1 00:00:00,000 --> 00:00:02,320 Welcome to the Daily AI News Podcast. 2 00:00:02,320 --> 00:00:05,800 Buckle up, because we have a ton of AI news to cover today. 3 00:00:05,800 --> 00:00:09,400 We've got Apple's new chips, AI image models, 4 00:00:09,400 --> 00:00:12,880 and even concerns about AI hallucinations and transcription. 5 00:00:12,880 --> 00:00:14,320 So let's just dive right in. 6 00:00:14,320 --> 00:00:16,160 It's amazing how these stories all connect, 7 00:00:16,160 --> 00:00:18,640 from hardware to software and even ethics. 8 00:00:18,640 --> 00:00:21,400 It really shows how intertwined the whole AI landscape is. 9 00:00:21,400 --> 00:00:22,600 Yeah, it really does. 10 00:00:22,600 --> 00:00:23,520 So let's start with Apple. 11 00:00:23,520 --> 00:00:26,460 They've just released a new family of chips, the M4. 12 00:00:26,460 --> 00:00:28,760 The M4 Pro and the M4 Max. 13 00:00:28,760 --> 00:00:30,040 And they're making some pretty big claims 14 00:00:30,040 --> 00:00:31,160 about what these chips can do. 15 00:00:31,160 --> 00:00:31,880 Oh, absolutely. 16 00:00:31,880 --> 00:00:34,680 We're talking up to four times faster performance 17 00:00:34,680 --> 00:00:36,880 than the latest AI PC chips and some tasks. 18 00:00:36,880 --> 00:00:39,880 I was like, can you imagine rendering complex 3D models 19 00:00:39,880 --> 00:00:42,000 in just seconds, not minutes? 20 00:00:42,000 --> 00:00:44,120 That's the kind of speed we're talking about here. 21 00:00:44,120 --> 00:00:46,320 It could totally change creative workflows. 22 00:00:46,320 --> 00:00:47,680 Wow, that's impressive. 23 00:00:47,680 --> 00:00:50,200 But what's actually driving these huge performance gains? 24 00:00:50,200 --> 00:00:51,800 Is it just raw processing power, 25 00:00:51,800 --> 00:00:53,160 or is there something else going on? 26 00:00:53,160 --> 00:00:55,200 It's not just about brute force. 27 00:00:55,200 --> 00:00:58,040 A key factor is the unified memory bandwidth, 28 00:00:58,040 --> 00:01:00,600 which they've increased by up to 75%. 29 00:01:00,600 --> 00:01:02,080 Think of it like a highway. 30 00:01:02,080 --> 00:01:04,560 Wider lanes mean less traffic jams. 31 00:01:04,560 --> 00:01:06,760 Data can move around the chip more efficiently, 32 00:01:06,760 --> 00:01:08,560 making the whole system much faster. 33 00:01:08,560 --> 00:01:10,520 So it's like they've optimized the whole architecture 34 00:01:10,520 --> 00:01:11,800 for speed and efficiency. 35 00:01:11,800 --> 00:01:12,600 Exactly. 36 00:01:12,600 --> 00:01:15,920 And that's really important for AI workloads, which often 37 00:01:15,920 --> 00:01:18,280 involve processing tons of data. 38 00:01:18,280 --> 00:01:21,160 Plus, the M4 chips have a faster neural engine, 39 00:01:21,160 --> 00:01:24,960 which is dedicated to handling AI tasks right on the device. 40 00:01:24,960 --> 00:01:27,280 So your Mac can do more AI processing locally 41 00:01:27,280 --> 00:01:28,880 without relying on the cloud. 42 00:01:28,880 --> 00:01:30,880 Oh, so there's a privacy angle to this as well. 43 00:01:30,880 --> 00:01:31,560 Absolutely. 44 00:01:31,560 --> 00:01:33,920 Processing data locally helps Apple keep your information 45 00:01:33,920 --> 00:01:35,480 more private and secure. 46 00:01:35,480 --> 00:01:38,960 And they've also really beefed up the M4 Max for video pros. 47 00:01:38,960 --> 00:01:41,440 It's got a media engine with two video encode engines 48 00:01:41,440 --> 00:01:43,280 and two ProRes accelerators. 49 00:01:43,280 --> 00:01:46,640 You can edit and process high resolution video in real time, 50 00:01:46,640 --> 00:01:49,280 like 8T video editing without breaking a sweat. 51 00:01:49,280 --> 00:01:50,320 That's amazing. 52 00:01:50,320 --> 00:01:52,480 It seems like Apple is really serious about pushing 53 00:01:52,480 --> 00:01:56,120 the boundaries of AI, both in terms of hardware and software. 54 00:01:56,120 --> 00:01:57,720 Yeah. 55 00:01:57,720 --> 00:02:00,360 And speaking of software, they're also introducing this new suite 56 00:02:00,360 --> 00:02:03,480 of features they're calling Apple Intelligence. 57 00:02:03,480 --> 00:02:06,280 It includes things like enhanced writing tools, 58 00:02:06,280 --> 00:02:09,320 a redesigned Siri, and other features focused 59 00:02:09,320 --> 00:02:12,080 on making your devices smarter and more personalized. 60 00:02:12,080 --> 00:02:14,720 This Apple Intelligence sounds intriguing, 61 00:02:14,720 --> 00:02:16,920 but I have to admit I'm a little hesitant about AI 62 00:02:16,920 --> 00:02:18,720 knowing too much about me. 63 00:02:18,720 --> 00:02:20,920 Can you give me an example of how it would personalize 64 00:02:20,920 --> 00:02:23,360 my experience but still protect my data? 65 00:02:23,360 --> 00:02:24,320 Sure. 66 00:02:24,320 --> 00:02:26,240 Let's say you're writing an email and you're struggling 67 00:02:26,240 --> 00:02:27,880 to find the right words. 68 00:02:27,880 --> 00:02:30,280 Apple Intelligence could analyze your past emails 69 00:02:30,280 --> 00:02:33,560 and suggest phrases or sentences that fit your style. 70 00:02:33,560 --> 00:02:35,840 And the context of the current email, 71 00:02:35,840 --> 00:02:38,720 all that processing happens right on your device. 72 00:02:38,720 --> 00:02:41,040 So your data never leaves your control. 73 00:02:41,040 --> 00:02:41,400 OK. 74 00:02:41,400 --> 00:02:43,560 So it's like having a really smart writing assistant 75 00:02:43,560 --> 00:02:46,080 that's always learning for me but never sharing my information. 76 00:02:46,080 --> 00:02:47,320 That makes me feel a little better. 77 00:02:47,320 --> 00:02:48,120 What about Siri? 78 00:02:48,120 --> 00:02:49,440 How are they redesigning it? 79 00:02:49,440 --> 00:02:51,640 Well, one big change is they're integrating chat 80 00:02:51,640 --> 00:02:54,600 GPT directly into Siri and the writing tools. 81 00:02:54,600 --> 00:02:56,640 It'll be available starting in December. 