I think, helping to, 108 00:03:56,120 --> 00:03:58,120 you know, democratize this tech, 109 00:03:58,120 --> 00:04:00,040 make it more affordable and efficient. 110 00:04:00,040 --> 00:04:01,360 It's not just about the hardware though. 111 00:04:01,360 --> 00:04:02,200 Right, exactly. 112 00:04:02,200 --> 00:04:04,440 They're also looking at distributed training, 113 00:04:04,440 --> 00:04:06,360 using a technique called DDP. 114 00:04:06,360 --> 00:04:07,480 DDP. 115 00:04:07,480 --> 00:04:09,000 Basically it's like having a bunch of computers 116 00:04:09,000 --> 00:04:09,840 working together. 117 00:04:09,840 --> 00:04:11,000 Yeah, like a team, right. 118 00:04:11,000 --> 00:04:12,240 To train a model faster. 119 00:04:12,240 --> 00:04:15,640 Yeah, and what they found is that this DDP is particularly 120 00:04:15,640 --> 00:04:17,240 effective for SLMs. 121 00:04:17,240 --> 00:04:18,080 Oh, interesting. 122 00:04:18,080 --> 00:04:20,400 Making the training process, you know, 123 00:04:20,400 --> 00:04:22,840 significantly faster and more efficient. 124 00:04:22,840 --> 00:04:26,280 And again, this is a game changer for smaller businesses. 125 00:04:26,280 --> 00:04:29,400 Or research teams that just might not have access 126 00:04:29,400 --> 00:04:31,520 to massive computing resources. 127 00:04:31,520 --> 00:04:33,840 Okay, so all this is great. 128 00:04:33,840 --> 00:04:35,520 It's fascinating. 129 00:04:35,520 --> 00:04:39,240 But what does this all mean for, you know, 130 00:04:39,240 --> 00:04:41,360 the average person out there? 131 00:04:41,360 --> 00:04:43,120 Well, think about it this way. 132 00:04:43,120 --> 00:04:47,840 The more accessible and affordable AI development becomes, 133 00:04:47,840 --> 00:04:51,160 the faster we're gonna see AI-powered solutions 134 00:04:51,160 --> 00:04:54,360 integrated into everyday products and services. 135 00:04:54,360 --> 00:04:55,480 Give me an example. 136 00:04:55,480 --> 00:04:58,600 Sure, think smarter virtual assistants. 137 00:04:58,600 --> 00:04:59,440 Okay. 138 00:04:59,440 --> 00:05:01,080 More personalized healthcare recommendations. 139 00:05:01,080 --> 00:05:01,920 Got it. 140 00:05:01,920 --> 00:05:04,600 All thanks to these advancements in SLM technology. 141 00:05:04,600 --> 00:05:05,440 Oh, that's cool. 142 00:05:05,440 --> 00:05:07,040 Now, shifting focus a bit. 143 00:05:07,040 --> 00:05:08,720 Let's talk about AI in the arts. 144 00:05:08,720 --> 00:05:09,840 Okay. 145 00:05:09,840 --> 00:05:14,520 We've got Kanye West using AI to recreate his daughters 146 00:05:14,520 --> 00:05:16,760 in his music video for Bomb. 147 00:05:16,760 --> 00:05:17,600 Interesting. 148 00:05:17,600 --> 00:05:22,600 It's a visually stunning example of AI's creative potential. 149 00:05:22,640 --> 00:05:25,720 But it's also sparked some debate. 150 00:05:25,720 --> 00:05:29,880 Kanye's video is a really powerful example 151 00:05:29,880 --> 00:05:33,200 of how AI is kind of pushing the boundaries 152 00:05:33,200 --> 00:05:35,800 of artistic expression. 153 00:05:35,800 --> 00:05:38,200 But it also raises these really important questions 154 00:05:38,200 --> 00:05:40,880 about the role of technology in art. 155 00:05:40,880 --> 00:05:45,880 Like some see it as this tool to enhance creativity. 156 00:05:46,000 --> 00:05:47,080 Others worry about it. 157 00:05:47,080 --> 00:05:49,840 Like replacing human artists. 158 00:05:49,840 --> 00:05:52,560 Some fans call the video lazy. 159 00:05:52,560 --> 00:05:53,400 Okay. 160 00:05:53,400 --> 00:05:55,840 Saying that like relying on AI kind of 161 00:05:55,840 --> 00:05:57,680 diminished the role of the artist. 162 00:05:57,680 --> 00:06:00,480 Others see it as a glimpse into the future of storytelling. 163 00:06:00,480 --> 00:06:02,080 Where technology and art merge 164 00:06:02,080 --> 00:06:04,560 in these like completely new ways. 165 00:06:04,560 --> 00:06:05,400 Interesting. 166 00:06:05,400 --> 00:06:06,760 It's definitely a conversation starter. 167 00:06:06,760 --> 00:06:08,000 It is. 168 00:06:08,000 --> 00:06:09,120 And this isn't even the first time 169 00:06:09,120 --> 00:06:13,280 Kanye has been linked to like AI in his music. 170 00:06:13,280 --> 00:06:14,120 Oh, really? 171 00:06:14,120 --> 00:06:14,960 Yeah. 172 00:06:14,960 --> 00:06:16,760 Something he might have used it to modify his vocals 173 00:06:16,760 --> 00:06:18,200 on the track Sky City. 174 00:06:18,200 --> 00:06:19,040 Interesting. 175 00:06:19,040 --> 00:06:23,400 Which raises another ethical consideration. 176 00:06:23,400 --> 00:06:26,160 Where do we draw the line between like artistic 177 00:06:26,160 --> 00:06:30,200 experimentation and potentially misleading the audience 178 00:06:30,200 --> 00:06:33,440 about the like origins of a creative work? 179 00:06:33,440 --> 00:06:34,480 That's a really good point. 180 00:06:34,480 --> 00:06:39,480 And it kind of ties into this bigger picture of AI's impact 181 00:06:39,560 --> 00:06:41,360 on creative industries as a whole. 182 00:06:41,360 --> 00:06:42,200 Totally. 183 00:06:42,200 --> 00:06:45,600 As these AI tools become more sophisticated, 184 00:06:45,600 --> 00:06:47,920 we're gonna have to like wrestle 185 00:06:47,920 --> 00:06:49,560 with these questions more and more. 186 00:06:49,560 --> 00:06:51,200 It's a conversation we need to have. 187 00:06:51,200 --> 00:06:52,040 Yeah. 188 00:06:52,040 --> 00:06:56,000 How do we like embrace the potential of AI in art? 189 00:06:56,000 --> 00:06:58,840 While also preserving the value of human creativity. 