1 00:00:00,000 --> 00:00:03,440 All right, so today we're diving into AI MV2, 2 00:00:03,440 --> 00:00:06,040 this new AI model from Apple. 3 00:00:06,040 --> 00:00:06,880 Yeah. 4 00:00:06,880 --> 00:00:09,160 That can handle both images and text. 5 00:00:09,160 --> 00:00:10,000 Pretty cool stuff. 6 00:00:10,000 --> 00:00:11,520 Pretty big deal in the AI world, right? 7 00:00:11,520 --> 00:00:12,360 Oh, absolutely. 8 00:00:12,360 --> 00:00:13,800 It is, like, you know, 9 00:00:13,800 --> 00:00:17,080 teaching a computer to see and read at the same time. 10 00:00:17,080 --> 00:00:19,120 Exactly, and traditionally, 11 00:00:19,120 --> 00:00:22,400 AI models have been kind of like specialists. 12 00:00:22,400 --> 00:00:23,960 You know, they're either really good at images 13 00:00:23,960 --> 00:00:25,240 or really good at text. 14 00:00:25,240 --> 00:00:26,080 Right. 15 00:00:26,080 --> 00:00:26,920 But not both. 16 00:00:26,920 --> 00:00:27,760 Uh-huh. 17 00:00:27,760 --> 00:00:29,960 AI MV2 is really trying to change that. 18 00:00:29,960 --> 00:00:32,920 Yeah, and the way it learns is super interesting. 19 00:00:32,920 --> 00:00:33,760 Okay. 20 00:00:33,760 --> 00:00:35,520 Instead of just, like, matching images and text, 21 00:00:35,520 --> 00:00:37,560 like, saying, okay, this picture of a cat 22 00:00:37,560 --> 00:00:38,960 goes with the word cat. 23 00:00:38,960 --> 00:00:39,800 Right. 24 00:00:39,800 --> 00:00:42,000 It actually learns to predict what comes next, 25 00:00:42,000 --> 00:00:43,960 whether it's the next part of an image 26 00:00:43,960 --> 00:00:45,280 or the next word in a sentence. 27 00:00:45,280 --> 00:00:46,760 So it's just memorizing pairs? 28 00:00:46,760 --> 00:00:47,600 Yeah. 29 00:00:47,600 --> 00:00:49,640 It's actually, like, figuring out the patterns 30 00:00:49,640 --> 00:00:50,640 and the relationships. 31 00:00:50,640 --> 00:00:52,560 It's more like how we learn. 32 00:00:52,560 --> 00:00:53,400 Yeah. 33 00:00:53,400 --> 00:00:56,160 You know, like, anticipating what's coming next. 34 00:00:56,160 --> 00:00:57,000 That's really cool. 35 00:00:57,000 --> 00:00:57,840 Yeah. 36 00:00:57,840 --> 00:00:59,480 And the researchers found that this way of learning, 37 00:00:59,480 --> 00:01:02,600 which is similar to how large language models are trained, 38 00:01:02,600 --> 00:01:05,160 it actually works really well for images, too. 39 00:01:05,160 --> 00:01:08,800 Like, they've unlocked a new way to teach AI 40 00:01:08,800 --> 00:01:10,240 about the visual world. 41 00:01:10,240 --> 00:01:11,400 So that's how it learns. 42 00:01:11,400 --> 00:01:13,120 But how does it actually stack up 43 00:01:13,120 --> 00:01:16,200 against other big AI models out there? 44 00:01:16,200 --> 00:01:18,320 So they put it through a whole bunch of tests. 45 00:01:18,320 --> 00:01:19,160 Yep. 46 00:01:19,160 --> 00:01:21,400 And it seems to be outperforming 47 00:01:21,400 --> 00:01:24,080 some of the, like, the heavy hitters out there. 48 00:01:24,080 --> 00:01:24,920 Oh, wow. 49 00:01:24,920 --> 00:01:26,760 Like, for example, it's better than DinoV2 50 00:01:26,760 --> 00:01:28,640 at identifying objects and images. 51 00:01:28,640 --> 00:01:30,680 And understanding descriptions like, 52 00:01:30,680 --> 00:01:32,480 the cat sitting on the blue rug. 53 00:01:32,480 --> 00:01:34,240 So it's not just recognizing the objects, 54 00:01:34,240 --> 00:01:36,040 but also understanding how they relate to each other 55 00:01:36,040 --> 00:01:36,880 in the scene. 56 00:01:36,880 --> 00:01:37,720 Exactly. 57 00:01:37,720 --> 00:01:38,560 It's getting the context. 58 00:01:38,560 --> 00:01:39,400 Yeah. 59 00:01:39,400 --> 00:01:42,240 And it's also scoring higher than CLIP and SIGLIP 60 00:01:42,240 --> 00:01:44,280 on some of those classic image recognition tests, 61 00:01:44,280 --> 00:01:45,280 like ImageNet. 62 00:01:45,280 --> 00:01:46,280 Wow, OK. 63 00:01:46,280 --> 00:01:47,600 Now, the paper mentions something called 64 00:01:47,600 --> 00:01:49,480 Native Resolution Fine Tuning. 65 00:01:49,480 --> 00:01:50,320 Right. 66 00:01:50,320 --> 00:01:51,160 Which sounds pretty technical. 67 00:01:51,160 --> 00:01:52,080 Yeah, it sounds fancy. 68 00:01:52,080 --> 00:01:54,080 But basically, it means that AI and V2 69 00:01:54,080 --> 00:01:56,680 can handle images of any size or shape. 70 00:01:56,680 --> 00:02:00,120 It doesn't need them to be perfectly formatted or resized. 