1 00:00:00,000 --> 00:00:02,700 Welcome to the Daily AI News podcast. 2 00:00:02,700 --> 00:00:04,340 We're gonna be diving into a whole bunch 3 00:00:04,340 --> 00:00:06,360 of different AI topics today. 4 00:00:06,360 --> 00:00:08,640 Everything from like the future of AI 5 00:00:08,640 --> 00:00:12,920 to some really practical stuff, even ethical concerns. 6 00:00:12,920 --> 00:00:14,520 We've got a lot to unpack. 7 00:00:14,520 --> 00:00:15,560 So let's get started. 8 00:00:15,560 --> 00:00:19,920 Our first source takes us to the Cerebral Valley AI Summit. 9 00:00:19,920 --> 00:00:20,960 Okay. 10 00:00:20,960 --> 00:00:23,320 Where experts debated whether AI progress 11 00:00:23,320 --> 00:00:25,720 is actually slowing down. 12 00:00:25,720 --> 00:00:26,840 Hitting a wall. 13 00:00:26,840 --> 00:00:29,120 So what were some of the big takeaways 14 00:00:29,120 --> 00:00:30,440 that you got from that? 15 00:00:30,440 --> 00:00:33,000 Well, there were some pretty different opinions. 16 00:00:33,000 --> 00:00:36,200 Alexandra Wang, the CEO of Scale AI, 17 00:00:36,200 --> 00:00:39,400 he admitted that just throwing more data 18 00:00:39,400 --> 00:00:43,560 at these AI models we have might not be the best way to go. 19 00:00:43,560 --> 00:00:46,500 He sees more potential in refining those models 20 00:00:46,500 --> 00:00:48,040 and focusing on AI agents. 21 00:00:48,040 --> 00:00:49,760 AI agents, that's like... 22 00:00:49,760 --> 00:00:50,600 It's the big thing. 23 00:00:50,600 --> 00:00:51,840 It's the buzzword right now. 24 00:00:51,840 --> 00:00:52,960 Everybody's talking about it. 25 00:00:52,960 --> 00:00:55,340 Yeah, can you kind of break down for our listeners 26 00:00:55,340 --> 00:00:57,440 what AI agents actually are? 27 00:00:57,440 --> 00:00:58,280 Sure. 28 00:00:58,280 --> 00:00:59,720 There's so much hype around them. 29 00:00:59,720 --> 00:01:01,400 Yeah, so think of an AI agent 30 00:01:01,400 --> 00:01:04,280 as like a super intelligent digital assistant. 31 00:01:04,280 --> 00:01:06,300 That can interact not just with computers, 32 00:01:06,300 --> 00:01:08,120 but with like the real world. 33 00:01:08,120 --> 00:01:08,960 Yeah, wow. 34 00:01:08,960 --> 00:01:09,920 To get things done. 35 00:01:09,920 --> 00:01:12,440 Like having a super powered helper, 36 00:01:12,440 --> 00:01:15,120 that can learn and adapt to what you need. 37 00:01:15,120 --> 00:01:17,960 So like if I needed to book a flight or a hotel. 38 00:01:17,960 --> 00:01:18,800 Exactly. 39 00:01:18,800 --> 00:01:21,160 An AI agent could theoretically do that for me. 40 00:01:21,160 --> 00:01:22,480 In theory. 41 00:01:22,480 --> 00:01:24,240 Yeah, and even more than that. 42 00:01:24,240 --> 00:01:27,560 So like Google, OpenAI, Meta, 43 00:01:27,560 --> 00:01:29,760 they're all racing to build these. 44 00:01:29,760 --> 00:01:31,240 And there's just a ton of speculation 45 00:01:31,240 --> 00:01:33,120 about what they'll be able to do. 46 00:01:33,120 --> 00:01:35,640 So are we talking about like a future where... 47 00:01:35,640 --> 00:01:36,640 It could be. 48 00:01:36,640 --> 00:01:40,160 We tell our AI agent to like do our grocery shopping for us. 49 00:01:40,160 --> 00:01:41,960 Write our emails, all that kind of stuff. 50 00:01:41,960 --> 00:01:42,880 Potentially. 51 00:01:42,880 --> 00:01:46,200 That's kind of the vision, although we're still very early. 52 00:01:46,200 --> 00:01:51,200 But Wang actually predicts a chat GPT moment for agents. 53 00:01:51,760 --> 00:01:54,400 Or like a super capable generic agent 54 00:01:54,400 --> 00:01:57,080 just becomes like the thing that everyone uses. 55 00:01:57,080 --> 00:01:58,640 Really, huh? 56 00:01:58,640 --> 00:02:00,000 That's an interesting prediction. 57 00:02:00,000 --> 00:02:00,840 It is. 58 00:02:00,840 --> 00:02:02,200 So from your perspective, 59 00:02:02,200 --> 00:02:04,320 what's it gonna take to get us to that point? 60 00:02:04,320 --> 00:02:05,160 Well, according to Wang, 61 00:02:05,160 --> 00:02:07,240 we need like new ways of training these things. 62 00:02:07,240 --> 00:02:08,720 Using data. 63 00:02:08,720 --> 00:02:11,000 You need to teach them to do complex tasks 64 00:02:11,000 --> 00:02:12,120 in the real world. 65 00:02:12,120 --> 00:02:12,960 Got it. 66 00:02:12,960 --> 00:02:17,440 And specifically, you need data that captures human actions 67 00:02:17,440 --> 00:02:18,680 and decision making. 68 00:02:18,680 --> 00:02:19,520 Right. 69 00:02:19,520 --> 00:02:21,840 Which isn't as easy to come by as you might think. 70 00:02:21,840 --> 00:02:23,400 So data is key. 71 00:02:23,400 --> 00:02:25,880 But of course, we can't just talk about the good stuff, right? 72 00:02:25,880 --> 00:02:27,920 We gotta talk about the potential risks as well. 73 00:02:27,920 --> 00:02:28,760 Of course. 74 00:02:28,760 --> 00:02:31,280 As AI gets more powerful, we can't ignore that. 75 00:02:31,280 --> 00:02:32,120 Absolutely. 76 00:02:32,120 --> 00:02:34,000 There are a lot of concerns around safety 77 00:02:34,000 --> 00:02:36,440 and that's where you start to see a clash of perspectives. 78 00:02:36,440 --> 00:02:37,280 Okay. 79 00:02:37,280 --> 00:02:39,520 So at the Cerebral Valley Summit, 80 00:02:39,520 --> 00:02:42,240 there was this interesting exchange 81 00:02:42,240 --> 00:02:47,120 between Anthropix CEO, Dario Amore. 82 00:02:47,120 --> 00:02:49,240 Who's actually really optimistic about AI. 83 00:02:49,240 --> 00:02:50,080 Okay. 84 00:02:50,080 --> 00:02:53,360 And Mark Andreessen, the venture capitalist 85 00:02:53,360 --> 00:02:56,520 who's known for his AI is just math stance. 86 00:02:56,520 --> 00:02:57,960 Oh, I've heard that argument before. 87 00:02:57,960 --> 00:02:58,800 Right. 88 00:02:58,800 --> 00:03:01,840 That because it's just math, it can't really be dangerous. 89 00:03:01,840 --> 00:03:02,680 Right. 90 00:03:02,680 --> 00:03:03,520 It's based on math. 91 00:03:03,520 --> 00:03:04,360 So what's the big deal? 92 00:03:04,360 --> 00:03:06,480 It just seems a little bit simplistic to me. 93 00:03:06,480 --> 00:03:07,320 Yeah. 94 00:03:07,320 --> 00:03:09,080 And Amode definitely thought so. 95 00:03:09,080 --> 00:03:11,320 His response to Andreessen was, 96 00:03:11,320 --> 00:03:13,680 isn't your brain just math? 97 00:03:13,680 --> 00:03:14,520 Oh. 98 00:03:14,520 --> 00:03:16,240 You know, which kind of highlights the flaw 99 00:03:16,240 --> 00:03:17,240 in that thinking. 100 00:03:17,240 --> 00:03:18,080 Right. 101 00:03:18,080 --> 00:03:20,400 The human brain can do some pretty terrible things 102 00:03:20,400 --> 00:03:22,200 even if it is based on math. 103 00:03:22,200 --> 00:03:23,200 That's a really good point. 104 00:03:23,200 --> 00:03:24,040 Yeah. 105 00:03:24,040 --> 00:03:25,200 So we have to think about this carefully. 106 00:03:25,200 --> 00:03:27,760 The potential benefits are amazing, 107 00:03:27,760 --> 00:03:29,280 but there are risks. 108 00:03:29,280 --> 00:03:30,120 Right. 109 00:03:30,120 --> 00:03:31,240 And we can't just ignore them. 110 00:03:31,240 --> 00:03:32,480 Absolutely. 111 00:03:32,480 --> 00:03:35,080 So speaking of Amode and Anthropix, 112 00:03:35,080 --> 00:03:37,920 our next source dives into their partnership 113 00:03:37,920 --> 00:03:39,680 with Amazon Web Services. 114 00:03:39,680 --> 00:03:40,520 Right. 115 00:03:40,520 --> 00:03:41,340 So this is interesting. 116 00:03:41,340 --> 00:03:44,240 Amazon just put a ton more money into Anthropix. 117 00:03:44,240 --> 00:03:45,080 Really? 118 00:03:45,080 --> 00:03:47,120 Like another $4 billion. 119 00:03:47,120 --> 00:03:47,960 Wow. 120 00:03:47,960 --> 00:03:50,320 On top of the 4 billion they already invested. 121 00:03:50,320 --> 00:03:51,160 Okay. 122 00:03:51,160 --> 00:03:52,200 So that's a pretty significant investment. 