82 00:02:56,640 --> 00:02:59,880 So you'll have access to chat GPT's vast knowledge base 83 00:02:59,880 --> 00:03:03,160 and generative capabilities right from your Apple devices. 84 00:03:03,160 --> 00:03:04,840 That's a huge change. 85 00:03:04,840 --> 00:03:07,920 It seems like Apple's going all in on AI. 86 00:03:07,920 --> 00:03:10,160 But how does this compare to what other companies are doing? 87 00:03:10,160 --> 00:03:12,000 Are they taking a similar approach? 88 00:03:12,000 --> 00:03:14,440 While other companies are investing in AI as well, 89 00:03:14,440 --> 00:03:17,520 Apple's focus on privacy and on-device processing 90 00:03:17,520 --> 00:03:19,040 definitely sets them apart. 91 00:03:19,040 --> 00:03:21,040 So it's not just about the cool new features 92 00:03:21,040 --> 00:03:21,880 that AI can offer. 93 00:03:21,880 --> 00:03:24,800 It's also about how AI is being used 94 00:03:24,800 --> 00:03:27,560 and who ultimately controls your data. 95 00:03:27,560 --> 00:03:28,680 Exactly. 96 00:03:28,680 --> 00:03:30,960 And speaking of AI pushing boundaries, 97 00:03:30,960 --> 00:03:35,160 there's this new AI image model called Recraft V3. 98 00:03:35,160 --> 00:03:38,360 It was originally released under the pseudonym Red Panda. 99 00:03:38,360 --> 00:03:40,960 And it's making quite a splash in the AI art scene. 100 00:03:40,960 --> 00:03:41,880 Red Panda. 101 00:03:41,880 --> 00:03:43,000 That's an interesting name. 102 00:03:43,000 --> 00:03:45,200 So what's so special about this Recraft V3? 103 00:03:45,200 --> 00:03:46,720 It's all about performance. 104 00:03:46,720 --> 00:03:48,960 It's currently at the top of the artificial analysis 105 00:03:48,960 --> 00:03:51,640 leaderboard, which ranks AI image models based 106 00:03:51,640 --> 00:03:54,800 on how good their output is and how much users like them. 107 00:03:54,800 --> 00:03:58,120 Recraft V3 is consistently beating the other models. 108 00:03:58,120 --> 00:04:00,120 The images it produces are incredibly 109 00:04:00,120 --> 00:04:01,680 realistic and beautiful. 110 00:04:01,680 --> 00:04:05,080 So it's like the current champion of the AI art world. 111 00:04:05,080 --> 00:04:07,760 Is it something that everyday users can use? 112 00:04:07,760 --> 00:04:09,320 Or is it more for professionals? 113 00:04:09,320 --> 00:04:11,520 It's gaining popularity with both professionals 114 00:04:11,520 --> 00:04:12,680 and hobbyists. 115 00:04:12,680 --> 00:04:14,880 Anyone who works with visuals could use it. 116 00:04:14,880 --> 00:04:17,920 Designers, marketers, you name it. 117 00:04:17,920 --> 00:04:20,960 Just imagine being able to create stunning visuals 118 00:04:20,960 --> 00:04:22,840 with just a few clicks. 119 00:04:22,840 --> 00:04:26,160 That's the power of AI image models like Recraft V3. 120 00:04:26,160 --> 00:04:27,000 That's incredible. 121 00:04:27,000 --> 00:04:28,400 It seems like AI is finding its way 122 00:04:28,400 --> 00:04:30,600 into every creative field these days. 123 00:04:30,600 --> 00:04:32,960 But with all these amazing AI models, 124 00:04:32,960 --> 00:04:35,360 it's important to remember that they're not perfect. 125 00:04:35,360 --> 00:04:36,360 You're absolutely right. 126 00:04:36,360 --> 00:04:38,000 And that brings us to a development that's 127 00:04:38,000 --> 00:04:39,000 a bit more concerning. 128 00:04:39,000 --> 00:04:40,480 OpenAI's Whisper. 129 00:04:40,480 --> 00:04:42,080 It's an AI transcription tool that's used 130 00:04:42,080 --> 00:04:43,600 in all sorts of applications. 131 00:04:43,600 --> 00:04:46,640 But it's been found to have a problem with hallucinations. 132 00:04:46,640 --> 00:04:47,800 Hallucinations. 133 00:04:47,800 --> 00:04:49,640 You mean the AI is making things up. 134 00:04:49,640 --> 00:04:51,160 That sounds a little unsettling. 135 00:04:51,160 --> 00:04:52,360 What's going on exactly? 136 00:04:52,360 --> 00:04:54,920 Basically, Whisper is sometimes fabricating information 137 00:04:54,920 --> 00:04:56,120 when it's transcribing. 138 00:04:56,120 --> 00:04:59,200 It might add words or phrases that weren't actually spoken, 139 00:04:59,200 --> 00:05:02,720 or even invent entire quotes or pieces of information. 140 00:05:02,720 --> 00:05:04,760 And it's happening more often than you'd think, 141 00:05:04,760 --> 00:05:06,280 which raises some serious questions 142 00:05:06,280 --> 00:05:08,200 about how reliable it is. 143 00:05:08,200 --> 00:05:08,800 Wow. 144 00:05:08,800 --> 00:05:10,480 That is a little scary. 145 00:05:10,480 --> 00:05:12,080 So where is Whisper being used? 146 00:05:12,080 --> 00:05:13,760 Is it just for personal use, or is it 147 00:05:13,760 --> 00:05:15,800 being used in more serious applications too? 148 00:05:15,800 --> 00:05:18,200 The issue is that Whisper is actually integrated 149 00:05:18,200 --> 00:05:22,520 into a lot of different systems, like chat GPT call centers, 150 00:05:22,520 --> 00:05:24,240 and even some medical settings. 151 00:05:24,240 --> 00:05:27,040 Can you imagine the problems inaccurate transcriptions 152 00:05:27,040 --> 00:05:28,360 could cause in health care? 153 00:05:28,360 --> 00:05:30,120 It really highlights the importance 154 00:05:30,120 --> 00:05:33,320 of checking AI output, especially in sensitive areas. 155 00:05:33,320 --> 00:05:37,000 It makes you think, if AI can't even transcribe accurately, 156 00:05:37,000 --> 00:05:39,200 what does that say about trusting it with other tasks? 157 00:05:39,200 --> 00:05:40,480 Researchers are trying to figure out 158 00:05:40,480 --> 00:05:44,160 why these hallucinations happen and how to stop them. 159 00:05:44,160 --> 00:05:46,520 One thing they're looking at is improving the quality 160 00:05:46,520 --> 00:05:49,120 of the data they use to train AI models. 161 00:05:49,120 --> 00:05:52,600 If the data is bad, the AI is more likely to make mistakes. 162 00:05:52,600 --> 00:05:54,640 So it's like garbage in garbage out. 163 00:05:54,640 --> 00:05:55,520 Exactly. 