1 00:00:00,000 --> 00:00:02,960 Welcome to the Daily AI News Podcast. 2 00:00:02,960 --> 00:00:05,840 Ready for a deep dive into all things AI? 3 00:00:05,840 --> 00:00:06,680 Always. 4 00:00:06,680 --> 00:00:08,040 What's on the menu for today? 5 00:00:08,040 --> 00:00:11,920 Oh, we've got a whole buffet of AI news and breakers. 6 00:00:11,920 --> 00:00:13,440 Some pretty big predictions too. 7 00:00:13,440 --> 00:00:14,520 Sounds exciting. 8 00:00:14,520 --> 00:00:15,560 Where should we start? 9 00:00:15,560 --> 00:00:19,280 Well, AWS just wrapped up their huge re-invent conference 10 00:00:19,280 --> 00:00:21,600 and Genitive AI was the star of the show. 11 00:00:21,600 --> 00:00:24,200 Ah, re-invent. 12 00:00:24,200 --> 00:00:26,040 Always a ton of news coming out of that. 13 00:00:26,040 --> 00:00:26,880 For sure. 14 00:00:26,880 --> 00:00:29,240 They really doubled down on Genitive AI this year, 15 00:00:29,240 --> 00:00:30,680 especially for businesses. 16 00:00:30,680 --> 00:00:31,240 Interesting. 17 00:00:31,240 --> 00:00:32,880 What were some of the highlights? 18 00:00:32,880 --> 00:00:37,040 So they made some big upgrades to their Bedrock platform. 19 00:00:37,040 --> 00:00:39,960 You know, the one for building and running generative AI apps. 20 00:00:39,960 --> 00:00:41,160 Yeah, I remember that. 21 00:00:41,160 --> 00:00:42,760 Well, now it's got this new thing called 22 00:00:42,760 --> 00:00:44,680 multi-agent orchestration. 23 00:00:44,680 --> 00:00:46,400 Multi-agent orchestration? 24 00:00:46,400 --> 00:00:47,640 What's that all about? 25 00:00:47,640 --> 00:00:49,720 It's like having a bunch of different AI agents 26 00:00:49,720 --> 00:00:52,480 that each specialize in something specific 27 00:00:52,480 --> 00:00:53,840 all working together like a team. 28 00:00:53,840 --> 00:00:56,080 Oh, so instead of one AI trying to do everything, 29 00:00:56,080 --> 00:00:58,200 you've got these specialists collaborating. 30 00:00:58,200 --> 00:00:59,080 Makes sense. 31 00:00:59,080 --> 00:00:59,600 Exactly. 32 00:00:59,600 --> 00:01:02,880 They gave the example of Moody's using it for risk assessment. 33 00:01:02,880 --> 00:01:04,400 They have different AIs handling 34 00:01:04,400 --> 00:01:05,760 different parts of the analysis. 35 00:01:05,760 --> 00:01:06,720 I see. 36 00:01:06,720 --> 00:01:09,440 Breaking down a complex problem into smaller chunks. 37 00:01:09,440 --> 00:01:11,880 Kind of like a team of expert doctors 38 00:01:11,880 --> 00:01:14,080 instead of just one general practitioner. 39 00:01:14,080 --> 00:01:15,400 Yeah, exactly. 40 00:01:15,400 --> 00:01:17,760 And remember all the talk about AI hallucinations. 41 00:01:17,760 --> 00:01:18,800 Oh, yeah. 42 00:01:18,800 --> 00:01:20,480 Where the AI just makes stuff up. 43 00:01:20,480 --> 00:01:23,000 Well, AWS claims they've solved that problem. 44 00:01:23,000 --> 00:01:23,400 Really? 45 00:01:23,400 --> 00:01:24,080 How? 46 00:01:24,080 --> 00:01:27,280 They've got this new feature called automated reasoning checks. 47 00:01:27,280 --> 00:01:31,680 They say it catches 100% of hallucinations on bedrock. 48 00:01:31,680 --> 00:01:33,280 That's a big claim. 49 00:01:33,280 --> 00:01:35,520 But if it works, it could be huge for businesses 50 00:01:35,520 --> 00:01:37,720 using AI for critical decisions. 51 00:01:37,720 --> 00:01:38,440 Definitely. 52 00:01:38,440 --> 00:01:40,520 And for people who don't have tons of resources 53 00:01:40,520 --> 00:01:42,880 to train those massive AI models, 54 00:01:42,880 --> 00:01:44,520 they introduced model distillation. 55 00:01:44,520 --> 00:01:45,360 What is that? 56 00:01:45,360 --> 00:01:47,760 Some kind of AI slimming down program. 57 00:01:47,760 --> 00:01:51,200 Basically, it creates smaller, faster AI models 58 00:01:51,200 --> 00:01:53,640 without sacrificing too much on performance. 59 00:01:53,640 --> 00:01:54,440 Makes sense. 60 00:01:54,440 --> 00:01:56,560 Efficiency is key, especially as AI 61 00:01:56,560 --> 00:01:58,120 becomes more and more common. 62 00:01:58,120 --> 00:01:59,120 Absolutely. 63 00:01:59,120 --> 00:02:00,840 And speaking of accessible, get this. 64 00:02:00,840 --> 00:02:04,640 AWS SageMaker, their platform for building and deploying 65 00:02:04,640 --> 00:02:07,720 machine learning models, it's getting a total makeover. 66 00:02:07,720 --> 00:02:09,400 Wow, a complete overhaul. 67 00:02:09,400 --> 00:02:10,160 What's changing? 68 00:02:10,160 --> 00:02:14,240 Thinked it as a one-stop shop for all things data and AI. 69 00:02:14,240 --> 00:02:16,800 They're merging all their analytics and machine learning 70 00:02:16,800 --> 00:02:18,960 tools into one platform. 71 00:02:18,960 --> 00:02:19,400 Interesting. 72 00:02:19,400 --> 00:02:22,320 So it's streamlining the whole process of developing 73 00:02:22,320 --> 00:02:23,880 and deploying AI applications. 74 00:02:23,880 --> 00:02:24,360 Yeah. 75 00:02:24,360 --> 00:02:26,360 They've added this thing called Lakehouse, 76 00:02:26,360 --> 00:02:29,240 which is designed to handle massive data sets. 77 00:02:29,240 --> 00:02:29,960 Lakehouse. 78 00:02:29,960 --> 00:02:30,280 OK. 79 00:02:30,280 --> 00:02:30,960 And what else? 80 00:02:30,960 --> 00:02:33,360 Oh, and then there's Unified Studio, basically 81 00:02:33,360 --> 00:02:36,840 a single interface for managing your entire AI workflow. 82 00:02:36,840 --> 00:02:39,040 That sounds like it could make AI development a lot more 83 00:02:39,040 --> 00:02:40,160 user-friendly. 84 00:02:40,160 --> 00:02:42,080 But wait, they didn't stop there, did they? 85 00:02:42,080 --> 00:02:44,680 What about those new AI models everyone's been buzzing about? 86 00:02:44,680 --> 00:02:45,760 Oh, you mean Amazon Nova? 87 00:02:45,760 --> 00:02:49,320 Yeah, the ones for generating text, images, even videos. 88 00:02:49,320 --> 00:02:50,880 Yep, that's them. 89 00:02:50,880 --> 00:02:53,360 They released a whole family of these models designed 90 00:02:53,360 --> 00:02:55,160 specifically for businesses. 91 00:02:55,160 --> 00:02:56,480 Businesses, huh? 92 00:02:56,480 --> 00:02:58,400 What are some potential use cases? 93 00:02:58,400 --> 00:03:01,480 Imagine automatically generating marketing materials, 94 00:03:01,480 --> 00:03:04,960 product demos, even personalized video messages. 95 00:03:04,960 --> 00:03:06,120 Pretty powerful stuff. 96 00:03:06,120 --> 00:03:07,000 For sure. 97 00:03:07,000 --> 00:03:08,800 And the really cool thing is that businesses 98 00:03:08,800 --> 00:03:12,880 can customize these Nova models to fit their specific needs. 