123 00:03:52,200 --> 00:03:53,200 It's massive. 124 00:03:53,200 --> 00:03:57,360 And it makes AWS their main cloud and training partner. 125 00:03:57,360 --> 00:03:58,600 Wow. That's huge. 126 00:03:58,600 --> 00:04:00,040 What does that actually mean for people 127 00:04:00,040 --> 00:04:01,240 who aren't super familiar with like 128 00:04:01,240 --> 00:04:02,720 the technical side of things? 129 00:04:02,720 --> 00:04:03,560 Sure. 130 00:04:03,560 --> 00:04:04,800 So they're going to be working together 131 00:04:04,800 --> 00:04:06,680 on both the hardware and the software. 132 00:04:06,680 --> 00:04:07,520 Okay. 133 00:04:07,520 --> 00:04:08,360 Specifically, they're going to improve 134 00:04:08,360 --> 00:04:10,200 this thing called Tranium. 135 00:04:10,200 --> 00:04:11,040 What is that? 136 00:04:11,040 --> 00:04:12,440 It's a machine learning chip. 137 00:04:12,440 --> 00:04:13,280 Okay. 138 00:04:13,280 --> 00:04:14,320 That AWS designed themselves. 139 00:04:14,320 --> 00:04:15,160 I had it. 140 00:04:15,160 --> 00:04:17,160 And they're also going to integrate 141 00:04:17,160 --> 00:04:20,120 Claude, which is Anthropix AI model, 142 00:04:20,120 --> 00:04:21,880 into Amazon Bedrock. 143 00:04:21,880 --> 00:04:23,960 Okay. So for those who don't know, 144 00:04:23,960 --> 00:04:25,120 what is Amazon Bedrock? 145 00:04:25,120 --> 00:04:26,080 Why is this a big deal? 146 00:04:26,080 --> 00:04:28,640 So Amazon Bedrock is basically a service 147 00:04:28,640 --> 00:04:33,640 that lets businesses use all sorts of powerful AI models, 148 00:04:33,680 --> 00:04:36,000 including these big language models. 149 00:04:36,000 --> 00:04:38,040 So they don't have to build them from scratch. 150 00:04:38,040 --> 00:04:38,880 Got it. 151 00:04:38,880 --> 00:04:41,560 So putting Claude into Bedrock means businesses 152 00:04:41,560 --> 00:04:44,560 will have another tool that they can use 153 00:04:44,560 --> 00:04:46,720 for things like generating content 154 00:04:46,720 --> 00:04:48,760 or more automating complex stuff. 155 00:04:48,760 --> 00:04:51,840 So this sounds like it's making AI much more accessible. 156 00:04:51,840 --> 00:04:52,680 Exactly. 157 00:04:52,680 --> 00:04:55,920 Lower the barrier to entry for all kinds of companies. 158 00:04:55,920 --> 00:04:59,080 But does that also raise any concerns about, you know, 159 00:04:59,080 --> 00:05:02,040 for sure, the power of these big tech companies? 160 00:05:02,040 --> 00:05:04,320 Yeah. That's a really important thing to consider. 161 00:05:04,320 --> 00:05:05,160 Right. 162 00:05:05,160 --> 00:05:07,000 As AI gets more powerful, 163 00:05:07,000 --> 00:05:08,680 we have to think about who controls it. 164 00:05:08,680 --> 00:05:09,520 Absolutely. 165 00:05:09,520 --> 00:05:11,480 And how that power is used. 166 00:05:11,480 --> 00:05:12,320 That's a great point. 167 00:05:12,320 --> 00:05:13,160 And it's something that I think 168 00:05:13,160 --> 00:05:15,400 we're going to continue to see discussed more and more. 169 00:05:15,400 --> 00:05:17,400 Definitely as AI gets more and more 170 00:05:17,400 --> 00:05:18,920 integrated into everything. 171 00:05:18,920 --> 00:05:21,000 Right. Now, shifting gears a little bit. 172 00:05:21,000 --> 00:05:21,820 Okay. 173 00:05:21,820 --> 00:05:23,360 Let's talk about Microsoft. 174 00:05:23,360 --> 00:05:24,320 All right. 175 00:05:24,320 --> 00:05:28,440 They recently previewed this new AI tool 176 00:05:28,440 --> 00:05:31,400 called Recall With Click To Do 177 00:05:31,400 --> 00:05:34,040 for their Co-Pilot Plus PCs. 178 00:05:34,040 --> 00:05:35,520 Yeah. It's in preview right now 179 00:05:35,520 --> 00:05:38,320 for people with Windows Insiders on the Dev Channel. 180 00:05:38,320 --> 00:05:39,880 So not widely available yet. 181 00:05:39,880 --> 00:05:40,720 Not yet. 182 00:05:40,720 --> 00:05:41,800 But it gives us a peek into the future. 183 00:05:41,800 --> 00:05:42,640 A little glimpse. 184 00:05:42,640 --> 00:05:44,320 So what does this tool actually do? 185 00:05:44,320 --> 00:05:47,900 So Recall uses AI to help you find things 186 00:05:47,900 --> 00:05:50,400 that you've seen or done on your PC. 187 00:05:50,400 --> 00:05:53,680 You can search using descriptions, visual cues, 188 00:05:53,680 --> 00:05:55,080 even a timeframe. 189 00:05:55,080 --> 00:05:56,640 So it's like a super search. 190 00:05:56,640 --> 00:05:58,560 Yeah. Super search for everything you've ever done. 191 00:05:58,560 --> 00:06:00,600 Exactly. Like your whole computer history. 192 00:06:00,600 --> 00:06:01,440 What? 193 00:06:01,440 --> 00:06:02,280 Click to do. 194 00:06:02,280 --> 00:06:03,960 Let's you actually act on those things. 195 00:06:03,960 --> 00:06:04,800 Okay. 196 00:06:04,800 --> 00:06:06,200 Like the images or text and the stuff you find. 197 00:06:06,200 --> 00:06:07,280 Okay. Can you give me an example 198 00:06:07,280 --> 00:06:08,520 of how that would actually work? 199 00:06:08,520 --> 00:06:10,360 Yeah. So say you're working on a project. 200 00:06:10,360 --> 00:06:11,200 Mm-hmm. 201 00:06:11,200 --> 00:06:12,480 And you remember seeing a graph 202 00:06:12,480 --> 00:06:15,120 with some sales trends like a few days ago. 203 00:06:15,120 --> 00:06:15,960 Right. 204 00:06:15,960 --> 00:06:18,200 You can use Recall to find that. 205 00:06:18,200 --> 00:06:23,200 And then click to do will open the document. 206 00:06:23,600 --> 00:06:24,440 Oh wow. 207 00:06:24,440 --> 00:06:25,680 Or even share it with someone. 208 00:06:25,680 --> 00:06:27,160 So it's not just finding it. 209 00:06:27,160 --> 00:06:28,000 It's doing something. 210 00:06:28,000 --> 00:06:30,640 But like doing something with that information. 211 00:06:30,640 --> 00:06:31,480 Exactly. 212 00:06:31,480 --> 00:06:32,320 That's really cool. 213 00:06:32,320 --> 00:06:34,640 Yeah. It could be huge for productivity. 214 00:06:34,640 --> 00:06:35,600 That sounds awesome. 215 00:06:35,600 --> 00:06:36,440 Oh yeah. 216 00:06:36,440 --> 00:06:37,880 But I do have one question. 217 00:06:37,880 --> 00:06:38,720 Yeah. 218 00:06:38,720 --> 00:06:41,040 If it's tracking everything I do on my computer. 219 00:06:41,040 --> 00:06:41,880 Uh huh. 220 00:06:41,880 --> 00:06:45,520 Isn't that a little bit scary from a privacy standpoint? 221 00:06:45,520 --> 00:06:46,600 That's a fair question. 222 00:06:46,600 --> 00:06:49,960 And Microsoft has said that they've taken steps. 223 00:06:49,960 --> 00:06:50,800 Okay. 224 00:06:50,800 --> 00:06:52,040 To protect user privacy. 225 00:06:52,040 --> 00:06:52,880 Okay. 226 00:06:52,880 --> 00:06:54,920 The snapshots are stored on your PC, 227 00:06:54,920 --> 00:06:56,360 not sent to Microsoft. 228 00:06:56,360 --> 00:06:59,800 And they've said that sensitive stuff like passwords 229 00:06:59,800 --> 00:07:01,720 and credit card numbers aren't saved. 230 00:07:01,720 --> 00:07:03,840 So it sounds like they're taking some precautions. 231 00:07:03,840 --> 00:07:05,960 Yeah. They seem to be aware of the concerns. 232 00:07:05,960 --> 00:07:07,040 Ooh, which is good. 233 00:07:07,040 --> 00:07:08,160 Yeah. It's important to be careful. 234 00:07:08,160 --> 00:07:09,320 Because anything that's collecting 235 00:07:09,320 --> 00:07:11,000 that much data about you. 236 00:07:11,000 --> 00:07:11,820 Right. 237 00:07:11,820 --> 00:07:13,160 You have to be a little bit cautious about it. 238 00:07:13,160 --> 00:07:15,240 Okay. So let's move on to a topic. 239 00:07:15,240 --> 00:07:16,080 Okay. 240 00:07:16,080 --> 00:07:17,920 We're going to highlight some of the ethical challenges 241 00:07:17,920 --> 00:07:19,000 around AI. 242 00:07:19,000 --> 00:07:19,840 Right. 243 00:07:19,840 --> 00:07:22,760 Our next source is about a Stanford professor, 244 00:07:22,760 --> 00:07:23,760 Jeff Hancock. 