1 00:00:00,000 --> 00:00:02,280 Welcome to the Daily AI News Podcast. 2 00:00:02,280 --> 00:00:03,800 We've got a lot to cover today. 3 00:00:03,800 --> 00:00:07,040 From new tools that make AI development easier to research, 4 00:00:07,040 --> 00:00:08,520 that really gets you thinking about 5 00:00:08,520 --> 00:00:10,040 how we learn as humans. 6 00:00:10,040 --> 00:00:11,640 AI is everywhere these days, 7 00:00:11,640 --> 00:00:14,000 and it's really changing how we work and create, 8 00:00:14,000 --> 00:00:16,240 and even how we understand our own intelligence. 9 00:00:16,240 --> 00:00:17,080 Definitely. 10 00:00:17,080 --> 00:00:18,400 Let's start with Anthropoc. 11 00:00:18,400 --> 00:00:21,240 They just came out with this cool open source toolkit 12 00:00:21,240 --> 00:00:23,280 called the Model Context Protocol, 13 00:00:23,280 --> 00:00:24,400 MCP for short. 14 00:00:24,400 --> 00:00:25,240 GMCP. 15 00:00:25,240 --> 00:00:26,080 Yeah. 16 00:00:26,080 --> 00:00:28,680 It's all about connecting AI models to other systems, 17 00:00:28,680 --> 00:00:30,520 making them way more useful. 18 00:00:30,520 --> 00:00:32,360 Like the apps and services we use every day. 19 00:00:32,360 --> 00:00:33,200 Exactly. 20 00:00:33,200 --> 00:00:35,400 And the best part is it's super simple. 21 00:00:35,400 --> 00:00:38,920 Connecting AI models to databases or cloud services 22 00:00:38,920 --> 00:00:40,280 used to be a real pain. 23 00:00:40,280 --> 00:00:41,440 Lots of coding in it. 24 00:00:41,440 --> 00:00:42,080 Yeah. 25 00:00:42,080 --> 00:00:44,320 But MCP streamlines the whole process. 26 00:00:44,320 --> 00:00:47,320 It can cut down development time from days to under an hour. 27 00:00:47,320 --> 00:00:47,600 Wow. 28 00:00:47,600 --> 00:00:48,680 That's a huge difference. 29 00:00:48,680 --> 00:00:49,320 It is. 30 00:00:49,320 --> 00:00:51,840 Developers can finally spend less time wrestling with code 31 00:00:51,840 --> 00:00:55,120 and more time actually building innovative AI applications. 32 00:00:55,120 --> 00:00:57,280 So think about an AI assistant that can pull up 33 00:00:57,280 --> 00:01:00,720 a troubleshooting guide from Google Drive instantly 34 00:01:00,720 --> 00:01:02,120 when a customer has a problem. 35 00:01:02,120 --> 00:01:04,360 Or a programming assistant that can test code 36 00:01:04,360 --> 00:01:07,040 in a live cloud environment right from your desktop. 37 00:01:07,040 --> 00:01:09,240 That's the kind of seamless integration 38 00:01:09,240 --> 00:01:10,960 that MCP makes possible. 39 00:01:10,960 --> 00:01:12,280 And it's not just theory either. 40 00:01:12,280 --> 00:01:14,880 Companies like Block Inc. and Apollo Inc. 41 00:01:14,880 --> 00:01:16,080 are already using it. 42 00:01:16,080 --> 00:01:18,800 That shows that the industry is really getting behind 43 00:01:18,800 --> 00:01:21,320 this new approach to connecting AI. 44 00:01:21,320 --> 00:01:21,960 Definitely. 45 00:01:21,960 --> 00:01:24,960 It's going to be exciting to see what developers create with MCP. 46 00:01:24,960 --> 00:01:27,160 Now continuing on this whole integration thing. 47 00:01:27,160 --> 00:01:29,480 Let's talk about IBM and AWS. 48 00:01:29,480 --> 00:01:30,120 Oh yeah. 49 00:01:30,120 --> 00:01:31,120 Big news there. 50 00:01:31,120 --> 00:01:36,560 IBM is launching its powerful Granite 3.0 AI models 51 00:01:36,560 --> 00:01:40,440 on Amazon's Bedrock and SageMaker Jumpstart services. 52 00:01:40,440 --> 00:01:42,040 This is great for developers because they'll 53 00:01:42,040 --> 00:01:45,240 have access to these advanced AI models within AWS, which 54 00:01:45,240 --> 00:01:46,520 they're already familiar with. 55 00:01:46,520 --> 00:01:48,400 And they can take advantage of all those integrations 56 00:01:48,400 --> 00:01:51,280 with things like AWS Neuron and Training 2 Chips. 