99 00:03:12,880 --> 00:03:15,160 It's not a one-size-fits-all approach anymore. 100 00:03:15,160 --> 00:03:16,640 That's impressive. 101 00:03:16,640 --> 00:03:20,440 Customization is key to making AI truly useful for businesses. 102 00:03:20,440 --> 00:03:21,080 Right. 103 00:03:21,080 --> 00:03:24,960 They also made some significant improvements to Hyperpod. 104 00:03:24,960 --> 00:03:27,480 You know, their service for training and deploying 105 00:03:27,480 --> 00:03:29,040 those massive AI models. 106 00:03:29,040 --> 00:03:30,320 Hyperpod, yeah, I've heard of it. 107 00:03:30,320 --> 00:03:31,360 What did they change? 108 00:03:31,360 --> 00:03:34,480 They got this new feature called Hyperpod Task Governance, 109 00:03:34,480 --> 00:03:37,080 which basically makes sure GPUs are used more efficiently 110 00:03:37,080 --> 00:03:37,840 during training. 111 00:03:37,840 --> 00:03:38,560 Well, that's smart. 112 00:03:38,560 --> 00:03:40,880 GPUs are expensive, so this helps reduce costs. 113 00:03:40,880 --> 00:03:43,320 Yeah, it can reduce idle time and infrastructure costs 114 00:03:43,320 --> 00:03:44,480 by up to 40%. 115 00:03:44,480 --> 00:03:46,520 Wow, that's significant. 116 00:03:46,520 --> 00:03:49,720 Anything else to help with those sky-high AI training costs? 117 00:03:49,720 --> 00:03:50,840 Actually, yeah. 118 00:03:50,840 --> 00:03:52,640 They've got Intelligent Prompt Routing, which 119 00:03:52,640 --> 00:03:55,920 sends your AI queries to the right model size. 120 00:03:55,920 --> 00:03:58,960 So you're not wasting resources on a big, complex model 121 00:03:58,960 --> 00:04:00,440 if a simpler one will do. 122 00:04:00,440 --> 00:04:03,120 That's clever, using the right tool for the job. 123 00:04:03,120 --> 00:04:05,160 And they also added prompt caching, right? 124 00:04:05,160 --> 00:04:07,080 Yeah, so if you use the same prompts a lot, 125 00:04:07,080 --> 00:04:10,160 they're stored and reused, saving time and processing power. 126 00:04:10,160 --> 00:04:12,400 It seems like AWS is really focused 127 00:04:12,400 --> 00:04:15,840 on making AI development more efficient and cost-effective. 128 00:04:15,840 --> 00:04:16,920 Good for them. 129 00:04:16,920 --> 00:04:18,040 What else is new? 130 00:04:18,040 --> 00:04:20,480 Well, they also rolled out some pretty sophisticated tools 131 00:04:20,480 --> 00:04:22,880 to make Argyé workflows easier, 132 00:04:22,880 --> 00:04:25,280 you know, retrieval augmented generation. 133 00:04:25,280 --> 00:04:26,720 Argyé? 134 00:04:26,720 --> 00:04:28,440 Rish, my memory on what that is again. 135 00:04:28,440 --> 00:04:30,680 So basically, it's like combining information 136 00:04:30,680 --> 00:04:32,600 retrieval with generative AI. 137 00:04:32,600 --> 00:04:35,200 OK, so you can pull in data from all sorts of sources 138 00:04:35,200 --> 00:04:37,600 and then use AI to make sense of it all. 139 00:04:37,600 --> 00:04:38,400 Exactly. 140 00:04:38,400 --> 00:04:40,920 And these new tools can even automatically generate 141 00:04:40,920 --> 00:04:44,040 SQL queries to get data from databases 142 00:04:44,040 --> 00:04:46,520 or create those knowledge graph visualizations. 143 00:04:46,520 --> 00:04:48,880 So you can ask it to, say, write a report 144 00:04:48,880 --> 00:04:50,400 on a specific topic, and it'll pull 145 00:04:50,400 --> 00:04:53,200 in all the relevant information from company databases, 146 00:04:53,200 --> 00:04:55,120 research papers, even news articles. 147 00:04:55,120 --> 00:04:56,680 Yeah, exactly like that. 148 00:04:56,680 --> 00:04:58,640 And last but not least, they launched 149 00:04:58,640 --> 00:05:00,200 Bedrock Data Automation. 150 00:05:00,200 --> 00:05:02,080 Bedrock Data Automation, OK. 151 00:05:02,080 --> 00:05:03,000 What does that do? 152 00:05:03,000 --> 00:05:06,920 It uses AI to convert all that messy, unstructured data, 153 00:05:06,920 --> 00:05:10,200 like PDFs and audio files, into structured formats 154 00:05:10,200 --> 00:05:11,720 that AI can actually work with. 155 00:05:11,720 --> 00:05:12,760 Oh, wow. 156 00:05:12,760 --> 00:05:15,840 Most companies have tons of unstructured data 157 00:05:15,840 --> 00:05:17,920 that they haven't been able to use for AI. 158 00:05:17,920 --> 00:05:18,840 I know, right? 159 00:05:18,840 --> 00:05:20,240 This could be a big deal for them. 160 00:05:20,240 --> 00:05:21,440 Definitely. 161 00:05:21,440 --> 00:05:23,280 It sounds like AWS is on a mission 162 00:05:23,280 --> 00:05:26,040 to make AI development easier, more efficient, 163 00:05:26,040 --> 00:05:28,480 and more powerful for everyone. 164 00:05:28,480 --> 00:05:31,240 And they're really focusing on making those powerful foundation 165 00:05:31,240 --> 00:05:32,960 models more accessible. 166 00:05:32,960 --> 00:05:36,000 The ones trained on tons of data that form the basis 167 00:05:36,000 --> 00:05:37,760 for so many AI applications. 168 00:05:37,760 --> 00:05:39,760 Right, like the large language models 169 00:05:39,760 --> 00:05:42,440 behind chatbots and content generators. 170 00:05:42,440 --> 00:05:43,440 Exactly. 171 00:05:43,440 --> 00:05:45,240 And to make them easier to work with, 172 00:05:45,240 --> 00:05:47,840 they've released these preconfigured recipes 173 00:05:47,840 --> 00:05:49,760 for SageMaker HyperPod. 174 00:05:49,760 --> 00:05:51,600 Recipes for AI. 175 00:05:51,600 --> 00:05:52,640 What do you mean by that? 176 00:05:52,640 --> 00:05:54,640 Think of them like easy-to-follow instructions 177 00:05:54,640 --> 00:05:56,600 that simplify the process of training 178 00:05:56,600 --> 00:05:58,880 and fine-tuning these massive foundation models. 179 00:05:58,880 --> 00:06:00,600 So even if you're not an AI expert, 180 00:06:00,600 --> 00:06:02,560 you can still use these powerful models. 181 00:06:02,560 --> 00:06:03,640 Exactly. 182 00:06:03,640 --> 00:06:07,120 And they claim these recipes can cut training time by up to 40% 183 00:06:07,120 --> 00:06:09,560 and let you scale your training across a massive amount 184 00:06:09,560 --> 00:06:10,720 of computing power. 185 00:06:10,720 --> 00:06:11,520 That's impressive. 186 00:06:11,520 --> 00:06:14,480 So even small teams can work with these huge models now. 187 00:06:14,480 --> 00:06:16,360 Yep, and they've got preconfigured recipes 188 00:06:16,360 --> 00:06:18,240 for some of the most popular models out there, 189 00:06:18,240 --> 00:06:20,440 like Lama 3.1 and Mixedroll. 190 00:06:20,440 --> 00:06:21,120 That's great. 