1 00:00:00,000 --> 00:00:02,240 Welcome back everyone to the deep dive. 2 00:00:02,240 --> 00:00:04,400 Ready for another exciting deep dive today? 3 00:00:04,400 --> 00:00:08,480 Absolutely, always ready to explore the latest in AI. 4 00:00:08,480 --> 00:00:09,600 Awesome. 5 00:00:09,600 --> 00:00:12,360 Well, today we're diving into something pretty remarkable. 6 00:00:12,360 --> 00:00:15,600 Amazon's brand new family of AI models. 7 00:00:15,600 --> 00:00:17,720 They call it Amazon Nova. 8 00:00:17,720 --> 00:00:19,400 These are foundation models, right? 9 00:00:19,400 --> 00:00:20,400 Exactly. 10 00:00:20,400 --> 00:00:22,840 Think of them like a Swiss army knife of AI. 11 00:00:22,840 --> 00:00:24,760 Oh, I like that analogy. 12 00:00:24,760 --> 00:00:26,360 Yeah, they're built to be super versatile, 13 00:00:26,360 --> 00:00:28,400 ready to tackle all sorts of different tasks. 14 00:00:28,400 --> 00:00:32,760 And get this, it's not just one model, it's a whole family. 15 00:00:32,760 --> 00:00:36,280 Nova Pro, Light, Micro, Canvas, and Real. 16 00:00:36,280 --> 00:00:37,560 Wow, a whole family. 17 00:00:37,560 --> 00:00:39,480 So each one has its own unique strengths. 18 00:00:39,480 --> 00:00:40,000 You got it. 19 00:00:40,000 --> 00:00:43,120 And not only are they designed to be incredibly intelligent, 20 00:00:43,120 --> 00:00:45,280 but they're also fast and cost effective. 21 00:00:45,280 --> 00:00:48,400 Speed and efficiency, those are big wins in the AI world, 22 00:00:48,400 --> 00:00:50,680 especially as these models get more and more complex. 23 00:00:50,680 --> 00:00:51,480 Absolutely. 24 00:00:51,480 --> 00:00:52,720 But let's break it down a bit. 25 00:00:52,720 --> 00:00:55,240 We've got Pro, Light, and Micro. 26 00:00:55,240 --> 00:00:56,640 What's the deal with those three? 27 00:00:56,640 --> 00:00:59,120 So those three are all built on what are called the transformer 28 00:00:59,120 --> 00:01:00,200 architecture. 29 00:01:00,200 --> 00:01:02,040 You can think of it like a powerful engine that 30 00:01:02,040 --> 00:01:03,880 allows them to really understand and work 31 00:01:03,880 --> 00:01:06,120 with all different types of information. 32 00:01:06,120 --> 00:01:06,560 Like what? 33 00:01:06,560 --> 00:01:07,720 Give us some examples. 34 00:01:07,720 --> 00:01:11,520 Well, we're talking text, images, videos, even documents. 35 00:01:11,520 --> 00:01:14,920 They can take all of that as input and then generate text, 36 00:01:14,920 --> 00:01:19,400 output, provide insights, answer questions, that kind of thing. 37 00:01:19,400 --> 00:01:21,800 So it's not just about understanding words on a page 38 00:01:21,800 --> 00:01:22,600 anymore. 39 00:01:22,600 --> 00:01:25,160 These models are processing the world around us 40 00:01:25,160 --> 00:01:26,840 in all its forms. 41 00:01:26,840 --> 00:01:28,280 That's pretty mind blowing. 42 00:01:28,280 --> 00:01:28,880 It is. 43 00:01:28,880 --> 00:01:31,960 And they do really well on industry standard tests. 44 00:01:31,960 --> 00:01:33,960 For example, there's MMLU. 45 00:01:33,960 --> 00:01:35,440 It measures language understanding 46 00:01:35,440 --> 00:01:37,200 across all kinds of subjects. 47 00:01:37,200 --> 00:01:40,280 OK, like a comprehensive language exam for AI. 48 00:01:40,280 --> 00:01:41,240 Exactly. 49 00:01:41,240 --> 00:01:44,680 And then there's Flores, which evaluates translation skills 50 00:01:44,680 --> 00:01:46,400 across a bunch of different languages. 51 00:01:46,400 --> 00:01:48,160 So these benchmarks are like a way 52 00:01:48,160 --> 00:01:50,880 to see how well rounded these AI models are, 53 00:01:50,880 --> 00:01:52,840 how well they can grasp information 54 00:01:52,840 --> 00:01:54,120 and apply their knowledge. 55 00:01:54,120 --> 00:01:54,680 Right. 56 00:01:54,680 --> 00:01:56,800 And it's interesting to see how each of those models 57 00:01:56,800 --> 00:02:00,320 Pro, Light, and Micro perform on those tests. 58 00:02:00,320 --> 00:02:01,920 They each have their own strengths. 59 00:02:01,920 --> 00:02:04,880 But one that's really been making waves is Nova Micro. 60 00:02:04,880 --> 00:02:05,800 You mentioned that before. 61 00:02:05,800 --> 00:02:06,880 What makes it so special? 62 00:02:06,880 --> 00:02:10,400 Well, for its size, it's punching way above its weight. 63 00:02:10,400 --> 00:02:13,320 It's actually outperforming some of the bigger, more complex 64 00:02:13,320 --> 00:02:15,000 models in certain areas. 65 00:02:15,000 --> 00:02:16,200 Like what kinds of areas? 