190 00:06:58,840 --> 00:06:59,680 Right. 191 00:06:59,680 --> 00:07:01,080 And making sure that things are transparent 192 00:07:01,080 --> 00:07:02,200 in the creative process. 193 00:07:02,200 --> 00:07:03,280 So much to unpack. 194 00:07:03,280 --> 00:07:04,400 Yeah. 195 00:07:04,400 --> 00:07:06,560 But moving on with this theme of regulation. 196 00:07:06,560 --> 00:07:08,640 Australia is making headlines. 197 00:07:08,640 --> 00:07:11,280 With their proposed ban on social media 198 00:07:11,280 --> 00:07:13,040 for anyone under 16. 199 00:07:13,040 --> 00:07:13,880 Interesting. 200 00:07:13,880 --> 00:07:15,080 It's one of the strictest measures 201 00:07:15,080 --> 00:07:15,960 we've seen globally. 202 00:07:15,960 --> 00:07:18,240 And it's definitely prompting like conversations 203 00:07:18,240 --> 00:07:19,560 about online safety. 204 00:07:19,560 --> 00:07:20,400 Right. 205 00:07:20,400 --> 00:07:22,880 And you know how governments should regulate technology. 206 00:07:22,880 --> 00:07:27,000 Australia's proposal is, I mean it's a bold move. 207 00:07:27,000 --> 00:07:30,600 And it has sparked concern. 208 00:07:30,600 --> 00:07:31,440 Oh yeah. 209 00:07:31,440 --> 00:07:33,120 Yeah, from the digital industry group. 210 00:07:33,120 --> 00:07:33,960 Okay. 211 00:07:33,960 --> 00:07:35,680 Which represents you know these tech giants 212 00:07:35,680 --> 00:07:37,920 like Snap and TikTok and Meta. 213 00:07:37,920 --> 00:07:40,280 They're worried about like the practicalities 214 00:07:40,280 --> 00:07:41,560 of age verification. 215 00:07:41,560 --> 00:07:42,400 Yeah. 216 00:07:42,400 --> 00:07:43,220 And how do you even do that? 217 00:07:43,220 --> 00:07:47,560 Exactly. And the potential impact on user privacy. 218 00:07:47,560 --> 00:07:49,160 Yeah. I mean that's understandable. 219 00:07:49,160 --> 00:07:50,000 Yeah. 220 00:07:50,000 --> 00:07:52,960 Collecting data to verify age raises 221 00:07:52,960 --> 00:07:55,400 like a whole host of privacy concerns. 222 00:07:55,400 --> 00:07:56,240 Totally. 223 00:07:56,240 --> 00:07:59,560 And then it's also like, is that even gonna work? 224 00:07:59,560 --> 00:08:00,400 Right. 225 00:08:00,400 --> 00:08:01,240 Like you know. 226 00:08:01,240 --> 00:08:02,080 Age is it effective. 227 00:08:02,080 --> 00:08:03,080 How effective would that even be 228 00:08:03,080 --> 00:08:06,480 at preventing you know, underage users 229 00:08:06,480 --> 00:08:08,360 from accessing these platforms? 230 00:08:08,360 --> 00:08:09,200 It's a tough one. 231 00:08:09,200 --> 00:08:12,880 And there are really no easy answers here. 232 00:08:12,880 --> 00:08:13,720 No. 233 00:08:13,720 --> 00:08:16,640 Australia's approach really contrasts with Florida's law. 234 00:08:16,640 --> 00:08:17,480 Okay. 235 00:08:17,480 --> 00:08:19,480 Which emphasizes parental consent. 236 00:08:19,480 --> 00:08:20,320 Right. Right. 237 00:08:20,320 --> 00:08:22,800 So you're seeing these like different approaches 238 00:08:22,800 --> 00:08:24,200 that governments are taking to try 239 00:08:24,200 --> 00:08:26,560 and address these challenges. 240 00:08:26,560 --> 00:08:28,400 And while Australia is tackling you know, 241 00:08:28,400 --> 00:08:29,720 social media access. 242 00:08:29,720 --> 00:08:30,560 Okay. 243 00:08:30,560 --> 00:08:34,160 Canada is taking aim at Google's dominance 244 00:08:34,160 --> 00:08:36,000 in the online advertising market. 245 00:08:36,000 --> 00:08:36,840 Interesting. 246 00:08:36,840 --> 00:08:39,960 They filed a lawsuit alleging anti-competitive practices. 247 00:08:39,960 --> 00:08:40,800 Okay. 248 00:08:40,800 --> 00:08:42,600 Seeking a hefty fine. 249 00:08:42,600 --> 00:08:44,760 And even demanding that Google sell off 250 00:08:44,760 --> 00:08:46,400 some of its ad tech tools. 251 00:08:46,400 --> 00:08:47,400 Wow. 252 00:08:47,400 --> 00:08:49,320 Like ad-ex and DFP. 253 00:08:49,320 --> 00:08:51,640 This lawsuit kind of echoes. 254 00:08:51,640 --> 00:08:54,200 Similar actions that the US Justice Department 255 00:08:54,200 --> 00:08:55,040 has been taking. 256 00:08:55,040 --> 00:08:58,280 Signaling this like growing global movement 257 00:08:58,280 --> 00:09:01,280 to kind of reign in the power of these tech giants. 258 00:09:01,280 --> 00:09:02,120 Yeah. 259 00:09:02,120 --> 00:09:04,080 This case could have huge implications 260 00:09:04,080 --> 00:09:06,920 for like how online advertising operates. 261 00:09:06,920 --> 00:09:07,760 Right. 262 00:09:07,760 --> 00:09:10,480 And how these dominant platforms are regulated. 263 00:09:10,480 --> 00:09:11,840 Seems like governments around the world 264 00:09:11,840 --> 00:09:14,120 are really starting to realize that like, 265 00:09:14,120 --> 00:09:17,080 we need clear rules for the digital landscape. 266 00:09:17,080 --> 00:09:19,960 Especially as AI becomes more integrated 267 00:09:19,960 --> 00:09:21,560 into these platforms and services. 268 00:09:21,560 --> 00:09:23,480 It's a tough balance. 269 00:09:23,480 --> 00:09:26,440 Regulation is like absolutely necessary. 270 00:09:26,440 --> 00:09:27,280 Oh yeah. 271 00:09:27,280 --> 00:09:29,080 To prevent abuses and protect consumers. 272 00:09:29,080 --> 00:09:31,320 But we also need to be really careful 273 00:09:31,320 --> 00:09:33,560 that we're not stifling innovation. 