71 00:02:00,120 --> 00:02:00,680 Got it. 72 00:02:00,680 --> 00:02:03,640 Which is a big advantage, because in the real world, 73 00:02:03,640 --> 00:02:07,920 images come in all sorts of sizes and resolutions. 74 00:02:07,920 --> 00:02:08,120 Right. 75 00:02:08,120 --> 00:02:09,440 Like, my phone takes a different picture 76 00:02:09,440 --> 00:02:10,200 than a big camera. 77 00:02:10,200 --> 00:02:10,960 Exactly. 78 00:02:10,960 --> 00:02:13,400 So it's more flexible and adaptable to, like, real world 79 00:02:13,400 --> 00:02:14,160 situations. 80 00:02:14,160 --> 00:02:18,200 Yeah, it can work with images in the wild, so to speak. 81 00:02:18,200 --> 00:02:18,760 I like that. 82 00:02:18,760 --> 00:02:21,920 Which is a big step towards making AI more practical 83 00:02:21,920 --> 00:02:22,760 for everyday use. 84 00:02:22,760 --> 00:02:23,680 That's really interesting. 85 00:02:23,680 --> 00:02:25,480 So we've talked about how it learns, 86 00:02:25,480 --> 00:02:28,880 how it compares to other models, but what can it actually do? 87 00:02:28,880 --> 00:02:29,280 Right. 88 00:02:29,280 --> 00:02:32,040 What kinds of tasks can it handle that 89 00:02:32,040 --> 00:02:34,280 involve both images and text? 90 00:02:34,280 --> 00:02:36,080 Well, one of the things it excels at 91 00:02:36,080 --> 00:02:38,280 is answering questions about images. 92 00:02:38,280 --> 00:02:38,560 OK. 93 00:02:38,560 --> 00:02:40,360 So you can show it a picture and ask, like, 94 00:02:40,360 --> 00:02:41,680 what color is the car? 95 00:02:41,680 --> 00:02:44,280 Or how many people are in this photo? 96 00:02:44,280 --> 00:02:46,480 And it can answer pretty accurately. 97 00:02:46,480 --> 00:02:48,760 So it's not just recognizing what's in the image, 98 00:02:48,760 --> 00:02:51,920 but also understanding the context and the relationships 99 00:02:51,920 --> 00:02:52,720 between the elements. 100 00:02:52,720 --> 00:02:53,400 Exactly. 101 00:02:53,400 --> 00:02:56,640 It's going beyond just basic recognition. 102 00:02:56,640 --> 00:02:57,360 That's amazing. 103 00:02:57,360 --> 00:02:59,360 It's starting to understand the meaning. 104 00:02:59,360 --> 00:03:01,120 And it's also pretty good at something 105 00:03:01,120 --> 00:03:03,560 called few-shot learning, which means 106 00:03:03,560 --> 00:03:06,640 it can learn new tasks with very little training data. 107 00:03:06,640 --> 00:03:07,920 So it's a fast learner. 108 00:03:07,920 --> 00:03:08,440 Yeah. 109 00:03:08,440 --> 00:03:09,680 It catches on quickly. 110 00:03:09,680 --> 00:03:10,480 That's impressive. 111 00:03:10,480 --> 00:03:12,520 And, you know, this is all coming out of Apple. 112 00:03:12,520 --> 00:03:12,880 Right. 113 00:03:12,880 --> 00:03:16,520 A company known more for its hardware than its AI research. 114 00:03:16,520 --> 00:03:17,000 Yeah. 115 00:03:17,000 --> 00:03:18,040 You think iPhones? 116 00:03:18,040 --> 00:03:18,840 Exactly. 117 00:03:18,840 --> 00:03:21,560 So it's interesting to see them making such strides 118 00:03:21,560 --> 00:03:22,440 in this field. 119 00:03:22,440 --> 00:03:22,800 Yeah. 120 00:03:22,800 --> 00:03:25,120 It's like Apple is saying, hey, we're not just 121 00:03:25,120 --> 00:03:27,480 about iPhones and Macs anymore. 122 00:03:27,480 --> 00:03:29,320 We're serious about AI, too. 123 00:03:29,320 --> 00:03:30,000 Exactly. 124 00:03:30,000 --> 00:03:32,680 And they're backing it up with some really impressive research. 125 00:03:32,680 --> 00:03:33,120 Yeah. 126 00:03:33,120 --> 00:03:34,600 You know what's really fascinating to me 127 00:03:34,600 --> 00:03:37,480 is that AI and V2's success hinges 128 00:03:37,480 --> 00:03:40,880 on this simple, yet powerful idea, training AI, 129 00:03:40,880 --> 00:03:43,080 to predict what comes next, whether it's 130 00:03:43,080 --> 00:03:45,600 a pixel in an image or a word in a sentence. 131 00:03:45,600 --> 00:03:47,800 So it's not just about the complexity of the model, 132 00:03:47,800 --> 00:03:50,040 but also about the cleverness of the training approach. 133 00:03:50,040 --> 00:03:53,320 Exactly. It's about finding those elegant solutions. 