57 00:01:51,280 --> 00:01:51,480 Yeah. 58 00:01:51,480 --> 00:01:53,600 For better performance and faster processing. 59 00:01:53,600 --> 00:01:55,160 So it's not just about access. 60 00:01:55,160 --> 00:01:57,440 It's about making sure those models run smoothly 61 00:01:57,440 --> 00:01:59,200 and efficiently on AWS. 62 00:01:59,200 --> 00:01:59,680 Right. 63 00:01:59,680 --> 00:02:01,480 And another important thing, IBM is really 64 00:02:01,480 --> 00:02:03,640 putting a focus on responsible AI. 65 00:02:03,640 --> 00:02:05,520 Which is so important these days. 66 00:02:05,520 --> 00:02:08,800 They're integrating their Watsonx.governance platform 67 00:02:08,800 --> 00:02:11,880 with SageMaker so developers can keep an eye on their AI apps 68 00:02:11,880 --> 00:02:13,840 and make sure they're being used ethically. 69 00:02:13,840 --> 00:02:16,280 It's like they're building responsible AI practices 70 00:02:16,280 --> 00:02:18,520 into the development process from the very beginning. 71 00:02:18,520 --> 00:02:19,800 Exactly. 72 00:02:19,800 --> 00:02:21,880 And speaking of responsible AI, there's 73 00:02:21,880 --> 00:02:24,040 been a lot of discussion about transparency 74 00:02:24,040 --> 00:02:26,400 in how AI models are trained. 75 00:02:26,400 --> 00:02:29,000 Especially when it comes to using copyrighted material. 76 00:02:29,000 --> 00:02:29,440 Yeah. 77 00:02:29,440 --> 00:02:32,880 Senator Welch has proposed the TRAIN Act, which 78 00:02:32,880 --> 00:02:35,080 would give copyright holders more power 79 00:02:35,080 --> 00:02:38,600 to investigate how their work is being used to train AI. 80 00:02:38,600 --> 00:02:41,040 Because a lot of artists and creative professionals 81 00:02:41,040 --> 00:02:43,600 are worried that their work is being used without their permission 82 00:02:43,600 --> 00:02:44,760 or compensation. 83 00:02:44,760 --> 00:02:45,160 Right. 84 00:02:45,160 --> 00:02:46,960 It's a really complex issue. 85 00:02:46,960 --> 00:02:50,280 You need huge data sets to train powerful AI models. 86 00:02:50,280 --> 00:02:52,880 But you also need to protect the rights of the creators. 87 00:02:52,880 --> 00:02:55,120 It'll be interesting to see how this all plays out 88 00:02:55,120 --> 00:02:57,920 and what kind of solutions emerge to balance innovation 89 00:02:57,920 --> 00:02:59,680 with protecting intellectual property. 90 00:02:59,680 --> 00:03:00,200 Definitely. 91 00:03:00,200 --> 00:03:02,000 It's a debate that's just getting started. 92 00:03:02,000 --> 00:03:02,520 OK. 93 00:03:02,520 --> 00:03:03,720 Shifting gears a bit. 94 00:03:03,720 --> 00:03:05,160 Let's talk about Zoom. 95 00:03:05,160 --> 00:03:06,920 You know, the video conferencing company. 96 00:03:06,920 --> 00:03:07,280 Oh yeah. 97 00:03:07,280 --> 00:03:08,800 They're going through some big changes. 98 00:03:08,800 --> 00:03:11,360 They've actually dropped video from their name. 99 00:03:11,360 --> 00:03:13,920 They're now just Zoom Communications Inc. 100 00:03:13,920 --> 00:03:16,640 It shows they're branching out from just video calls. 101 00:03:16,640 --> 00:03:19,320 They want to be known as an AI first work platform. 102 00:03:19,320 --> 00:03:20,720 So more than just video calls. 103 00:03:20,720 --> 00:03:21,400 Exactly. 104 00:03:21,400 --> 00:03:24,360 They're offering a whole suite of communication and productivity 105 00:03:24,360 --> 00:03:27,640 tools that use AI to help people work better together. 106 00:03:27,640 --> 00:03:30,160 Like their Zoom AI companion 2.0. 