274 00:09:33,560 --> 00:09:34,400 Right. 275 00:09:34,400 --> 00:09:37,600 Finding that sweet spot is gonna be super crucial 276 00:09:37,600 --> 00:09:38,640 as we move forward. 277 00:09:38,640 --> 00:09:39,480 Right. 278 00:09:39,480 --> 00:09:40,320 Okay. 279 00:09:40,320 --> 00:09:41,160 So it's tricky. 280 00:09:41,160 --> 00:09:43,680 But shifting gears to the US. 281 00:09:43,680 --> 00:09:44,520 Okay. 282 00:09:44,520 --> 00:09:48,520 There's like a lot of uncertainty around AI regulation 283 00:09:48,520 --> 00:09:50,640 with the Republicans taking control. 284 00:09:50,640 --> 00:09:51,480 Right. 285 00:09:51,480 --> 00:09:55,040 President-elect Trump has pledged to overturn 286 00:09:55,040 --> 00:09:58,160 President Biden's AI executive order. 287 00:09:58,160 --> 00:10:00,680 Which focused on safety and ethical use. 288 00:10:00,680 --> 00:10:01,520 Right. 289 00:10:01,520 --> 00:10:02,560 And the Republican National Committee 290 00:10:02,560 --> 00:10:06,120 seems pretty like gung-ho about AI development. 291 00:10:06,120 --> 00:10:08,720 So what does that mean for AI in the US? 292 00:10:08,720 --> 00:10:09,560 Yeah. 293 00:10:09,560 --> 00:10:11,680 It's headed for a wild west scenario. 294 00:10:11,680 --> 00:10:12,520 Right. 295 00:10:12,520 --> 00:10:13,360 With no rules. 296 00:10:13,360 --> 00:10:14,760 It's hard to say for sure. 297 00:10:14,760 --> 00:10:18,200 There is bipartisan interest in using AI 298 00:10:18,200 --> 00:10:19,440 for national security. 299 00:10:19,440 --> 00:10:20,280 Okay. 300 00:10:20,280 --> 00:10:22,200 And things like combating. 301 00:10:22,200 --> 00:10:23,040 Yeah. 302 00:10:23,040 --> 00:10:24,960 Non-consensual explicit imagery. 303 00:10:24,960 --> 00:10:25,800 Right. 304 00:10:25,800 --> 00:10:29,040 But the Republican focus on free speech 305 00:10:29,040 --> 00:10:31,280 and limited government might lead to 306 00:10:31,280 --> 00:10:34,600 like a more hands-off approach to AI development. 307 00:10:34,600 --> 00:10:36,880 But doesn't that raise concerns about things like. 308 00:10:38,040 --> 00:10:38,880 Yeah. 309 00:10:38,880 --> 00:10:40,640 Things being used in elections. 310 00:10:40,640 --> 00:10:41,480 Absolutely. 311 00:10:41,480 --> 00:10:42,560 I mean that's. 312 00:10:42,560 --> 00:10:45,320 Especially with the Republicans wanting fewer regulations. 313 00:10:45,320 --> 00:10:46,160 It does. 314 00:10:46,160 --> 00:10:47,000 It does. 315 00:10:47,000 --> 00:10:50,720 And we're already seeing the potential for AI generated 316 00:10:50,720 --> 00:10:54,680 content to be used in like very misleading or harmful ways. 317 00:10:54,680 --> 00:10:55,520 Yeah. 318 00:10:55,520 --> 00:10:57,120 So it's you know. 319 00:10:57,120 --> 00:10:58,160 It's a tough balancing act. 320 00:10:58,160 --> 00:10:59,000 It is. 321 00:10:59,000 --> 00:10:59,840 Yeah. 322 00:10:59,840 --> 00:11:01,000 How do we protect against potential harms. 323 00:11:01,000 --> 00:11:01,840 Right. 324 00:11:01,840 --> 00:11:03,840 Without stifling innovation. 325 00:11:03,840 --> 00:11:05,960 It sounds like there are no easy answers. 326 00:11:05,960 --> 00:11:06,800 Yeah. 327 00:11:06,800 --> 00:11:08,000 Especially when it comes to regulating something. 328 00:11:08,000 --> 00:11:08,840 Yeah. 329 00:11:08,840 --> 00:11:11,720 It's complex and like rapidly evolving as AI. 330 00:11:11,720 --> 00:11:12,560 Exactly. 331 00:11:12,560 --> 00:11:15,160 And while the future of AI regulation in the US is uncertain. 332 00:11:15,160 --> 00:11:16,240 It's definitely something. 333 00:11:16,240 --> 00:11:17,080 Yeah. 334 00:11:17,080 --> 00:11:20,880 Will continue to be debating and discussing for years to come. 335 00:11:20,880 --> 00:11:21,720 Yeah. 336 00:11:21,720 --> 00:11:22,560 Yeah. 337 00:11:22,560 --> 00:11:23,400 For sure. 338 00:11:23,400 --> 00:11:24,240 Speaking of uncertainty. 339 00:11:24,240 --> 00:11:25,080 Okay. 340 00:11:25,080 --> 00:11:29,440 This story about a crypto user outsmarting an AI bot. 341 00:11:29,440 --> 00:11:30,280 Yeah. 342 00:11:30,280 --> 00:11:31,240 Named Fraysa. 343 00:11:31,240 --> 00:11:32,320 Okay. 344 00:11:32,320 --> 00:11:36,360 And walking away with a $47,000 prize pool. 345 00:11:36,360 --> 00:11:37,200 Wow. 346 00:11:37,200 --> 00:11:38,040 Really caught my eye. 347 00:11:38,040 --> 00:11:40,480 It's like something out of a movie. 348 00:11:40,480 --> 00:11:41,320 Yeah. 349 00:11:41,320 --> 00:11:42,160 Yeah. 350 00:11:42,160 --> 00:11:45,120 It makes you wonder if someone can like trick an AI bot that's 351 00:11:45,120 --> 00:11:45,960 you know. 352 00:11:45,960 --> 00:11:47,280 Designed for security. 353 00:11:47,280 --> 00:11:48,120 Right. 354 00:11:48,120 --> 00:11:51,200 What does that say about like the potential vulnerabilities 355 00:11:51,200 --> 00:11:52,160 of these systems. 356 00:11:52,160 --> 00:11:54,720 It's a fascinating story. 357 00:11:54,720 --> 00:11:58,040 And I think you know it highlights this fact that even 358 00:11:58,040 --> 00:11:59,800 the most sophisticated AI systems. 359 00:11:59,800 --> 00:12:00,640 Yeah. 360 00:12:00,640 --> 00:12:03,480 Can still be susceptible to these like unexpected loopholes. 361 00:12:03,480 --> 00:12:04,320 Right. 362 00:12:04,320 --> 00:12:06,880 This particular user exploited a logic flaw. 363 00:12:06,880 --> 00:12:07,720 Okay. 364 00:12:07,720 --> 00:12:08,560 This is programming. 