134 00:03:53,320 --> 00:03:56,480 And this approach could have far-reaching implications 135 00:03:56,480 --> 00:03:59,640 for the future of multimodal AI. 136 00:03:59,640 --> 00:04:02,880 It could lead to even more powerful and versatile AI 137 00:04:02,880 --> 00:04:05,480 models that can truly understand and interact 138 00:04:05,480 --> 00:04:06,880 with the world around us. 139 00:04:06,880 --> 00:04:07,380 OK. 140 00:04:07,380 --> 00:04:09,520 So we've talked about the what and the why. 141 00:04:09,520 --> 00:04:11,240 Now, let's get into the how. 142 00:04:11,240 --> 00:04:15,760 Can you walk us through how AI and V2 is actually built 143 00:04:15,760 --> 00:04:16,680 and trained? 144 00:04:16,680 --> 00:04:18,920 What's going on under the hood, so to speak? 145 00:04:18,920 --> 00:04:23,000 So at its core, AI and V2 uses a popular architecture 146 00:04:23,000 --> 00:04:26,520 called the Vision Transformer, or VIT for short. 147 00:04:26,520 --> 00:04:26,760 OK. 148 00:04:26,760 --> 00:04:28,560 So what's a Vision Transformer? 149 00:04:28,560 --> 00:04:29,400 Think of it like this. 150 00:04:29,400 --> 00:04:31,560 A Vision Transformer takes an image 151 00:04:31,560 --> 00:04:34,320 and breaks it down into smaller chunks, 152 00:04:34,320 --> 00:04:36,240 kind of like a jigsaw puzzle. 153 00:04:36,240 --> 00:04:38,960 And it analyzes the relationships between those chunks 154 00:04:38,960 --> 00:04:40,640 to understand the bigger picture. 155 00:04:40,640 --> 00:04:42,880 So it's not just looking at the whole image at once, 156 00:04:42,880 --> 00:04:46,120 but rather dissecting it and analyzing the pieces. 157 00:04:46,120 --> 00:04:46,640 Exactly. 158 00:04:46,640 --> 00:04:48,320 It's breaking it down to understand the whole. 159 00:04:48,320 --> 00:04:50,680 And this approach has proven to be very effective 160 00:04:50,680 --> 00:04:52,440 for image recognition tasks. 161 00:04:52,440 --> 00:04:52,960 Oh, yeah. 162 00:04:52,960 --> 00:04:54,760 It's become very popular in recent years. 163 00:04:54,760 --> 00:04:56,720 So VIT is the foundation. 164 00:04:56,720 --> 00:04:59,560 But what makes AI and V2 special? 165 00:04:59,560 --> 00:05:02,760 What did the researchers add to this basic architecture 166 00:05:02,760 --> 00:05:05,720 to make it so good at handling both images and text? 167 00:05:05,720 --> 00:05:07,720 Well, they incorporated some clever techniques 168 00:05:07,720 --> 00:05:09,720 that have been successful in language modeling. 169 00:05:09,720 --> 00:05:10,120 OK. 170 00:05:10,120 --> 00:05:12,080 One of them is called prefix attention. 171 00:05:12,080 --> 00:05:13,240 Prefix attention? 172 00:05:13,240 --> 00:05:14,000 Yeah. 173 00:05:14,000 --> 00:05:16,040 That sounds intriguing. 174 00:05:16,040 --> 00:05:17,160 What does that do? 175 00:05:17,160 --> 00:05:19,280 Think of it like a spotlight. 176 00:05:19,280 --> 00:05:21,840 Prefix attention helps the model focus 177 00:05:21,840 --> 00:05:24,480 on the most important parts of the input, 178 00:05:24,480 --> 00:05:26,640 whether it's an image or a sentence. 179 00:05:26,640 --> 00:05:26,920 OK. 180 00:05:26,920 --> 00:05:28,960 It helps filter out the noise and highlight 181 00:05:28,960 --> 00:05:30,200 the key information. 182 00:05:30,200 --> 00:05:32,440 So it's like having a built-in attention mechanism. 183 00:05:32,440 --> 00:05:32,880 Exactly. 184 00:05:32,880 --> 00:05:36,680 That helps AI and V2 prioritize what's important. 185 00:05:36,680 --> 00:05:38,680 It's learning what to pay attention to. 186 00:05:38,680 --> 00:05:41,680 And that's crucial when you're dealing with complex data, 187 00:05:41,680 --> 00:05:43,560 like images and texts. 188 00:05:43,560 --> 00:05:45,360 You don't want the model to get bogged down 189 00:05:45,360 --> 00:05:46,480 in irrelevant details. 190 00:05:46,480 --> 00:05:48,880 You want to focus on the stuff that really matters. 191 00:05:48,880 --> 00:05:49,240 Right. 192 00:05:49,240 --> 00:05:51,000 It's about efficiency and accuracy. 193 00:05:51,000 --> 00:05:51,800 Makes sense. 194 00:05:51,800 --> 00:05:53,320 What else did they add to the mix? 195 00:05:53,320 --> 00:05:56,760 They also use techniques called SUGLU and RMSNorm. 196 00:05:56,760 --> 00:05:57,520 Ooh. 197 00:05:57,520 --> 00:05:58,840 Those sound complicated. 198 00:05:58,840 --> 00:06:01,440 They sound fancy, but they basically 199 00:06:01,440 --> 00:06:05,160 help the model learn more complex relationships in the data 200 00:06:05,160 --> 00:06:08,440 and prevent it from jumping to conclusions too quickly. 