191 00:06:21,120 --> 00:06:23,240 So you can easily fine-tune an existing model 192 00:06:23,240 --> 00:06:24,960 for your specific needs. 193 00:06:24,960 --> 00:06:27,360 Yep, and the recipes are super flexible. 194 00:06:27,360 --> 00:06:29,680 You can switch between different types of computing 195 00:06:29,680 --> 00:06:33,760 instances like GPUs or Tranium to optimize for performance 196 00:06:33,760 --> 00:06:34,560 or cost. 197 00:06:34,560 --> 00:06:36,960 That kind of flexibility is so important, 198 00:06:36,960 --> 00:06:40,320 especially with AI development costs always going up. 199 00:06:40,320 --> 00:06:42,320 What else is cool about these recipes? 200 00:06:42,320 --> 00:06:44,320 Well, they're also making them available on GitHub, 201 00:06:44,320 --> 00:06:46,360 so anyone can access and modify them. 202 00:06:46,360 --> 00:06:48,320 They even include code snippets to show 203 00:06:48,320 --> 00:06:49,840 how to customize the recipes. 204 00:06:49,840 --> 00:06:51,040 That's a fantastic move. 205 00:06:51,040 --> 00:06:53,800 It'll create a whole community of developers working together, 206 00:06:53,800 --> 00:06:54,800 a real win-win. 207 00:06:54,800 --> 00:06:58,880 Speaking of AI pioneers, Sam Altman, the CEO of Open AI, 208 00:06:58,880 --> 00:07:00,920 has been making some pretty bold predictions 209 00:07:00,920 --> 00:07:02,200 about the future of AI. 210 00:07:02,200 --> 00:07:05,440 Oh, Sam Altman, the man behind chat GPT. 211 00:07:05,440 --> 00:07:07,640 I'm always interested to hear what he has to say. 212 00:07:07,640 --> 00:07:08,960 What's he predicting now? 213 00:07:08,960 --> 00:07:11,080 Well, he's putting his money on the arrival 214 00:07:11,080 --> 00:07:13,840 of Artificial General Intelligence, or AGI, 215 00:07:13,840 --> 00:07:15,280 within the next few years. 216 00:07:15,280 --> 00:07:16,480 AGI? 217 00:07:16,480 --> 00:07:19,240 That's the holy grail of AI research. 218 00:07:19,240 --> 00:07:22,720 The idea that AI could perform any intellectual task a human 219 00:07:22,720 --> 00:07:23,280 can. 220 00:07:23,280 --> 00:07:24,840 Yeah, and he thinks it's going to be much bigger 221 00:07:24,840 --> 00:07:26,000 than most people realize. 222 00:07:26,000 --> 00:07:27,640 That's a bold prediction considering 223 00:07:27,640 --> 00:07:29,960 how complex human intelligence really is. 224 00:07:29,960 --> 00:07:33,440 Does he give any specifics about how AGI will impact us? 225 00:07:33,440 --> 00:07:36,840 Well, he does acknowledge that job displacement is a real 226 00:07:36,840 --> 00:07:39,840 concern as AI becomes more capable. 227 00:07:39,840 --> 00:07:43,640 But he also compares AI to the invention of the transistor. 228 00:07:43,640 --> 00:07:45,120 Interesting comparison. 229 00:07:45,120 --> 00:07:47,080 The transistor revolutionized electronics 230 00:07:47,080 --> 00:07:48,800 and led to so many innovations. 231 00:07:48,800 --> 00:07:51,840 Is he suggesting that AI will have a similar transformative 232 00:07:51,840 --> 00:07:53,000 effect on the economy? 233 00:07:53,000 --> 00:07:54,280 Yeah, exactly. 234 00:07:54,280 --> 00:07:56,840 He thinks AI will become as ubiquitous and essential 235 00:07:56,840 --> 00:08:00,040 as the transistor, used by companies in every industry, 236 00:08:00,040 --> 00:08:02,200 and completely changing how our world works. 237 00:08:02,200 --> 00:08:03,760 That's a pretty grand vision. 238 00:08:03,760 --> 00:08:06,040 It's exciting, but also a bit domping. 239 00:08:06,040 --> 00:08:06,720 It is. 240 00:08:06,720 --> 00:08:09,000 And he's not shying away from the challenges either. 241 00:08:09,000 --> 00:08:11,920 He's been talking about the lawsuits Open AI is facing, 242 00:08:11,920 --> 00:08:13,760 including one from the New York Times 243 00:08:13,760 --> 00:08:15,280 over copyright issues. 244 00:08:15,280 --> 00:08:17,880 Yes, the big debate about who owns the data 245 00:08:17,880 --> 00:08:19,840 used to train these AI models. 246 00:08:19,840 --> 00:08:20,880 That's a tough one. 247 00:08:20,880 --> 00:08:22,000 It is. 248 00:08:22,000 --> 00:08:23,800 He acknowledges the need for new ways 249 00:08:23,800 --> 00:08:26,720 to pay creators in this age of AI. 250 00:08:26,720 --> 00:08:28,360 But he also thinks the current discussions 251 00:08:28,360 --> 00:08:30,040 about fair use are missing the point. 252 00:08:30,040 --> 00:08:32,360 It's definitely a conversation that needs to happen. 253 00:08:32,360 --> 00:08:34,680 And then, of course, there's all the drama with Elon Musk 254 00:08:34,680 --> 00:08:36,440 and his lawsuit against Open AI. 255 00:08:36,440 --> 00:08:37,960 Oh, yeah, that's been making headlines. 256 00:08:37,960 --> 00:08:38,480 Yeah. 257 00:08:38,480 --> 00:08:41,880 Musk claims that Open AI has abandoned its original nonprofit 258 00:08:41,880 --> 00:08:44,760 mission and become too focused on profits. 259 00:08:44,760 --> 00:08:47,200 He's even accusing Altman of betrayal. 260 00:08:47,200 --> 00:08:49,240 It's a pretty public and messy fight, 261 00:08:49,240 --> 00:08:51,800 especially considering they co-founded Open AI together. 262 00:08:51,800 --> 00:08:53,800 How's Altman responding to all of this? 263 00:08:53,800 --> 00:08:55,600 He says he's sad about the conflict. 264 00:08:55,600 --> 00:08:57,840 He used to consider Musk a hero, but he also 265 00:08:57,840 --> 00:08:59,440 sees it as a business rivalry now. 266 00:08:59,440 --> 00:09:02,200 Yeah, with Musk's new company, XAI, 267 00:09:02,200 --> 00:09:04,440 emerging as a competitor to Open AI, 268 00:09:04,440 --> 00:09:07,080 it's a rivalry that's definitely got everyone's attention. 269 00:09:07,080 --> 00:09:09,880 It's a reminder that AI isn't just about algorithms. 270 00:09:09,880 --> 00:09:13,000 It's about people and their visions for the future. 271 00:09:13,000 --> 00:09:14,720 And sometimes those visions clash. 272 00:09:14,720 --> 00:09:15,800 True. 273 00:09:15,800 --> 00:09:18,680 And as AI becomes more powerful, those clashes 274 00:09:18,680 --> 00:09:20,680 become even more significant. 275 00:09:20,680 --> 00:09:21,880 What else has been happening? 276 00:09:21,880 --> 00:09:23,880 Any news on the AI safety front? 277 00:09:23,880 --> 00:09:25,160 Actually, yeah. 278 00:09:25,160 --> 00:09:26,760 There's been some interesting research 279 00:09:26,760 --> 00:09:29,800 on how easily AI-powered robots can be tricked 280 00:09:29,800 --> 00:09:31,280 into doing harmful things. 281 00:09:31,280 --> 00:09:32,120 Really? 