245 00:07:23,760 --> 00:07:24,600 Uh huh. 246 00:07:24,600 --> 00:07:28,520 Who was accused of using AI to fake testimony. 247 00:07:28,520 --> 00:07:29,360 Yeah. 248 00:07:29,360 --> 00:07:30,440 This is a pretty wild one. 249 00:07:30,440 --> 00:07:31,800 Wait, a Stanford professor. 250 00:07:31,800 --> 00:07:32,640 No. 251 00:07:32,640 --> 00:07:35,320 Accused of using AI to create false evidence. 252 00:07:35,320 --> 00:07:36,160 Yeah. 253 00:07:36,160 --> 00:07:37,800 That's pretty serious. 254 00:07:37,800 --> 00:07:39,200 So what are the details here? 255 00:07:39,200 --> 00:07:42,240 So Hancock is actually an expert on misinformation. 256 00:07:42,240 --> 00:07:43,080 Okay. 257 00:07:43,080 --> 00:07:44,000 Which is kind of ironic. 258 00:07:44,000 --> 00:07:47,760 But he submitted a statement for a case in Minnesota 259 00:07:47,760 --> 00:07:50,920 that was challenging their ban on political deepfakes. 260 00:07:50,920 --> 00:07:51,760 Okay. 261 00:07:51,760 --> 00:07:54,320 And in that statement, he cited a study 262 00:07:54,320 --> 00:07:56,160 that seems to have never existed. 263 00:07:56,160 --> 00:07:58,480 He cited a study that doesn't exist. 264 00:07:58,480 --> 00:07:59,600 It doesn't seem to. 265 00:07:59,600 --> 00:08:01,200 How is that even possible? 266 00:08:01,200 --> 00:08:02,480 That's the scary part. 267 00:08:02,480 --> 00:08:05,160 The lawyers on the other side of the case, 268 00:08:05,160 --> 00:08:07,240 they think it was an AI hallucination. 269 00:08:07,240 --> 00:08:09,080 Like it was just made up by an AI. 270 00:08:09,080 --> 00:08:09,920 Wow. 271 00:08:09,920 --> 00:08:10,880 Like chat GPT. 272 00:08:10,880 --> 00:08:12,240 So if that's true. 273 00:08:12,240 --> 00:08:13,080 Yeah. 274 00:08:13,080 --> 00:08:14,280 It's incredibly concerning. 275 00:08:14,280 --> 00:08:15,120 It is. 276 00:08:15,120 --> 00:08:17,160 Because if you can just create fake studies. 277 00:08:17,160 --> 00:08:18,000 Yeah. 278 00:08:18,000 --> 00:08:19,040 And present them as real. 279 00:08:19,040 --> 00:08:20,280 It undermines everything. 280 00:08:20,280 --> 00:08:22,360 It undermines the entire concept of trust. 281 00:08:22,360 --> 00:08:23,200 Exactly. 282 00:08:23,200 --> 00:08:24,800 Like how do you know what to believe? 283 00:08:24,800 --> 00:08:26,360 It's like a very slippery slope. 284 00:08:26,360 --> 00:08:27,200 It is. 285 00:08:27,200 --> 00:08:29,960 And especially in the context like legal proceedings. 286 00:08:29,960 --> 00:08:30,800 Yeah. 287 00:08:30,800 --> 00:08:31,920 The implications are huge. 288 00:08:31,920 --> 00:08:33,200 Where stakes are really high. 289 00:08:33,200 --> 00:08:34,600 Absolutely. 290 00:08:34,600 --> 00:08:36,640 This is a case we need to pay attention to. 291 00:08:36,640 --> 00:08:37,480 Yeah. 292 00:08:37,480 --> 00:08:38,920 This is a really important one to highlight. 293 00:08:38,920 --> 00:08:41,240 And I think it really underscores the need 294 00:08:41,240 --> 00:08:42,840 for critical thinking. 295 00:08:42,840 --> 00:08:45,560 Especially in this age of AI. 296 00:08:45,560 --> 00:08:46,400 Yeah. 297 00:08:46,400 --> 00:08:48,200 We can't just take everything at face value. 298 00:08:48,200 --> 00:08:50,680 Especially when it sounds really believable. 299 00:08:50,680 --> 00:08:51,520 Right. 300 00:08:51,520 --> 00:08:52,800 Because as AI gets better. 301 00:08:52,800 --> 00:08:53,640 Right. 302 00:08:53,640 --> 00:08:55,840 It's gonna be harder to tell what's real and what's not. 303 00:08:55,840 --> 00:08:56,680 You're sure. 304 00:08:56,680 --> 00:08:58,080 So we need to be vigilant. 305 00:08:58,080 --> 00:08:58,920 Absolutely. 306 00:08:58,920 --> 00:08:59,760 All right. 307 00:08:59,760 --> 00:09:00,600 So let's lighten things up a bit. 308 00:09:00,600 --> 00:09:01,440 Okay. 309 00:09:01,440 --> 00:09:03,280 I could use a break from the doom and gloom. 310 00:09:03,280 --> 00:09:04,120 Yeah. 311 00:09:04,120 --> 00:09:05,440 Me too. 312 00:09:05,440 --> 00:09:07,920 Let's talk about how AI is even impacting the way 313 00:09:07,920 --> 00:09:09,320 we shop for the holidays. 314 00:09:10,280 --> 00:09:11,280 Wait what? 315 00:09:11,280 --> 00:09:12,600 I know it sounds kind of crazy right. 316 00:09:12,600 --> 00:09:14,200 AI and holiday shopping. 317 00:09:14,200 --> 00:09:16,320 That's an interesting combination. 318 00:09:16,320 --> 00:09:17,280 Tell me about it. 319 00:09:17,280 --> 00:09:18,120 Okay. 320 00:09:18,120 --> 00:09:18,960 I'm intrigued. 321 00:09:18,960 --> 00:09:19,800 Lay it on me. 322 00:09:19,800 --> 00:09:22,280 So some people are actually using chat GPT. 323 00:09:22,280 --> 00:09:23,120 Okay. 324 00:09:23,120 --> 00:09:24,880 To come up with Christmas gift ideas. 325 00:09:24,880 --> 00:09:26,400 Like what to buy people. 326 00:09:26,400 --> 00:09:27,240 Yeah. 327 00:09:27,240 --> 00:09:28,080 Yeah. 328 00:09:28,080 --> 00:09:28,920 Interesting. 329 00:09:28,920 --> 00:09:30,920 So this is obviously sparked a bit of a debate. 330 00:09:30,920 --> 00:09:31,760 I bet. 331 00:09:31,760 --> 00:09:33,120 You know among gift giving experts. 332 00:09:33,120 --> 00:09:33,960 It exists. 333 00:09:33,960 --> 00:09:35,000 What do they think about this? 334 00:09:35,000 --> 00:09:36,080 Well on the one hand, 335 00:09:36,080 --> 00:09:38,280 you can see how AI could be helpful. 336 00:09:38,280 --> 00:09:39,120 Yeah. 337 00:09:39,120 --> 00:09:39,960 For brainstorming. 338 00:09:39,960 --> 00:09:40,800 Yeah. 339 00:09:40,800 --> 00:09:41,840 If you don't know where to start. 340 00:09:41,840 --> 00:09:42,680 Right. 341 00:09:42,680 --> 00:09:43,520 If you're feeling stuck. 342 00:09:43,520 --> 00:09:44,360 It could give you some ideas. 343 00:09:44,360 --> 00:09:45,200 Right. 344 00:09:45,200 --> 00:09:46,400 But then again, you run the risk of just getting 345 00:09:46,400 --> 00:09:47,920 like really generic gifts. 346 00:09:47,920 --> 00:09:48,760 Okay. 347 00:09:48,760 --> 00:09:50,000 That don't really have any thought behind them. 348 00:09:50,000 --> 00:09:50,840 Right. 349 00:09:50,840 --> 00:09:51,760 So it's kind of a double-edged sword. 350 00:09:51,760 --> 00:09:52,600 Exactly. 351 00:09:52,600 --> 00:09:53,720 So what are some of the things 352 00:09:53,720 --> 00:09:55,560 that the experts are actually saying? 353 00:09:55,560 --> 00:09:58,000 Well gift giving experts like Betsy Ben, 354 00:09:58,000 --> 00:09:59,240 they really emphasize. 355 00:09:59,240 --> 00:10:00,080 Okay. 356 00:10:00,080 --> 00:10:02,440 The importance of thoughtful, personalized gifts. 357 00:10:02,440 --> 00:10:03,280 Right. 358 00:10:03,280 --> 00:10:05,120 That show that you really know the person. 359 00:10:05,120 --> 00:10:05,960 Of course. 360 00:10:05,960 --> 00:10:09,560 So for them, using AI generated gift lists. 361 00:10:09,560 --> 00:10:10,400 Uh huh. 362 00:10:10,400 --> 00:10:11,520 Is missing the whole point. 363 00:10:11,520 --> 00:10:12,360 Right. 364 00:10:12,360 --> 00:10:13,880 The point is to show you care. 365 00:10:13,880 --> 00:10:14,720 Right. 366 00:10:14,720 --> 00:10:16,640 And appreciate the people you're giving gifts to. 367 00:10:16,640 --> 00:10:17,480 Right. 368 00:10:17,480 --> 00:10:19,000 Not just go through the motions. 369 00:10:19,000 --> 00:10:19,840 Okay. 370 00:10:19,840 --> 00:10:21,840 So is using AI for Christmas shopping 371 00:10:21,840 --> 00:10:23,640 like a bad thing then? 372 00:10:23,640 --> 00:10:26,000 I don't know if it's necessarily bad. 373 00:10:26,000 --> 00:10:26,840 Okay. 374 00:10:26,840 --> 00:10:28,400 Professor Catherine Janssen Boyd, 375 00:10:28,400 --> 00:10:30,520 she's an expert in consumer psychology. 376 00:10:30,520 --> 00:10:31,360 Okay. 377 00:10:31,360 --> 00:10:33,560 She says that AI has its limits. 378 00:10:33,560 --> 00:10:34,400 Okay. 379 00:10:34,400 --> 00:10:35,720 Especially when it comes to emotions. 