164 00:05:55,520 --> 00:05:57,600 They're also working on more advanced algorithms 165 00:05:57,600 --> 00:06:00,200 that can catch and fix errors in real time. 166 00:06:00,200 --> 00:06:02,480 It's a tough problem, but it's a top priority 167 00:06:02,480 --> 00:06:03,760 for AI developers. 168 00:06:03,760 --> 00:06:05,120 That's good to know. 169 00:06:05,120 --> 00:06:06,840 But this whole situation with Whisper 170 00:06:06,840 --> 00:06:10,400 shows that it's important to be critical of AI output 171 00:06:10,400 --> 00:06:12,720 and not blindly trust everything it gives you. 172 00:06:12,720 --> 00:06:13,480 Absolutely. 173 00:06:13,480 --> 00:06:16,280 And that leads us to another development, SimpleQA. 174 00:06:16,280 --> 00:06:19,320 It's a new benchmark open AI developed for evaluating 175 00:06:19,320 --> 00:06:21,360 how factual language models are. 176 00:06:21,360 --> 00:06:24,480 So it's like a test to see how well AI can stick to the facts. 177 00:06:24,480 --> 00:06:25,720 Why is that so important? 178 00:06:25,720 --> 00:06:29,000 If we want AI to be truly useful and trustworthy, 179 00:06:29,000 --> 00:06:31,160 it needs to be based on reality. 180 00:06:31,160 --> 00:06:34,760 SimpleQA is designed to assess how well AI models can do that, 181 00:06:34,760 --> 00:06:36,240 how well they can follow the facts 182 00:06:36,240 --> 00:06:37,640 and avoid making things up. 183 00:06:37,640 --> 00:06:39,200 You know, the SimpleQA makes me think 184 00:06:39,200 --> 00:06:41,720 of something we talked about earlier, calibration. 185 00:06:41,720 --> 00:06:42,880 Is there a connection there? 186 00:06:42,880 --> 00:06:44,840 You're spot on connecting those. 187 00:06:44,840 --> 00:06:48,880 SimpleQA actually explores that idea of calibration, 188 00:06:48,880 --> 00:06:52,160 which is how well an AI model knows what it doesn't know. 189 00:06:52,160 --> 00:06:55,240 A well-calibrated AI is like a doctor who not only gives you 190 00:06:55,240 --> 00:06:57,920 a diagnosis, but also tells you how confident 191 00:06:57,920 --> 00:06:59,640 they are in that diagnosis. 192 00:06:59,640 --> 00:07:01,960 That helps us understand how much we can rely 193 00:07:01,960 --> 00:07:03,160 on the information. 194 00:07:03,160 --> 00:07:04,160 That's a great way to put it. 195 00:07:04,160 --> 00:07:07,280 So a well-calibrated AI would be more likely to say, 196 00:07:07,280 --> 00:07:11,480 I don't know, or I need more information 197 00:07:11,480 --> 00:07:12,160 when it's not sure it's right. 198 00:07:12,160 --> 00:07:12,800 Exactly. 199 00:07:12,800 --> 00:07:15,360 SimpleQA helps us figure out how well-calibrated 200 00:07:15,360 --> 00:07:17,400 different AI models are, which is 201 00:07:17,400 --> 00:07:20,600 crucial for avoiding situations where AI might confidently 202 00:07:20,600 --> 00:07:21,960 give us false information. 203 00:07:21,960 --> 00:07:24,480 Remember, AI is a tool. 204 00:07:24,480 --> 00:07:26,120 And like any tool, it has limits. 205 00:07:26,120 --> 00:07:26,760 That's a great point. 206 00:07:26,760 --> 00:07:28,080 It seems like the big takeaway here 207 00:07:28,080 --> 00:07:31,240 is that while AI is getting more powerful and complex, 208 00:07:31,240 --> 00:07:33,040 we still need to remember its limitations. 209 00:07:33,040 --> 00:07:34,960 And always look at what it produces with a critical eye. 210 00:07:34,960 --> 00:07:36,120 Absolutely. 211 00:07:36,120 --> 00:07:38,320 And speaking of pushing the limits of AI, 212 00:07:38,320 --> 00:07:41,360 let's move on to another area where it's making big progress. 213 00:07:41,360 --> 00:07:42,160 Coding. 214 00:07:42,160 --> 00:07:44,920 Have you heard of SWE Bench Verified? 215 00:07:44,920 --> 00:07:46,440 SWE Bench Verified. 216 00:07:46,440 --> 00:07:47,480 I think I've heard of it. 217 00:07:47,480 --> 00:07:48,760 But can you remind me what it is? 218 00:07:48,760 --> 00:07:52,320 It's a benchmark that tests how well AI can handle real-world 219 00:07:52,320 --> 00:07:53,400 coding tasks. 220 00:07:53,400 --> 00:07:56,080 It's not just about answering basic coding questions. 221 00:07:56,080 --> 00:07:58,360 It's about understanding and solving complex problems, 222 00:07:58,360 --> 00:08:00,160 just like a human software engineer would. 223 00:08:00,160 --> 00:08:03,320 So it's like a test to see if AI can code like a pro. 224 00:08:03,320 --> 00:08:04,200 That's ambitious. 225 00:08:04,200 --> 00:08:05,880 What kinds of tasks does it involve? 226 00:08:05,880 --> 00:08:08,640 SWE Bench Verified tests AI's ability 227 00:08:08,640 --> 00:08:10,560 to understand project requirements. 228 00:08:10,560 --> 00:08:12,880 Write code that works, debug errors, 229 00:08:12,880 --> 00:08:14,600 and even work with other developers. 230 00:08:14,600 --> 00:08:16,280 It's a pretty comprehensive assessment 231 00:08:16,280 --> 00:08:17,800 of AI's coding skills. 232 00:08:17,800 --> 00:08:18,920 That's impressive. 233 00:08:18,920 --> 00:08:21,920 So has any AI model actually passed this test? 234 00:08:21,920 --> 00:08:22,640 It's tough. 235 00:08:22,640 --> 00:08:25,800 But a new AI model called Clawd 3.5 Sonnet 236 00:08:25,800 --> 00:08:30,600 has gotten the highest score so far on SWE Bench Verified, 49%. 237 00:08:30,600 --> 00:08:32,960 And what's amazing is that it did this using a simple prompt 238 00:08:32,960 --> 00:08:34,360 and general purpose tools. 239 00:08:34,360 --> 00:08:36,640 It really shows its ability to think and solve problems 240 00:08:36,640 --> 00:08:37,520 effectively. 241 00:08:37,520 --> 00:08:39,120 So it's not just following instructions. 242 00:08:39,120 --> 00:08:40,720 It's actually thinking for itself 243 00:08:40,720 --> 00:08:42,600 and coming up with creative solutions. 244 00:08:42,600 --> 00:08:43,400 That's incredible. 245 00:08:43,400 --> 00:08:45,400 But if it only got a 49%, does that 246 00:08:45,400 --> 00:08:48,640 mean AI is still a long way from replacing human programmers? 