282 00:09:32,120 --> 00:09:33,080 That's a bit unsettling. 283 00:09:33,080 --> 00:09:34,480 How are they doing this? 284 00:09:34,480 --> 00:09:36,920 Researchers have found that you can essentially jailbreak 285 00:09:36,920 --> 00:09:40,240 these robots by using cleverly crafted prompts 286 00:09:40,240 --> 00:09:42,080 to bypass their safety controls. 287 00:09:42,080 --> 00:09:43,680 Jailbreaking robots. 288 00:09:43,680 --> 00:09:45,600 Sounds like something out of a movie. 289 00:09:45,600 --> 00:09:46,480 It does. 290 00:09:46,480 --> 00:09:48,840 They were able to get a simulated self-driving car 291 00:09:48,840 --> 00:09:51,360 to ignore stop signs, a wheeled robot 292 00:09:51,360 --> 00:09:53,480 to search for a bomb detonation point, 293 00:09:53,480 --> 00:09:56,200 and even a four-legged robot to spy on people. 294 00:09:56,200 --> 00:09:57,760 Wow, that's scary stuff. 295 00:09:57,760 --> 00:10:00,280 It really highlights the potential dangers of AI, 296 00:10:00,280 --> 00:10:02,080 especially as it starts controlling things 297 00:10:02,080 --> 00:10:03,000 in the physical world. 298 00:10:03,000 --> 00:10:03,520 No doubt. 299 00:10:03,520 --> 00:10:05,240 The researchers are warning that this is just 300 00:10:05,240 --> 00:10:08,600 the tip of the iceberg when it comes to AI vulnerabilities. 301 00:10:08,600 --> 00:10:10,120 It's a good reminder that we need 302 00:10:10,120 --> 00:10:12,080 to prioritize safety and security 303 00:10:12,080 --> 00:10:15,440 as AI becomes more integrated into our lives. 304 00:10:15,440 --> 00:10:16,560 What are some of the ways people 305 00:10:16,560 --> 00:10:18,360 are addressing these concerns? 306 00:10:18,360 --> 00:10:20,200 Well, there are organizations working 307 00:10:20,200 --> 00:10:21,880 on developing standards and benchmarks 308 00:10:21,880 --> 00:10:24,080 to measure the safety of AI systems. 309 00:10:24,080 --> 00:10:25,520 That's good to hear. 310 00:10:25,520 --> 00:10:29,000 For instance, ML Commons just released this new benchmark 311 00:10:29,000 --> 00:10:30,560 called A-Aluminate. 312 00:10:30,560 --> 00:10:33,360 It's designed to measure the safety of those large language 313 00:10:33,360 --> 00:10:33,920 models. 314 00:10:33,920 --> 00:10:34,480 That's interesting. 315 00:10:34,480 --> 00:10:35,960 Tell me more about it. 316 00:10:35,960 --> 00:10:39,920 So AI-Aluminate uses over 24,000 prompts 317 00:10:39,920 --> 00:10:43,520 to test LLMs for potentially harmful content. 318 00:10:43,520 --> 00:10:45,720 It covers a wide range of risks from things 319 00:10:45,720 --> 00:10:49,400 like telling a robot to do something dangerous to generating 320 00:10:49,400 --> 00:10:50,560 bias language. 321 00:10:50,560 --> 00:10:53,240 So it's looking for any signs that an AI might behave 322 00:10:53,240 --> 00:10:55,040 in a harmful or unethical way. 323 00:10:55,040 --> 00:10:56,000 Exactly. 324 00:10:56,000 --> 00:10:58,560 And they've tested a bunch of popular LLMs, 325 00:10:58,560 --> 00:11:00,480 and the results are all over the map. 326 00:11:00,480 --> 00:11:02,320 Some models perform much better than others 327 00:11:02,320 --> 00:11:03,400 when it comes to safety. 328 00:11:03,400 --> 00:11:04,520 That makes sense. 329 00:11:04,520 --> 00:11:06,600 AI models are all trained on different data, 330 00:11:06,600 --> 00:11:08,360 so their behaviors will vary. 331 00:11:08,360 --> 00:11:10,280 It highlights the importance of these benchmarks 332 00:11:10,280 --> 00:11:12,960 for tracking safety and holding developers accountable. 333 00:11:12,960 --> 00:11:13,440 For sure. 334 00:11:13,440 --> 00:11:15,040 And on a more positive note, there's 335 00:11:15,040 --> 00:11:16,600 some really exciting research happening 336 00:11:16,600 --> 00:11:19,200 on how AI can be used to speed up scientific discoveries. 337 00:11:19,200 --> 00:11:20,120 Oh, yeah. 338 00:11:20,120 --> 00:11:21,160 I've heard a bit about this. 339 00:11:21,160 --> 00:11:22,880 What are some of the promising areas? 340 00:11:22,880 --> 00:11:25,880 Well, researchers have created this virtual lab, 341 00:11:25,880 --> 00:11:27,960 powered by a team of AI scientists. 342 00:11:27,960 --> 00:11:29,400 AI scientists? 343 00:11:29,400 --> 00:11:30,360 That's fascinating. 344 00:11:30,360 --> 00:11:31,520 What do you mean by that? 345 00:11:31,520 --> 00:11:33,280 So they've got these different AI agents. 346 00:11:33,280 --> 00:11:35,480 Each with specific roles and expertise, 347 00:11:35,480 --> 00:11:37,080 just like a real research team. 348 00:11:37,080 --> 00:11:37,760 Wow. 349 00:11:37,760 --> 00:11:40,240 So you have an AI playing the role of, say, 350 00:11:40,240 --> 00:11:42,400 the principal investigator, the immunologist, 351 00:11:42,400 --> 00:11:43,880 the computational biologist. 352 00:11:43,880 --> 00:11:44,680 That's incredible. 353 00:11:44,680 --> 00:11:45,360 It is. 354 00:11:45,360 --> 00:11:49,480 And they actually gave this AI team a real-world challenge, 355 00:11:49,480 --> 00:11:53,160 designing nanobuddies that could target the SARS-CoV-2 356 00:11:53,160 --> 00:11:53,800 virus. 357 00:11:53,800 --> 00:11:54,760 No way. 358 00:11:54,760 --> 00:11:55,840 Did it work? 359 00:11:55,840 --> 00:11:56,440 It did. 360 00:11:56,440 --> 00:11:59,720 The AI team was able to design 92 nanobuddies, 361 00:11:59,720 --> 00:12:03,720 and over 90% of them actually bound to the virus in tests. 362 00:12:03,720 --> 00:12:04,800 That's amazing. 363 00:12:04,800 --> 00:12:06,800 How long did it take the AI team to do this? 364 00:12:06,800 --> 00:12:08,840 Just a fraction of the time it would take a human team. 365 00:12:08,840 --> 00:12:10,120 That's the power of AI, right? 366 00:12:10,120 --> 00:12:12,320 They're speeding up scientific discovery 367 00:12:12,320 --> 00:12:14,040 and potentially leading to breakthroughs 368 00:12:14,040 --> 00:12:15,320 that could save lives. 369 00:12:15,320 --> 00:12:17,880 And while the virtual lab is largely autonomous, 370 00:12:17,880 --> 00:12:20,320 the researchers emphasize that human guidance is still 371 00:12:20,320 --> 00:12:22,000 really important for success. 372 00:12:22,000 --> 00:12:22,520 Makes sense. 373 00:12:22,520 --> 00:12:24,240 It's not about replacing scientists. 374 00:12:24,240 --> 00:12:26,480 It's about giving them a powerful new tool. 375 00:12:26,480 --> 00:12:28,240 It's like giving them a superpower. 376 00:12:28,240 --> 00:12:29,240 Exactly. 377 00:12:29,240 --> 00:12:31,720 And speaking of tackling real-world problems, 378 00:12:31,720 --> 00:12:36,040 AI is making big strides in the field of weather forecasting. 