380 00:10:35,720 --> 00:10:36,560 Right. 381 00:10:36,560 --> 00:10:37,400 And personalization. 382 00:10:37,400 --> 00:10:38,240 Right. 383 00:10:38,240 --> 00:10:40,320 But she also points out that AI can help us 384 00:10:40,320 --> 00:10:41,880 understand ourselves better. 385 00:10:41,880 --> 00:10:43,200 Oh, that's interesting. 386 00:10:43,200 --> 00:10:44,040 Yeah. 387 00:10:44,040 --> 00:10:45,280 It can give us insights we wouldn't have thought of. 388 00:10:45,280 --> 00:10:47,960 So maybe it's about finding that balance. 389 00:10:47,960 --> 00:10:48,800 I think so. 390 00:10:48,800 --> 00:10:50,720 Using AI as a starting point. 391 00:10:50,720 --> 00:10:51,560 Yeah. 392 00:10:51,560 --> 00:10:52,400 Like brainstorming. 393 00:10:52,400 --> 00:10:53,240 Right. 394 00:10:53,240 --> 00:10:54,280 But then adding our own personal touch. 395 00:10:54,280 --> 00:10:55,120 Exactly. 396 00:10:55,120 --> 00:10:55,960 You gotta make it your own. 397 00:10:55,960 --> 00:10:56,800 Right. 398 00:10:56,800 --> 00:10:57,640 AI can help us. 399 00:10:57,640 --> 00:10:58,480 Yeah. 400 00:10:58,480 --> 00:11:00,040 But it can't do everything for us. 401 00:11:00,040 --> 00:11:00,880 Not yet anyway. 402 00:11:00,880 --> 00:11:01,720 Right. 403 00:11:01,720 --> 00:11:02,760 So it's all about finding that balance. 404 00:11:02,760 --> 00:11:04,440 And that's a really good point to keep in mind 405 00:11:04,440 --> 00:11:06,160 as we head into the holiday season. 406 00:11:06,160 --> 00:11:07,440 For sure. 407 00:11:07,440 --> 00:11:09,160 But let's shift gears now. 408 00:11:09,160 --> 00:11:12,400 To a topic that I think is on a lot of people's minds. 409 00:11:12,400 --> 00:11:14,880 And that's AI's impact on jobs. 410 00:11:14,880 --> 00:11:15,720 Yeah. 411 00:11:15,720 --> 00:11:16,640 Especially for coders. 412 00:11:16,640 --> 00:11:17,480 Exactly. 413 00:11:17,480 --> 00:11:18,320 Yeah. 414 00:11:18,320 --> 00:11:20,200 So we're seeing all these AI coding tools popping up. 415 00:11:20,200 --> 00:11:21,320 Left and right. 416 00:11:21,320 --> 00:11:23,840 Is learning to code even worth it anymore? 417 00:11:23,840 --> 00:11:24,680 Yeah. 418 00:11:24,680 --> 00:11:25,640 That's the big question. 419 00:11:25,640 --> 00:11:28,640 Are these skills gonna be obsolete in a few years? 420 00:11:28,640 --> 00:11:29,880 It's tricky. 421 00:11:29,880 --> 00:11:32,360 There are a lot of different opinions out there. 422 00:11:32,360 --> 00:11:33,200 Okay. 423 00:11:33,200 --> 00:11:35,560 Some experts think AI is gonna create 424 00:11:35,560 --> 00:11:37,400 even more jobs for coders. 425 00:11:37,400 --> 00:11:38,240 Really? 426 00:11:38,240 --> 00:11:39,080 How so? 427 00:11:39,080 --> 00:11:40,880 Doesn't it just automate everything? 428 00:11:40,880 --> 00:11:42,680 That's the other side of the argument. 429 00:11:42,680 --> 00:11:43,520 And it's valid. 430 00:11:43,520 --> 00:11:45,920 One of our sources talks about this guy, 431 00:11:45,920 --> 00:11:47,360 Florence Rindone. 432 00:11:47,360 --> 00:11:49,760 He graduated from a coding boot camp. 433 00:11:49,760 --> 00:11:50,600 Okay. 434 00:11:50,600 --> 00:11:52,200 And he's actually struggling to find a job. 435 00:11:52,200 --> 00:11:53,040 Oh wow. 436 00:11:53,040 --> 00:11:53,880 Yeah. 437 00:11:53,880 --> 00:11:56,160 He's finding that even with the training, 438 00:11:56,160 --> 00:11:57,800 there's just so much competition 439 00:11:57,800 --> 00:11:59,400 from experienced coders. 440 00:11:59,400 --> 00:12:00,240 Right. 441 00:12:00,240 --> 00:12:01,800 And AI is changing the game. 442 00:12:01,800 --> 00:12:04,640 So is this the end of learn to code? 443 00:12:04,640 --> 00:12:05,600 Not necessarily. 444 00:12:05,600 --> 00:12:08,240 But it means the skills you need to succeed 445 00:12:08,240 --> 00:12:09,160 are changing. 446 00:12:09,160 --> 00:12:10,000 Okay. 447 00:12:10,000 --> 00:12:11,120 So what are some of those skills? 448 00:12:11,120 --> 00:12:12,200 What do people need to know? 449 00:12:12,200 --> 00:12:14,320 Well, it's not just about the code anymore. 450 00:12:14,320 --> 00:12:16,480 You need to be a good problem solver. 451 00:12:16,480 --> 00:12:17,600 You need business skills 452 00:12:17,600 --> 00:12:19,400 and you have to be a great communicator. 453 00:12:19,400 --> 00:12:20,720 So it's more holistic. 454 00:12:20,720 --> 00:12:21,560 Yeah. 455 00:12:21,560 --> 00:12:23,080 You need to be able to think critically. 456 00:12:23,080 --> 00:12:23,920 Right. 457 00:12:23,920 --> 00:12:24,760 And understand how things work. 458 00:12:24,760 --> 00:12:25,600 In a broader sense. 459 00:12:25,600 --> 00:12:26,440 Exactly. 460 00:12:26,440 --> 00:12:28,200 But let's get into some specifics. 461 00:12:28,200 --> 00:12:32,200 What are these GPT monkeys that we keep hearing about? 462 00:12:32,200 --> 00:12:33,040 Right. 463 00:12:33,040 --> 00:12:35,760 So GPT monkey is kind of a funny term. 464 00:12:35,760 --> 00:12:36,600 Okay. 465 00:12:36,600 --> 00:12:39,360 It refers to the idea that AI can automate 466 00:12:39,360 --> 00:12:41,440 the basic stuff in coding. 467 00:12:41,440 --> 00:12:42,280 Okay. 468 00:12:42,280 --> 00:12:44,840 Which could actually displace junior programmers. 469 00:12:44,840 --> 00:12:46,800 So it's like AI is taking over. 470 00:12:46,800 --> 00:12:48,480 It's taking over the boring parts. 471 00:12:48,480 --> 00:12:49,320 Okay. 472 00:12:49,320 --> 00:12:50,160 The repetitive stuff. 473 00:12:50,160 --> 00:12:51,000 Right. 474 00:12:51,000 --> 00:12:51,840 Which could be good or bad. 475 00:12:51,840 --> 00:12:52,680 Right. 476 00:12:52,680 --> 00:12:55,520 It frees up humans to focus on the more complex stuff. 477 00:12:55,520 --> 00:12:56,680 The more interesting stuff. 478 00:12:56,680 --> 00:12:57,680 Exactly. 479 00:12:57,680 --> 00:12:59,920 But it could also mean job losses. 480 00:12:59,920 --> 00:13:01,680 For people who are just starting out. 481 00:13:01,680 --> 00:13:02,520 Right. 482 00:13:02,520 --> 00:13:04,720 And haven't developed those higher level skills yet. 483 00:13:04,720 --> 00:13:07,840 Yeah. And Professor Matt Bean who's actually studying this. 484 00:13:07,840 --> 00:13:08,680 Okay. 485 00:13:08,680 --> 00:13:12,000 He said some entry level coders feel like they're being 486 00:13:12,000 --> 00:13:14,320 treated like these GPT monkeys. 487 00:13:14,320 --> 00:13:15,160 Oh wow. 488 00:13:15,160 --> 00:13:18,080 Just churning through code with the help of AI. 489 00:13:18,080 --> 00:13:19,400 So it's a double-edged sword. 490 00:13:19,400 --> 00:13:20,240 It really is. 491 00:13:20,240 --> 00:13:21,960 AI could make things more efficient. 492 00:13:21,960 --> 00:13:23,400 What happens to the people. 493 00:13:23,400 --> 00:13:24,240 Exactly. 494 00:13:24,240 --> 00:13:25,800 Who are just trying to get their foot in the door. 495 00:13:25,800 --> 00:13:27,320 It's a real concern. 496 00:13:27,320 --> 00:13:28,400 So what's the solution. 497 00:13:28,400 --> 00:13:30,240 I think it's about adapting. 498 00:13:30,240 --> 00:13:31,360 Evolving your skills. 499 00:13:31,360 --> 00:13:32,200 Right. 500 00:13:32,200 --> 00:13:33,760 Don't just focus on the basics. 501 00:13:33,760 --> 00:13:36,520 Learn how to solve problems, understand business. 502 00:13:36,520 --> 00:13:37,360 Right. 503 00:13:37,360 --> 00:13:38,520 And communicate effectively. 504 00:13:38,520 --> 00:13:40,640 So it's about being more well-rounded. 505 00:13:40,640 --> 00:13:41,480 It is. 506 00:13:41,480 --> 00:13:43,760 And I think this applies to more than just coding. 507 00:13:43,760 --> 00:13:44,600 Oh absolutely. 508 00:13:44,600 --> 00:13:49,480 This applies to any field that's going to be impacted by AI. 509 00:13:49,480 --> 00:13:50,320 Right. 510 00:13:50,320 --> 00:13:52,920 Because AI is coming for everything eventually. 