365 00:12:08,560 --> 00:12:09,400 Got it. 366 00:12:09,400 --> 00:12:14,400 Demonstrating that AI at its core is still based on code. 367 00:12:14,400 --> 00:12:16,520 And algorithms that can be manipulated. 368 00:12:16,520 --> 00:12:17,360 Right. 369 00:12:17,360 --> 00:12:19,000 So it's not you know all knowing all powerful. 370 00:12:19,000 --> 00:12:19,840 It's not magic. 371 00:12:19,840 --> 00:12:20,680 Yeah. 372 00:12:20,680 --> 00:12:21,520 Exactly. 373 00:12:21,520 --> 00:12:22,360 Exactly. 374 00:12:22,360 --> 00:12:24,200 It's a reminder that we need to be cautious. 375 00:12:24,200 --> 00:12:27,920 And like constantly evaluating the security of these systems 376 00:12:27,920 --> 00:12:30,160 as they become more integrated into our lives. 377 00:12:30,160 --> 00:12:33,840 But I think it also highlights you know human ingenuity. 378 00:12:33,840 --> 00:12:34,680 Right. 379 00:12:34,680 --> 00:12:39,680 And the ability to kind of find creative solutions. 380 00:12:39,680 --> 00:12:40,520 Yeah. 381 00:12:40,520 --> 00:12:43,800 Even when we're faced with this like seemingly impenetrable 382 00:12:43,800 --> 00:12:44,720 technology. 383 00:12:44,720 --> 00:12:45,560 Right. 384 00:12:45,560 --> 00:12:49,040 And this guy turned it into like a $47,000 payday. 385 00:12:49,040 --> 00:12:49,880 So. 386 00:12:49,880 --> 00:12:50,720 Yeah. 387 00:12:50,720 --> 00:12:51,560 I mean, do you know impressive. 388 00:12:51,560 --> 00:12:54,720 This story reminds us that AI is a tool. 389 00:12:54,720 --> 00:12:55,720 And like any tool. 390 00:12:55,720 --> 00:12:56,560 Right. 391 00:12:56,560 --> 00:12:58,440 It can be used for good or for bad. 392 00:12:58,440 --> 00:12:59,280 Right. 393 00:12:59,280 --> 00:13:02,360 So it's up to us to like understand its limitations. 394 00:13:02,360 --> 00:13:03,200 Yeah. 395 00:13:03,200 --> 00:13:07,800 And develop you know the safeguards to prevent misuse. 396 00:13:07,800 --> 00:13:09,760 Speaking of cutting edge technology. 397 00:13:09,760 --> 00:13:10,600 Okay. 398 00:13:10,600 --> 00:13:12,760 Microsoft and Adam Computing. 399 00:13:12,760 --> 00:13:15,720 Have made a huge breakthrough in quantum computing. 400 00:13:15,720 --> 00:13:16,560 Yeah. 401 00:13:16,560 --> 00:13:19,480 They've managed to entangle a record number 402 00:13:19,480 --> 00:13:21,120 of logical quibits. 403 00:13:21,120 --> 00:13:21,960 Okay. 404 00:13:21,960 --> 00:13:26,000 Using remarkably few physical quibits. 405 00:13:26,000 --> 00:13:26,840 Okay. 406 00:13:26,840 --> 00:13:27,680 I'll be honest. 407 00:13:27,680 --> 00:13:28,640 I'm not a quantum physicist. 408 00:13:28,640 --> 00:13:30,000 Can you break this down for me? 409 00:13:30,000 --> 00:13:31,680 Like why is this a big deal? 410 00:13:31,680 --> 00:13:32,520 Sure. 411 00:13:32,520 --> 00:13:35,560 Think of quibits like the building blocks. 412 00:13:35,560 --> 00:13:37,640 Of quantum computers. 413 00:13:37,640 --> 00:13:38,480 Right. 414 00:13:38,480 --> 00:13:41,960 The more quibits you can entangle and control. 415 00:13:41,960 --> 00:13:44,280 The more powerful the computer becomes. 416 00:13:44,280 --> 00:13:46,600 What Microsoft and Adam Computing have achieved 417 00:13:46,600 --> 00:13:49,680 is really significant because they've increased 418 00:13:49,680 --> 00:13:51,640 the number of logical quibits. 419 00:13:51,640 --> 00:13:52,480 Right. 420 00:13:52,480 --> 00:13:53,880 While using fewer physical quibits. 421 00:13:53,880 --> 00:13:56,000 Making it more efficient and scalable. 422 00:13:56,000 --> 00:13:56,840 Okay. 423 00:13:56,840 --> 00:13:58,360 So more power with less hardware. 424 00:13:58,360 --> 00:13:59,200 Exactly. 425 00:13:59,200 --> 00:14:00,040 Got it. 426 00:14:00,040 --> 00:14:00,880 Yeah. 427 00:14:00,880 --> 00:14:04,960 And what does that do with cryptocurrency mining? 428 00:14:04,960 --> 00:14:07,200 This is where it gets really interesting. 429 00:14:07,200 --> 00:14:08,160 What can it lead on me? 430 00:14:08,160 --> 00:14:09,480 Quantum computers. 431 00:14:09,480 --> 00:14:10,320 Yeah. 432 00:14:10,320 --> 00:14:13,080 With their immense processing power. 433 00:14:13,080 --> 00:14:13,920 Right. 434 00:14:13,920 --> 00:14:15,120 Could potentially, yeah. 435 00:14:15,120 --> 00:14:19,520 Crack the encryption algorithms that secure 436 00:14:19,520 --> 00:14:21,080 many cryptocurrencies. 437 00:14:21,080 --> 00:14:22,120 Including Bitcoin. 438 00:14:22,120 --> 00:14:22,960 Okay. 439 00:14:22,960 --> 00:14:25,200 Specifically Grover's algorithm. 440 00:14:25,200 --> 00:14:28,560 Which is a quantum computing technique. 441 00:14:28,560 --> 00:14:30,560 Could be used to break. 442 00:14:30,560 --> 00:14:31,400 Okay. 443 00:14:31,400 --> 00:14:32,840 SHA-256 encryption. 444 00:14:32,840 --> 00:14:33,680 Okay. 445 00:14:33,680 --> 00:14:37,080 Which underpins Bitcoin's proof of work system. 446 00:14:37,080 --> 00:14:37,920 Hold on. 447 00:14:37,920 --> 00:14:40,560 So you're saying that quantum computers could potentially 448 00:14:40,560 --> 00:14:43,560 like render Bitcoin mining as we know it. 449 00:14:43,560 --> 00:14:44,480 Like obsolete. 450 00:14:44,480 --> 00:14:46,480 That sounds a little dramatic. 451 00:14:46,480 --> 00:14:48,880 It's not a question of if but when. 452 00:14:48,880 --> 00:14:49,720 Okay. 453 00:14:49,720 --> 00:14:51,760 Right now quantum computers aren't powerful enough. 454 00:14:51,760 --> 00:14:52,600 Right. 