379 00:12:36,040 --> 00:12:37,200 Weather forecasting. 380 00:12:37,200 --> 00:12:38,680 That's always been a tough one to crack. 381 00:12:38,680 --> 00:12:39,760 I know, right? 382 00:12:39,760 --> 00:12:43,880 But Google just unveiled this new AI model called GenCast. 383 00:12:43,880 --> 00:12:46,840 And it's claiming some really impressive accuracy 384 00:12:46,840 --> 00:12:50,200 in predicting both day-to-day weather and extreme events. 385 00:12:50,200 --> 00:12:52,600 GenCast, what's so special about it? 386 00:12:52,600 --> 00:12:53,800 Well, it's an ensemble model. 387 00:12:53,800 --> 00:12:54,680 Ensemble model. 388 00:12:54,680 --> 00:12:55,520 What does that mean? 389 00:12:55,520 --> 00:12:57,440 So instead of giving you a single forecast, 390 00:12:57,440 --> 00:13:00,440 it predicts a range of possible weather scenarios. 391 00:13:00,440 --> 00:13:01,240 Oh, that's smart. 392 00:13:01,240 --> 00:13:04,120 So it's not just about the most likely outcome, 393 00:13:04,120 --> 00:13:06,720 but also about understanding the uncertainty involved 394 00:13:06,720 --> 00:13:07,880 in weather prediction. 395 00:13:07,880 --> 00:13:08,480 Yep. 396 00:13:08,480 --> 00:13:10,920 And they've tested GenCast against some of the best systems 397 00:13:10,920 --> 00:13:11,680 out there. 398 00:13:11,680 --> 00:13:14,880 And it consistently outperforms them, even up to 15 days 399 00:13:14,880 --> 00:13:15,480 in advance. 400 00:13:15,480 --> 00:13:16,560 15 days. 401 00:13:16,560 --> 00:13:18,600 That's a long-range forecast. 402 00:13:18,600 --> 00:13:21,560 What are some of the applications for a model like this? 403 00:13:21,560 --> 00:13:24,440 Well, imagine being able to predict extreme weather events 404 00:13:24,440 --> 00:13:27,600 like hurricanes or heat waves, days, or even weeks 405 00:13:27,600 --> 00:13:28,240 before they hit. 406 00:13:28,240 --> 00:13:31,160 That could be a game changer for disaster preparedness, 407 00:13:31,160 --> 00:13:32,960 saving lives and property. 408 00:13:32,960 --> 00:13:33,960 Exactly. 409 00:13:33,960 --> 00:13:37,360 And the best part is that Google is open-sourcing GenCast, 410 00:13:37,360 --> 00:13:39,760 so the whole research community can benefit from it. 411 00:13:39,760 --> 00:13:40,840 That's fantastic. 412 00:13:40,840 --> 00:13:42,640 Open Science at its best. 413 00:13:42,640 --> 00:13:44,800 It's great to see these powerful tools becoming 414 00:13:44,800 --> 00:13:46,320 more accessible. 415 00:13:46,320 --> 00:13:49,680 Speaking of powerful tools, have you heard about Genie 2? 416 00:13:49,680 --> 00:13:50,240 Genie 2. 417 00:13:50,240 --> 00:13:53,640 Is that the one for creating those amazing virtual worlds? 418 00:13:53,640 --> 00:13:54,560 Yeah. 419 00:13:54,560 --> 00:13:57,080 It can generate these incredible 3D environments 420 00:13:57,080 --> 00:13:58,480 from just a single image. 421 00:13:58,480 --> 00:13:58,960 I know. 422 00:13:58,960 --> 00:14:02,200 It's like stepping into a video game or a simulation. 423 00:14:02,200 --> 00:14:04,840 It's incredible what they're doing with AI these days. 424 00:14:04,840 --> 00:14:05,440 It really is. 425 00:14:05,440 --> 00:14:06,760 And we're just getting started. 426 00:14:06,760 --> 00:14:08,200 It's like they're handing us the keys 427 00:14:08,200 --> 00:14:09,800 to create whole new worlds. 428 00:14:09,800 --> 00:14:10,960 I know, right? 429 00:14:10,960 --> 00:14:12,600 And it's not just for fun either. 430 00:14:12,600 --> 00:14:16,200 Genie 2 has some really exciting potential applications 431 00:14:16,200 --> 00:14:17,680 for research and development. 432 00:14:17,680 --> 00:14:18,840 Yeah, like what? 433 00:14:18,840 --> 00:14:21,200 How could scientists use something like this? 434 00:14:21,200 --> 00:14:24,120 Well, they could create super realistic simulations 435 00:14:24,120 --> 00:14:26,040 of real world environments. 436 00:14:26,040 --> 00:14:28,640 Test different theories or train AI agents. 437 00:14:28,640 --> 00:14:29,080 Oh, I see. 438 00:14:29,080 --> 00:14:32,280 So like simulating the effects of climate change on a city 439 00:14:32,280 --> 00:14:34,200 or the spread of a disease? 440 00:14:34,200 --> 00:14:35,280 Exactly. 441 00:14:35,280 --> 00:14:38,520 And because Genie 2 models physics and object interaction 442 00:14:38,520 --> 00:14:42,560 so accurately, you could use it to train robots or autonomous 443 00:14:42,560 --> 00:14:45,320 vehicles in a virtual world before they're ever 444 00:14:45,320 --> 00:14:46,600 released in the real world. 445 00:14:46,600 --> 00:14:47,680 That makes a lot of sense. 446 00:14:47,680 --> 00:14:50,400 Much safer to have a robot crash in a simulation 447 00:14:50,400 --> 00:14:51,560 than in real life. 448 00:14:51,560 --> 00:14:52,320 For sure. 449 00:14:52,320 --> 00:14:54,280 But of course, with any powerful tech 450 00:14:54,280 --> 00:14:56,840 comes potential risks and ethical concerns. 451 00:14:56,840 --> 00:14:57,840 Right. 452 00:14:57,840 --> 00:15:01,800 What kind of ethical issues come up with creating virtual worlds? 453 00:15:01,800 --> 00:15:04,440 Well, for one, the line between reality and virtual reality 454 00:15:04,440 --> 00:15:06,280 could become increasingly blurred. 455 00:15:06,280 --> 00:15:06,680 Oh, yeah. 456 00:15:06,680 --> 00:15:08,320 That's a classic sci-fi trope. 457 00:15:08,320 --> 00:15:09,520 Think the matrix. 458 00:15:09,520 --> 00:15:11,240 But it's not so far-fetched anymore, is it? 459 00:15:11,240 --> 00:15:11,800 Nope. 460 00:15:11,800 --> 00:15:13,880 And then there's the potential for misuse. 461 00:15:13,880 --> 00:15:16,160 Imagine someone creating a virtual world 462 00:15:16,160 --> 00:15:19,520 to spread fake news or train AI for warfare. 463 00:15:19,520 --> 00:15:20,560 Pretty scary stuff. 464 00:15:20,560 --> 00:15:21,060 It is. 465 00:15:21,060 --> 00:15:22,280 We definitely need to be thinking 466 00:15:22,280 --> 00:15:23,680 about these ethical implications 467 00:15:23,680 --> 00:15:25,040 and putting safeguards in place. 468 00:15:25,040 --> 00:15:25,920 Absolutely. 469 00:15:25,920 --> 00:15:28,280 We need to make sure we're using this incredible technology 470 00:15:28,280 --> 00:15:29,040 for good. 471 00:15:29,040 --> 00:15:29,640 Agreed. 472 00:15:29,640 --> 00:15:31,360 It's a big responsibility. 