201 00:06:08,440 --> 00:06:10,760 So it's like adding some safeguards and fine tuning 202 00:06:10,760 --> 00:06:12,040 to the learning process. 203 00:06:12,040 --> 00:06:12,520 Exactly. 204 00:06:12,520 --> 00:06:14,680 To make sure AI and V2 is learning effectively. 205 00:06:14,680 --> 00:06:16,880 It's all about optimizing the training. 206 00:06:16,880 --> 00:06:20,080 And all of this is powered by a massive amount of data, right? 207 00:06:20,080 --> 00:06:20,520 Oh, yeah. 208 00:06:20,520 --> 00:06:22,160 What kind of data are we talking about? 209 00:06:22,160 --> 00:06:25,680 A mix of public data sets and a private data set from Apple. 210 00:06:25,680 --> 00:06:26,200 OK. 211 00:06:26,200 --> 00:06:29,400 They used millions of images and text captions 212 00:06:29,400 --> 00:06:34,520 to train AI and V2, giving it a huge knowledge base to draw from. 213 00:06:34,520 --> 00:06:36,560 So it's not just random images and text 214 00:06:36,560 --> 00:06:37,480 scraped from the internet. 215 00:06:37,480 --> 00:06:37,960 No. 216 00:06:37,960 --> 00:06:39,680 They've carefully curated the data. 217 00:06:39,680 --> 00:06:41,520 They've put a lot of effort into making sure 218 00:06:41,520 --> 00:06:43,080 it's high quality and relevant. 219 00:06:43,080 --> 00:06:45,680 To make sure it's high quality and relevant to the task at hand. 220 00:06:45,680 --> 00:06:46,680 Exactly. 221 00:06:46,680 --> 00:06:50,040 And they even used a technique called synthetic captioning 222 00:06:50,040 --> 00:06:54,280 to create additional captions for the images giving AI and V2 223 00:06:54,280 --> 00:06:55,840 even more examples to learn from. 224 00:06:55,840 --> 00:06:57,840 It's like giving it extra study material. 225 00:06:57,840 --> 00:06:59,920 So they're basically creating artificial captions 226 00:06:59,920 --> 00:07:01,240 to supplement the real ones. 227 00:07:01,240 --> 00:07:02,040 Exactly. 228 00:07:02,040 --> 00:07:02,840 That's pretty clever. 229 00:07:02,840 --> 00:07:05,040 It's a smart way to boost the training data. 230 00:07:05,040 --> 00:07:07,960 And it seems to have paid off AI and V2's performance 231 00:07:07,960 --> 00:07:09,360 is truly impressive. 232 00:07:09,360 --> 00:07:10,120 It really is. 233 00:07:10,120 --> 00:07:13,360 And it points to a bright future for multimodal AI. 234 00:07:13,360 --> 00:07:13,840 Yeah. 235 00:07:13,840 --> 00:07:14,920 This is just the beginning. 236 00:07:14,920 --> 00:07:16,200 This is all very interesting. 237 00:07:16,200 --> 00:07:17,720 But I have a question. 238 00:07:17,720 --> 00:07:20,520 We've talked a lot about how AI and V2 239 00:07:20,520 --> 00:07:23,960 learns from paired image text data, 240 00:07:23,960 --> 00:07:25,840 like images with captions. 241 00:07:25,840 --> 00:07:28,560 But what about situations where you don't have those captions? 242 00:07:28,560 --> 00:07:31,480 Can AI and V2 still understand images on its own? 243 00:07:31,480 --> 00:07:32,560 That's a great question. 244 00:07:32,560 --> 00:07:35,120 And it's something the researchers are exploring. 245 00:07:35,120 --> 00:07:38,080 While AI and V2 relies heavily on paired data 246 00:07:38,080 --> 00:07:39,320 for its initial training. 247 00:07:39,320 --> 00:07:39,840 OK. 248 00:07:39,840 --> 00:07:42,080 They've also shown that it can be adapted to handle 249 00:07:42,080 --> 00:07:44,000 images without captions. 250 00:07:44,000 --> 00:07:46,440 So it's not entirely dependent on having text 251 00:07:46,440 --> 00:07:47,280 to go with the images. 252 00:07:47,280 --> 00:07:47,760 Exactly. 253 00:07:47,760 --> 00:07:50,280 It can still extract meaningful information from images 254 00:07:50,280 --> 00:07:53,120 even without explicit textual descriptions. 255 00:07:53,120 --> 00:07:56,560 So even though it's trained on these image text pairs, 256 00:07:56,560 --> 00:07:58,560 it's not limited to those scenarios. 257 00:07:58,560 --> 00:08:01,560 It can still learn and understand in a more visual way. 258 00:08:01,560 --> 00:08:01,880 Right. 259 00:08:01,880 --> 00:08:04,560 It's building a deeper understanding of the visual world. 260 00:08:04,560 --> 00:08:05,400 That's really interesting. 261 00:08:05,400 --> 00:08:07,160 It opens up a lot of possibilities 262 00:08:07,160 --> 00:08:11,320 for using AI and V2 in situations where paired data is 263 00:08:11,320 --> 00:08:13,760 scarce or difficult to obtain. 264 00:08:13,760 --> 00:08:14,280 That makes sense. 