511 00:13:52,920 --> 00:13:53,760 Right. 512 00:13:53,760 --> 00:13:55,240 So we have to learn to learn. 513 00:13:55,240 --> 00:13:56,080 Yeah. 514 00:13:56,080 --> 00:13:56,920 That's the key. 515 00:13:56,920 --> 00:13:57,760 Be adaptable. 516 00:13:57,760 --> 00:13:59,240 That's how you stay ahead. 517 00:13:59,240 --> 00:14:01,960 So we've covered a ton of ground here in this first part. 518 00:14:01,960 --> 00:14:02,800 We have. 519 00:14:02,800 --> 00:14:03,640 Of our deep dive. 520 00:14:03,640 --> 00:14:04,640 And it's all connected. 521 00:14:04,640 --> 00:14:05,520 And it's all connected. 522 00:14:05,520 --> 00:14:07,200 Like all these different pieces. 523 00:14:07,200 --> 00:14:08,040 Exactly. 524 00:14:08,040 --> 00:14:08,880 They all fit together. 525 00:14:08,880 --> 00:14:11,840 And they paint a picture of AI's impact on our world. 526 00:14:11,840 --> 00:14:12,360 For sure. 527 00:14:12,360 --> 00:14:13,840 And we're just scratching the surface. 528 00:14:13,840 --> 00:14:14,240 We are. 529 00:14:14,240 --> 00:14:16,120 There's so much more to explore. 530 00:14:16,120 --> 00:14:17,240 Absolutely. 531 00:14:17,240 --> 00:14:19,200 But we'll be back in the next part of our deep dive 532 00:14:19,200 --> 00:14:20,560 to discuss even more. 533 00:14:20,560 --> 00:14:21,320 Can't wait. 534 00:14:21,320 --> 00:14:24,080 Fascinating developments in the world of AI. 535 00:14:24,080 --> 00:14:26,040 Stay tuned. 536 00:14:26,040 --> 00:14:28,960 Welcome back to the Daily AI News podcast. 537 00:14:28,960 --> 00:14:31,280 So we've been talking a lot about AI. 538 00:14:31,280 --> 00:14:32,240 We have. 539 00:14:32,240 --> 00:14:34,880 And its impact on everything. 540 00:14:34,880 --> 00:14:39,320 From jobs to ethical concerns. 541 00:14:39,320 --> 00:14:40,400 Even holiday shopping. 542 00:14:40,400 --> 00:14:40,800 Right. 543 00:14:40,800 --> 00:14:41,640 It's everywhere. 544 00:14:41,640 --> 00:14:43,920 Let's pick back up with that Stanford professor. 545 00:14:43,920 --> 00:14:44,320 Oh yeah. 546 00:14:44,320 --> 00:14:45,160 Jeff Hancock. 547 00:14:45,160 --> 00:14:49,920 Who was accused of using AI to create fake testimony. 548 00:14:49,920 --> 00:14:52,480 That case is just wild. 549 00:14:52,480 --> 00:14:55,520 It really raises some questions about trust 550 00:14:55,520 --> 00:14:58,360 and accountability in this age of AI. 551 00:14:58,360 --> 00:14:58,600 Yeah. 552 00:14:58,600 --> 00:14:59,080 How you know. 553 00:14:59,080 --> 00:15:02,080 Like if AI can be used to create fake evidence. 554 00:15:02,080 --> 00:15:02,880 Right. 555 00:15:02,880 --> 00:15:05,320 That could potentially influence legal decisions. 556 00:15:05,320 --> 00:15:05,960 Uh-huh. 557 00:15:05,960 --> 00:15:07,440 That's a really scary thought. 558 00:15:07,440 --> 00:15:08,800 It's a slippery slope. 559 00:15:08,800 --> 00:15:09,160 It is. 560 00:15:09,160 --> 00:15:12,560 And it shows that we need like clear safeguards. 561 00:15:12,560 --> 00:15:12,800 Right. 562 00:15:12,800 --> 00:15:13,960 Ethical guidelines. 563 00:15:13,960 --> 00:15:15,320 As AI gets more powerful. 564 00:15:15,320 --> 00:15:15,840 Absolutely. 565 00:15:15,840 --> 00:15:17,720 We got to make sure it's used responsibly. 566 00:15:17,720 --> 00:15:18,160 For sure. 567 00:15:18,160 --> 00:15:19,840 Speaking of responsible use. 568 00:15:19,840 --> 00:15:22,320 Let's circle back to that AI holiday shopping thing. 569 00:15:22,320 --> 00:15:22,880 Right. 570 00:15:22,880 --> 00:15:24,240 It's kind of a funny example. 571 00:15:24,240 --> 00:15:24,480 Yeah. 572 00:15:24,480 --> 00:15:26,080 Of how AI is creeping into like. 573 00:15:26,080 --> 00:15:26,640 Everything. 574 00:15:26,640 --> 00:15:28,600 All these unexpected areas of our lives. 575 00:15:28,600 --> 00:15:29,800 Or even our holidays. 576 00:15:29,800 --> 00:15:32,560 So some people are turning to chat GPT. 577 00:15:32,560 --> 00:15:32,680 Yeah. 578 00:15:32,680 --> 00:15:34,400 For Christmas gift ideas. 579 00:15:34,400 --> 00:15:35,560 Which makes sense. 580 00:15:35,560 --> 00:15:36,080 Right. 581 00:15:36,080 --> 00:15:37,920 But it's also sparked this debate about. 582 00:15:37,920 --> 00:15:38,360 Yeah. 583 00:15:38,360 --> 00:15:39,640 Whether that's appropriate. 584 00:15:39,640 --> 00:15:39,880 Right. 585 00:15:39,880 --> 00:15:42,000 Because gift giving is so personal. 586 00:15:42,000 --> 00:15:42,200 Right. 587 00:15:42,200 --> 00:15:43,200 It's supposed to be thoughtful. 588 00:15:43,200 --> 00:15:43,880 Exactly. 589 00:15:43,880 --> 00:15:46,360 So you can see how AI could be helpful. 590 00:15:46,360 --> 00:15:47,080 Uh-huh. 591 00:15:47,080 --> 00:15:47,800 You know. 592 00:15:47,800 --> 00:15:49,720 Maybe breaking us out of our old habits. 593 00:15:49,720 --> 00:15:49,880 Yeah. 594 00:15:49,880 --> 00:15:51,760 Getting you out of your comfort zone. 595 00:15:51,760 --> 00:15:53,120 Giving us new ideas. 596 00:15:53,120 --> 00:15:53,400 But. 597 00:15:53,400 --> 00:15:55,200 There's that risk of losing. 598 00:15:55,200 --> 00:15:55,480 Yes. 599 00:15:55,480 --> 00:15:56,440 Personal touch. 600 00:15:56,440 --> 00:15:57,440 That human element. 601 00:15:57,440 --> 00:15:57,760 Yeah. 602 00:15:57,760 --> 00:15:58,840 That's what makes it special. 603 00:15:58,840 --> 00:15:59,400 Exactly. 604 00:15:59,400 --> 00:16:01,360 So how do we find that balance? 605 00:16:01,360 --> 00:16:02,320 That's the question. 606 00:16:02,320 --> 00:16:04,040 Is there a way to use AI. 607 00:16:04,040 --> 00:16:04,800 Uh-huh. 608 00:16:04,800 --> 00:16:06,280 For gift giving. 609 00:16:06,280 --> 00:16:09,200 Without sacrificing that thoughtfulness. 610 00:16:09,200 --> 00:16:12,160 I think maybe AI can be a good starting point. 611 00:16:12,160 --> 00:16:12,440 Okay. 612 00:16:12,440 --> 00:16:14,000 Like a brainstorming tool. 613 00:16:14,000 --> 00:16:14,640 Okay. 614 00:16:14,640 --> 00:16:16,680 But then you gotta take those ideas. 615 00:16:16,680 --> 00:16:17,800 And make them your own. 616 00:16:17,800 --> 00:16:18,120 Right. 617 00:16:18,120 --> 00:16:19,880 So AI can help us. 618 00:16:19,880 --> 00:16:20,200 Yeah. 619 00:16:20,200 --> 00:16:22,200 But it shouldn't replace our own thinking. 620 00:16:22,200 --> 00:16:23,000 It's a tool. 621 00:16:23,000 --> 00:16:23,400 Right. 622 00:16:23,400 --> 00:16:24,280 Not a solution. 623 00:16:24,280 --> 00:16:26,920 Now let's switch gears to something completely different. 624 00:16:26,920 --> 00:16:27,760 Okay. 625 00:16:27,760 --> 00:16:28,520 We're going now. 626 00:16:28,520 --> 00:16:30,160 Let's talk about the legal battle. 627 00:16:30,160 --> 00:16:30,560 Oh yeah. 628 00:16:30,560 --> 00:16:32,160 Between open AI. 629 00:16:32,160 --> 00:16:33,440 And the New York Times. 630 00:16:33,440 --> 00:16:34,520 This is an interesting one. 631 00:16:34,520 --> 00:16:35,720 It is. 632 00:16:35,720 --> 00:16:40,320 So open AI tried to force the New York Times. 633 00:16:40,320 --> 00:16:40,920 Uh-huh. 634 00:16:40,920 --> 00:16:45,440 To tell them how their reporters are using AI tools. 635 00:16:45,440 --> 00:16:46,280 Wow. 636 00:16:46,280 --> 00:16:47,240 In their work. 637 00:16:47,240 --> 00:16:48,920 I wonder why they did that. 638 00:16:48,920 --> 00:16:50,800 It's not totally clear why. 639 00:16:50,800 --> 00:16:51,120 Okay. 640 00:16:51,120 --> 00:16:53,840 But there's speculation that they might have been worried about. 641 00:16:53,840 --> 00:16:54,200 Okay. 642 00:16:54,200 --> 00:16:55,800 Copyright infringement. 643 00:16:55,800 --> 00:16:56,520 That makes sense. 644 00:16:56,520 --> 00:16:59,240 Or maybe misuse of their technology. 645 00:16:59,240 --> 00:17:00,600 So they wanted to know. 646 00:17:00,600 --> 00:17:00,720 Right. 647 00:17:00,720 --> 00:17:03,360 How it was being used in a journalistic context. 