247 00:08:48,640 --> 00:08:51,480 Even though Clawd 3.5 Sonnet getting that score 248 00:08:51,480 --> 00:08:55,120 is a big milestone, there are definitely still challenges. 249 00:08:55,120 --> 00:08:57,080 One of the biggest is how expensive it 250 00:08:57,080 --> 00:08:58,880 is to run these large language models. 251 00:08:58,880 --> 00:09:01,720 It takes a lot of computing power and energy for AI 252 00:09:01,720 --> 00:09:03,400 to solve complex coding problems. 253 00:09:03,400 --> 00:09:04,720 That makes sense. 254 00:09:04,720 --> 00:09:08,040 Are there other challenges in this field besides the cost? 255 00:09:08,040 --> 00:09:11,760 Another challenge is how to grade code that AI generates. 256 00:09:11,760 --> 00:09:14,400 How do you decide if the code not only works, 257 00:09:14,400 --> 00:09:18,320 but is also well written, efficient, and easy to maintain? 258 00:09:18,320 --> 00:09:21,360 These are subjective things that are hard to measure objectively. 259 00:09:21,360 --> 00:09:21,760 Yeah. 260 00:09:21,760 --> 00:09:23,000 I see how that would be tricky. 261 00:09:23,000 --> 00:09:24,480 It's like grading an essay. 262 00:09:24,480 --> 00:09:26,120 There's more to it than just grammar. 263 00:09:26,120 --> 00:09:26,880 Exactly. 264 00:09:26,880 --> 00:09:28,840 And of course, current AI models still 265 00:09:28,840 --> 00:09:29,560 have limitations. 266 00:09:29,560 --> 00:09:31,400 They can do amazing things. 267 00:09:31,400 --> 00:09:33,880 But they're not quite at the level of understanding 268 00:09:33,880 --> 00:09:36,240 and creativity that human programmers have. 269 00:09:36,240 --> 00:09:38,560 So it seems like we're not going to see AI programmers 270 00:09:38,560 --> 00:09:40,760 replacing humans anytime soon. 271 00:09:40,760 --> 00:09:42,960 But AI is definitely making a big impact 272 00:09:42,960 --> 00:09:44,160 in software development. 273 00:09:44,160 --> 00:09:44,800 Definitely. 274 00:09:44,800 --> 00:09:47,480 And one area that's really promising for the future of AI 275 00:09:47,480 --> 00:09:49,720 coding is multimodal integration. 276 00:09:49,720 --> 00:09:51,880 That means combining different kinds of data, 277 00:09:51,880 --> 00:09:55,800 like text, images, and code, to give AI 278 00:09:55,800 --> 00:09:58,000 a more complete understanding of the task. 279 00:09:58,000 --> 00:09:59,320 Multimodal integration. 280 00:09:59,320 --> 00:10:01,720 Can you give an example of how that would work in coding? 281 00:10:01,720 --> 00:10:05,160 Imagine an AI that can not only read and write code, 282 00:10:05,160 --> 00:10:09,240 but also understand diagrams, user interface designs, 283 00:10:09,240 --> 00:10:11,280 and even spoken instructions. 284 00:10:11,280 --> 00:10:13,520 That would help it see the bigger picture of a software 285 00:10:13,520 --> 00:10:15,720 project and make smarter decisions 286 00:10:15,720 --> 00:10:17,280 during the development process. 287 00:10:17,280 --> 00:10:18,000 That's fascinating. 288 00:10:18,000 --> 00:10:20,040 It's like giving AI the ability to see the world 289 00:10:20,040 --> 00:10:20,960 through multiple lenses. 290 00:10:20,960 --> 00:10:21,800 Exactly. 291 00:10:21,800 --> 00:10:23,720 And that's a big goal in AI research, 292 00:10:23,720 --> 00:10:26,040 to create systems that can understand and interact 293 00:10:26,040 --> 00:10:27,920 with the world in a more human-like way. 294 00:10:27,920 --> 00:10:30,680 Multimodal integration is a big step in that direction. 295 00:10:30,680 --> 00:10:30,920 Wow. 296 00:10:30,920 --> 00:10:32,960 We've covered a lot today from Apple's new ships 297 00:10:32,960 --> 00:10:36,000 and the rise of Recraft V3 to the problems with AI 298 00:10:36,000 --> 00:10:39,400 hallucinations and the potential of AI-assisted coding. 299 00:10:39,400 --> 00:10:41,240 It's clear that AI is changing quickly, 300 00:10:41,240 --> 00:10:43,320 and it's affecting all parts of our lives. 301 00:10:43,320 --> 00:10:44,760 So what's next on the agenda? 302 00:10:44,760 --> 00:10:47,200 Let's take a look at some of the ethical and philosophical 303 00:10:47,200 --> 00:10:49,320 questions these developments bring up. 304 00:10:49,320 --> 00:10:51,920 Because AI isn't just about code and algorithms. 305 00:10:51,920 --> 00:10:55,960 It's also about the nature of intelligence, consciousness, 306 00:10:55,960 --> 00:10:57,760 and what it means to be human. 307 00:10:57,760 --> 00:10:59,320 That sounds like an interesting discussion. 308 00:10:59,320 --> 00:11:01,680 I'm looking forward to exploring those deeper questions. 309 00:11:01,680 --> 00:11:03,240 Until then, I encourage you to think 310 00:11:03,240 --> 00:11:06,000 about how AI is already affecting your life 311 00:11:06,000 --> 00:11:08,000 and how you want to see it change in the future. 312 00:11:08,000 --> 00:11:09,680 What are your hopes and fears? 313 00:11:09,680 --> 00:11:12,720 What ethical limits do you think we should set? 314 00:11:12,720 --> 00:11:14,680 These are important questions to think about 315 00:11:14,680 --> 00:11:17,320 as AI becomes more a part of our world. 316 00:11:17,320 --> 00:11:19,400 Those are great questions to think about. 317 00:11:19,400 --> 00:11:21,880 Thanks for listening to the Daily AI News Podcast. 318 00:11:21,880 --> 00:11:23,280 And stay tuned for more. 319 00:11:23,280 --> 00:11:26,040 It's wild how these developments blur the lines 320 00:11:26,040 --> 00:11:28,880 between reality and what used to be sci-fi. 321 00:11:28,880 --> 00:11:29,520 It really is. 322 00:11:29,520 --> 00:11:31,440 It feels like something huge is about to happen, 323 00:11:31,440 --> 00:11:32,760 but it's also kind of unsettling. 324 00:11:32,760 --> 00:11:33,600 I get that. 325 00:11:33,600 --> 00:11:36,240 With powerful technology, there's always excitement 326 00:11:36,240 --> 00:11:37,400 and a little fear. 327 00:11:37,400 --> 00:11:40,320 It's so important to look at AI from both sides, 328 00:11:40,320 --> 00:11:43,320 recognizing the good, but not ignoring the risks. 