455 00:14:52,600 --> 00:14:54,840 To like crack SHA-256. 456 00:14:54,840 --> 00:14:55,680 Okay. 457 00:14:55,680 --> 00:14:58,960 But the technology is, it's advancing rapidly. 458 00:14:58,960 --> 00:14:59,800 Right. 459 00:14:59,800 --> 00:15:01,520 And the breakthrough by Microsoft and 460 00:15:01,520 --> 00:15:02,360 and computing. 461 00:15:02,360 --> 00:15:03,200 Right. 462 00:15:03,200 --> 00:15:04,280 Is a sign that we're getting closer. 463 00:15:04,280 --> 00:15:05,440 So what happens then? 464 00:15:05,440 --> 00:15:06,280 Yeah. 465 00:15:06,280 --> 00:15:07,880 Does Bitcoin just like disappear? 466 00:15:07,880 --> 00:15:09,000 Not necessarily. 467 00:15:09,000 --> 00:15:09,840 Okay. 468 00:15:09,840 --> 00:15:11,960 The cryptocurrency industry is aware of this threat. 469 00:15:11,960 --> 00:15:12,800 Okay. 470 00:15:12,800 --> 00:15:15,840 And they're working on developing quantum resistant 471 00:15:15,840 --> 00:15:16,760 algorithms. 472 00:15:16,760 --> 00:15:18,640 It's like this race against time. 473 00:15:18,640 --> 00:15:19,480 Yeah. 474 00:15:19,480 --> 00:15:22,120 To create these new cryptographic methods. 475 00:15:22,120 --> 00:15:22,960 Right. 476 00:15:22,960 --> 00:15:24,600 That can withstand the power. 477 00:15:24,600 --> 00:15:25,440 Okay. 478 00:15:25,440 --> 00:15:28,120 Of quantum computers. 479 00:15:28,120 --> 00:15:29,680 So it's a high stakes game. 480 00:15:29,680 --> 00:15:30,520 It is. 481 00:15:30,520 --> 00:15:32,200 Shifting gears once again. 482 00:15:32,200 --> 00:15:33,040 Yeah. 483 00:15:33,040 --> 00:15:36,520 Let's talk about the potential for AI 484 00:15:36,520 --> 00:15:38,920 to address the US deficit. 485 00:15:38,920 --> 00:15:39,760 Okay. 486 00:15:39,760 --> 00:15:43,280 I was surprised to read that economists are optimistic. 487 00:15:43,280 --> 00:15:44,360 Uh huh. 488 00:15:44,360 --> 00:15:49,120 About AI's ability to narrow the gap, 489 00:15:49,120 --> 00:15:50,240 especially in healthcare. 490 00:15:50,240 --> 00:15:51,800 It might seem counterintuitive, 491 00:15:51,800 --> 00:15:54,520 but AI could totally revolutionize healthcare. 492 00:15:54,520 --> 00:15:55,360 Okay. 493 00:15:55,360 --> 00:15:57,320 And make it significantly more efficient. 494 00:15:57,320 --> 00:15:58,160 Okay. 495 00:15:58,160 --> 00:16:01,120 This in turn could lead to substantial cost savings. 496 00:16:01,120 --> 00:16:01,960 Right. 497 00:16:01,960 --> 00:16:03,920 Potentially contributing to a reduction 498 00:16:03,920 --> 00:16:05,160 in the US deficit. 499 00:16:05,160 --> 00:16:06,000 Okay. 500 00:16:06,000 --> 00:16:06,840 I'm intrigued. 501 00:16:06,840 --> 00:16:07,680 Yeah. 502 00:16:07,680 --> 00:16:09,960 How could AI actually make healthcare more efficient? 503 00:16:09,960 --> 00:16:12,760 So think about all the like administrative tasks 504 00:16:12,760 --> 00:16:14,200 that involved in healthcare. 505 00:16:14,200 --> 00:16:15,960 Appointment scheduling, billing, 506 00:16:15,960 --> 00:16:17,840 patient record management. 507 00:16:17,840 --> 00:16:20,160 These tasks are so time consuming. 508 00:16:20,160 --> 00:16:21,000 Right. 509 00:16:21,000 --> 00:16:21,840 And expensive. 510 00:16:21,840 --> 00:16:22,680 Yeah. 511 00:16:22,680 --> 00:16:23,520 For sure. 512 00:16:23,520 --> 00:16:25,320 AI can automate a lot of these processes. 513 00:16:25,320 --> 00:16:26,160 Okay. 514 00:16:26,160 --> 00:16:27,760 Freeing up healthcare professionals. 515 00:16:27,760 --> 00:16:28,600 Right. 516 00:16:28,600 --> 00:16:31,600 To actually focus on patient care. 517 00:16:31,600 --> 00:16:35,560 And wouldn't that also like reduce errors? 518 00:16:35,560 --> 00:16:37,760 And make things like more accurate. 519 00:16:37,760 --> 00:16:38,600 Exactly. 520 00:16:38,600 --> 00:16:39,440 Exactly. 521 00:16:39,440 --> 00:16:40,280 Right. 522 00:16:40,280 --> 00:16:41,280 AI can help eliminate human error. 523 00:16:41,280 --> 00:16:42,120 Right. 524 00:16:42,120 --> 00:16:44,640 Leading to more efficient and accurate 525 00:16:44,640 --> 00:16:45,480 record keeping billing. 526 00:16:45,480 --> 00:16:46,320 Oh, right. 527 00:16:46,320 --> 00:16:48,680 And overall administrative processes. 528 00:16:48,680 --> 00:16:50,640 So it makes things run smoother. 529 00:16:50,640 --> 00:16:51,480 Yeah. 530 00:16:51,480 --> 00:16:52,320 Exactly. 531 00:16:52,320 --> 00:16:53,280 And that not only saves money. 532 00:16:53,280 --> 00:16:54,120 Yeah. 533 00:16:54,120 --> 00:16:56,040 But it also improves the quality of care. 534 00:16:56,040 --> 00:16:56,880 Right. 535 00:16:56,880 --> 00:16:58,040 And ensuring that patients are getting 536 00:16:58,040 --> 00:16:58,920 the right treatments. 537 00:16:58,920 --> 00:16:59,760 Yeah. 538 00:16:59,760 --> 00:17:00,600 And medications. 539 00:17:00,600 --> 00:17:01,560 But it's not just about like 540 00:17:01,560 --> 00:17:03,680 making things run smoothly behind the scenes. 541 00:17:03,680 --> 00:17:04,520 Right. 542 00:17:04,520 --> 00:17:05,360 Exactly. 543 00:17:05,360 --> 00:17:06,200 Yeah. 544 00:17:06,200 --> 00:17:07,040 What about? 545 00:17:07,040 --> 00:17:08,600 What about actually improving. 546 00:17:08,600 --> 00:17:09,440 Yeah. 547 00:17:09,440 --> 00:17:10,280 Improving patient care. 548 00:17:10,280 --> 00:17:11,120 Patient care. 549 00:17:11,120 --> 00:17:11,960 Right. 550 00:17:11,960 --> 00:17:12,800 Right. 