473 00:15:31,360 --> 00:15:33,680 Speaking of responsibility, one of the biggest concerns 474 00:15:33,680 --> 00:15:36,320 around AI is its impact on jobs. 475 00:15:36,320 --> 00:15:37,800 Yeah, the fear that robots are going 476 00:15:37,800 --> 00:15:39,560 to take over everyone's jobs. 477 00:15:39,560 --> 00:15:40,800 It's a valid concern. 478 00:15:40,800 --> 00:15:41,920 It is. 479 00:15:41,920 --> 00:15:44,280 But technological advancements have always 480 00:15:44,280 --> 00:15:45,720 disrupted the job market. 481 00:15:45,720 --> 00:15:47,600 Think about the industrial revolution. 482 00:15:47,600 --> 00:15:48,120 Right. 483 00:15:48,120 --> 00:15:50,640 Machines replaced a lot of manual labor back then. 484 00:15:50,640 --> 00:15:53,440 But they also created new industries and new jobs. 485 00:15:53,440 --> 00:15:54,160 Exactly. 486 00:15:54,160 --> 00:15:57,040 And I think we'll see a similar pattern with AI. 487 00:15:57,040 --> 00:16:00,360 Some jobs will be lost, but new opportunities will emerge. 488 00:16:00,360 --> 00:16:02,560 It's about adapting and learning new skills. 489 00:16:02,560 --> 00:16:03,400 That's the key. 490 00:16:03,400 --> 00:16:05,520 We need to focus on developing those skills 491 00:16:05,520 --> 00:16:08,440 that AI can't replicate easily. 492 00:16:08,440 --> 00:16:11,200 Like creativity, critical thinking, problem solving. 493 00:16:11,200 --> 00:16:11,840 Exactly. 494 00:16:11,840 --> 00:16:14,520 The human skills that will always be valuable. 495 00:16:14,520 --> 00:16:16,480 But even with training and education, 496 00:16:16,480 --> 00:16:19,720 some people will inevitably be displaced by AI. 497 00:16:19,720 --> 00:16:20,600 What happens to them? 498 00:16:20,600 --> 00:16:23,040 That's where things like universal basic income 499 00:16:23,040 --> 00:16:25,840 and robust retraining programs become really important. 500 00:16:25,840 --> 00:16:28,000 Yeah, we need to make sure that everyone benefits 501 00:16:28,000 --> 00:16:31,760 from these advancements in AI and that no one gets left behind. 502 00:16:31,760 --> 00:16:32,600 Agreed. 503 00:16:32,600 --> 00:16:34,400 It's a societal challenge that we all 504 00:16:34,400 --> 00:16:36,080 need to work together to solve. 505 00:16:36,080 --> 00:16:39,320 Speaking of challenges, remember Sam Altman's bold prediction 506 00:16:39,320 --> 00:16:42,080 about AGI arriving within the next few years? 507 00:16:42,080 --> 00:16:42,960 Oh, yeah. 508 00:16:42,960 --> 00:16:45,400 And the drama with Elon Musk accusing him 509 00:16:45,400 --> 00:16:47,360 of leading open AI astray. 510 00:16:47,360 --> 00:16:50,320 It's like a real life battle of the tech titans. 511 00:16:50,320 --> 00:16:51,520 It is. 512 00:16:51,520 --> 00:16:53,080 And it highlights the fact that AI 513 00:16:53,080 --> 00:16:54,720 isn't just about technology. 514 00:16:54,720 --> 00:16:57,760 It's about people's values and visions for the future. 515 00:16:57,760 --> 00:16:59,560 And sometimes those visions collide. 516 00:16:59,560 --> 00:17:00,360 True. 517 00:17:00,360 --> 00:17:02,920 And as AI becomes more powerful, those collisions 518 00:17:02,920 --> 00:17:04,680 have bigger and bigger consequences. 519 00:17:04,680 --> 00:17:07,240 It's what makes this field so captivating. 520 00:17:07,240 --> 00:17:10,200 It's a story about technology and humanity all wrapped up 521 00:17:10,200 --> 00:17:10,920 in one. 522 00:17:10,920 --> 00:17:11,800 Exactly. 523 00:17:11,800 --> 00:17:12,000 Yeah. 524 00:17:12,000 --> 00:17:13,880 So before we move on to something a little lighter, 525 00:17:13,880 --> 00:17:16,280 I want to touch on something we talked about earlier. 526 00:17:16,280 --> 00:17:19,600 You know how AI is being used to speed up scientific discoveries? 527 00:17:19,600 --> 00:17:20,120 Right. 528 00:17:20,120 --> 00:17:23,560 Like those AI scientists designing nanobodies. 529 00:17:23,560 --> 00:17:24,560 Pretty impressive stuff. 530 00:17:24,560 --> 00:17:25,400 It is. 531 00:17:25,400 --> 00:17:28,280 But could AI actually change the very nature 532 00:17:28,280 --> 00:17:29,480 of how research is done? 533 00:17:29,480 --> 00:17:30,080 Hmm. 534 00:17:30,080 --> 00:17:31,120 That's a good question. 535 00:17:31,120 --> 00:17:33,200 Could AI not only assist scientists, 536 00:17:33,200 --> 00:17:35,280 but actually become a scientist itself? 537 00:17:35,280 --> 00:17:35,920 Right. 538 00:17:35,920 --> 00:17:39,560 It's a fascinating idea, but one that raises a lot of questions. 539 00:17:39,560 --> 00:17:42,160 What would an AI scientist even look like? 540 00:17:42,160 --> 00:17:42,880 How would it think? 541 00:17:42,880 --> 00:17:43,720 I don't know. 542 00:17:43,720 --> 00:17:45,640 But researchers are already experimenting 543 00:17:45,640 --> 00:17:48,040 with different approaches to AI-driven science. 544 00:17:48,040 --> 00:17:51,920 Like using AI agents to explore and test hypotheses 545 00:17:51,920 --> 00:17:54,320 in those virtual labs we were talking about earlier. 546 00:17:54,320 --> 00:17:55,240 Virtual labs. 547 00:17:55,240 --> 00:17:58,400 So instead of doing experiments in a physical lab, 548 00:17:58,400 --> 00:18:02,200 you can run simulations and gather data virtually. 549 00:18:02,200 --> 00:18:02,760 Exactly. 550 00:18:02,760 --> 00:18:05,520 It's like a digital playground for scientific discovery. 551 00:18:05,520 --> 00:18:07,120 That sounds really powerful. 552 00:18:07,120 --> 00:18:08,800 What are some of the benefits? 553 00:18:08,800 --> 00:18:10,920 Well, it's much faster and more efficient 554 00:18:10,920 --> 00:18:12,440 than traditional lab work. 555 00:18:12,440 --> 00:18:13,320 Makes sense. 556 00:18:13,320 --> 00:18:15,160 And I bet it's a lot cheaper too, right? 557 00:18:15,160 --> 00:18:15,660 Yeah. 558 00:18:15,660 --> 00:18:18,440 You don't need all that expensive equipment in lab space. 559 00:18:18,440 --> 00:18:20,440 Plus, it's a lot safer, especially if you're 560 00:18:20,440 --> 00:18:22,480 dealing with hazardous materials. 561 00:18:22,480 --> 00:18:23,720 That's true. 562 00:18:23,720 --> 00:18:27,280 But it's not just about speed, efficiency, and safety, is it? 563 00:18:27,280 --> 00:18:30,360 Virtual labs also let scientists explore a much wider 564 00:18:30,360 --> 00:18:31,560 range of possibilities. 565 00:18:31,560 --> 00:18:32,160 Exactly. 566 00:18:32,160 --> 00:18:34,120 You're not limited by the laws of physics 567 00:18:34,120 --> 00:18:35,480 or the availability of materials. 568 00:18:35,480 --> 00:18:37,600 Oh, so you could simulate conditions 569 00:18:37,600 --> 00:18:40,240 that would be impossible to create in the real world. 