66 00:02:16,200 --> 00:02:20,000 Especially when it comes to things like logic, reasoning, 67 00:02:20,000 --> 00:02:23,000 following complex instructions, really impressive stuff 68 00:02:23,000 --> 00:02:24,720 for a model of its size. 69 00:02:24,720 --> 00:02:28,480 So does that mean we might start seeing more powerful AI 70 00:02:28,480 --> 00:02:30,920 capabilities becoming available to, say, 71 00:02:30,920 --> 00:02:34,200 smaller companies or individual developers who might not 72 00:02:34,200 --> 00:02:36,280 have the resources of a big tech company? 73 00:02:36,280 --> 00:02:37,720 It's definitely a possibility. 74 00:02:37,720 --> 00:02:40,520 This kind of efficiency could be a game changer 75 00:02:40,520 --> 00:02:43,080 for making advanced AI more accessible. 76 00:02:43,080 --> 00:02:44,400 That's super exciting. 77 00:02:44,400 --> 00:02:46,960 Now, you mentioned that these models can be customized. 78 00:02:46,960 --> 00:02:48,720 What does that look like in practice? 79 00:02:48,720 --> 00:02:50,720 Well, developers can actually fine tune them 80 00:02:50,720 --> 00:02:51,960 for specific tasks. 81 00:02:51,960 --> 00:02:52,680 It's really cool. 82 00:02:52,680 --> 00:02:56,200 They can adjust things like accuracy, speed, and even 83 00:02:56,200 --> 00:02:57,520 cost to fit their needs. 84 00:02:57,520 --> 00:02:59,640 So it's not a one-size-fits-all situation. 85 00:02:59,640 --> 00:03:01,560 You can actually mold these models 86 00:03:01,560 --> 00:03:05,000 to create the perfect AI assistant for a specific job. 87 00:03:05,000 --> 00:03:05,800 Exactly. 88 00:03:05,800 --> 00:03:08,800 It's all about flexibility and making these models work 89 00:03:08,800 --> 00:03:11,240 for you, not the other way around. 90 00:03:11,240 --> 00:03:13,360 And they're using some pretty sophisticated techniques 91 00:03:13,360 --> 00:03:14,000 to do it. 92 00:03:14,000 --> 00:03:14,880 Like what? 93 00:03:14,880 --> 00:03:16,800 Things like supervised fine tuning, 94 00:03:16,800 --> 00:03:19,920 where they train the models further on specific data sets. 95 00:03:19,920 --> 00:03:21,400 And then there's reinforcement learning 96 00:03:21,400 --> 00:03:24,040 from human feedback, which is super interesting. 97 00:03:24,040 --> 00:03:24,560 Oh, yeah. 98 00:03:24,560 --> 00:03:25,360 I've heard about that. 99 00:03:25,360 --> 00:03:27,640 It's like the AI is learning from our feedback 100 00:03:27,640 --> 00:03:28,880 and getting better over time. 101 00:03:28,880 --> 00:03:29,400 Precisely. 102 00:03:29,400 --> 00:03:31,480 It's constantly learning and evolving. 103 00:03:31,480 --> 00:03:32,760 That's wild. 104 00:03:32,760 --> 00:03:35,880 But we've only talked about one part of the family so far. 105 00:03:35,880 --> 00:03:39,680 What about those creative models, Nova Canvas and Real? 106 00:03:39,680 --> 00:03:42,160 Yes, those are the artists of the family. 107 00:03:42,160 --> 00:03:44,400 Nova Canvas focuses on generating images 108 00:03:44,400 --> 00:03:47,720 from text descriptions, while Nova Real takes it a step 109 00:03:47,720 --> 00:03:49,600 further and creates videos. 110 00:03:49,600 --> 00:03:51,800 Wait, AI that can make art? 111 00:03:51,800 --> 00:03:53,920 This is where things start to feel like science fiction. 112 00:03:53,920 --> 00:03:54,800 It does, doesn't it? 113 00:03:54,800 --> 00:03:57,520 Imagine typing in a cat wearing a tiny hat, 114 00:03:57,520 --> 00:03:59,880 riding a unicorn through a rainbow field, 115 00:03:59,880 --> 00:04:03,280 and having Canvas generate a stunning high resolution 116 00:04:03,280 --> 00:04:05,280 image of exactly that. 117 00:04:05,280 --> 00:04:06,400 That's amazing. 118 00:04:06,400 --> 00:04:08,120 But do you have any control over the image, 119 00:04:08,120 --> 00:04:09,840 or does it just kind of do its own thing? 120 00:04:09,840 --> 00:04:11,800 Oh, you have control. 121 00:04:11,800 --> 00:04:15,400 With Canvas, you can specify the resolution, the aspect ratio 122 00:04:15,400 --> 00:04:18,480 you're not stuck with, just a standard square image. 123 00:04:18,480 --> 00:04:21,080 And get this, you can actually edit the image 124 00:04:21,080 --> 00:04:22,600 using text commands. 125 00:04:22,600 --> 00:04:23,080 Hold on. 126 00:04:23,080 --> 00:04:26,320 So I can talk to the AI and tell it to make changes 127 00:04:26,320 --> 00:04:26,920 to the image. 128 00:04:26,920 --> 00:04:29,960 Like, what if I wanted the cat's hat to be blue instead of red? 129 00:04:29,960 --> 00:04:30,760 Exactly. 130 00:04:30,760 --> 00:04:33,400 You could say, change the cat's hat to blue, 131 00:04:33,400 --> 00:04:35,360 and it would actually make the change. 