107 00:03:30,160 --> 00:03:30,800 Yes. 108 00:03:30,800 --> 00:03:34,320 It can summarize meetings, draft emails, schedule appointments, 109 00:03:34,320 --> 00:03:37,080 all sorts of things to save time and boost productivity. 110 00:03:37,080 --> 00:03:39,120 They're aiming to be the go-to platform 111 00:03:39,120 --> 00:03:41,080 for everything work related. 112 00:03:41,080 --> 00:03:43,200 And AI is a big part of that strategy. 113 00:03:43,200 --> 00:03:44,480 Makes sense. 114 00:03:44,480 --> 00:03:46,600 Now let's dive into some really interesting research 115 00:03:46,600 --> 00:03:48,280 from Cold Spring Harbor Laboratory. 116 00:03:48,280 --> 00:03:48,840 Oh yeah. 117 00:03:48,840 --> 00:03:50,920 The work by Zador and Kulikov. 118 00:03:50,920 --> 00:03:51,520 Right. 119 00:03:51,520 --> 00:03:52,840 They've been studying this concept 120 00:03:52,840 --> 00:03:54,520 called the genomic bottleneck. 121 00:03:54,520 --> 00:03:57,520 Which basically means our genome, the instructions 122 00:03:57,520 --> 00:03:59,000 that make us who we are. 123 00:03:59,000 --> 00:04:01,120 Can only hold a limited amount of information. 124 00:04:01,120 --> 00:04:04,360 And surprisingly, they think that this limitation might 125 00:04:04,360 --> 00:04:07,680 actually be what makes us intelligent and adaptable. 126 00:04:07,680 --> 00:04:07,960 Wait. 127 00:04:07,960 --> 00:04:11,280 So you're saying our limited genetic capacity is a good thing. 128 00:04:11,280 --> 00:04:13,400 It sounds strange, but think of it this way. 129 00:04:13,400 --> 00:04:15,680 Our genome is like a compressed file. 130 00:04:15,680 --> 00:04:18,080 It has the essential instructions, but not all the details. 131 00:04:18,080 --> 00:04:19,160 Exactly. 132 00:04:19,160 --> 00:04:21,520 So our brains have to learn and adapt 133 00:04:21,520 --> 00:04:24,600 based on our experiences, which makes us more flexible 134 00:04:24,600 --> 00:04:25,480 and resilient. 135 00:04:25,480 --> 00:04:28,120 Instead of being programmed for specific tasks, 136 00:04:28,120 --> 00:04:30,040 we can learn and solve problems in a bunch 137 00:04:30,040 --> 00:04:31,040 of different situations. 138 00:04:31,040 --> 00:04:31,680 Exactly. 139 00:04:31,680 --> 00:04:33,680 And what's really fascinating is that they've 140 00:04:33,680 --> 00:04:35,920 developed an algorithm based on this idea. 141 00:04:35,920 --> 00:04:38,200 An algorithm that learns from compressed data. 142 00:04:38,200 --> 00:04:38,880 Yes. 143 00:04:38,880 --> 00:04:41,200 And it's showing remarkable abilities. 144 00:04:41,200 --> 00:04:43,280 It can do things like image recognition 145 00:04:43,280 --> 00:04:44,680 and even play video games. 146 00:04:44,680 --> 00:04:45,160 Wow. 147 00:04:45,160 --> 00:04:48,680 So they're suggesting that we can make AI more intelligent 148 00:04:48,680 --> 00:04:50,640 by embracing limitations. 149 00:04:50,640 --> 00:04:53,960 It's a really interesting approach to AI development. 150 00:04:53,960 --> 00:04:56,960 Now let's move on to Neuralink, Elon Musk's company that's 151 00:04:56,960 --> 00:05:00,240 all about brain computer interfaces or BCIs. 152 00:05:00,240 --> 00:05:04,200 They just announced a new trial using their N1 implant 153 00:05:04,200 --> 00:05:06,080 to control a robotic arm. 154 00:05:06,080 --> 00:05:08,880 That's a huge step forward for people with paralysis 155 00:05:08,880 --> 00:05:10,560 or other mobility challenges. 156 00:05:10,560 --> 00:05:13,400 It really shows the potential of BCI technology 157 00:05:13,400 --> 00:05:16,280 to give people back their physical freedom. 158 00:05:16,280 --> 00:05:17,240 Absolutely. 159 00:05:17,240 --> 00:05:19,760 It's amazing how technology can be used to tackle 160 00:05:19,760 --> 00:05:20,720 such important issues. 