265 00:08:14,280 --> 00:08:16,040 So it's a very promising area of research. 266 00:08:16,040 --> 00:08:16,800 That's really cool. 267 00:08:16,800 --> 00:08:20,480 It's one of the things that makes AI and V2 so exciting. 268 00:08:20,480 --> 00:08:22,760 It's pushing the boundaries of what's 269 00:08:22,760 --> 00:08:27,520 possible with AI showing us that we can teach machines 270 00:08:27,520 --> 00:08:30,760 to understand the world in more nuanced and sophisticated 271 00:08:30,760 --> 00:08:31,320 ways. 272 00:08:31,320 --> 00:08:33,920 And that's what makes this field so fascinating. 273 00:08:33,920 --> 00:08:35,680 It's constantly evolving. 274 00:08:35,680 --> 00:08:38,760 And we're just scratching the surface of what AI can do. 275 00:08:38,760 --> 00:08:40,120 Yeah, we're just getting started. 276 00:08:40,120 --> 00:08:40,800 Exactly. 277 00:08:40,800 --> 00:08:44,400 And it's not just about recognizing what's in a picture. 278 00:08:44,400 --> 00:08:47,360 It's about understanding the relationships between things. 279 00:08:47,360 --> 00:08:49,720 Like it's not just seeing a cat and a dog, 280 00:08:49,720 --> 00:08:51,880 but understanding that the cat is chasing the dog 281 00:08:51,880 --> 00:08:53,400 or that they're playing together. 282 00:08:53,400 --> 00:08:55,080 So it can kind of understand the story. 283 00:08:55,080 --> 00:08:55,560 Exactly. 284 00:08:55,560 --> 00:08:58,360 It's like AI and V2 can grasp the narrative 285 00:08:58,360 --> 00:09:00,200 within the image, which is pretty amazing. 286 00:09:00,200 --> 00:09:01,320 Yeah, that is amazing. 287 00:09:01,320 --> 00:09:03,320 And to test this, the researchers 288 00:09:03,320 --> 00:09:05,880 gave AI and V2 some tricky tasks, 289 00:09:05,880 --> 00:09:08,160 like predicting what would happen next 290 00:09:08,160 --> 00:09:09,440 in a series of pictures. 291 00:09:09,440 --> 00:09:10,320 Oh, that's interesting. 292 00:09:10,320 --> 00:09:13,240 So if you showed it pictures of someone burking a cake, 293 00:09:13,240 --> 00:09:15,120 could it predict that the next step would be 294 00:09:15,120 --> 00:09:16,200 to put the cake in the oven? 295 00:09:16,200 --> 00:09:16,880 Exactly. 296 00:09:16,880 --> 00:09:19,600 And what's remarkable is that AI and V2 297 00:09:19,600 --> 00:09:22,040 showed real promise in these tasks, 298 00:09:22,040 --> 00:09:25,200 even though it wasn't specifically trained for them. 299 00:09:25,200 --> 00:09:26,520 So it's kind of thinking ahead. 300 00:09:26,520 --> 00:09:28,800 Yeah, it's like it's learning to think about images 301 00:09:28,800 --> 00:09:31,000 in a more human-like way. 302 00:09:31,000 --> 00:09:31,760 That's really cool. 303 00:09:31,760 --> 00:09:33,800 It seems like it's not just a one-trick pony. 304 00:09:33,800 --> 00:09:36,160 It's actually learning to think about images more like we do. 305 00:09:36,160 --> 00:09:36,880 Exactly. 306 00:09:36,880 --> 00:09:38,880 And that kind of visual reasoning ability 307 00:09:38,880 --> 00:09:40,480 just opens up so many possibilities 308 00:09:40,480 --> 00:09:42,880 for how AI and V2 could be used. 309 00:09:42,880 --> 00:09:44,760 Well, let's talk about some of those possibilities. 310 00:09:44,760 --> 00:09:46,560 What are some real-world applications 311 00:09:46,560 --> 00:09:49,680 where AI and V2 could really shine? 312 00:09:49,680 --> 00:09:52,680 Well, one area that immediately comes to mind is image search. 313 00:09:52,680 --> 00:09:54,880 Imagine being able to search for images, 314 00:09:54,880 --> 00:09:58,560 not by typing in keywords, but by describing what you see, 315 00:09:58,560 --> 00:10:01,320 like a photo of a sunset over a mountain range 316 00:10:01,320 --> 00:10:02,760 with a lake in the foreground. 317 00:10:02,760 --> 00:10:04,280 Wow, that was so much easier. 318 00:10:04,280 --> 00:10:06,120 Right, so much more intuitive. 319 00:10:06,120 --> 00:10:07,880 Than trying to come up with the perfect keywords. 320 00:10:07,880 --> 00:10:09,000 Exactly. 321 00:10:09,000 --> 00:10:11,920 AI and V2 could totally revolutionize the way we 322 00:10:11,920 --> 00:10:14,640 search for and discover visual content. 323 00:10:14,640 --> 00:10:15,560 That would be huge. 324 00:10:15,560 --> 00:10:17,920 And another area is in content creation. 325 00:10:17,920 --> 00:10:21,080 Like automatically generating captions for images and videos. 326 00:10:21,080 --> 00:10:23,000 Yes, exactly. 