132 00:04:35,360 --> 00:04:36,080 No way. 133 00:04:36,080 --> 00:04:36,920 That's incredible. 134 00:04:36,920 --> 00:04:37,960 And what about real? 135 00:04:37,960 --> 00:04:39,160 How does that work? 136 00:04:39,160 --> 00:04:41,200 It's similar, but focuses on videos. 137 00:04:41,200 --> 00:04:44,040 You could give it a prompt, like a robot learning 138 00:04:44,040 --> 00:04:46,360 to dance in a bustling city square, 139 00:04:46,360 --> 00:04:47,960 and it would generate a short video 140 00:04:47,960 --> 00:04:49,520 bringing that scene to life. 141 00:04:49,520 --> 00:04:50,320 Wow. 142 00:04:50,320 --> 00:04:53,560 So I could basically write a mini movie script, 143 00:04:53,560 --> 00:04:55,360 and this AI would direct it for me. 144 00:04:55,360 --> 00:04:56,840 In a way, yes. 145 00:04:56,840 --> 00:04:58,680 You could even control the camera movement 146 00:04:58,680 --> 00:05:00,000 using natural language. 147 00:05:00,000 --> 00:05:02,360 Imagine saying, zoom in on the robot's face, 148 00:05:02,360 --> 00:05:05,680 or pan across the cityscape, and having the AI 149 00:05:05,680 --> 00:05:08,800 seamlessly incorporate those camera moves into the video. 150 00:05:08,800 --> 00:05:09,440 That's insane. 151 00:05:09,440 --> 00:05:12,200 It sounds like these models could revolutionize filmmaking, 152 00:05:12,200 --> 00:05:14,080 animation, even advertising. 153 00:05:14,080 --> 00:05:15,440 The possibilities are endless. 154 00:05:15,440 --> 00:05:17,640 The potential applications are vast. 155 00:05:17,640 --> 00:05:20,160 And what's even more impressive is how they achieve 156 00:05:20,160 --> 00:05:21,920 this level of creative output. 157 00:05:21,920 --> 00:05:24,840 They use something called latent diffusion models. 158 00:05:24,840 --> 00:05:27,720 OK, latent diffusion models. 159 00:05:27,720 --> 00:05:30,640 Now you're just using big words to make me feel dumb. 160 00:05:30,640 --> 00:05:31,440 Break that down for us. 161 00:05:31,440 --> 00:05:32,600 How do they actually work? 162 00:05:32,600 --> 00:05:35,520 All right, imagine you have a perfectly clear picture, 163 00:05:35,520 --> 00:05:38,680 and then you start slowly adding noise to it, 164 00:05:38,680 --> 00:05:41,200 making it blurrier and blurrier until it's just 165 00:05:41,200 --> 00:05:43,080 a mess of random pixels. 166 00:05:43,080 --> 00:05:44,560 OK, following so far. 167 00:05:44,560 --> 00:05:46,160 Kind of like those scrambled pictures 168 00:05:46,160 --> 00:05:47,280 you used to see in puzzle books. 169 00:05:47,280 --> 00:05:48,600 Yeah, kind of like that. 170 00:05:48,600 --> 00:05:51,840 So latent diffusion models learn to reverse that process. 171 00:05:51,840 --> 00:05:54,560 They start with a jumbled mess, and then, based on your text 172 00:05:54,560 --> 00:05:56,760 prompts, they gradually remove the noise 173 00:05:56,760 --> 00:05:59,360 to create a coherent image or video. 174 00:05:59,360 --> 00:06:01,840 So it's like taking that blurry pixelated image 175 00:06:01,840 --> 00:06:05,240 and having the AI de-blur it based on your description, 176 00:06:05,240 --> 00:06:08,160 except instead of an image, it's a whole video. 177 00:06:08,160 --> 00:06:08,840 That's wild. 178 00:06:08,840 --> 00:06:11,080 How do they train these models to do this? 179 00:06:11,080 --> 00:06:13,120 Well, the training process is extensive. 180 00:06:13,120 --> 00:06:14,440 There are two main phases. 181 00:06:14,440 --> 00:06:16,760 First, they pre-train the models on a massive data 182 00:06:16,760 --> 00:06:18,480 set of images and videos. 183 00:06:18,480 --> 00:06:19,600 OK, hold on. 184 00:06:19,600 --> 00:06:21,040 Where do they get all this data from? 185 00:06:21,040 --> 00:06:22,680 Are they just grabbing everything off the internet? 186 00:06:22,680 --> 00:06:24,320 They don't go into specific details, 187 00:06:24,320 --> 00:06:27,120 but they mention using a mix of sources. 188 00:06:27,120 --> 00:06:27,880 Like what? 189 00:06:27,880 --> 00:06:31,960 They use licensed data, their own data, open source data sets, 190 00:06:31,960 --> 00:06:36,080 and publicly available data, where it's appropriate to use it. 191 00:06:36,080 --> 00:06:38,320 So it's a blend of different sources. 192 00:06:38,320 --> 00:06:40,720 And I'm assuming they're being super careful about things 193 00:06:40,720 --> 00:06:42,880 like data privacy and responsible use from them. 194 00:06:42,880 --> 00:06:43,480 Absolutely. 