161 00:05:20,720 --> 00:05:24,480 And the possibilities with DCIs go way beyond movement. 162 00:05:24,480 --> 00:05:26,760 Imagine enhancing our cognitive abilities 163 00:05:26,760 --> 00:05:29,000 or even creating new ways for us to communicate. 164 00:05:29,000 --> 00:05:31,120 It's a field that's full of possibilities. 165 00:05:31,120 --> 00:05:32,240 Definitely. 166 00:05:32,240 --> 00:05:35,640 It's exciting to think about what the future holds for BCIs. 167 00:05:35,640 --> 00:05:39,080 We've covered so much today from simplifying AI development 168 00:05:39,080 --> 00:05:41,720 to the very nature of intelligence and brain computer 169 00:05:41,720 --> 00:05:42,520 interfaces. 170 00:05:42,520 --> 00:05:45,560 It's incredible how fast the world of AI is moving 171 00:05:45,560 --> 00:05:47,720 and its impact is everywhere. 172 00:05:47,720 --> 00:05:49,680 It's definitely an exciting time to be following 173 00:05:49,680 --> 00:05:50,680 all these developments. 174 00:05:50,680 --> 00:05:53,760 And it's important for everyone to stay informed and engaged 175 00:05:53,760 --> 00:05:56,240 as AI continues to shape our world. 176 00:05:56,240 --> 00:05:59,200 OK, so let's break down how MCP actually works. 177 00:05:59,200 --> 00:06:00,560 Well, it's pretty straightforward. 178 00:06:00,560 --> 00:06:02,680 Developers can either make their data accessible 179 00:06:02,680 --> 00:06:05,120 through these things called MCP servers 180 00:06:05,120 --> 00:06:07,520 or they can create AI applications that 181 00:06:07,520 --> 00:06:10,360 act as MCP clients and connect to those servers. 182 00:06:10,360 --> 00:06:12,560 So like a two-way communication channel. 183 00:06:12,560 --> 00:06:13,360 Exactly. 184 00:06:13,360 --> 00:06:16,400 It allows for seamless data flow between the AI model 185 00:06:16,400 --> 00:06:18,440 and the external system. 186 00:06:18,440 --> 00:06:21,320 And Anthropic has made it really easy for developers 187 00:06:21,320 --> 00:06:22,080 to get started. 188 00:06:22,080 --> 00:06:22,600 They have. 189 00:06:22,600 --> 00:06:25,400 They've got local MCP server support built right 190 00:06:25,400 --> 00:06:27,240 into their cloud desktop apps. 191 00:06:27,240 --> 00:06:30,880 And they even have an open source repository of MCP servers. 192 00:06:30,880 --> 00:06:31,320 Oh, wow. 193 00:06:31,320 --> 00:06:31,880 That's handy. 194 00:06:31,880 --> 00:06:33,200 Yeah, super convenient. 195 00:06:33,200 --> 00:06:35,600 They've also created pre-built MCT servers 196 00:06:35,600 --> 00:06:39,040 for all these popular platforms like Google Drive, Slack, 197 00:06:39,040 --> 00:06:42,360 GitHub, Git Postgres, even Puppeteer 198 00:06:42,360 --> 00:06:44,200 for controlling web browser. 199 00:06:44,200 --> 00:06:46,720 So developers don't have to build everything from scratch. 200 00:06:46,720 --> 00:06:48,480 Right, they can just grab these pre-built servers 201 00:06:48,480 --> 00:06:49,880 and save a ton of time and effort. 202 00:06:49,880 --> 00:06:52,200 It's like Anthropic is giving them the building blocks 203 00:06:52,200 --> 00:06:55,160 for creating these powerful connected AI applications. 204 00:06:55,160 --> 00:06:57,000 It's all about making AI development more 205 00:06:57,000 --> 00:06:58,640 efficient and accessible. 206 00:06:58,640 --> 00:07:01,400 Now let's go back to IBM and AWS for a second. 207 00:07:01,400 --> 00:07:04,080 We talked about how they're making IBM's granite models 208 00:07:04,080 --> 00:07:05,240 available on AWS. 209 00:07:05,240 --> 00:07:05,740 Right. 