648 00:17:03,360 --> 00:17:03,960 Interesting. 649 00:17:03,960 --> 00:17:07,520 But a federal judge actually denied. 650 00:17:07,520 --> 00:17:08,240 Oh really? 651 00:17:08,240 --> 00:17:09,760 Open AI's request. 652 00:17:09,760 --> 00:17:10,040 Sure. 653 00:17:10,040 --> 00:17:11,720 Saying that it was too broad. 654 00:17:11,720 --> 00:17:13,840 Like asking a video game company. 655 00:17:13,840 --> 00:17:14,240 Okay. 656 00:17:14,240 --> 00:17:17,840 To provide information about their copyright holders employees. 657 00:17:17,840 --> 00:17:19,400 And their gaming habits. 658 00:17:19,400 --> 00:17:20,040 Right. 659 00:17:20,040 --> 00:17:21,640 Just because they're phasing a lawsuit. 660 00:17:21,640 --> 00:17:24,240 So basically they said no phishing expedition. 661 00:17:24,240 --> 00:17:24,680 Right. 662 00:17:24,680 --> 00:17:25,800 Open AI was overreached. 663 00:17:25,800 --> 00:17:27,760 How are you ever trying to get too much information? 664 00:17:27,760 --> 00:17:28,280 Exactly. 665 00:17:28,280 --> 00:17:30,000 So I think this highlights. 666 00:17:30,000 --> 00:17:30,480 Yeah. 667 00:17:30,480 --> 00:17:34,960 This growing need to define the boundaries. 668 00:17:34,960 --> 00:17:35,320 Yeah. 669 00:17:35,320 --> 00:17:37,480 Of data access and transparency. 670 00:17:37,480 --> 00:17:38,600 Especially for AR. 671 00:17:38,600 --> 00:17:39,920 When it comes to AI tools. 672 00:17:39,920 --> 00:17:40,480 Right. 673 00:17:40,480 --> 00:17:43,520 And this is especially important in fields like journalism. 674 00:17:43,520 --> 00:17:43,880 Yeah. 675 00:17:43,880 --> 00:17:45,960 Where freedom of the press is so important. 676 00:17:45,960 --> 00:17:46,160 Right. 677 00:17:46,160 --> 00:17:48,840 You can't just force journalists to reveal their sources. 678 00:17:48,840 --> 00:17:49,400 Right. 679 00:17:49,400 --> 00:17:50,320 Or their methods. 680 00:17:50,320 --> 00:17:51,000 Exactly. 681 00:17:51,000 --> 00:17:52,880 So this is going to be interesting to watch. 682 00:17:52,880 --> 00:17:57,720 It is because the legal and ethical frameworks around AI. 683 00:17:57,720 --> 00:17:58,320 Mm-hmm. 684 00:17:58,320 --> 00:17:59,440 Are still evolving. 685 00:17:59,440 --> 00:18:01,320 We're in uncharted territory. 686 00:18:01,320 --> 00:18:02,040 We are. 687 00:18:02,040 --> 00:18:04,680 And we can expect to see a lot more cases like this. 688 00:18:04,680 --> 00:18:05,280 Probably. 689 00:18:05,280 --> 00:18:08,280 As AI becomes more and more integrated into our lives. 690 00:18:08,280 --> 00:18:08,520 Yeah. 691 00:18:08,520 --> 00:18:10,560 This is just the beginning. 692 00:18:10,560 --> 00:18:13,160 Now let's go back to the topic of AI and coding. 693 00:18:13,160 --> 00:18:13,640 Okay. 694 00:18:13,640 --> 00:18:15,360 And how it's impacting the job market. 695 00:18:15,360 --> 00:18:15,800 Right. 696 00:18:15,800 --> 00:18:17,800 Because we talked about how the skills. 697 00:18:17,800 --> 00:18:17,960 Yeah. 698 00:18:17,960 --> 00:18:20,480 That you need to succeed are changing. 699 00:18:20,480 --> 00:18:20,720 Right. 700 00:18:20,720 --> 00:18:22,560 And it's not just about the technical stuff anymore. 701 00:18:22,560 --> 00:18:23,080 Exactly. 702 00:18:23,080 --> 00:18:24,280 You need problem solving. 703 00:18:24,280 --> 00:18:24,760 Uh-huh. 704 00:18:24,760 --> 00:18:26,160 You need communication. 705 00:18:26,160 --> 00:18:27,040 Business skills. 706 00:18:27,040 --> 00:18:27,320 Right. 707 00:18:27,320 --> 00:18:28,920 That story about Florencia Orendan. 708 00:18:28,920 --> 00:18:29,160 Yeah. 709 00:18:29,160 --> 00:18:30,680 The guy who went to the coding boot camp. 710 00:18:30,680 --> 00:18:33,960 Really highlights the need to be adaptable. 711 00:18:33,960 --> 00:18:34,640 Absolutely. 712 00:18:34,640 --> 00:18:35,840 And to keep learning. 713 00:18:35,840 --> 00:18:37,520 It's all about continuous learning. 714 00:18:37,520 --> 00:18:40,120 And that's a lesson I think applies to everyone. 715 00:18:40,120 --> 00:18:41,080 Any field. 716 00:18:41,080 --> 00:18:43,520 In any field that's being touched by AI. 717 00:18:43,520 --> 00:18:43,960 Yeah. 718 00:18:43,960 --> 00:18:45,680 Because things are changing so fast. 719 00:18:45,680 --> 00:18:46,160 Right. 720 00:18:46,160 --> 00:18:47,760 And the only way to stay ahead. 721 00:18:47,760 --> 00:18:49,000 Is to keep learning. 722 00:18:49,000 --> 00:18:52,760 New things and adapting to those changes. 723 00:18:52,760 --> 00:18:55,280 And AI itself can actually help us learn. 724 00:18:55,280 --> 00:18:56,000 That's a good point. 725 00:18:56,000 --> 00:18:57,880 It's a tool for learning as well. 726 00:18:57,880 --> 00:19:01,080 So let's dive back into Microsoft's new AI tool. 727 00:19:01,080 --> 00:19:01,720 Okay. 728 00:19:01,720 --> 00:19:04,880 Recall with click to do. 729 00:19:04,880 --> 00:19:05,720 This thing is supposed to. 730 00:19:05,720 --> 00:19:07,000 I'm still wrapping my head around it. 731 00:19:07,000 --> 00:19:09,720 It's supposed to change the way we interact with our computers. 732 00:19:09,720 --> 00:19:10,280 Yeah. 733 00:19:10,280 --> 00:19:11,120 How so. 734 00:19:11,120 --> 00:19:13,040 So it uses AI to help us find. 735 00:19:13,040 --> 00:19:13,520 Okay. 736 00:19:13,520 --> 00:19:14,960 And act on information. 737 00:19:14,960 --> 00:19:16,120 That we've seen before. 738 00:19:16,120 --> 00:19:17,520 Like our computer history. 739 00:19:17,520 --> 00:19:20,160 It's basically creating this searchable history. 740 00:19:20,160 --> 00:19:20,400 Yeah. 741 00:19:20,400 --> 00:19:20,720 Okay. 742 00:19:20,720 --> 00:19:22,360 Of everything you've done on your computer. 743 00:19:22,360 --> 00:19:24,960 So it's more than just a keyword search. 744 00:19:24,960 --> 00:19:27,520 It's like it understands the context. 745 00:19:27,520 --> 00:19:28,320 Oh wow. 746 00:19:28,320 --> 00:19:29,440 Of what you've seen and done. 747 00:19:29,440 --> 00:19:32,160 So if I'm remembering a presentation I saw. 748 00:19:32,160 --> 00:19:32,640 Yes. 749 00:19:32,640 --> 00:19:34,280 But I can't remember the file name. 750 00:19:34,280 --> 00:19:35,000 Exactly. 751 00:19:35,000 --> 00:19:36,640 Recall could help me find it. 752 00:19:36,640 --> 00:19:37,200 Exactly. 753 00:19:37,200 --> 00:19:37,560 And then. 754 00:19:37,560 --> 00:19:38,440 In what. 755 00:19:38,440 --> 00:19:40,040 Click to do. 756 00:19:40,040 --> 00:19:41,120 Takes it a step further. 757 00:19:41,120 --> 00:19:41,520 Okay. 758 00:19:41,520 --> 00:19:41,920 How so. 759 00:19:41,920 --> 00:19:44,320 It lets you take actions based on that information. 760 00:19:44,320 --> 00:19:45,080 Oh that's cool. 761 00:19:45,080 --> 00:19:48,520 So say you find a snapshot of a website. 762 00:19:48,520 --> 00:19:49,800 With a product you want to buy. 763 00:19:49,800 --> 00:19:50,720 Right. 764 00:19:50,720 --> 00:19:52,160 You can just click on the product. 765 00:19:52,160 --> 00:19:53,320 Oh within the snapshot. 766 00:19:53,320 --> 00:19:53,640 Yeah. 767 00:19:53,640 --> 00:19:55,080 And it'll take you to the page. 768 00:19:55,080 --> 00:19:55,480 Wow. 769 00:19:55,480 --> 00:19:56,240 Where you can buy it. 770 00:19:56,240 --> 00:19:57,160 That's amazing. 771 00:19:57,160 --> 00:19:58,280 It's super seamless. 772 00:19:58,280 --> 00:19:58,520 Yeah. 773 00:19:58,520 --> 00:19:59,520 It connects everything. 774 00:19:59,520 --> 00:20:03,480 Like it bridges the gap between your past activity 775 00:20:03,480 --> 00:20:05,360 and what you're doing right now. 776 00:20:05,360 --> 00:20:07,160 It's like time travel for your computer. 777 00:20:07,160 --> 00:20:07,560 It is. 778 00:20:07,560 --> 00:20:10,040 It's like AI is remembering for us. 779 00:20:10,040 --> 00:20:12,120 And helping us get things done. 780 00:20:12,120 --> 00:20:13,640 And helping us get things done. 781 00:20:13,640 --> 00:20:15,200 But it's important to note. 782 00:20:15,200 --> 00:20:15,440 Yeah. 783 00:20:15,440 --> 00:20:17,320 That it's still in that limited preview phase. 