195 00:06:43,480 --> 00:06:45,840 In fact, they have a whole section of their report 196 00:06:45,840 --> 00:06:47,800 dedicated to responsible AI. 197 00:06:47,800 --> 00:06:51,880 It's all about ensuring these powerful tools are used ethically. 198 00:06:51,880 --> 00:06:55,360 Maybe we can delve into that a little bit more after a quick. 199 00:06:55,360 --> 00:06:57,040 Oh, we don't really have breaks here, do we? 200 00:06:57,040 --> 00:06:57,480 Nope. 201 00:06:57,480 --> 00:06:58,520 This is the deep dive. 202 00:06:58,520 --> 00:06:59,600 We just keep on diving. 203 00:06:59,600 --> 00:07:01,880 So let's jump right into responsible AI 204 00:07:01,880 --> 00:07:05,040 and see what Amazon is doing to make sure these incredible models 205 00:07:05,040 --> 00:07:06,680 are used for good. 206 00:07:06,680 --> 00:07:10,640 So responsible AI, it's a big topic these days 207 00:07:10,640 --> 00:07:11,720 and for good reason. 208 00:07:11,720 --> 00:07:12,400 It is. 209 00:07:12,400 --> 00:07:14,480 It's one thing to create powerful AI, 210 00:07:14,480 --> 00:07:16,920 but it's another to make sure it's used ethically. 211 00:07:16,920 --> 00:07:18,760 Responsibly, it's a must-have. 212 00:07:18,760 --> 00:07:21,240 Right, with great power comes great responsibility 213 00:07:21,240 --> 00:07:22,520 or something like that. 214 00:07:22,520 --> 00:07:27,000 So how is Amazon approaching this with the Nova models? 215 00:07:27,000 --> 00:07:28,320 Well, they've got this framework. 216 00:07:28,320 --> 00:07:30,640 They're calling it the eight core dimensions 217 00:07:30,640 --> 00:07:32,360 of responsible AI. 218 00:07:32,360 --> 00:07:33,920 Eight dimensions, OK. 219 00:07:33,920 --> 00:07:34,560 Lay it on us. 220 00:07:34,560 --> 00:07:35,080 What are they? 221 00:07:35,080 --> 00:07:38,400 Fairness, explainability, privacy and security, 222 00:07:38,400 --> 00:07:43,040 safety, controllability, and veracity and robustness, governance, 223 00:07:43,040 --> 00:07:43,960 and transparency. 224 00:07:43,960 --> 00:07:45,040 OK, that's quite the list. 225 00:07:45,040 --> 00:07:46,360 So those are the dimensions. 226 00:07:46,360 --> 00:07:49,520 But what do they actually mean when it comes to these AI models? 227 00:07:49,520 --> 00:07:51,440 How does it all play out in the real world? 228 00:07:51,440 --> 00:07:52,560 Good question. 229 00:07:52,560 --> 00:07:53,800 Let's start with fairness. 230 00:07:53,800 --> 00:07:56,240 What we're talking about here is making sure these models don't 231 00:07:56,240 --> 00:07:57,640 have any bias baked in. 232 00:07:57,640 --> 00:07:58,880 Baked in, OK. 233 00:07:58,880 --> 00:08:02,320 So no unfair treatment of certain groups of people. 234 00:08:02,320 --> 00:08:03,120 Exactly. 235 00:08:03,120 --> 00:08:05,480 Like let's say you're using Nova to analyze resumes. 236 00:08:05,480 --> 00:08:07,680 You want to make sure it's a value in candidates fairly, 237 00:08:07,680 --> 00:08:10,920 regardless of their background, their gender, their ethnicity, 238 00:08:10,920 --> 00:08:11,680 all of that. 239 00:08:11,680 --> 00:08:12,400 That's crucial. 240 00:08:12,400 --> 00:08:15,160 We've seen examples in the past where AI systems have actually 241 00:08:15,160 --> 00:08:17,280 perpetuated existing biases. 242 00:08:17,280 --> 00:08:20,160 So it's good to know they're addressing this head on. 243 00:08:20,160 --> 00:08:21,480 What about explainability? 244 00:08:21,480 --> 00:08:23,520 What does that mean in this context? 245 00:08:23,520 --> 00:08:26,040 Explainability is all about understanding 246 00:08:26,040 --> 00:08:28,160 how the model makes its decisions. 247 00:08:28,160 --> 00:08:30,480 Like why did it recommend this candidate? 248 00:08:30,480 --> 00:08:33,280 Or why did it generate this particular image? 249 00:08:33,280 --> 00:08:35,200 So it's like a peek behind the curtain. 250 00:08:35,200 --> 00:08:37,800 We get to see the reasoning behind the AI's actions. 251 00:08:37,800 --> 00:08:38,480 Exactly. 252 00:08:38,480 --> 00:08:42,440 That transparency is so important for building trust, 253 00:08:42,440 --> 00:08:44,800 for holding these AI systems accountable. 254 00:08:44,800 --> 00:08:45,480 I can see that. 255 00:08:45,480 --> 00:08:47,280 And privacy and security, I imagine, 256 00:08:47,280 --> 00:08:49,360 those are huge priorities as well, 257 00:08:49,360 --> 00:08:51,920 especially with these models handling so much data. 258 00:08:51,920 --> 00:08:52,640 Absolutely. 259 00:08:52,640 --> 00:08:55,360 I mean, you're talking about people's personal information, 260 00:08:55,360 --> 00:08:56,600 sensitive data. 261 00:08:56,600 --> 00:09:00,600 Amazon has implemented measures to protect all of that 262 00:09:00,600 --> 00:09:03,240 and to prevent these models from being misused. 