329 00:11:43,320 --> 00:11:43,840 Definitely. 330 00:11:43,840 --> 00:11:46,680 It's like finding the right balance between innovation 331 00:11:46,680 --> 00:11:48,000 and being cautious. 332 00:11:48,000 --> 00:11:49,280 Exactly. 333 00:11:49,280 --> 00:11:52,640 And speaking of being cautious, let's go back to calibration. 334 00:11:52,640 --> 00:11:56,040 It's surprisingly crucial for making AI we can trust. 335 00:11:56,040 --> 00:11:56,960 You're right. 336 00:11:56,960 --> 00:11:59,760 It's not just about the AI getting the right answer. 337 00:11:59,760 --> 00:12:03,200 It's also about knowing how sure it is about that answer. 338 00:12:03,200 --> 00:12:04,840 Why is that so important? 339 00:12:04,840 --> 00:12:06,560 Think about an AI doctor. 340 00:12:06,560 --> 00:12:07,560 Diagnosing a patient. 341 00:12:07,560 --> 00:12:09,840 It's not enough to just give a diagnosis. 342 00:12:09,840 --> 00:12:13,280 The AI needs to say how certain it is about that diagnosis. 343 00:12:13,280 --> 00:12:16,040 If it's confident but wrong, that could be a big problem. 344 00:12:16,040 --> 00:12:16,400 Yeah. 345 00:12:16,400 --> 00:12:17,040 It's a great point. 346 00:12:17,040 --> 00:12:18,360 I hadn't thought about it that way. 347 00:12:18,360 --> 00:12:22,240 So calibration is about helping AI understand its own limits 348 00:12:22,240 --> 00:12:23,520 and not being overconfident. 349 00:12:23,520 --> 00:12:24,360 Exactly. 350 00:12:24,360 --> 00:12:26,360 That's what makes simple QA so valuable. 351 00:12:26,360 --> 00:12:28,960 By measuring calibration, we can spot 352 00:12:28,960 --> 00:12:31,760 AI models that are prone to being overconfident 353 00:12:31,760 --> 00:12:33,360 and then work on getting them better 354 00:12:33,360 --> 00:12:34,960 at recognizing their limits. 355 00:12:34,960 --> 00:12:37,720 It's almost like teaching AI to be humble. 356 00:12:37,720 --> 00:12:39,800 Helping it understand it doesn't know everything. 357 00:12:39,800 --> 00:12:42,360 And sometimes it's OK to say, I don't know. 358 00:12:42,360 --> 00:12:43,160 Exactly. 359 00:12:43,160 --> 00:12:45,400 We need AI that can show uncertainty 360 00:12:45,400 --> 00:12:47,920 instead of confidently stating something that might be wrong. 361 00:12:47,920 --> 00:12:49,480 So let's shift gears a bit. 362 00:12:49,480 --> 00:12:51,400 What about those huge performance gains 363 00:12:51,400 --> 00:12:54,640 we were talking about with the new Apple chips? 364 00:12:54,640 --> 00:12:56,680 What do you think those will mean for us? 365 00:12:56,680 --> 00:12:59,240 It's crazy to think what we'll be able to do 366 00:12:59,240 --> 00:13:00,800 with that kind of power. 367 00:13:00,800 --> 00:13:03,440 It opens up possibilities for AI applications 368 00:13:03,440 --> 00:13:05,720 that we can barely even imagine. 369 00:13:05,720 --> 00:13:07,760 It's like having a supercomputer in our pockets. 370 00:13:07,760 --> 00:13:08,200 Yeah. 371 00:13:08,200 --> 00:13:11,320 What areas do you see this having the biggest impact? 372 00:13:11,320 --> 00:13:13,480 Well, in scientific research, these chips 373 00:13:13,480 --> 00:13:15,720 could really speed up things like drug discovery 374 00:13:15,720 --> 00:13:19,560 and climate modeling, tasks that need a lot of computing power. 375 00:13:19,560 --> 00:13:21,760 We might see breakthroughs happening a lot faster. 376 00:13:21,760 --> 00:13:22,520 That's exciting. 377 00:13:22,520 --> 00:13:24,000 What about creative fields? 378 00:13:24,000 --> 00:13:27,960 Think about things like real time 3D rendering, video editing, 379 00:13:27,960 --> 00:13:29,480 and music production. 380 00:13:29,480 --> 00:13:33,320 It could revolutionize how we create and experience media. 381 00:13:33,320 --> 00:13:34,720 It seems like there's no limit. 382 00:13:34,720 --> 00:13:37,280 And don't forget personalized learning and health care. 383 00:13:37,280 --> 00:13:39,440 AI-powered apps running on these chips 384 00:13:39,440 --> 00:13:42,760 could give us customized education and medical insights, 385 00:13:42,760 --> 00:13:44,800 leading to better results for individuals. 386 00:13:44,800 --> 00:13:45,400 You got it. 387 00:13:45,400 --> 00:13:46,600 It's an exciting time. 388 00:13:46,600 --> 00:13:49,960 But of course, with great power comes great responsibility, 389 00:13:49,960 --> 00:13:50,440 right? 390 00:13:50,440 --> 00:13:51,040 Absolutely. 391 00:13:51,040 --> 00:13:54,440 More processing power also means a greater chance of misuse. 392 00:13:54,440 --> 00:13:57,920 We have to make sure these tools are used ethically and for good. 393 00:13:57,920 --> 00:13:59,720 It's a responsibility we all share. 394 00:13:59,720 --> 00:14:00,200 Definitely. 395 00:14:00,200 --> 00:14:02,160 Technology itself isn't good or bad. 396 00:14:02,160 --> 00:14:03,560 It's all about how we use it. 397 00:14:03,560 --> 00:14:05,880 Oh, before we move on from Apple, they mentioned something 398 00:14:05,880 --> 00:14:07,400 called Image Playground. 399 00:14:07,400 --> 00:14:08,120 What's that about? 400 00:14:08,120 --> 00:14:09,880 We don't know a lot yet. 401 00:14:09,880 --> 00:14:11,760 But it seems like Image Playground 402 00:14:11,760 --> 00:14:15,920 will be a way for people to create original images using AI, 403 00:14:15,920 --> 00:14:18,320 like having an AI art studio right on your Mac. 404 00:14:18,320 --> 00:14:20,360 So anyone could be an artist, even 405 00:14:20,360 --> 00:14:21,760 if they've never done art before. 406 00:14:21,760 --> 00:14:22,760 That's the idea. 407 00:14:22,760 --> 00:14:24,480 It could give anyone the power to create 408 00:14:24,480 --> 00:14:26,720 unique and expressive images. 409 00:14:26,720 --> 00:14:28,760 It'll be interesting to see what Apple does with it. 410 00:14:28,760 --> 00:14:30,040 I'm curious to try it out. 411 00:14:30,040 --> 00:14:33,360 It seems like AI is making it possible for anyone to be creative. 