570 00:18:40,240 --> 00:18:43,480 Yep, like the inside of a black hole or testing a new drug 571 00:18:43,480 --> 00:18:44,840 on a virtual population. 572 00:18:44,840 --> 00:18:45,760 That's incredible. 573 00:18:45,760 --> 00:18:48,960 Are virtual labs actually being used for research today? 574 00:18:48,960 --> 00:18:49,960 They are. 575 00:18:49,960 --> 00:18:52,520 One big area is drug discovery. 576 00:18:52,520 --> 00:18:54,520 Pharmaceutical companies are using AI 577 00:18:54,520 --> 00:18:57,560 to simulate how drug molecules interact with proteins. 578 00:18:57,560 --> 00:19:00,480 That's so cool, helping to find new treatments faster 579 00:19:00,480 --> 00:19:01,160 and cheaper. 580 00:19:01,160 --> 00:19:02,440 Are there other examples? 581 00:19:02,440 --> 00:19:03,480 Tons. 582 00:19:03,480 --> 00:19:07,720 Material science, climate modeling, even astrophysics. 583 00:19:07,720 --> 00:19:10,080 Scientists are simulating the formation of galaxies 584 00:19:10,080 --> 00:19:11,360 in these virtual labs. 585 00:19:11,360 --> 00:19:13,840 It's mind blowing what's possible with these simulations. 586 00:19:13,840 --> 00:19:16,120 And I bet they'll just keep getting more powerful and more 587 00:19:16,120 --> 00:19:17,760 realistic as AI advances. 588 00:19:17,760 --> 00:19:18,480 Definitely. 589 00:19:18,480 --> 00:19:20,360 It's an area to watch closely. 590 00:19:20,360 --> 00:19:21,760 But before we get too carried away 591 00:19:21,760 --> 00:19:24,080 with all the positive potential of AI, 592 00:19:24,080 --> 00:19:27,280 let's bring it back to those concerns about safety and ethics. 593 00:19:27,280 --> 00:19:27,680 Right. 594 00:19:27,680 --> 00:19:30,560 It's a balancing act, pushing the boundaries of innovation 595 00:19:30,560 --> 00:19:33,000 while making sure AI is used for good. 596 00:19:33,000 --> 00:19:35,920 Exactly, like that whole robot jailbreaking thing. 597 00:19:35,920 --> 00:19:38,840 It's a good reminder that we can't just assume 598 00:19:38,840 --> 00:19:41,160 AI will always behave the way we intend. 599 00:19:41,160 --> 00:19:44,040 We need strong safety protocols and ethical guidelines, 600 00:19:44,040 --> 00:19:46,400 especially as AI gets more powerful and starts making 601 00:19:46,400 --> 00:19:48,560 decisions that could impact people's lives. 602 00:19:48,560 --> 00:19:50,360 It's not just a technological challenge. 603 00:19:50,360 --> 00:19:51,840 It's a societal one. 604 00:19:51,840 --> 00:19:56,200 We need input from everyone, ethicists, social scientists, 605 00:19:56,200 --> 00:19:59,840 policymakers, to make sure AI aligns with our values. 606 00:19:59,840 --> 00:20:00,920 Couldn't agree more. 607 00:20:00,920 --> 00:20:02,840 And that brings us back to that big concern 608 00:20:02,840 --> 00:20:04,600 about job displacement. 609 00:20:04,600 --> 00:20:07,160 How do we make sure everyone benefits from AI 610 00:20:07,160 --> 00:20:08,680 and no one is left behind? 611 00:20:08,680 --> 00:20:10,160 It's a tough question. 612 00:20:10,160 --> 00:20:12,680 We need to invest in education and training programs 613 00:20:12,680 --> 00:20:14,120 that teach people the skills they'll 614 00:20:14,120 --> 00:20:16,160 need in an AI-powered world. 615 00:20:16,160 --> 00:20:16,520 Right. 616 00:20:16,520 --> 00:20:19,200 Skills like critical thinking, creativity, problem 617 00:20:19,200 --> 00:20:22,280 solving, those things that AI isn't so good at yet. 618 00:20:22,280 --> 00:20:23,960 It's not just about what you know. 619 00:20:23,960 --> 00:20:26,480 It's about how you learn and how you adapt. 620 00:20:26,480 --> 00:20:27,600 Exactly. 621 00:20:27,600 --> 00:20:29,200 And these educational opportunities 622 00:20:29,200 --> 00:20:30,920 need to be available to everyone, 623 00:20:30,920 --> 00:20:32,560 not just the privileged few. 624 00:20:32,560 --> 00:20:33,360 Agreed. 625 00:20:33,360 --> 00:20:35,320 We need to build a more equitable society 626 00:20:35,320 --> 00:20:38,520 where everyone has a chance to thrive in this new world. 627 00:20:38,520 --> 00:20:40,000 But let's be realistic. 628 00:20:40,000 --> 00:20:42,040 Even with the best education programs, 629 00:20:42,040 --> 00:20:43,680 some jobs will be lost. 630 00:20:43,680 --> 00:20:44,960 So what do we do about that? 631 00:20:44,960 --> 00:20:48,240 Well, we need to explore options like universal basic income 632 00:20:48,240 --> 00:20:51,360 and retraining programs to help people transition to new roles. 633 00:20:51,360 --> 00:20:54,120 It'll take a lot of effort from individuals, the government, 634 00:20:54,120 --> 00:20:55,600 and companies working together. 635 00:20:55,600 --> 00:20:56,480 It will. 636 00:20:56,480 --> 00:21:00,000 But it's an opportunity to create a better future for everyone. 637 00:21:00,000 --> 00:21:02,600 And speaking of the future, let's get back to Sam Altman's 638 00:21:02,600 --> 00:21:06,720 predictions about AI becoming as commonplace as the transistor. 639 00:21:06,720 --> 00:21:07,240 Right. 640 00:21:07,240 --> 00:21:09,400 And the conflict with Elon Musk, who's saying 641 00:21:09,400 --> 00:21:11,000 Altman's gone rogue. 642 00:21:11,000 --> 00:21:12,920 It's a fascinating clash of visions, 643 00:21:12,920 --> 00:21:15,040 and it's playing out in the public eye. 644 00:21:15,040 --> 00:21:18,080 It's a good reminder that AI isn't just about algorithms. 645 00:21:18,080 --> 00:21:19,160 It's about people. 646 00:21:19,160 --> 00:21:20,920 And they're very different ideas about what 647 00:21:20,920 --> 00:21:22,200 the future should look like. 648 00:21:22,200 --> 00:21:25,280 And those ideas have real world consequences. 649 00:21:25,280 --> 00:21:26,920 It's what makes this field so exciting. 650 00:21:26,920 --> 00:21:35,560 Yeah, I'm writing text. 651 00:21:35,560 --> 00:21:37,320 I'm making images, even videos. 652 00:21:37,320 --> 00:21:39,040 But what about something even bigger? 653 00:21:39,040 --> 00:21:41,240 Like creating whole virtual worlds. 654 00:21:41,240 --> 00:21:42,040 Exactly. 655 00:21:42,040 --> 00:21:44,120 Google DeepMind's Genie 2. 656 00:21:44,120 --> 00:21:45,840 That's where things get really interesting. 657 00:21:45,840 --> 00:21:46,880 It really is. 658 00:21:46,880 --> 00:21:48,680 And you give it an image, and boom, 659 00:21:48,680 --> 00:21:50,880 it builds this whole interactive 3D world. 660 00:21:50,880 --> 00:21:51,800 It's wild. 661 00:21:51,800 --> 00:21:53,840 It's like having a digital paintbrush that 662 00:21:53,840 --> 00:21:55,800 can create entire universes. 