263 00:09:03,240 --> 00:09:03,840 Misused? 264 00:09:03,840 --> 00:09:04,440 How so? 265 00:09:04,440 --> 00:09:05,480 Give us an example. 266 00:09:05,480 --> 00:09:08,280 Well, you don't want someone using these powerful AI models 267 00:09:08,280 --> 00:09:12,600 to say generate harmful content or spread misinformation. 268 00:09:12,600 --> 00:09:13,720 It's a real concern. 269 00:09:13,720 --> 00:09:14,400 Definitely. 270 00:09:14,400 --> 00:09:17,640 It's like we're giving these AI's incredibly powerful tools, 271 00:09:17,640 --> 00:09:20,160 and we need to make sure they use them for good, not for harm. 272 00:09:20,160 --> 00:09:20,840 Exactly. 273 00:09:20,840 --> 00:09:22,440 That's where the safety dimension comes in. 274 00:09:22,440 --> 00:09:24,680 It's about ensuring these models are being used in a way that 275 00:09:24,680 --> 00:09:26,200 doesn't put people at risk. 276 00:09:26,200 --> 00:09:27,440 Makes sense. 277 00:09:27,440 --> 00:09:29,720 It's like putting safeguards around these tools, 278 00:09:29,720 --> 00:09:31,400 like safety rails. 279 00:09:31,400 --> 00:09:33,880 But how do they actually control these models? 280 00:09:33,880 --> 00:09:35,960 How do you make sure they stay within the bounds 281 00:09:35,960 --> 00:09:37,200 of responsible use? 282 00:09:37,200 --> 00:09:38,480 It seems like a tough challenge. 283 00:09:38,480 --> 00:09:41,240 It is, but that's what controllability is all about. 284 00:09:41,240 --> 00:09:45,120 They've built in mechanisms to monitor the AI's behavior, 285 00:09:45,120 --> 00:09:48,400 to steer it in the right direction, to keep it on track. 286 00:09:48,400 --> 00:09:50,240 So it's not just about setting rules. 287 00:09:50,240 --> 00:09:54,200 It's about having the power to actually enforce those rules, 288 00:09:54,200 --> 00:09:56,960 to make sure the AI doesn't go off the rails, so to speak. 289 00:09:56,960 --> 00:09:57,240 Right. 290 00:09:57,240 --> 00:09:58,360 You need both. 291 00:09:58,360 --> 00:10:01,080 Clear guidelines and the ability to make sure 292 00:10:01,080 --> 00:10:02,800 those guidelines are followed. 293 00:10:02,800 --> 00:10:05,240 Now, you mentioned veracity, robustness, governance, 294 00:10:05,240 --> 00:10:07,360 transparency, want to dig into those a bit more. 295 00:10:07,360 --> 00:10:07,960 Let's do it. 296 00:10:07,960 --> 00:10:08,920 Break those down for us. 297 00:10:08,920 --> 00:10:09,200 All right. 298 00:10:09,200 --> 00:10:11,160 So veracity and robustness, that's 299 00:10:11,160 --> 00:10:13,000 about making sure the models are giving us 300 00:10:13,000 --> 00:10:15,600 accurate information, reliable results, 301 00:10:15,600 --> 00:10:17,920 even when they're faced with something unexpected. 302 00:10:17,920 --> 00:10:18,960 Like a curveball. 303 00:10:18,960 --> 00:10:20,400 Yes, like a curveball. 304 00:10:20,400 --> 00:10:20,720 Yeah. 305 00:10:20,720 --> 00:10:22,120 A challenging input. 306 00:10:22,120 --> 00:10:24,680 You need these models to be tough, resilient, 307 00:10:24,680 --> 00:10:26,920 to stand up to the test and still give you 308 00:10:26,920 --> 00:10:28,440 trustworthy outputs. 309 00:10:28,440 --> 00:10:30,600 So it's about building them to be reliable, 310 00:10:30,600 --> 00:10:32,320 no matter what we throw at them. 311 00:10:32,320 --> 00:10:33,120 What about governance? 312 00:10:33,120 --> 00:10:34,040 Where does that fit in? 313 00:10:34,040 --> 00:10:35,960 Governance is all about setting up 314 00:10:35,960 --> 00:10:39,560 clear guidelines and procedures for developing and deploying 315 00:10:39,560 --> 00:10:41,720 these AI systems responsibly. 316 00:10:41,720 --> 00:10:44,520 It's about having a solid framework in place 317 00:10:44,520 --> 00:10:46,360 to bake those ethical considerations 318 00:10:46,360 --> 00:10:48,000 into every step of the process. 319 00:10:48,000 --> 00:10:48,400 Got it. 320 00:10:48,400 --> 00:10:50,480 And lastly, transparency. 321 00:10:50,480 --> 00:10:53,600 Transparency is all about being open and honest, 322 00:10:53,600 --> 00:10:55,520 making sure all the information about the model's 323 00:10:55,520 --> 00:10:57,800 capabilities, its limitations, all of that 324 00:10:57,800 --> 00:11:00,560 is easily accessible to users and stakeholders. 325 00:11:00,560 --> 00:11:01,360 No secrets. 326 00:11:01,360 --> 00:11:01,680 Right. 327 00:11:01,680 --> 00:11:02,720 No secrets. 