784 00:20:17,320 --> 00:20:17,720 Right. 785 00:20:17,720 --> 00:20:19,360 Not everyone has access to it yet. 786 00:20:19,360 --> 00:20:21,000 But wider availability is coming soon. 787 00:20:21,000 --> 00:20:21,400 Okay. 788 00:20:21,400 --> 00:20:22,400 Good to know. 789 00:20:22,400 --> 00:20:24,080 So keep an eye out for that. 790 00:20:24,080 --> 00:20:27,480 Now back to Anthropic and AWS. 791 00:20:27,480 --> 00:20:27,840 Okay. 792 00:20:27,840 --> 00:20:29,120 The Dynamic Duo. 793 00:20:29,120 --> 00:20:31,040 This partnership is a pretty big deal. 794 00:20:31,040 --> 00:20:31,440 Yeah. 795 00:20:31,440 --> 00:20:33,440 It could really shape the future of AI. 796 00:20:33,440 --> 00:20:33,720 Right. 797 00:20:33,720 --> 00:20:36,720 They're working on enhancing that Tranium hardware. 798 00:20:36,720 --> 00:20:37,200 Uh huh. 799 00:20:37,200 --> 00:20:39,320 And software for AI model training. 800 00:20:39,320 --> 00:20:39,600 Right. 801 00:20:39,600 --> 00:20:40,560 So just to recap. 802 00:20:40,560 --> 00:20:40,960 Yeah. 803 00:20:40,960 --> 00:20:44,240 Tranium is that custom designed machine learning chip. 804 00:20:44,240 --> 00:20:44,680 Right. 805 00:20:44,680 --> 00:20:45,960 Made by AWS. 806 00:20:45,960 --> 00:20:47,360 That AWS developed. 807 00:20:47,360 --> 00:20:48,440 And they're optimizing it. 808 00:20:48,440 --> 00:20:48,960 Yeah. 809 00:20:48,960 --> 00:20:50,120 For Anthropic's needs. 810 00:20:50,120 --> 00:20:52,000 To make AI training faster. 811 00:20:52,000 --> 00:20:53,320 And more efficient. 812 00:20:53,320 --> 00:20:53,680 Right. 813 00:20:53,680 --> 00:20:55,200 It's all about pushing the limits. 814 00:20:55,200 --> 00:20:58,200 And this partnership goes beyond just infrastructure. 815 00:20:58,200 --> 00:20:58,720 Uh huh. 816 00:20:58,720 --> 00:21:00,400 They're also integrating Claude. 817 00:21:00,400 --> 00:21:02,040 Which is Anthropic's AI model. 818 00:21:02,040 --> 00:21:03,440 Into Amazon Bedrock. 819 00:21:03,440 --> 00:21:04,120 Right. 820 00:21:04,120 --> 00:21:06,000 And Amazon Bedrock. 821 00:21:06,000 --> 00:21:09,200 Is that service that gives businesses easy access 822 00:21:09,200 --> 00:21:11,440 to all those different AI models. 823 00:21:11,440 --> 00:21:11,760 Right. 824 00:21:11,760 --> 00:21:13,080 So they don't have to build their own. 825 00:21:13,080 --> 00:21:16,600 So what does it mean to add Claude to this platform? 826 00:21:16,600 --> 00:21:19,120 It means more options for businesses. 827 00:21:19,120 --> 00:21:19,480 Okay. 828 00:21:19,480 --> 00:21:21,400 More AI tools to play with. 829 00:21:21,400 --> 00:21:24,080 So it's all about making AI more accessible. 830 00:21:24,080 --> 00:21:24,320 Yeah. 831 00:21:24,320 --> 00:21:25,360 Democratizing it. 832 00:21:25,360 --> 00:21:26,280 Two more people. 833 00:21:26,280 --> 00:21:26,880 Exactly. 834 00:21:26,880 --> 00:21:29,280 And that could lead to a lot more innovation. 835 00:21:29,280 --> 00:21:29,560 Right. 836 00:21:29,560 --> 00:21:31,040 Because more people would be able to use it. 837 00:21:31,040 --> 00:21:31,320 Right. 838 00:21:31,320 --> 00:21:32,480 And experiment with it. 839 00:21:32,480 --> 00:21:34,080 To come up with new ideas. 840 00:21:34,080 --> 00:21:35,240 And new applications. 841 00:21:35,240 --> 00:21:36,560 It's exciting to think about. 842 00:21:36,560 --> 00:21:39,840 It is and this partnership is really at the forefront. 843 00:21:39,840 --> 00:21:40,280 Uh huh. 844 00:21:40,280 --> 00:21:41,280 Of that trend. 845 00:21:41,280 --> 00:21:42,800 Of democratizing AI. 846 00:21:42,800 --> 00:21:44,320 It's definitely something to watch. 847 00:21:44,320 --> 00:21:47,760 Now before we wrap up this part of our deep dive. 848 00:21:47,760 --> 00:21:48,200 Okay. 849 00:21:48,200 --> 00:21:50,680 I want to touch on AI and journalism. 850 00:21:50,680 --> 00:21:51,000 Right. 851 00:21:51,000 --> 00:21:52,960 We talked about that case with open AI. 852 00:21:52,960 --> 00:21:53,760 And the New York Times. 853 00:21:53,760 --> 00:21:54,040 Yeah. 854 00:21:54,040 --> 00:21:54,960 That was a big one. 855 00:21:54,960 --> 00:21:56,600 It raises some important questions about. 856 00:21:56,600 --> 00:21:56,800 Yeah. 857 00:21:56,800 --> 00:21:57,720 Like where's the line? 858 00:21:57,720 --> 00:21:57,840 Yeah. 859 00:21:57,840 --> 00:21:59,920 Where's the line between AI and journalism. 860 00:21:59,920 --> 00:22:01,800 How much AI is too much. 861 00:22:01,800 --> 00:22:02,040 Right. 862 00:22:02,040 --> 00:22:04,120 How do we protect. 863 00:22:04,120 --> 00:22:05,200 Journalistic practices. 864 00:22:05,200 --> 00:22:05,840 Age source. 865 00:22:05,840 --> 00:22:08,120 In this age of AI. 866 00:22:08,120 --> 00:22:09,440 It's a tricky balance. 867 00:22:09,440 --> 00:22:10,320 It is. 868 00:22:10,320 --> 00:22:10,600 Yeah. 869 00:22:10,600 --> 00:22:12,560 And it's clear that this is just the beginning. 870 00:22:12,560 --> 00:22:13,560 Of the conversation. 871 00:22:13,560 --> 00:22:14,480 About AI. 872 00:22:14,480 --> 00:22:14,800 Yeah. 873 00:22:14,800 --> 00:22:15,680 And its impact. 874 00:22:15,680 --> 00:22:16,560 On everything. 875 00:22:16,560 --> 00:22:17,120 Yeah. 876 00:22:17,120 --> 00:22:18,720 On our lives, our jobs. 877 00:22:18,720 --> 00:22:20,080 Our laws. 878 00:22:20,080 --> 00:22:21,040 It's changing everything. 879 00:22:21,040 --> 00:22:23,760 It's changing everything and we're just starting to see. 880 00:22:23,760 --> 00:22:24,560 Yeah. 881 00:22:24,560 --> 00:22:25,360 The ripple effect. 882 00:22:25,360 --> 00:22:27,520 We need to be having these conversations now. 883 00:22:27,520 --> 00:22:28,080 Absolutely. 884 00:22:28,080 --> 00:22:29,200 Before it's too late. 885 00:22:29,200 --> 00:22:31,520 We need to think about the ethical implications. 886 00:22:31,520 --> 00:22:32,040 Yeah. 887 00:22:32,040 --> 00:22:33,280 The legal implications. 888 00:22:33,280 --> 00:22:34,160 Uh huh. 889 00:22:34,160 --> 00:22:36,640 And figure out how to use this technology. 890 00:22:36,640 --> 00:22:37,360 Responsibly. 891 00:22:37,360 --> 00:22:39,680 Responsibly for the benefit of everyone. 892 00:22:39,680 --> 00:22:40,800 Exactly. 893 00:22:40,800 --> 00:22:42,800 So we've covered a ton of ground. 894 00:22:42,800 --> 00:22:44,080 In this second part. 895 00:22:44,080 --> 00:22:44,800 We have. 896 00:22:44,800 --> 00:22:45,760 Of our deep dive. 897 00:22:45,760 --> 00:22:46,000 Yeah. 898 00:22:46,000 --> 00:22:46,800 It's been a whirlwind. 899 00:22:46,800 --> 00:22:48,320 It's been a whirlwind of AI. 900 00:22:48,320 --> 00:22:50,640 From fake news to holiday shopping. 901 00:22:50,640 --> 00:22:51,920 It's mind boggling. 902 00:22:51,920 --> 00:22:53,200 AI is everywhere. 903 00:22:53,200 --> 00:22:54,640 But we're going to take a short break. 904 00:22:54,640 --> 00:22:54,960 Okay. 905 00:22:54,960 --> 00:22:56,720 And come back to wrap things up. 906 00:22:56,720 --> 00:22:57,440 Sounds good. 907 00:22:57,440 --> 00:23:00,880 With some final thoughts on the future of AI. 908 00:23:00,880 --> 00:23:01,440 I'm ready. 909 00:23:01,440 --> 00:23:04,880 Welcome back to the Daily AI News Podcast. 910 00:23:04,880 --> 00:23:08,160 We've been exploring this crazy world of AI. 911 00:23:08,160 --> 00:23:09,440 It's been quite a journey. 912 00:23:09,440 --> 00:23:10,000 It has. 913 00:23:10,000 --> 00:23:12,240 And I think one of the big takeaways for me. 914 00:23:12,240 --> 00:23:13,760 Is this idea of adaptability. 915 00:23:13,760 --> 00:23:14,400 Uh huh. 916 00:23:14,400 --> 00:23:15,600 And how important it is. 917 00:23:15,600 --> 00:23:16,080 Yeah. 918 00:23:16,080 --> 00:23:17,200 To keep learning. 