210 00:07:05,740 --> 00:07:07,400 But it's more than just that. 211 00:07:07,400 --> 00:07:10,080 They're really integrating their tools and services deeply 212 00:07:10,080 --> 00:07:11,840 within the AWS ecosystem. 213 00:07:11,840 --> 00:07:13,720 For optimal performance, yeah. 214 00:07:13,720 --> 00:07:15,880 They want to make sure those models run as smoothly 215 00:07:15,880 --> 00:07:18,480 as possible on AWS. 216 00:07:18,480 --> 00:07:20,840 So for example, IBM's granite models 217 00:07:20,840 --> 00:07:22,600 will support AWS Neuron. 218 00:07:22,600 --> 00:07:23,640 AWS Neuron. 219 00:07:23,640 --> 00:07:26,200 It's a service that lets developers run deep learning 220 00:07:26,200 --> 00:07:28,920 workloads on those special inferential and tranium 221 00:07:28,920 --> 00:07:30,680 instances that Amazon has. 222 00:07:30,680 --> 00:07:31,640 Powerful hardware. 223 00:07:31,640 --> 00:07:32,640 Exactly. 224 00:07:32,640 --> 00:07:35,160 And they're also working on integrating IBM's WatsonX 225 00:07:35,160 --> 00:07:38,800 platform with AWS's Tranium 2 chip. 226 00:07:38,800 --> 00:07:40,840 Which is their most advanced AI chip, right? 227 00:07:40,840 --> 00:07:43,600 Yep, designed for high performance training and inference. 228 00:07:43,600 --> 00:07:44,880 So it's like a perfect match. 229 00:07:44,880 --> 00:07:46,600 They're really optimizing these models 230 00:07:46,600 --> 00:07:50,440 to take full advantage of AWS's infrastructure. 231 00:07:50,440 --> 00:07:53,120 And of course, we can't forget about responsible AI. 232 00:07:53,120 --> 00:07:56,080 IBM is bringing their focus on that to AWS as well. 233 00:07:56,080 --> 00:07:56,720 They are. 234 00:07:56,720 --> 00:07:59,400 But integrating their WatsonX.governance platform 235 00:07:59,400 --> 00:08:01,040 with Amazon SageMaker developers 236 00:08:01,040 --> 00:08:03,440 can assess and manage any ethical risks 237 00:08:03,440 --> 00:08:04,760 throughout the AI lifecycle. 238 00:08:04,760 --> 00:08:06,760 But within the AWS environment. 239 00:08:06,760 --> 00:08:07,280 Exactly. 240 00:08:07,280 --> 00:08:10,240 It's about making responsible AI practices a core part 241 00:08:10,240 --> 00:08:11,480 of the development process. 242 00:08:11,480 --> 00:08:13,360 And speaking of responsible AI, let's 243 00:08:13,360 --> 00:08:15,160 circle back to the whole discussion about AI 244 00:08:15,160 --> 00:08:16,400 and creative fields. 245 00:08:16,400 --> 00:08:21,160 And the use of copyrighted material in training AI models. 246 00:08:21,160 --> 00:08:22,360 Yeah, that's a tricky one. 247 00:08:22,360 --> 00:08:23,280 It is. 248 00:08:23,280 --> 00:08:25,440 There are strong arguments on both sides. 249 00:08:25,440 --> 00:08:27,880 Artists and creators are understandably concerned 250 00:08:27,880 --> 00:08:31,240 about their work being used without permission or compensation. 251 00:08:31,240 --> 00:08:31,720 Right. 252 00:08:31,720 --> 00:08:34,240 They should have control over how their creations are used. 253 00:08:34,240 --> 00:08:36,720 But on the other hand, AI development, especially 254 00:08:36,720 --> 00:08:40,200 in areas like image generation or music composition, 255 00:08:40,200 --> 00:08:43,040 requires access to massive data sets. 256 00:08:43,040 --> 00:08:44,640 Including existing creative works. 257 00:08:44,640 --> 00:08:45,440 Exactly. 258 00:08:45,440 --> 00:08:45,640 Yeah. 259 00:08:45,640 --> 00:08:48,160 So how do we balance the rights of creators 260 00:08:48,160 --> 00:08:50,480 with the need to advance AI technology? 261 00:08:50,480 --> 00:08:51,840 That's the million dollar question. 