328 00:11:02,720 --> 00:11:05,840 It's about building trust by being upfront about how 329 00:11:05,840 --> 00:11:07,720 these models work, what they're good at, 330 00:11:07,720 --> 00:11:09,400 and where they might fall short. 331 00:11:09,400 --> 00:11:11,600 So it's not just about building responsible AI. 332 00:11:11,600 --> 00:11:14,040 It's about being open and communicating clearly 333 00:11:14,040 --> 00:11:15,000 about the whole process. 334 00:11:15,000 --> 00:11:15,400 I like that. 335 00:11:15,400 --> 00:11:16,480 It sounds like they're really taking 336 00:11:16,480 --> 00:11:17,960 a holistic approach to all of this. 337 00:11:17,960 --> 00:11:18,520 They are. 338 00:11:18,520 --> 00:11:20,320 And they've taken some concrete steps. 339 00:11:20,320 --> 00:11:21,960 They've been really careful about using 340 00:11:21,960 --> 00:11:23,760 a wide range of data for training, 341 00:11:23,760 --> 00:11:25,960 making sure it's diverse, unbiased. 342 00:11:25,960 --> 00:11:29,400 Plus they've done a ton of these red teaming exercises. 343 00:11:29,400 --> 00:11:30,880 Red teaming exercises. 344 00:11:30,880 --> 00:11:31,560 What are those? 345 00:11:31,560 --> 00:11:32,640 Imagine this. 346 00:11:32,640 --> 00:11:35,520 They bring in these internal and external experts 347 00:11:35,520 --> 00:11:38,880 and basically ask them to try to break the models. 348 00:11:38,880 --> 00:11:39,640 Break them? 349 00:11:39,640 --> 00:11:40,160 How? 350 00:11:40,160 --> 00:11:42,480 Well, by trying to find ways to trick them 351 00:11:42,480 --> 00:11:46,120 into generating harmful content, inappropriate outputs, 352 00:11:46,120 --> 00:11:46,880 that kind of thing. 353 00:11:46,880 --> 00:11:48,080 So like a stress test. 354 00:11:48,080 --> 00:11:48,400 Yeah. 355 00:11:48,400 --> 00:11:50,280 Finding the weak points and fixing them 356 00:11:50,280 --> 00:11:52,600 before these models get released into the wild. 357 00:11:52,600 --> 00:11:53,720 Exactly. 358 00:11:53,720 --> 00:11:56,480 It's a proactive way to identify and address 359 00:11:56,480 --> 00:12:00,000 potential problems before they become real world issues. 360 00:12:00,000 --> 00:12:01,600 And they don't stop there. 361 00:12:01,600 --> 00:12:03,320 Once these models are out in the world, 362 00:12:03,320 --> 00:12:05,320 they've got more safeguards in place. 363 00:12:05,320 --> 00:12:06,000 Like what? 364 00:12:06,000 --> 00:12:08,800 Things like runtime input and output moderation. 365 00:12:08,800 --> 00:12:10,440 Basically, they have systems that filter out 366 00:12:10,440 --> 00:12:13,360 harmful or inappropriate prompts and responses. 367 00:12:13,360 --> 00:12:17,000 So even if someone tries to use these models for bad stuff, 368 00:12:17,000 --> 00:12:19,920 these moderation systems are there to stop them, 369 00:12:19,920 --> 00:12:21,160 to prevent misuse. 370 00:12:21,160 --> 00:12:21,840 Exactly. 371 00:12:21,840 --> 00:12:23,800 They're like the gatekeepers, making sure everything 372 00:12:23,800 --> 00:12:26,280 stays within the bounds of responsible use. 373 00:12:26,280 --> 00:12:28,720 And get this, they even came up with this really 374 00:12:28,720 --> 00:12:31,800 innovative watermarking technique for images and videos 375 00:12:31,800 --> 00:12:33,760 generated by Canvas and Reel. 376 00:12:33,760 --> 00:12:34,400 A watermark? 377 00:12:34,400 --> 00:12:35,120 What does that do? 378 00:12:35,120 --> 00:12:36,960 It's like a digital fingerprint. 379 00:12:36,960 --> 00:12:39,280 It helps you tell the difference between AI-generated 380 00:12:39,280 --> 00:12:43,320 content and real content, which is super important when 381 00:12:43,320 --> 00:12:46,120 you think about the rise of deep fakes and all the concerns 382 00:12:46,120 --> 00:12:47,480 about misinformation. 383 00:12:47,480 --> 00:12:49,600 So you can always tell something was created by an AI. 384 00:12:49,600 --> 00:12:52,040 It's like a built-in truth detector. 385 00:12:52,040 --> 00:12:53,120 In a way, yes. 386 00:12:53,120 --> 00:12:56,400 And again, that transparency is key to building trust 387 00:12:56,400 --> 00:12:58,600 and encouraging responsible use. 388 00:12:58,600 --> 00:13:02,120 OK, so we've talked about the what of responsible AI, 389 00:13:02,120 --> 00:13:03,840 the principles, the different dimensions. 390 00:13:03,840 --> 00:13:06,480 But I'm also curious about the how. 391 00:13:06,480 --> 00:13:09,000 What kind of infrastructure and technology 392 00:13:09,000 --> 00:13:10,880 did it take to actually build and train 393 00:13:10,880 --> 00:13:13,000 these massive AI models? 