412 00:14:33,360 --> 00:14:35,840 And speaking of AI and creativity, 413 00:14:35,840 --> 00:14:39,360 we can't forget about Recraft V3 and how it's 414 00:14:39,360 --> 00:14:41,600 dominating the AI image generation scene. 415 00:14:41,600 --> 00:14:44,640 It's impressive how it went from being unknown as Red Panda 416 00:14:44,640 --> 00:14:45,960 to a leader in its field. 417 00:14:45,960 --> 00:14:47,480 It's like it came out of nowhere. 418 00:14:47,480 --> 00:14:49,600 I think what makes Recraft V3 stand out 419 00:14:49,600 --> 00:14:51,600 is not just how good it is technically, 420 00:14:51,600 --> 00:14:53,680 but also its focus on user experience. 421 00:14:53,680 --> 00:14:55,720 It seems to be hitting the spot for people who want 422 00:14:55,720 --> 00:14:59,480 a simple but powerful way to create amazing visuals. 423 00:14:59,480 --> 00:15:01,080 It's powerful and easy to use. 424 00:15:01,080 --> 00:15:02,720 That's a great combination. 425 00:15:02,720 --> 00:15:05,640 I wonder how Recraft will change in the future. 426 00:15:05,640 --> 00:15:07,280 And if it can stay on top, there's 427 00:15:07,280 --> 00:15:09,040 a lot of competition in AI. 428 00:15:09,040 --> 00:15:09,880 There is. 429 00:15:09,880 --> 00:15:11,640 Now going back to the challenges of AI, 430 00:15:11,640 --> 00:15:14,320 we should talk more about those AI hallucinations. 431 00:15:14,320 --> 00:15:17,840 What happened with OpenAI's whisper is definitely worrying, 432 00:15:17,840 --> 00:15:20,400 especially since it's used so widely. 433 00:15:20,400 --> 00:15:22,680 It shows us that AI isn't perfect, 434 00:15:22,680 --> 00:15:25,080 even when it's made by the best organizations. 435 00:15:25,080 --> 00:15:27,760 And these hallucinations can have real consequences, 436 00:15:27,760 --> 00:15:29,640 especially in areas like health care. 437 00:15:29,640 --> 00:15:32,360 It's especially concerning that these hallucinations often 438 00:15:32,360 --> 00:15:33,560 seem very believable. 439 00:15:33,560 --> 00:15:36,280 It's hard for us to spot them unless we really look closely. 440 00:15:36,280 --> 00:15:39,080 We can't just trust AI output without question, 441 00:15:39,080 --> 00:15:40,840 no matter how advanced it seems. 442 00:15:40,840 --> 00:15:41,440 You're right. 443 00:15:41,440 --> 00:15:43,840 It shows how important it is to have humans checking 444 00:15:43,840 --> 00:15:47,040 AI's work and using our own judgment. 445 00:15:47,040 --> 00:15:50,040 That's where things like simple QA are so important. 446 00:15:50,040 --> 00:15:52,920 By creating strong benchmarks for factuality, 447 00:15:52,920 --> 00:15:56,760 we can encourage the development of more trustworthy AI systems. 448 00:15:56,760 --> 00:15:58,840 It's about holding AI accountable, 449 00:15:58,840 --> 00:16:01,280 just like we would with any other technology. 450 00:16:01,280 --> 00:16:03,040 Accountability is key. 451 00:16:03,040 --> 00:16:05,040 We can't let AI work in the dark. 452 00:16:05,040 --> 00:16:09,080 We need to understand how it works, what its limits are, 453 00:16:09,080 --> 00:16:11,640 and how to make sure it's used responsibly. 454 00:16:11,640 --> 00:16:14,360 Now let's dig a little deeper into the technical challenges 455 00:16:14,360 --> 00:16:16,120 of preventing AI hallucinations. 456 00:16:16,120 --> 00:16:17,720 It's a complex problem. 457 00:16:17,720 --> 00:16:19,320 There are no easy answers. 458 00:16:19,320 --> 00:16:20,840 It seems like a really tough task. 459 00:16:20,840 --> 00:16:22,920 Where do you even start with something like that? 460 00:16:22,920 --> 00:16:25,400 It's not as simple as changing a few algorithms. 461 00:16:25,400 --> 00:16:28,520 We need to fundamentally understand how AI models learn 462 00:16:28,520 --> 00:16:29,760 and process information. 463 00:16:29,760 --> 00:16:32,760 We have to look at how they're built, the data they learn from, 464 00:16:32,760 --> 00:16:34,360 and how they make decisions. 465 00:16:34,360 --> 00:16:36,960 So it's about figuring out why these hallucinations are 466 00:16:36,960 --> 00:16:37,360 happening. 467 00:16:37,360 --> 00:16:38,360 Exactly. 468 00:16:38,360 --> 00:16:40,560 One thing they're focusing on is making the data used 469 00:16:40,560 --> 00:16:42,400 to train AI models better. 470 00:16:42,400 --> 00:16:44,720 If the data is biased or has mistakes, 471 00:16:44,720 --> 00:16:47,400 it'll likely show up in what the AI produces. 472 00:16:47,400 --> 00:16:48,960 We need to make sure AI is learning 473 00:16:48,960 --> 00:16:51,600 from a diverse, accurate, and fair representation 474 00:16:51,600 --> 00:16:52,600 of the real world. 475 00:16:52,600 --> 00:16:54,280 It's kind of like raising a child. 476 00:16:54,280 --> 00:16:56,560 You want to expose them to lots of different experiences 477 00:16:56,560 --> 00:16:58,240 and perspectives so they can develop 478 00:16:58,240 --> 00:16:59,800 a good understanding of the world. 479 00:16:59,800 --> 00:17:01,320 That's a great way to put it. 480 00:17:01,320 --> 00:17:02,680 They're also working on better ways 481 00:17:02,680 --> 00:17:05,480 to catch and correct hallucinations in real time, 482 00:17:05,480 --> 00:17:08,800 maybe by using multiple AI models to check each other's work, 483 00:17:08,800 --> 00:17:11,360 or by having humans point out possible problems. 484 00:17:11,360 --> 00:17:13,880 It's like having a system of checks and balances 485 00:17:13,880 --> 00:17:15,280 to keep the AI on track. 486 00:17:15,280 --> 00:17:16,040 Precisely. 487 00:17:16,040 --> 00:17:18,920 And as AI models become more complex and powerful, 488 00:17:18,920 --> 00:17:20,280 these checks and balances are going 489 00:17:20,280 --> 00:17:22,080 to become even more important. 490 00:17:22,080 --> 00:17:24,960 Speaking of complex and powerful AI models, 491 00:17:24,960 --> 00:17:27,840 let's go back to Claude 3.5's Sonnet, 492 00:17:27,840 --> 00:17:31,520 and how well it did on SWE Bench Verified. 