551 00:17:12,800 --> 00:17:15,680 That's where AI gets really exciting. 552 00:17:15,680 --> 00:17:19,320 AI can analyze like vast amounts of medical data. 553 00:17:19,320 --> 00:17:20,160 Okay. 554 00:17:20,160 --> 00:17:22,040 To identify patterns and insights. 555 00:17:22,040 --> 00:17:22,880 Yeah. 556 00:17:22,880 --> 00:17:24,840 That humans might miss. 557 00:17:24,840 --> 00:17:25,680 Okay. 558 00:17:25,680 --> 00:17:26,520 Okay. 559 00:17:26,520 --> 00:17:29,560 And then we'll go to earlier and more accurate diagnoses. 560 00:17:29,560 --> 00:17:31,200 Personalized treatment plans. 561 00:17:31,200 --> 00:17:32,440 And even the development. 562 00:17:32,440 --> 00:17:33,280 Right. 563 00:17:33,280 --> 00:17:35,240 Of new drugs. 564 00:17:35,240 --> 00:17:36,720 And therapies. 565 00:17:36,720 --> 00:17:38,640 So it's like having a super smart assistant. 566 00:17:38,640 --> 00:17:39,480 It is. 567 00:17:39,480 --> 00:17:40,520 Helping doctors. 568 00:17:40,520 --> 00:17:41,360 Exactly. 569 00:17:41,360 --> 00:17:42,360 Make better decisions. 570 00:17:42,360 --> 00:17:43,200 Exactly. 571 00:17:43,200 --> 00:17:44,040 That sounds amazing. 572 00:17:44,040 --> 00:17:44,880 Yeah. 573 00:17:44,880 --> 00:17:45,840 But are there any risks? 574 00:17:45,840 --> 00:17:46,680 Of course. 575 00:17:46,680 --> 00:17:48,680 I mean with any new technology. 576 00:17:48,680 --> 00:17:49,520 Right. 577 00:17:49,520 --> 00:17:51,560 There are potential challenges. 578 00:17:51,560 --> 00:17:52,400 Okay. 579 00:17:52,400 --> 00:17:55,800 One major concern is the potential for bias. 580 00:17:55,800 --> 00:17:56,480 Okay. 581 00:17:56,480 --> 00:17:57,960 And AI algorithms. 582 00:17:57,960 --> 00:17:58,800 Right. 583 00:17:58,800 --> 00:18:03,440 If the data used to train these algorithms is like skewed. 584 00:18:03,440 --> 00:18:04,280 Right. 585 00:18:04,280 --> 00:18:05,120 Or incomplete. 586 00:18:05,120 --> 00:18:05,960 Right. 587 00:18:05,960 --> 00:18:07,160 It could lead to discriminatory outcomes. 588 00:18:07,160 --> 00:18:08,000 Right. 589 00:18:08,000 --> 00:18:09,640 So we need to be careful about the data that's going in. 590 00:18:09,640 --> 00:18:10,480 Exactly. 591 00:18:10,480 --> 00:18:11,320 Exactly. 592 00:18:11,320 --> 00:18:12,160 How do we even. 593 00:18:12,160 --> 00:18:13,120 Yeah. 594 00:18:13,120 --> 00:18:13,960 Police that. 595 00:18:13,960 --> 00:18:14,800 That's a great question. 596 00:18:14,800 --> 00:18:15,640 Yeah. 597 00:18:15,640 --> 00:18:16,480 What other concerns are there? 598 00:18:16,480 --> 00:18:18,360 Data privacy is another one. 599 00:18:18,360 --> 00:18:19,200 Okay. 600 00:18:19,200 --> 00:18:20,040 Right. 601 00:18:20,040 --> 00:18:21,520 Healthcare data is incredibly sensitive. 602 00:18:21,520 --> 00:18:22,360 Yeah. 603 00:18:22,360 --> 00:18:23,200 And we need to make sure. 604 00:18:23,200 --> 00:18:24,040 Yeah. 605 00:18:24,040 --> 00:18:26,160 That any use of AI in healthcare. 606 00:18:26,160 --> 00:18:27,000 Right. 607 00:18:27,000 --> 00:18:29,120 Adheres to very strict. 608 00:18:29,120 --> 00:18:29,960 Yeah. 609 00:18:29,960 --> 00:18:30,800 Privacy regulations. 610 00:18:30,800 --> 00:18:31,640 Right. 611 00:18:31,640 --> 00:18:32,480 And protects. 612 00:18:32,480 --> 00:18:33,320 Yeah. 613 00:18:33,320 --> 00:18:34,160 Patient confidentiality. 614 00:18:34,160 --> 00:18:36,920 It seems like there's a lot to consider in terms of. 615 00:18:36,920 --> 00:18:37,760 You know. 616 00:18:37,760 --> 00:18:38,600 Yeah. 617 00:18:38,600 --> 00:18:39,440 The benefits. 618 00:18:39,440 --> 00:18:40,280 Yeah. 619 00:18:40,280 --> 00:18:41,120 But also the risks. 620 00:18:41,120 --> 00:18:41,960 Absolutely. 621 00:18:41,960 --> 00:18:43,840 Implementing AI in healthcare requires. 622 00:18:43,840 --> 00:18:44,680 Like. 623 00:18:44,680 --> 00:18:45,520 I does. 624 00:18:45,520 --> 00:18:46,360 A lot of careful planning. 625 00:18:46,360 --> 00:18:47,200 Mm hmm. 626 00:18:47,200 --> 00:18:48,040 Ethical considerations. 627 00:18:48,040 --> 00:18:48,880 Yeah. 628 00:18:48,880 --> 00:18:49,720 For sure. 629 00:18:49,720 --> 00:18:50,560 But. 630 00:18:50,560 --> 00:18:51,400 The potential is huge. 631 00:18:51,400 --> 00:18:52,240 It is. 632 00:18:52,240 --> 00:18:53,080 But. 633 00:18:53,080 --> 00:18:54,520 Before we get too carried away. 634 00:18:54,520 --> 00:18:55,360 Okay. 635 00:18:55,360 --> 00:18:56,100 With the future of AI. 636 00:18:56,100 --> 00:18:56,940 We're going to rewind a bit. 637 00:18:56,940 --> 00:18:57,760 Okay. 638 00:18:57,760 --> 00:18:58,840 And talk about something a little closer at home. 639 00:18:58,840 --> 00:19:00,280 Remember that story about Kanye West. 640 00:19:00,280 --> 00:19:01,120 Yeah. 641 00:19:01,120 --> 00:19:03,800 Using AI to recreate his daughters in his music video. 642 00:19:03,800 --> 00:19:04,880 Mm hmm. 643 00:19:04,880 --> 00:19:08,000 That got me thinking about like the. 644 00:19:08,000 --> 00:19:09,480 The issue of access. 645 00:19:09,480 --> 00:19:10,400 Okay. 646 00:19:10,400 --> 00:19:13,080 If AI powered healthcare is going to be the future. 647 00:19:13,080 --> 00:19:13,920 Yeah. 648 00:19:13,920 --> 00:19:16,040 How do we make sure that it's not just a luxury. 649 00:19:16,040 --> 00:19:16,880 Yeah. 650 00:19:16,880 --> 00:19:17,720 For the wealthy. 651 00:19:17,720 --> 00:19:18,560 That's a really important point. 