394 00:13:13,000 --> 00:13:14,640 Well, it's safe to say they went all out. 395 00:13:14,640 --> 00:13:17,760 They used custom design chips, the most powerful GPUs out 396 00:13:17,760 --> 00:13:19,960 there, huge, massive computing systems. 397 00:13:19,960 --> 00:13:22,680 They really pushed the limits of what's possible with the AI 398 00:13:22,680 --> 00:13:23,440 infrastructure. 399 00:13:23,440 --> 00:13:25,720 It sounds like we're about to get really technical. 400 00:13:25,720 --> 00:13:28,360 All right, so let's get into the nuts and bolts of it all. 401 00:13:28,360 --> 00:13:30,200 What kind of tech firepower did they 402 00:13:30,200 --> 00:13:33,120 need to create these Amazon Nova models? 403 00:13:33,120 --> 00:13:35,160 Well, they definitely didn't hold back. 404 00:13:35,160 --> 00:13:38,080 For starters, they used their own Trainium One chips. 405 00:13:38,080 --> 00:13:39,480 Trainium One chips. 406 00:13:39,480 --> 00:13:41,640 Yeah, these chips are custom designed 407 00:13:41,640 --> 00:13:44,360 for machine learning, high performance stuff. 408 00:13:44,360 --> 00:13:46,120 And they didn't stop there. 409 00:13:46,120 --> 00:13:50,240 They also used those NVIDIA A100 and H100 GPUs. 410 00:13:50,240 --> 00:13:50,880 GPUs. 411 00:13:50,880 --> 00:13:52,200 Those are the graphics cards, right? 412 00:13:52,200 --> 00:13:53,160 It's like the gamers use. 413 00:13:53,160 --> 00:13:55,480 You got it, but these are like the supercharged versions. 414 00:13:55,480 --> 00:13:56,640 Some of them was powerful out there, 415 00:13:56,640 --> 00:13:59,320 perfect for handling those massive AI workloads. 416 00:13:59,320 --> 00:14:03,640 So they've got the hardware, but these AI models are huge. 417 00:14:03,640 --> 00:14:06,200 How do they even manage the training process 418 00:14:06,200 --> 00:14:07,400 with all that data? 419 00:14:07,400 --> 00:14:09,240 That's where AWS comes in. 420 00:14:09,240 --> 00:14:11,000 They used Amazon SageMaker. 421 00:14:11,000 --> 00:14:13,040 SageMaker, that's their cloud-based machine 422 00:14:13,040 --> 00:14:14,000 learning platform, right? 423 00:14:14,000 --> 00:14:14,840 Exactly. 424 00:14:14,840 --> 00:14:17,800 They used it to orchestrate the whole training process. 425 00:14:17,800 --> 00:14:19,720 And to make things even smoother, 426 00:14:19,720 --> 00:14:23,760 they used Amazon's Elastic Kubernetes Service, or EKS 427 00:14:23,760 --> 00:14:24,600 for short. 428 00:14:24,600 --> 00:14:25,960 Kubernetes, no, that's a word I've 429 00:14:25,960 --> 00:14:27,280 heard thrown around a lot, but I'm not 430 00:14:27,280 --> 00:14:28,360 sure I fully understand it. 431 00:14:28,360 --> 00:14:29,360 Think of it like this. 432 00:14:29,360 --> 00:14:32,200 You've got this massive training workload, right? 433 00:14:32,200 --> 00:14:34,840 Kubernetes helps distribute that workload 434 00:14:34,840 --> 00:14:37,480 across a whole bunch of different servers. 435 00:14:37,480 --> 00:14:40,040 It's like having an army of computers all working together 436 00:14:40,040 --> 00:14:41,440 in Perfect Sync. 437 00:14:41,440 --> 00:14:42,520 So it's all about teamwork. 438 00:14:42,520 --> 00:14:43,560 Makes sense. 439 00:14:43,560 --> 00:14:46,760 But we're still talking about mountains of data, text, images, 440 00:14:46,760 --> 00:14:47,600 video. 441 00:14:47,600 --> 00:14:49,400 Where do they even store all of that? 442 00:14:49,400 --> 00:14:52,720 They used a smart combination of storage solutions. 443 00:14:52,720 --> 00:14:55,920 For frequently used data, they went with Amazon FSx. 444 00:14:55,920 --> 00:14:59,440 It's a high-performance file system, so super fast access. 445 00:14:59,440 --> 00:15:01,680 And then for storing all those massive data sets 446 00:15:01,680 --> 00:15:04,760 and model checkpoints, they used Amazon S3. 447 00:15:04,760 --> 00:15:07,240 Amazon S3, that's their cloud storage service. 448 00:15:07,240 --> 00:15:08,840 Pretty much everyone uses it these days. 449 00:15:08,840 --> 00:15:11,560 Yeah, it's known for being super reliable and scalable. 450 00:15:11,560 --> 00:15:13,160 You can store pretty much anything on there, 451 00:15:13,160 --> 00:15:15,160 perfect for those giant AI models. 452 00:15:15,160 --> 00:15:16,640 So they built this whole ecosystem 453 00:15:16,640 --> 00:15:19,800 for training these models, optimizing every step of the way. 454 00:15:19,800 --> 00:15:21,000 It's pretty impressive. 455 00:15:21,000 --> 00:15:23,280 But beyond the hardware and the cloud stuff, 456 00:15:23,280 --> 00:15:25,720 did they do anything to make the training process itself 457 00:15:25,720 --> 00:15:26,320 more efficient? 