663 00:21:55,800 --> 00:21:58,960 What I find really cool is that it's not just about the visuals. 664 00:21:58,960 --> 00:21:59,460 Right. 665 00:21:59,460 --> 00:22:01,280 It's not just a pretty picture. 666 00:22:01,280 --> 00:22:04,600 It simulates things like physics, how objects interact, 667 00:22:04,600 --> 00:22:06,880 and even how other agents behave in the world. 668 00:22:06,880 --> 00:22:09,760 So it's like creating a world that feels real, 669 00:22:09,760 --> 00:22:12,080 a world you can actually experiment in and see 670 00:22:12,080 --> 00:22:13,200 how different things play out. 671 00:22:13,200 --> 00:22:13,700 Exactly. 672 00:22:13,700 --> 00:22:17,320 It's like a giant digital sandbox for your imagination. 673 00:22:17,320 --> 00:22:18,800 And the possibilities are endless. 674 00:22:18,800 --> 00:22:19,880 Oh, yeah. 675 00:22:19,880 --> 00:22:23,400 Games, training simulations, even modeling complex systems 676 00:22:23,400 --> 00:22:24,160 like cities. 677 00:22:24,160 --> 00:22:26,200 And the best part is Google is open sourcing it. 678 00:22:26,200 --> 00:22:26,840 Oh, wow. 679 00:22:26,840 --> 00:22:27,880 So anyone can use it. 680 00:22:27,880 --> 00:22:28,380 Yep. 681 00:22:28,380 --> 00:22:31,600 Anyone can access Genie 2 and contribute to its development. 682 00:22:31,600 --> 00:22:33,440 It's going to be interesting to see what people come up with. 683 00:22:33,440 --> 00:22:34,400 I bet. 684 00:22:34,400 --> 00:22:36,440 I can already imagine artists and designers 685 00:22:36,440 --> 00:22:39,120 using this to create some mind blowing stuff. 686 00:22:39,120 --> 00:22:40,040 Definitely. 687 00:22:40,040 --> 00:22:42,560 But it's not just about entertainment. 688 00:22:42,560 --> 00:22:45,800 Researchers could use Genie 2 to run all sorts of simulations. 689 00:22:45,800 --> 00:22:46,600 Like what? 690 00:22:46,600 --> 00:22:47,680 Give me an example. 691 00:22:47,680 --> 00:22:50,560 Well, imagine simulating the effects of climate change 692 00:22:50,560 --> 00:22:53,960 on a coastal city or modeling how a disease might 693 00:22:53,960 --> 00:22:55,600 spread through a population. 694 00:22:55,600 --> 00:22:57,760 Wow, those are some pretty heavy applications. 695 00:22:57,760 --> 00:22:58,440 They are. 696 00:22:58,440 --> 00:23:02,320 And because Genie 2 is so good at simulating physics, 697 00:23:02,320 --> 00:23:05,200 you could use it to train robots and self-driving cars 698 00:23:05,200 --> 00:23:07,080 in a virtual environment before they ever 699 00:23:07,080 --> 00:23:08,160 hit the real world. 700 00:23:08,160 --> 00:23:09,880 That's actually a really good idea, 701 00:23:09,880 --> 00:23:11,880 a virtual driving test for robots. 702 00:23:11,880 --> 00:23:13,040 Makes them a lot safer. 703 00:23:13,040 --> 00:23:13,960 Exactly. 704 00:23:13,960 --> 00:23:16,960 But we have to remember with any powerful technology, 705 00:23:16,960 --> 00:23:19,240 there's always the potential for misuse. 706 00:23:19,240 --> 00:23:19,840 Right. 707 00:23:19,840 --> 00:23:21,840 It's like that old saying with great power. 708 00:23:21,840 --> 00:23:23,440 Comes great responsibility. 709 00:23:23,440 --> 00:23:24,240 Yep. 710 00:23:24,240 --> 00:23:26,280 And with something like Genie 2, which 711 00:23:26,280 --> 00:23:29,360 can create such realistic virtual worlds, 712 00:23:29,360 --> 00:23:31,520 there are some serious ethical questions 713 00:23:31,520 --> 00:23:32,560 we need to consider. 714 00:23:32,560 --> 00:23:34,240 Like what happens when we can no longer 715 00:23:34,240 --> 00:23:36,800 tell the difference between what's real and what's virtual? 716 00:23:36,800 --> 00:23:37,440 Exactly. 717 00:23:37,440 --> 00:23:39,600 It's like something straight out of the matrix. 718 00:23:39,600 --> 00:23:41,560 We need to be having those conversations now 719 00:23:41,560 --> 00:23:42,600 before it's too late. 720 00:23:42,600 --> 00:23:43,320 Agreed. 721 00:23:43,320 --> 00:23:45,000 And then there's the potential for someone 722 00:23:45,000 --> 00:23:48,000 to use this technology for harmful purposes, 723 00:23:48,000 --> 00:23:51,680 like creating a virtual world to spread misinformation 724 00:23:51,680 --> 00:23:53,600 or train AI for warfare. 725 00:23:53,600 --> 00:23:56,880 It's a scary thought, but a very real possibility. 726 00:23:56,880 --> 00:24:00,080 We need to figure out how to use this technology responsibly. 727 00:24:00,080 --> 00:24:02,720 It's a challenge, but it's one we have to face head on. 728 00:24:02,720 --> 00:24:03,600 Well said. 729 00:24:03,600 --> 00:24:05,200 We've covered a lot of ground today. 730 00:24:05,200 --> 00:24:09,680 Generative AI, AI safety, ethical questions, 731 00:24:09,680 --> 00:24:10,800 the future of work. 732 00:24:10,800 --> 00:24:12,400 It's been quite a deep dive. 733 00:24:12,400 --> 00:24:12,880 It has. 734 00:24:12,880 --> 00:24:15,000 It's amazing how fast this field is moving. 735 00:24:15,000 --> 00:24:16,200 I know, right? 736 00:24:16,200 --> 00:24:19,320 Every day, there's something new to learn and explore. 737 00:24:19,320 --> 00:24:22,280 It's both exciting and a little bit overwhelming. 738 00:24:22,280 --> 00:24:23,520 I agree. 739 00:24:23,520 --> 00:24:24,880 Before we wrap up, though, I want 740 00:24:24,880 --> 00:24:27,040 to leave our listeners with something to think about. 741 00:24:27,040 --> 00:24:29,480 We've talked about AI solving problems 742 00:24:29,480 --> 00:24:31,160 and automating tasks. 743 00:24:31,160 --> 00:24:33,520 But what about the role of human creativity 744 00:24:33,520 --> 00:24:35,360 in an AI-powered world? 745 00:24:35,360 --> 00:24:36,920 That's a great question. 746 00:24:36,920 --> 00:24:40,480 Will there still be a need for human imagination and innovation 747 00:24:40,480 --> 00:24:42,400 when AI can do so much? 748 00:24:42,400 --> 00:24:44,760 Or will we become too reliant on AI 749 00:24:44,760 --> 00:24:46,320 to do our thinking for us? 750 00:24:46,320 --> 00:24:48,680 It's something we all need to consider as we move forward. 751 00:24:48,680 --> 00:24:49,840 It's a conversation we'll definitely 752 00:24:49,840 --> 00:24:51,480 continue to have here on the deep dive. 753 00:24:51,480 --> 00:24:54,280 Thanks for joining us on this exploration of all things AI. 754 00:24:54,280 --> 00:24:55,680 It's been a pleasure, as always. 755 00:24:55,680 --> 00:24:58,360 Thanks for listening to the Daily AI News Podcast. 756 00:24:58,360 --> 00:25:00,600 And we'll catch you next time.