327 00:10:23,000 --> 00:10:25,400 That could make content more accessible to people 328 00:10:25,400 --> 00:10:26,680 with visual impairments. 329 00:10:26,680 --> 00:10:27,640 Oh, that's a good point. 330 00:10:27,640 --> 00:10:30,320 And also help with things like organizing and managing 331 00:10:30,320 --> 00:10:33,240 huge libraries of images and videos. 332 00:10:33,240 --> 00:10:34,160 Yeah, that makes sense. 333 00:10:34,160 --> 00:10:35,600 It's a huge time saver. 334 00:10:35,600 --> 00:10:37,640 What about other creative applications? 335 00:10:37,640 --> 00:10:41,400 Could AI and V2 be used to generate art or write 336 00:10:41,400 --> 00:10:43,320 stories based on images? 337 00:10:43,320 --> 00:10:44,440 It's definitely possible. 338 00:10:44,440 --> 00:10:47,920 We're already seeing AI being used to create art and music. 339 00:10:47,920 --> 00:10:50,800 And AI and V2's ability to understand the relationship 340 00:10:50,800 --> 00:10:54,560 between images and text could take things to a whole new level. 341 00:10:54,560 --> 00:10:55,320 That's pretty exciting. 342 00:10:55,320 --> 00:10:55,600 It is. 343 00:10:55,600 --> 00:10:58,200 It's like the fusion of creativity and technology. 344 00:10:58,200 --> 00:11:00,760 What about areas like robotics or self-driving cars? 345 00:11:00,760 --> 00:11:01,760 Oh, absolutely. 346 00:11:01,760 --> 00:11:04,720 In robotics, AI and V2 could help robots better 347 00:11:04,720 --> 00:11:06,880 understand their surroundings, allowing them 348 00:11:06,880 --> 00:11:09,040 to navigate more complex environments 349 00:11:09,040 --> 00:11:11,800 and interact with objects in a more sophisticated way. 350 00:11:11,800 --> 00:11:15,000 So instead of just seeing a chair as an obstacle, 351 00:11:15,000 --> 00:11:19,080 a robot equipped with AI and V2 could understand that it's 352 00:11:19,080 --> 00:11:22,320 a chair and could potentially even use it to sit down 353 00:11:22,320 --> 00:11:23,920 or to reach something high up. 354 00:11:23,920 --> 00:11:24,680 Exactly. 355 00:11:24,680 --> 00:11:27,280 It's like giving robots a deeper understanding of the world. 356 00:11:27,280 --> 00:11:29,000 And in self-driving cars? 357 00:11:29,000 --> 00:11:29,520 Yeah. 358 00:11:29,520 --> 00:11:30,520 Could it help there? 359 00:11:30,520 --> 00:11:31,560 Absolutely. 360 00:11:31,560 --> 00:11:34,080 It could be used to enhance the perception systems, 361 00:11:34,080 --> 00:11:37,200 helping the cars to understand complex traffic situations 362 00:11:37,200 --> 00:11:38,800 and make safer driving decisions. 363 00:11:38,800 --> 00:11:41,120 So it could really have a big impact on safety? 364 00:11:41,120 --> 00:11:41,720 It could. 365 00:11:41,720 --> 00:11:44,600 It has the potential to make our roads much safer. 366 00:11:44,600 --> 00:11:45,440 That's amazing. 367 00:11:45,440 --> 00:11:46,160 Yeah. 368 00:11:46,160 --> 00:11:48,000 But with any powerful technology, 369 00:11:48,000 --> 00:11:50,280 it's important to consider the potential downsides. 370 00:11:50,280 --> 00:11:51,280 Right. 371 00:11:51,280 --> 00:11:53,720 Are there any concerns about how AI and V2 372 00:11:53,720 --> 00:11:55,560 might be used or misused? 373 00:11:55,560 --> 00:11:57,120 One thing that's really important to remember 374 00:11:57,120 --> 00:12:00,080 is that AI and V2, like any AI model, 375 00:12:00,080 --> 00:12:02,280 is only as good as the data it's trained on. 376 00:12:02,280 --> 00:12:06,560 So if the data used to train AI and V2 contains biases, 377 00:12:06,560 --> 00:12:08,880 those biases could be reflected in the model's output. 378 00:12:08,880 --> 00:12:09,520 That's right. 379 00:12:09,520 --> 00:12:11,880 And that could have real world consequences, 380 00:12:11,880 --> 00:12:15,360 especially if AI and V2 is used in applications that 381 00:12:15,360 --> 00:12:17,640 make decisions affecting people's lives. 382 00:12:17,640 --> 00:12:20,400 So it's really crucial to ensure that the data used 383 00:12:20,400 --> 00:12:24,680 to train these models is as unbiased and represented 384 00:12:24,680 --> 00:12:25,440 as a possible. 385 00:12:25,440 --> 00:12:27,000 Absolutely. 386 00:12:27,000 --> 00:12:29,000 We need to be very careful about the data we 387 00:12:29,000 --> 00:12:30,080 feed these models. 