458 00:15:26,320 --> 00:15:27,120 They did. 459 00:15:27,120 --> 00:15:30,200 They actually came up with this new activation checkpointing 460 00:15:30,200 --> 00:15:30,920 scheme. 461 00:15:30,920 --> 00:15:32,520 Activation checkpointing scheme? 462 00:15:32,520 --> 00:15:33,960 Try saying that five times fast. 463 00:15:33,960 --> 00:15:34,760 I know, right? 464 00:15:34,760 --> 00:15:35,640 It's a mouthful. 465 00:15:35,640 --> 00:15:38,520 But basically, it's a way to reduce the amount of memory 466 00:15:38,520 --> 00:15:40,080 needed during training. 467 00:15:40,080 --> 00:15:42,920 So they found a way to make it less memory intensive. 468 00:15:42,920 --> 00:15:43,840 How does that work? 469 00:15:43,840 --> 00:15:45,600 Well, I won't bore you with the technical details, 470 00:15:45,600 --> 00:15:46,760 but it's pretty clever. 471 00:15:46,760 --> 00:15:50,320 They call it super selective activation checkpointing. 472 00:15:50,320 --> 00:15:51,640 Super selective, I like it. 473 00:15:51,640 --> 00:15:53,080 It sounds very precise. 474 00:15:53,080 --> 00:15:53,920 It is. 475 00:15:53,920 --> 00:15:57,400 And this allows them to either train even larger models 476 00:15:57,400 --> 00:16:00,040 or use even more data during training. 477 00:16:00,040 --> 00:16:02,120 It opens up a lot of possibilities. 478 00:16:02,120 --> 00:16:02,920 That's amazing. 479 00:16:02,920 --> 00:16:04,400 Any other optimizations they made? 480 00:16:04,400 --> 00:16:04,880 Oh, yeah. 481 00:16:04,880 --> 00:16:08,120 They also made some tweaks to their communication protocols. 482 00:16:08,120 --> 00:16:09,080 Communication protocols. 483 00:16:09,080 --> 00:16:09,440 Yeah. 484 00:16:09,440 --> 00:16:13,400 Basically, it's how different parts of this whole distributed 485 00:16:13,400 --> 00:16:15,520 system talk to each other. 486 00:16:15,520 --> 00:16:17,320 And by improving that communication, 487 00:16:17,320 --> 00:16:19,960 they're able to minimize the time it takes for everything 488 00:16:19,960 --> 00:16:20,760 to sync up. 489 00:16:20,760 --> 00:16:24,320 So it's not just about individual components being fast. 490 00:16:24,320 --> 00:16:26,240 It's about making sure everything works together 491 00:16:26,240 --> 00:16:28,200 smoothly, efficiently. 492 00:16:28,200 --> 00:16:29,720 Kind of like a well-oiled machine. 493 00:16:29,720 --> 00:16:30,480 Exactly. 494 00:16:30,480 --> 00:16:31,360 A well-oiled machine. 495 00:16:31,360 --> 00:16:32,400 That's a great way to put it. 496 00:16:32,400 --> 00:16:34,640 Well, I think we've covered a lot of ground today. 497 00:16:34,640 --> 00:16:37,280 We've explored these fascinating new AI models 498 00:16:37,280 --> 00:16:39,600 from Amazon, the Nova family. 499 00:16:39,600 --> 00:16:43,400 We've dived into the complex world of responsible AI 500 00:16:43,400 --> 00:16:45,800 and how they're working to make sure these powerful tools are 501 00:16:45,800 --> 00:16:47,200 used for good. 502 00:16:47,200 --> 00:16:48,880 And we even got a little technical, 503 00:16:48,880 --> 00:16:51,720 exploring the infrastructure and the clever optimizations 504 00:16:51,720 --> 00:16:53,720 they made to make all of this possible. 505 00:16:53,720 --> 00:16:56,160 It's been a really exciting deep dive. 506 00:16:56,160 --> 00:16:59,280 The Amazon Nova family represents a real step forward 507 00:16:59,280 --> 00:17:00,680 in the world of AI. 508 00:17:00,680 --> 00:17:03,440 They're pushing the limits of what we thought was possible. 509 00:17:03,440 --> 00:17:05,440 And the most exciting part is that we're just 510 00:17:05,440 --> 00:17:07,800 at the beginning of this journey. 511 00:17:07,800 --> 00:17:10,560 As developers get their hands on these models, 512 00:17:10,560 --> 00:17:12,880 who knows what incredible applications they'll come up 513 00:17:12,880 --> 00:17:13,160 with. 514 00:17:13,160 --> 00:17:15,600 The possibilities are truly endless. 515 00:17:15,600 --> 00:17:18,200 So to our listeners, we'd love to hear your thoughts. 516 00:17:18,200 --> 00:17:20,320 What applications of the Amazon Nova family 517 00:17:20,320 --> 00:17:22,200 are you most excited about? 518 00:17:22,200 --> 00:17:24,200 Head over to our website and let us know. 519 00:17:24,200 --> 00:17:26,160 And be sure to check out the show notes for links 520 00:17:26,160 --> 00:17:28,840 to all the reports and resources we mentioned today. 521 00:17:28,840 --> 00:17:30,520 Thanks for joining us on this deep dive. 522 00:17:30,520 --> 00:17:50,280 We'll see you next time.