262 00:08:51,840 --> 00:08:52,200 It is. 263 00:08:52,200 --> 00:08:55,360 Senator Welch's train act is one attempt to address this. 264 00:08:55,360 --> 00:08:57,960 By giving copyright holders the right to investigate 265 00:08:57,960 --> 00:08:59,400 how their work is being used. 266 00:08:59,400 --> 00:08:59,920 Yeah. 267 00:08:59,920 --> 00:09:03,480 It's a step towards more transparency and accountability. 268 00:09:03,480 --> 00:09:06,040 But we'll have to see how it plays out in practice. 269 00:09:06,040 --> 00:09:07,480 It could be a game changer. 270 00:09:07,480 --> 00:09:10,520 Or it could create a lot of legal headaches for AI developers. 271 00:09:10,520 --> 00:09:12,720 It's a conversation that's just getting started. 272 00:09:12,720 --> 00:09:14,800 And it's going to be interesting to see how it evolves. 273 00:09:14,800 --> 00:09:17,120 Now let's switch gears again and go back to Neuralink 274 00:09:17,120 --> 00:09:19,760 and their work on brain computer interfaces. 275 00:09:19,760 --> 00:09:22,320 They're making some incredible progress. 276 00:09:22,320 --> 00:09:24,920 Their new trial using the N1 implant 277 00:09:24,920 --> 00:09:28,640 to control a robotic arm is really groundbreaking. 278 00:09:28,640 --> 00:09:30,760 It could give people with paralysis 279 00:09:30,760 --> 00:09:33,920 a level of independence they never thought possible. 280 00:09:33,920 --> 00:09:36,320 It's amazing to see how technology can be used 281 00:09:36,320 --> 00:09:37,880 to overcome such challenges. 282 00:09:37,880 --> 00:09:42,080 And the potential applications for BCIs are so vast. 283 00:09:42,080 --> 00:09:43,080 Beyond just movement. 284 00:09:43,080 --> 00:09:44,120 Exactly. 285 00:09:44,120 --> 00:09:47,600 Imagine enhancing our cognitive abilities 286 00:09:47,600 --> 00:09:50,120 or even creating new forms of communication. 287 00:09:50,120 --> 00:09:51,400 It's like something out of science fiction. 288 00:09:51,400 --> 00:09:53,280 It's a field that's full of potential. 289 00:09:53,280 --> 00:09:55,040 Now finally, let's revisit that research 290 00:09:55,040 --> 00:09:56,920 on the genomic bottleneck. 291 00:09:56,920 --> 00:09:59,600 The idea that our limitations might actually 292 00:09:59,600 --> 00:10:01,360 be the key to our intelligence. 293 00:10:01,360 --> 00:10:03,040 It's a fascinating concept. 294 00:10:03,040 --> 00:10:05,320 It really makes you think differently about intelligence. 295 00:10:05,320 --> 00:10:07,640 The researchers who developed the algorithm based 296 00:10:07,640 --> 00:10:10,040 on this idea showed that you can achieve surprisingly 297 00:10:10,040 --> 00:10:14,200 sophisticated abilities by training on compressed data. 298 00:10:14,200 --> 00:10:16,360 So maybe limiting the amount of information 299 00:10:16,360 --> 00:10:19,960 an AI model has access to could actually make it smarter. 300 00:10:19,960 --> 00:10:23,240 It's a counterintuitive idea, but it seems to have merit. 301 00:10:23,240 --> 00:10:26,000 It challenges the way we think about AI development. 302 00:10:26,000 --> 00:10:29,360 Maybe instead of building bigger and more complex models, 303 00:10:29,360 --> 00:10:32,440 we should be exploring the power of constraint. 304 00:10:32,440 --> 00:10:33,720 That's a really interesting thought. 305 00:10:33,720 --> 00:10:36,320 It's a reminder that we still have so much to learn 306 00:10:36,320 --> 00:10:39,680 about both biological and artificial intelligence. 307 00:10:39,680 --> 00:10:42,240 It's an exciting time to be following all these developments. 308 00:10:42,240 --> 00:10:43,200 For sure. 309 00:10:43,200 --> 00:10:45,560 The world of AI is constantly evolving, 310 00:10:45,560 --> 00:10:48,000 and there's always something new to discover. 311 00:10:48,000 --> 00:10:51,000 So much to think about with all this AI stuff. 