652 00:19:18,560 --> 00:19:23,600 And you know we can't let AI driven healthcare. 653 00:19:23,600 --> 00:19:24,440 Right. 654 00:19:24,440 --> 00:19:26,320 To exacerbate these existing inequalities. 655 00:19:26,320 --> 00:19:27,160 Right. 656 00:19:27,160 --> 00:19:29,800 Policy makers, healthcare providers, tech developers. 657 00:19:29,800 --> 00:19:30,640 Yeah. 658 00:19:30,640 --> 00:19:32,160 All need to be working together. 659 00:19:32,160 --> 00:19:33,000 Yeah. 660 00:19:33,000 --> 00:19:34,600 To make sure that these advancements. 661 00:19:34,600 --> 00:19:35,440 Mm hmm. 662 00:19:35,440 --> 00:19:36,280 Are accessible to everyone. 663 00:19:36,280 --> 00:19:37,120 Mm hmm. 664 00:19:37,120 --> 00:19:37,960 Regardless of their. 665 00:19:37,960 --> 00:19:39,000 Uh. 666 00:19:39,000 --> 00:19:39,840 Income. 667 00:19:39,840 --> 00:19:40,660 Yeah. 668 00:19:40,660 --> 00:19:41,500 Location. 669 00:19:41,500 --> 00:19:42,340 It's like we're on the cusp of this. 670 00:19:42,340 --> 00:19:43,180 Yeah. 671 00:19:43,180 --> 00:19:45,480 Like amazing technological revolutions in healthcare. 672 00:19:45,480 --> 00:19:46,320 Mm hmm. 673 00:19:46,320 --> 00:19:47,640 But we need to make sure that we're like. 674 00:19:47,640 --> 00:19:48,480 Yeah. 675 00:19:48,480 --> 00:19:49,520 Running everyone along for the ride. 676 00:19:49,520 --> 00:19:50,360 Exactly. 677 00:19:50,360 --> 00:19:54,920 AI has the potential to really democratize healthcare. 678 00:19:54,920 --> 00:19:55,760 Right. 679 00:19:55,760 --> 00:19:56,600 Yeah. 680 00:19:56,600 --> 00:19:57,440 Make it more personalized. 681 00:19:57,440 --> 00:19:58,280 Mm hmm. 682 00:19:58,280 --> 00:19:59,120 Affordable and accessible. 683 00:19:59,120 --> 00:19:59,960 Right. 684 00:19:59,960 --> 00:20:02,080 But that will only happen if we prioritize. 685 00:20:02,080 --> 00:20:02,920 Yeah. 686 00:20:02,920 --> 00:20:04,800 Equity and inclusivity from the very start. 687 00:20:04,800 --> 00:20:06,600 So it's not just about the technology itself. 688 00:20:06,600 --> 00:20:07,440 Right. 689 00:20:07,440 --> 00:20:10,080 It's about like how we implement it. 690 00:20:10,080 --> 00:20:10,920 Exactly. 691 00:20:10,920 --> 00:20:11,760 And who benefits from it. 692 00:20:11,760 --> 00:20:13,240 It's a big responsibility. 693 00:20:13,240 --> 00:20:16,440 And I think you know it really makes you realize. 694 00:20:16,440 --> 00:20:19,880 That these conversations about AI are about so much more. 695 00:20:19,880 --> 00:20:20,720 Yeah. 696 00:20:20,720 --> 00:20:23,480 Than just like lines of code and algorithms. 697 00:20:23,480 --> 00:20:26,920 It's about shaping the future of society. 698 00:20:26,920 --> 00:20:27,760 Yeah. 699 00:20:27,760 --> 00:20:30,720 And ensuring that technology serves humanity as a whole. 700 00:20:30,720 --> 00:20:31,560 Well said. 701 00:20:31,560 --> 00:20:32,400 So. 702 00:20:32,400 --> 00:20:33,240 Yeah. 703 00:20:33,240 --> 00:20:34,080 This deep dive has. 704 00:20:36,040 --> 00:20:38,960 I think really highlighted the fact that. 705 00:20:38,960 --> 00:20:39,800 Uh huh. 706 00:20:39,800 --> 00:20:42,960 AI isn't just some futuristic concept. 707 00:20:42,960 --> 00:20:43,800 Right. 708 00:20:43,800 --> 00:20:44,640 It's already here. 709 00:20:44,640 --> 00:20:45,480 Yeah. 710 00:20:45,480 --> 00:20:48,640 And it's transforming the world in some really profound ways. 711 00:20:48,640 --> 00:20:49,480 Yeah. 712 00:20:49,480 --> 00:20:51,160 From the rise of small language models. 713 00:20:51,160 --> 00:20:52,000 Right. 714 00:20:52,000 --> 00:20:54,520 To the potential of quantum computing. 715 00:20:54,520 --> 00:20:56,560 The promise of AI powered healthcare. 716 00:20:56,560 --> 00:20:57,400 Yeah. 717 00:20:57,400 --> 00:21:00,120 We're seeing AI's influence across like. 718 00:21:00,120 --> 00:21:00,960 Yeah. 719 00:21:00,960 --> 00:21:01,800 Almost every aspect of our lives. 720 00:21:01,800 --> 00:21:03,120 It's everywhere. 721 00:21:03,120 --> 00:21:03,960 Yeah. 722 00:21:03,960 --> 00:21:04,800 Yeah. 723 00:21:04,800 --> 00:21:05,640 It's been a wild ride. 724 00:21:05,640 --> 00:21:06,480 It has. 725 00:21:06,480 --> 00:21:08,440 Exploring all these different facets of AI. 726 00:21:08,440 --> 00:21:09,760 And it's clear that like. 727 00:21:09,760 --> 00:21:12,240 We're just at the beginning of this journey. 728 00:21:12,240 --> 00:21:14,600 What stood out to you the most? 729 00:21:14,600 --> 00:21:17,120 I think for me it's the sheer like. 730 00:21:17,120 --> 00:21:19,560 The breadth of AI's impact. 731 00:21:19,560 --> 00:21:20,600 It's huge. 732 00:21:20,600 --> 00:21:21,440 Yeah. 733 00:21:21,440 --> 00:21:22,280 Yeah. 734 00:21:22,280 --> 00:21:25,640 We talked about how it's like revolutionizing creative industries. 735 00:21:25,640 --> 00:21:27,960 Challenging the financial markets. 736 00:21:27,960 --> 00:21:30,400 Pushing the boundaries of scientific research. 737 00:21:30,400 --> 00:21:31,240 Yeah. 738 00:21:31,240 --> 00:21:32,640 And even like. 739 00:21:32,640 --> 00:21:34,120 Prompting us to rethink. 740 00:21:34,120 --> 00:21:35,480 Like the very nature. 741 00:21:35,480 --> 00:21:36,320 Yeah. 742 00:21:36,320 --> 00:21:37,160 Of intelligence. 743 00:21:37,160 --> 00:21:38,000 Uh huh. 744 00:21:38,000 --> 00:21:38,840 And consciousness. 