919 00:23:17,200 --> 00:23:18,000 Absolutely. 920 00:23:18,000 --> 00:23:19,840 Continuous learning is key. 921 00:23:19,840 --> 00:23:21,760 Especially in this age of AI. 922 00:23:21,760 --> 00:23:22,080 Yeah. 923 00:23:22,080 --> 00:23:23,520 Things are moving so fast. 924 00:23:23,520 --> 00:23:23,840 Right. 925 00:23:23,840 --> 00:23:25,360 We saw how AI is like. 926 00:23:26,240 --> 00:23:28,160 Changing the game for coders. 927 00:23:28,160 --> 00:23:28,640 Right. 928 00:23:28,640 --> 00:23:29,840 And it's not just about. 929 00:23:29,840 --> 00:23:30,160 Yeah. 930 00:23:30,160 --> 00:23:31,120 Knowing how to code anything. 931 00:23:31,120 --> 00:23:32,240 It's called package. 932 00:23:32,240 --> 00:23:32,560 Right. 933 00:23:32,560 --> 00:23:34,800 You need those problem solving skills. 934 00:23:34,800 --> 00:23:36,240 You got to be able to think on your feet. 935 00:23:36,240 --> 00:23:37,360 Communication. 936 00:23:37,360 --> 00:23:38,320 Business skills. 937 00:23:38,320 --> 00:23:39,520 The story about. 938 00:23:40,720 --> 00:23:41,840 Florencio Rendon. 939 00:23:42,720 --> 00:23:44,400 The coding boot camp grad. 940 00:23:44,400 --> 00:23:44,720 Yeah. 941 00:23:44,720 --> 00:23:45,920 That really stuck with me. 942 00:23:45,920 --> 00:23:46,240 Yeah. 943 00:23:46,240 --> 00:23:47,360 It's a wake up call. 944 00:23:47,360 --> 00:23:50,480 Because it shows that even if you have the technical skills. 945 00:23:50,480 --> 00:23:50,880 Yeah. 946 00:23:50,880 --> 00:23:52,080 You need more than that. 947 00:23:52,080 --> 00:23:52,400 Right. 948 00:23:52,400 --> 00:23:53,520 You got to be adaptable. 949 00:23:53,520 --> 00:23:54,720 To survive. 950 00:23:54,720 --> 00:23:55,760 In this new world. 951 00:23:55,760 --> 00:23:57,040 The AI world. 952 00:23:57,040 --> 00:23:58,320 And I think that applies to. 953 00:23:58,320 --> 00:23:58,800 Definitely. 954 00:23:58,800 --> 00:24:00,080 More than just coding. 955 00:24:00,080 --> 00:24:01,680 Any field really. 956 00:24:01,680 --> 00:24:02,960 It's a lesson for everyone. 957 00:24:02,960 --> 00:24:03,200 Yeah. 958 00:24:04,000 --> 00:24:05,840 AI is going to impact all of us. 959 00:24:05,840 --> 00:24:07,040 So we need to be prepared. 960 00:24:07,760 --> 00:24:08,960 To learn new things. 961 00:24:08,960 --> 00:24:09,680 To evolve. 962 00:24:09,680 --> 00:24:11,360 To evolve and adapt. 963 00:24:11,360 --> 00:24:13,280 That's how we stay ahead of the curve. 964 00:24:13,280 --> 00:24:16,240 It's not just about learning a specific skill. 965 00:24:16,240 --> 00:24:17,680 It's about learning how to learn. 966 00:24:17,680 --> 00:24:19,280 It's about being a lifelong learner. 967 00:24:19,280 --> 00:24:20,000 Exactly. 968 00:24:20,000 --> 00:24:21,280 Always be curious. 969 00:24:21,280 --> 00:24:23,200 And what's cool is that AI. 970 00:24:23,760 --> 00:24:24,400 Yeah. 971 00:24:24,400 --> 00:24:25,920 Can actually help us with that. 972 00:24:25,920 --> 00:24:27,440 It's a tool for learning. 973 00:24:27,440 --> 00:24:28,800 It's not just about automation. 974 00:24:28,800 --> 00:24:29,120 Right. 975 00:24:29,120 --> 00:24:31,760 It's not just about replacing jobs. 976 00:24:31,760 --> 00:24:33,680 It's about augmenting our abilities. 977 00:24:33,680 --> 00:24:34,320 Exactly. 978 00:24:34,320 --> 00:24:38,160 We saw that with Microsoft's recall with click to do. 979 00:24:38,160 --> 00:24:38,880 Right. 980 00:24:38,880 --> 00:24:40,400 That thing is amazing. 981 00:24:40,400 --> 00:24:41,440 It's pretty mind blowing. 982 00:24:41,440 --> 00:24:43,600 It's like AI is helping us be more productive. 983 00:24:43,600 --> 00:24:44,880 And it's making information. 984 00:24:44,880 --> 00:24:45,840 Mm hmm. 985 00:24:45,840 --> 00:24:46,560 More accessible. 986 00:24:46,560 --> 00:24:47,680 More actionable. 987 00:24:47,680 --> 00:24:48,560 And then we have. 988 00:24:48,560 --> 00:24:48,960 Yeah. 989 00:24:48,960 --> 00:24:50,720 Anthropic and AWS. 990 00:24:50,720 --> 00:24:52,720 That partnership is huge. 991 00:24:52,720 --> 00:24:53,360 It really is. 992 00:24:53,360 --> 00:24:55,200 They're making AI more accessible. 993 00:24:55,200 --> 00:24:55,280 Yeah. 994 00:24:55,280 --> 00:24:56,000 To more people. 995 00:24:56,000 --> 00:24:57,520 To businesses developers. 996 00:24:57,520 --> 00:24:58,560 Which could lead to. 997 00:24:58,560 --> 00:24:59,040 Yeah. 998 00:24:59,040 --> 00:25:00,480 A wave of innovation. 999 00:25:00,480 --> 00:25:02,240 More people using AI. 1000 00:25:02,240 --> 00:25:03,840 More people experimenting with it. 1001 00:25:03,840 --> 00:25:04,720 More ideas. 1002 00:25:04,720 --> 00:25:05,280 Exactly. 1003 00:25:05,280 --> 00:25:06,560 So AI is not just. 1004 00:25:06,560 --> 00:25:07,280 Wow. 1005 00:25:07,280 --> 00:25:07,520 Right. 1006 00:25:07,520 --> 00:25:08,960 This scary thing. 1007 00:25:08,960 --> 00:25:10,560 It's not all doom and gloom. 1008 00:25:10,560 --> 00:25:12,000 It has the potential to do. 1009 00:25:12,560 --> 00:25:12,720 Yeah. 1010 00:25:12,720 --> 00:25:14,080 A lot of good in the world. 1011 00:25:14,080 --> 00:25:16,000 To solve problems we couldn't solve before. 1012 00:25:16,000 --> 00:25:17,040 To prove our lives. 1013 00:25:17,040 --> 00:25:18,080 To make things better. 1014 00:25:18,080 --> 00:25:19,760 But we have to be careful. 1015 00:25:19,760 --> 00:25:20,480 Oh, absolutely. 1016 00:25:20,480 --> 00:25:22,000 We have to guide its development. 1017 00:25:22,000 --> 00:25:23,280 Responsibly. 1018 00:25:23,280 --> 00:25:24,640 Responsibly and ethically. 1019 00:25:24,640 --> 00:25:25,280 Yeah. 1020 00:25:25,280 --> 00:25:27,200 With our values in mind. 1021 00:25:27,200 --> 00:25:27,920 And that means. 1022 00:25:27,920 --> 00:25:28,240 Yeah. 1023 00:25:28,240 --> 00:25:29,600 Having these conversations. 1024 00:25:29,600 --> 00:25:30,560 We got to talk about it. 1025 00:25:30,560 --> 00:25:32,400 Talking about the ethical implications. 1026 00:25:32,400 --> 00:25:33,600 The potential risks. 1027 00:25:33,600 --> 00:25:34,720 The legal challenges. 1028 00:25:34,720 --> 00:25:36,240 And how we can mitigate them. 1029 00:25:36,240 --> 00:25:36,800 Exactly. 1030 00:25:36,800 --> 00:25:37,920 This is just the beginning. 1031 00:25:37,920 --> 00:25:38,160 Yeah. 1032 00:25:38,160 --> 00:25:40,560 The AI conversation is just getting started. 1033 00:25:40,560 --> 00:25:42,000 It's an ongoing exploration. 1034 00:25:42,000 --> 00:25:43,760 And we all need to be a part of it. 1035 00:25:43,760 --> 00:25:45,840 Because AI is going to impact. 1036 00:25:45,840 --> 00:25:46,560 Yeah. 1037 00:25:46,560 --> 00:25:47,360 All of us. 1038 00:25:47,360 --> 00:25:48,160 Our lives. 1039 00:25:48,160 --> 00:25:49,040 Our work. 1040 00:25:49,040 --> 00:25:50,080 Our future. 1041 00:25:50,080 --> 00:25:50,480 Everything. 1042 00:25:50,480 --> 00:25:51,600 So it's up to us. 1043 00:25:51,600 --> 00:25:52,080 Right. 1044 00:25:52,080 --> 00:25:53,280 To shape that future. 1045 00:25:53,280 --> 00:25:55,840 To make sure AI is a force for good. 1046 00:25:56,640 --> 00:25:57,760 So let's stay informed. 1047 00:25:57,760 --> 00:25:58,000 Yeah. 1048 00:25:58,000 --> 00:25:59,280 Let's ask questions. 1049 00:25:59,280 --> 00:26:00,160 Let's be critical. 1050 00:26:00,160 --> 00:26:01,040 Let's be thoughtful. 1051 00:26:01,040 --> 00:26:02,080 Let's work together. 1052 00:26:02,080 --> 00:26:04,160 To make sure AI benefits everyone. 1053 00:26:04,160 --> 00:26:05,280 That's the goal. 1054 00:26:05,280 --> 00:26:08,800 Thanks for joining us for this deep dive into the world of AI. 1055 00:26:08,800 --> 00:26:09,920 It's been a pleasure. 1056 00:26:09,920 --> 00:26:13,200 And thanks for listening to the Daily AI News Podcast. 1057 00:26:13,200 --> 00:26:29,200 We'll see you next time.