388 00:12:30,080 --> 00:12:32,960 And we also need to be mindful of how AI and V2 is used 389 00:12:32,960 --> 00:12:34,200 and who has access to it. 390 00:12:34,200 --> 00:12:34,800 Exactly. 391 00:12:34,800 --> 00:12:38,760 Like any powerful tool, it could potentially be misused. 392 00:12:38,760 --> 00:12:41,400 So it's important to have ethical guidelines and regulations 393 00:12:41,400 --> 00:12:42,000 in place. 394 00:12:42,000 --> 00:12:44,120 So it's not just about the technology itself, 395 00:12:44,120 --> 00:12:45,560 but also about how we use it. 396 00:12:45,560 --> 00:12:46,000 Exactly. 397 00:12:46,000 --> 00:12:47,600 And what safeguards we put in place. 398 00:12:47,600 --> 00:12:50,720 It's about responsible AI development and deployment. 399 00:12:50,720 --> 00:12:52,240 That makes a lot of sense. 400 00:12:52,240 --> 00:12:54,400 It's not just about what AI can do, 401 00:12:54,400 --> 00:12:55,800 but also about what it should do. 402 00:12:55,800 --> 00:12:57,520 We need to make sure it's used for good. 403 00:12:57,520 --> 00:12:58,520 Exactly. 404 00:12:58,520 --> 00:13:01,200 And speaking of advancements, one aspect of AI and V2 405 00:13:01,200 --> 00:13:04,800 that we haven't discussed yet is its potential for scalability. 406 00:13:04,800 --> 00:13:05,720 Right. 407 00:13:05,720 --> 00:13:07,040 What do you mean by scalability? 408 00:13:07,040 --> 00:13:09,400 Well, the researchers found that AI and V2 409 00:13:09,400 --> 00:13:11,800 gets even better as it's trained on more data. 410 00:13:11,800 --> 00:13:13,880 And as the size of the model increases. 411 00:13:13,880 --> 00:13:15,880 So the more data you feed it, the smarter it gets. 412 00:13:15,880 --> 00:13:16,560 Exactly. 413 00:13:16,560 --> 00:13:20,600 And that scalability is what makes AI and V2 so exciting. 414 00:13:20,600 --> 00:13:22,040 It means that it has the potential 415 00:13:22,040 --> 00:13:25,400 to keep learning and improving as we gather more data 416 00:13:25,400 --> 00:13:27,800 and develop more powerful computing resources. 417 00:13:27,800 --> 00:13:30,880 So we're just seeing the tip of the iceberg with AI and V2 418 00:13:30,880 --> 00:13:33,280 as it continues to learn and grow. 419 00:13:33,280 --> 00:13:35,760 Who knows what amazing things it'll be capable of. 420 00:13:35,760 --> 00:13:37,760 It's really mind blowing to think about. 421 00:13:37,760 --> 00:13:38,360 It is. 422 00:13:38,360 --> 00:13:40,480 But as we delve into those possibilities, 423 00:13:40,480 --> 00:13:44,440 it's important to remember that AI and V2 is still a tool. 424 00:13:44,440 --> 00:13:46,440 Its impact on the world will ultimately 425 00:13:46,440 --> 00:13:48,480 depend on how we choose to use it. 426 00:13:48,480 --> 00:13:49,120 That's a good point. 427 00:13:49,120 --> 00:13:50,840 Technology is neutral. 428 00:13:50,840 --> 00:13:53,200 It's up to us to use it wisely and ethically. 429 00:13:53,200 --> 00:13:55,120 And that brings us to another interesting point. 430 00:13:55,120 --> 00:13:57,800 The paper on AI and V2 focuses mainly 431 00:13:57,800 --> 00:13:59,760 on its technical capabilities. 432 00:13:59,760 --> 00:14:02,480 But it doesn't delve deeply into the broader societal 433 00:14:02,480 --> 00:14:03,240 implications. 434 00:14:03,240 --> 00:14:05,640 So it tells us what AI and V2 can do. 435 00:14:05,640 --> 00:14:07,400 But not necessarily what it should do. 436 00:14:07,400 --> 00:14:08,160 Exactly. 437 00:14:08,160 --> 00:14:10,480 And that's a question that goes beyond the scope 438 00:14:10,480 --> 00:14:12,640 of any single research paper. 439 00:14:12,640 --> 00:14:14,600 It's something that we as society 440 00:14:14,600 --> 00:14:17,760 need to grapple with as AI technology becomes 441 00:14:17,760 --> 00:14:19,800 more and more integrated into our lives. 442 00:14:19,800 --> 00:14:21,320 So it's a bigger conversation. 443 00:14:21,320 --> 00:14:21,720 It is. 444 00:14:21,720 --> 00:14:24,040 That needs to involve not just AI experts, 445 00:14:24,040 --> 00:14:27,120 but also ethicists, policymakers, 446 00:14:27,120 --> 00:14:28,120 and the general public. 447 00:14:28,120 --> 00:14:28,800 Absolutely. 448 00:14:28,800 --> 00:14:31,160 We need to have a broad societal discussion 449 00:14:31,160 --> 00:14:32,640 about the future of AI. 450 00:14:32,640 --> 00:14:33,600 That's a great point. 451 00:14:33,600 --> 00:14:35,840 We can't just leave it to the tech companies 452 00:14:35,840 --> 00:14:37,080 to figure this out on their own. 453 00:14:37,080 --> 00:14:37,480 Right. 