493 00:17:31,520 --> 00:17:35,120 It's amazing to see AI being used to tackle real coding 494 00:17:35,120 --> 00:17:35,800 problems. 495 00:17:35,800 --> 00:17:39,040 It really shows how much progress is being made in using AI 496 00:17:39,040 --> 00:17:40,680 for software development. 497 00:17:40,680 --> 00:17:43,480 And it could really change how we build software in the future. 498 00:17:43,480 --> 00:17:47,040 I can imagine a future where AI and human programmers work 499 00:17:47,040 --> 00:17:49,280 together, each using their strength 500 00:17:49,280 --> 00:17:50,960 to create even better software. 501 00:17:50,960 --> 00:17:51,960 That's a cool idea. 502 00:17:51,960 --> 00:17:53,720 But it also brings up important questions 503 00:17:53,720 --> 00:17:55,920 about the role of human software engineers. 504 00:17:55,920 --> 00:17:56,840 Yeah. 505 00:17:56,840 --> 00:18:01,120 Will AI completely replace human coders? 506 00:18:01,120 --> 00:18:03,680 Or will it just help them do their jobs better 507 00:18:03,680 --> 00:18:05,960 so they can focus on more complex tasks? 508 00:18:05,960 --> 00:18:07,200 That's a discussion that's probably 509 00:18:07,200 --> 00:18:08,360 going to go on for a while. 510 00:18:08,360 --> 00:18:11,480 But one thing's for sure, AI is becoming more and more 511 00:18:11,480 --> 00:18:13,280 a part of how we develop software. 512 00:18:13,280 --> 00:18:15,480 The question is how we adapt to that 513 00:18:15,480 --> 00:18:19,040 and make sure AI is used to enhance what humans can do, 514 00:18:19,040 --> 00:18:20,080 not replace them. 515 00:18:20,080 --> 00:18:23,840 It's all about finding that balance and using AI's power 516 00:18:23,840 --> 00:18:26,440 while still valuing the skills and creativity 517 00:18:26,440 --> 00:18:27,280 that humans bring. 518 00:18:27,280 --> 00:18:29,200 Now let's switch gears again and talk 519 00:18:29,200 --> 00:18:32,000 about something that's related to both AI hallucinations 520 00:18:32,000 --> 00:18:34,000 and AI assisted coding. 521 00:18:34,000 --> 00:18:35,800 Explainability. 522 00:18:35,800 --> 00:18:36,920 Explainability. 523 00:18:36,920 --> 00:18:38,040 That sounds interesting. 524 00:18:38,040 --> 00:18:39,640 What that mean when we're talking that AI? 525 00:18:39,640 --> 00:18:42,800 It means being able to understand how an AI model reaches 526 00:18:42,800 --> 00:18:44,080 its conclusions. 527 00:18:44,080 --> 00:18:47,000 It's about making the AI's thought process clear to humans. 528 00:18:47,000 --> 00:18:47,920 What is this so important? 529 00:18:47,920 --> 00:18:50,400 Can't we just trust the AI if it's usually right? 530 00:18:50,400 --> 00:18:53,040 Well, if we don't understand why the AI is doing what it's 531 00:18:53,040 --> 00:18:55,280 doing, it's hard to trust it, especially 532 00:18:55,280 --> 00:18:57,600 for important things like medical diagnoses 533 00:18:57,600 --> 00:18:59,360 or financial decisions. 534 00:18:59,360 --> 00:19:02,120 We need to be able to see the reasoning behind the AI's 535 00:19:02,120 --> 00:19:05,280 choices, not just accept the results without question. 536 00:19:05,280 --> 00:19:07,880 So it's not enough for the AI to just give us an answer. 537 00:19:07,880 --> 00:19:09,800 We need to know how it got to that answer. 538 00:19:09,800 --> 00:19:10,880 Exactly. 539 00:19:10,880 --> 00:19:13,200 That's where explainable AI comes in. 540 00:19:13,200 --> 00:19:16,120 Researchers are working on ways to make AI models more 541 00:19:16,120 --> 00:19:19,680 transparent so we can see how they came to their conclusions. 542 00:19:19,680 --> 00:19:21,520 That sounds really difficult. 543 00:19:21,520 --> 00:19:23,760 How do you even start to understand what's going on 544 00:19:23,760 --> 00:19:25,960 inside a complex AI model? 545 00:19:25,960 --> 00:19:27,400 It's a tough job. 546 00:19:27,400 --> 00:19:29,480 But there are some promising approaches. 547 00:19:29,480 --> 00:19:33,160 Some techniques show us the AI's decision-making process, 548 00:19:33,160 --> 00:19:35,280 highlighting the factors that were most important 549 00:19:35,280 --> 00:19:36,520 in its decision. 550 00:19:36,520 --> 00:19:38,600 It's like looking inside the AI's brain 551 00:19:38,600 --> 00:19:40,000 to see how it's thinking. 552 00:19:40,000 --> 00:19:41,480 That's a great way to describe it. 553 00:19:41,480 --> 00:19:43,560 Other techniques involve creating explanations 554 00:19:43,560 --> 00:19:45,200 that humans can understand. 555 00:19:45,200 --> 00:19:47,880 This could mean translating the AI's internal language 556 00:19:47,880 --> 00:19:49,280 into something we can grasp. 557 00:19:49,280 --> 00:19:52,920 So it's about closing the gap between how AI thinks 558 00:19:52,920 --> 00:19:54,000 and how we think. 559 00:19:54,000 --> 00:19:54,720 Exactly. 560 00:19:54,720 --> 00:19:57,240 And as AI becomes more a part of our lives, 561 00:19:57,240 --> 00:20:00,200 explainability will be crucial for building trust 562 00:20:00,200 --> 00:20:02,880 and making sure these systems are used in the right way. 563 00:20:02,880 --> 00:20:04,720 It's like building a bridge of understanding 564 00:20:04,720 --> 00:20:06,520 between humans and AI. 565 00:20:06,520 --> 00:20:08,320 OK, we've talked about a lot today. 566 00:20:08,320 --> 00:20:12,160 From Apple's new chips and the rise of re-craft V3 567 00:20:12,160 --> 00:20:14,320 to the problems with AI hallucinations 568 00:20:14,320 --> 00:20:16,440 and the importance of explainability, 569 00:20:16,440 --> 00:20:19,440 it's obvious that AI is changing quickly. 570 00:20:19,440 --> 00:20:21,840 And it's having a big impact on our world. 571 00:20:21,840 --> 00:20:24,800 It's both exciting and a little scary. 572 00:20:24,800 --> 00:20:25,320 I agree. 573 00:20:25,320 --> 00:20:27,040 It's like we're at the start of something new. 574 00:20:27,040 --> 00:20:30,400 It's thrilling and a bit frightening at the same time. 575 00:20:30,400 --> 00:20:32,080 What's next on our AI journey? 