745 00:21:38,840 --> 00:21:40,400 It's pretty mind blowing when you. 746 00:21:41,320 --> 00:21:42,600 When you really think about it. 747 00:21:42,600 --> 00:21:43,440 It is. 748 00:21:43,440 --> 00:21:44,280 Yeah. 749 00:21:44,280 --> 00:21:45,240 And the pace of change is like. 750 00:21:45,240 --> 00:21:46,200 It's incredible. 751 00:21:46,200 --> 00:21:47,040 Yeah. 752 00:21:47,040 --> 00:21:48,400 It's like every day. 753 00:21:48,400 --> 00:21:49,240 Yeah. 754 00:21:49,240 --> 00:21:50,080 There's some new. 755 00:21:50,080 --> 00:21:50,920 Right. 756 00:21:50,920 --> 00:21:52,200 Breakthrough or discovery. 757 00:21:52,200 --> 00:21:53,200 It's exciting. 758 00:21:53,200 --> 00:21:54,040 But it is. 759 00:21:54,040 --> 00:21:55,720 But it's also like a little overwhelming. 760 00:21:55,720 --> 00:21:56,840 It's a lot to keep up with. 761 00:21:56,840 --> 00:21:57,680 Yeah. 762 00:21:57,680 --> 00:21:58,520 It is. 763 00:21:58,520 --> 00:22:00,160 So as we wrap up. 764 00:22:00,160 --> 00:22:01,000 Yeah. 765 00:22:01,000 --> 00:22:02,560 What can our listeners do. 766 00:22:02,560 --> 00:22:03,400 Yeah. 767 00:22:03,400 --> 00:22:04,360 To like. 768 00:22:04,360 --> 00:22:06,120 Make sense of all this. 769 00:22:06,120 --> 00:22:07,680 Well first and foremost. 770 00:22:07,680 --> 00:22:08,520 Yeah. 771 00:22:08,520 --> 00:22:09,800 Stay curious. 772 00:22:09,800 --> 00:22:10,640 Okay. 773 00:22:10,640 --> 00:22:11,600 Read articles. 774 00:22:11,600 --> 00:22:12,440 Yeah. 775 00:22:12,440 --> 00:22:15,280 Listen to podcasts like this one. 776 00:22:15,280 --> 00:22:18,320 Don't be afraid to ask questions. 777 00:22:18,320 --> 00:22:19,880 Challenge assumptions. 778 00:22:19,880 --> 00:22:20,720 Yeah. 779 00:22:20,720 --> 00:22:22,080 Explore different perspectives. 780 00:22:22,080 --> 00:22:23,600 And think about how. 781 00:22:25,040 --> 00:22:27,720 How AI might impact. 782 00:22:27,720 --> 00:22:29,000 Your own life. 783 00:22:29,000 --> 00:22:30,640 And the world around you. 784 00:22:30,640 --> 00:22:31,800 It's about like. 785 00:22:31,800 --> 00:22:33,320 Being an active participant. 786 00:22:33,320 --> 00:22:34,160 Exactly. 787 00:22:34,160 --> 00:22:35,880 In shaping the future of AI. 788 00:22:35,880 --> 00:22:36,720 Yeah. 789 00:22:36,720 --> 00:22:38,160 Not just a passive observer. 790 00:22:38,160 --> 00:22:39,000 Exactly. 791 00:22:39,000 --> 00:22:40,280 The choices that we make today. 792 00:22:40,280 --> 00:22:41,720 Are gonna determine. 793 00:22:41,720 --> 00:22:43,680 The trajectory of AI development. 794 00:22:43,680 --> 00:22:44,520 Yeah. 795 00:22:44,520 --> 00:22:45,560 And the impact on society. 796 00:22:45,560 --> 00:22:46,400 So it's. 797 00:22:46,400 --> 00:22:47,240 Yeah. 798 00:22:47,240 --> 00:22:48,080 It's up to all of us. 799 00:22:48,080 --> 00:22:48,920 Mm hmm. 800 00:22:48,920 --> 00:22:50,960 To make sure that AI is used. 801 00:22:50,960 --> 00:22:51,800 Ethically. 802 00:22:51,800 --> 00:22:52,640 Right. 803 00:22:52,640 --> 00:22:53,480 Responsibly. 804 00:22:53,480 --> 00:22:54,320 Mm hmm. 805 00:22:54,320 --> 00:22:55,640 And for the benefit of all. 806 00:22:55,640 --> 00:22:57,080 This deep dive has been. 807 00:22:57,080 --> 00:22:57,920 Yeah. 808 00:22:57,920 --> 00:22:59,400 A whirlwind tour. 809 00:22:59,400 --> 00:23:00,720 Of the AI landscape. 810 00:23:00,720 --> 00:23:01,560 It has. 811 00:23:01,560 --> 00:23:02,640 And it's clear that there's like. 812 00:23:02,640 --> 00:23:03,480 Yes. 813 00:23:03,480 --> 00:23:04,800 Still so much more to explore. 814 00:23:04,800 --> 00:23:05,640 So much more. 815 00:23:05,640 --> 00:23:07,160 But I think the most important take away 816 00:23:07,160 --> 00:23:08,640 for our listeners is this. 817 00:23:08,640 --> 00:23:09,480 Yeah. 818 00:23:09,480 --> 00:23:11,760 AI is not just some abstract concept. 819 00:23:11,760 --> 00:23:12,600 Right. 820 00:23:12,600 --> 00:23:13,800 It's a powerful force. 821 00:23:13,800 --> 00:23:14,480 Yeah. 822 00:23:14,480 --> 00:23:15,320 That's what we're doing. 823 00:23:15,320 --> 00:23:16,280 We're already shaping our world. 824 00:23:16,280 --> 00:23:18,760 And understanding it is crucial. 825 00:23:18,760 --> 00:23:19,600 It's essential. 826 00:23:19,600 --> 00:23:20,440 Yeah. 827 00:23:20,440 --> 00:23:21,280 For navigating the future. 828 00:23:21,280 --> 00:23:25,520 We need to approach AI with a sense of both wonder. 829 00:23:25,520 --> 00:23:26,360 Mm hmm. 830 00:23:26,360 --> 00:23:27,200 And responsibility. 831 00:23:27,200 --> 00:23:28,040 Right. 832 00:23:28,040 --> 00:23:30,760 Recognizing its potential to solve these. 833 00:23:30,760 --> 00:23:32,080 Really complex problems. 834 00:23:32,080 --> 00:23:32,920 Right. 835 00:23:32,920 --> 00:23:34,880 But also being mindful of its. 836 00:23:34,880 --> 00:23:37,440 You know limitations and potential risks. 837 00:23:37,440 --> 00:23:38,280 Well said. 838 00:23:38,280 --> 00:23:39,120 Well. 839 00:23:39,120 --> 00:23:39,960 Yeah. 840 00:23:39,960 --> 00:23:40,800 Thanks for joining us on this deep dive. 841 00:23:40,800 --> 00:23:41,640 Of course. 842 00:23:41,640 --> 00:23:42,480 Into the world of AI. 843 00:23:42,480 --> 00:23:43,320 Yeah. 844 00:23:43,320 --> 00:23:45,480 And we're going to be very excited to see what 845 00:23:45,480 --> 00:23:47,760 we can do to help you find it insightful. 846 00:23:47,760 --> 00:23:48,600 Mm hmm. 847 00:23:48,600 --> 00:23:49,600 And thought provoking. 848 00:23:49,600 --> 00:23:52,760 Thanks for listening to the Daily AI News Podcast.