312 00:10:51,000 --> 00:10:52,960 It's really amazing how it's impacting 313 00:10:52,960 --> 00:10:55,520 so many different parts of our lives. 314 00:10:55,520 --> 00:10:57,680 Everything from the tools we use at work 315 00:10:57,680 --> 00:11:00,000 to the way we understand our own intelligence. 316 00:11:00,000 --> 00:11:02,160 Yeah, it's pretty mind blowing when you think about it. 317 00:11:02,160 --> 00:11:04,400 And the pace of innovation is just incredible. 318 00:11:04,400 --> 00:11:05,720 There's always something new happening. 319 00:11:05,720 --> 00:11:06,760 It's hard to keep up sometimes. 320 00:11:06,760 --> 00:11:07,840 It really is. 321 00:11:07,840 --> 00:11:09,040 You know, something else I came across 322 00:11:09,040 --> 00:11:10,680 while we were researching for this episode. 323 00:11:10,680 --> 00:11:11,520 Oh, yeah. 324 00:11:11,520 --> 00:11:14,680 Back in 2016, there were reports 325 00:11:14,680 --> 00:11:16,280 that President Trump was thinking about 326 00:11:16,280 --> 00:11:18,000 appointing an AI czar. 327 00:11:18,000 --> 00:11:18,840 An AI czar. 328 00:11:18,840 --> 00:11:20,480 To oversee AI policy. 329 00:11:20,480 --> 00:11:21,720 Hmm, interesting. 330 00:11:21,720 --> 00:11:24,360 It just shows you how seriously 331 00:11:24,360 --> 00:11:26,280 governments are starting to take AI. 332 00:11:26,280 --> 00:11:27,800 They're realizing that it's gonna have 333 00:11:27,800 --> 00:11:29,440 a huge impact on everything. 334 00:11:29,440 --> 00:11:31,520 And they're trying to figure out how to regulate it 335 00:11:31,520 --> 00:11:33,680 and promote innovation at the same time. 336 00:11:33,680 --> 00:11:35,120 It's a tough balance to strike. 337 00:11:35,120 --> 00:11:35,960 It is. 338 00:11:35,960 --> 00:11:38,640 You don't wanna stifle progress with too many rules, 339 00:11:38,640 --> 00:11:39,800 but you also need to make sure 340 00:11:39,800 --> 00:11:42,480 that AI is being developed and used responsibly. 341 00:11:42,480 --> 00:11:44,000 To protect people and society. 342 00:11:44,000 --> 00:11:44,840 Exactly. 343 00:11:44,840 --> 00:11:46,800 It's a conversation that's happening all over the world. 344 00:11:46,800 --> 00:11:49,280 Right now, there are no easy answers. 345 00:11:49,280 --> 00:11:51,440 Well, this has been a really fascinating deep dive 346 00:11:51,440 --> 00:11:52,640 into the world of AI. 347 00:11:52,640 --> 00:11:54,120 We've covered a lot of ground today. 348 00:11:54,120 --> 00:11:55,120 We have. 349 00:11:55,120 --> 00:11:58,960 From those new tools that make AI development easier 350 00:11:58,960 --> 00:12:01,120 to those big ethical questions, 351 00:12:01,120 --> 00:12:02,520 and the incredible potential 352 00:12:02,520 --> 00:12:04,320 of brain-computer interfaces. 353 00:12:04,320 --> 00:12:07,080 It's clear that AI is rapidly evolving. 354 00:12:07,080 --> 00:12:08,760 And it's gonna continue to shape our world 355 00:12:08,760 --> 00:12:10,360 in ways we can't even imagine yet. 356 00:12:10,360 --> 00:12:12,680 It's an exciting time to be alive, that's for sure. 357 00:12:12,680 --> 00:12:13,920 Absolutely. 358 00:12:13,920 --> 00:12:15,920 Well, that's all the time we have for today's episode. 359 00:12:15,920 --> 00:12:17,000 We hope you enjoyed it. 360 00:12:17,000 --> 00:12:18,520 And we encourage you to keep learning 361 00:12:18,520 --> 00:12:21,160 and exploring the amazing world of AI. 362 00:12:21,160 --> 00:12:23,120 Because the future of AI is a future 363 00:12:23,120 --> 00:12:24,920 that we're all creating together. 364 00:12:24,920 --> 00:12:28,120 Thanks for listening to the Daily AI News Podcast. 365 00:12:28,120 --> 00:12:31,280 We'll catch you next time.