576 00:20:32,080 --> 00:20:33,960 Well, let's explore the philosophical side 577 00:20:33,960 --> 00:20:35,120 of these developments. 578 00:20:35,120 --> 00:20:36,960 AI isn't just about code. 579 00:20:36,960 --> 00:20:39,680 It's about intelligence, consciousness, 580 00:20:39,680 --> 00:20:41,160 and what it means to be human. 581 00:20:41,160 --> 00:20:42,680 That sounds like a fascinating discussion. 582 00:20:42,680 --> 00:20:44,480 I can't wait to get into those bigger questions. 583 00:20:44,480 --> 00:20:46,080 Until then, I encourage you to really think 584 00:20:46,080 --> 00:20:47,440 about what we've discussed today. 585 00:20:47,440 --> 00:20:49,440 How do you see AI shaping the future? 586 00:20:49,440 --> 00:20:51,360 What role should it play in our lives? 587 00:20:51,360 --> 00:20:53,520 Those are great questions to consider. 588 00:20:53,520 --> 00:20:55,960 Thanks for listening to the Daily AI News Podcast. 589 00:20:55,960 --> 00:20:57,440 And stay tuned for more. 590 00:20:57,440 --> 00:20:59,680 AI is really pushing the boundaries of what 591 00:20:59,680 --> 00:21:01,360 we thought was possible. 592 00:21:01,360 --> 00:21:02,120 It's amazing. 593 00:21:02,120 --> 00:21:02,440 I know. 594 00:21:02,440 --> 00:21:04,920 It feels like we're living in a sci-fi novel with how fast 595 00:21:04,920 --> 00:21:05,720 things are changing. 596 00:21:05,720 --> 00:21:07,160 And it's only getting faster. 597 00:21:07,160 --> 00:21:09,520 So it's really important that we stay informed and talk 598 00:21:09,520 --> 00:21:13,440 about what role AI should play in our lives. 599 00:21:13,440 --> 00:21:16,800 We need to be shaping the future, not just watching it happen. 600 00:21:16,800 --> 00:21:17,520 Absolutely. 601 00:21:17,520 --> 00:21:19,480 We can't just sit back and let things 602 00:21:19,480 --> 00:21:22,160 happen without thinking about the consequences we 603 00:21:22,160 --> 00:21:23,920 need to be asking questions and having 604 00:21:23,920 --> 00:21:26,880 thoughtful conversations about how we develop and use AI. 605 00:21:26,880 --> 00:21:27,560 Exactly. 606 00:21:27,560 --> 00:21:28,840 Now, before we finish up, I wanted 607 00:21:28,840 --> 00:21:30,880 to go back to something we talked about earlier, 608 00:21:30,880 --> 00:21:33,880 integrating chat GPT into Apple's world. 609 00:21:33,880 --> 00:21:36,000 What do you think about what that means? 610 00:21:36,000 --> 00:21:37,640 Yeah, that's a big move, isn't it? 611 00:21:37,640 --> 00:21:41,440 It feels like large language models like Chat GPT 612 00:21:41,440 --> 00:21:44,400 are going from labs and demos to being 613 00:21:44,400 --> 00:21:46,440 a real part of our digital lives. 614 00:21:46,440 --> 00:21:46,840 You're right. 615 00:21:46,840 --> 00:21:49,680 It makes you wonder how we'll interact with AI in the future. 616 00:21:49,680 --> 00:21:52,440 Will we depend on AI assistance for more and more things? 617 00:21:52,440 --> 00:21:55,720 Will AI become how we mainly access information 618 00:21:55,720 --> 00:21:56,880 and get things done? 619 00:21:56,880 --> 00:21:58,600 It's almost like that sci-fi idea of having 620 00:21:58,600 --> 00:22:00,920 your own personal Jarvis like an Iron Man. 621 00:22:00,920 --> 00:22:02,240 That's a great comparison. 622 00:22:02,240 --> 00:22:04,360 And just like in those stories, we 623 00:22:04,360 --> 00:22:06,920 need to think about both the good and the bad AI 624 00:22:06,920 --> 00:22:09,160 assistance could make our lives easier, 625 00:22:09,160 --> 00:22:12,400 automate boring tasks, and give us personalized information. 626 00:22:12,400 --> 00:22:15,440 But we also have to be careful about relying on them 627 00:22:15,440 --> 00:22:18,280 too much potential bias and even manipulation. 628 00:22:18,280 --> 00:22:21,680 We need to stay critical and use these tools wisely. 629 00:22:21,680 --> 00:22:22,360 I totally agree. 630 00:22:22,360 --> 00:22:25,400 It's not about blindly accepting or rejecting AI. 631 00:22:25,400 --> 00:22:27,760 It's about finding that middle ground, 632 00:22:27,760 --> 00:22:30,280 using it in a way that fits with our values 633 00:22:30,280 --> 00:22:32,800 and makes our lives better without losing 634 00:22:32,800 --> 00:22:34,480 our independence or creativity. 635 00:22:34,480 --> 00:22:35,000 Well said. 636 00:22:35,000 --> 00:22:36,280 One last thought for you to take away 637 00:22:36,280 --> 00:22:37,880 with all the amazing things we've talked about. 638 00:22:37,880 --> 00:22:39,400 It's easy to get caught up in the excitement, 639 00:22:39,400 --> 00:22:41,880 but it's important to remember that AI is a tool 640 00:22:41,880 --> 00:22:43,280 at the end of the day. 641 00:22:43,280 --> 00:22:44,360 That's a powerful point. 642 00:22:44,360 --> 00:22:46,400 The future of AI isn't set in stone. 643 00:22:46,400 --> 00:22:48,160 It's being shaped right now by the choices 644 00:22:48,160 --> 00:22:50,360 we make, the questions we ask, and the things we do. 645 00:22:50,360 --> 00:22:53,320 It's up to all of us to guide this technology in a direction 646 00:22:53,320 --> 00:22:56,000 that's good for humanity and makes a better future for everyone. 647 00:22:56,000 --> 00:22:56,680 Exactly. 648 00:22:56,680 --> 00:22:59,920 So as you go about your day, I encourage 649 00:22:59,920 --> 00:23:02,880 you to think about how AI is already part of your life 650 00:23:02,880 --> 00:23:04,560 and how you'd like to see it develop. 651 00:23:04,560 --> 00:23:06,320 What are your hopes and fears? 652 00:23:06,320 --> 00:23:08,200 What ethical lines do we need to draw? 653 00:23:08,200 --> 00:23:10,520 These are important things to think about as we move forward 654 00:23:10,520 --> 00:23:12,200 into this unknown territory. 655 00:23:12,200 --> 00:23:14,440 Those are great questions to think about. 656 00:23:14,440 --> 00:23:16,720 Thanks for listening to the Daily AI News Podcast, 657 00:23:16,720 --> 00:23:30,800 and stay tuned for more and more.