454 00:14:37,480 --> 00:14:39,120 It needs to be a collaborative effort. 455 00:14:39,120 --> 00:14:39,760 Absolutely. 456 00:14:39,760 --> 00:14:40,160 Yeah. 457 00:14:40,160 --> 00:14:41,920 Yeah, it really highlights the need 458 00:14:41,920 --> 00:14:46,600 for a more holistic approach to AI development, one that 459 00:14:46,600 --> 00:14:48,520 considers not just the technical aspects, 460 00:14:48,520 --> 00:14:51,400 but also the societal and ethical implications. 461 00:14:51,400 --> 00:14:52,040 Absolutely. 462 00:14:52,040 --> 00:14:54,800 And it's not just about putting safeguards in place. 463 00:14:54,800 --> 00:14:57,480 It's about actively shaping the development of AI 464 00:14:57,480 --> 00:15:00,840 in a way that aligns with our values and goals as a society. 465 00:15:00,840 --> 00:15:03,120 So it's not just about what AI can do, 466 00:15:03,120 --> 00:15:04,280 but what AI should do. 467 00:15:04,280 --> 00:15:04,760 Exactly. 468 00:15:04,760 --> 00:15:07,120 We need to be asking ourselves, what kind of future 469 00:15:07,120 --> 00:15:10,200 do we want to create with AI, and then guide its development 470 00:15:10,200 --> 00:15:11,000 accordingly? 471 00:15:11,000 --> 00:15:12,440 That's a really great point. 472 00:15:12,440 --> 00:15:16,280 It's a reminder that we're not just passive observers 473 00:15:16,280 --> 00:15:18,200 in this technological revolution. 474 00:15:18,200 --> 00:15:20,800 We have a role to play in shaping its trajectory. 475 00:15:20,800 --> 00:15:24,640 And one way to do that is by staying informed and engaged. 476 00:15:24,640 --> 00:15:27,880 The more people understand about AI, its potential, 477 00:15:27,880 --> 00:15:30,000 and its limitations, the better equipped 478 00:15:30,000 --> 00:15:31,640 will be to make those decisions. 479 00:15:31,640 --> 00:15:35,120 That's why I think this research on AI and V2 is so important. 480 00:15:35,120 --> 00:15:38,400 It's pushing the boundaries of what's possible with AI 481 00:15:38,400 --> 00:15:40,440 and forcing us to think about the implications. 482 00:15:40,440 --> 00:15:42,480 And it's sparking some really interesting discussions 483 00:15:42,480 --> 00:15:43,640 in the AI community. 484 00:15:43,640 --> 00:15:44,160 Oh, yeah. 485 00:15:44,160 --> 00:15:46,280 People are excited about the potential 486 00:15:46,280 --> 00:15:49,480 of multimodal models like AI and V2, 487 00:15:49,480 --> 00:15:52,760 but also cautious about the potential risks. 488 00:15:52,760 --> 00:15:55,320 Yeah, it's good to have that balance of excitement and caution. 489 00:15:55,320 --> 00:15:56,440 Right. 490 00:15:56,440 --> 00:15:59,560 We need both to navigate this new landscape responsibly. 491 00:15:59,560 --> 00:16:00,160 Absolutely. 492 00:16:00,160 --> 00:16:02,400 And as we continue to explore this landscape, 493 00:16:02,400 --> 00:16:03,600 I think it's important to remember 494 00:16:03,600 --> 00:16:06,560 that AI is still in its early stages of development. 495 00:16:06,560 --> 00:16:06,840 Right. 496 00:16:06,840 --> 00:16:07,800 We're just getting started. 497 00:16:07,800 --> 00:16:10,640 We're just starting to scratch the surface of what's possible. 498 00:16:10,640 --> 00:16:12,720 So what we're seeing with AI and V2 499 00:16:12,720 --> 00:16:15,840 is just a glimpse of what the future of AI might hold. 500 00:16:15,840 --> 00:16:16,360 Exactly. 501 00:16:16,360 --> 00:16:20,440 Imagine a world where AI can not only understand images and text, 502 00:16:20,440 --> 00:16:24,320 but also generate new content, combining those modalities 503 00:16:24,320 --> 00:16:25,920 in creative and innovative ways. 504 00:16:25,920 --> 00:16:29,280 So like an AI that could write a song based on a painting 505 00:16:29,280 --> 00:16:31,520 or create a 3D model from a sketch. 506 00:16:31,520 --> 00:16:32,440 Precisely. 507 00:16:32,440 --> 00:16:34,800 The possibilities are pretty mind-boggling. 508 00:16:34,800 --> 00:16:36,160 Yeah, it is kind of mind-boggling. 509 00:16:36,160 --> 00:16:39,520 It's an exciting time to be following the world of AI. 510 00:16:39,520 --> 00:16:43,200 We're witnessing the births of a new era of intelligent machines 511 00:16:43,200 --> 00:16:45,240 capable of things we never thought possible. 512 00:16:45,240 --> 00:16:47,200 And as we move forward, it's up to all of us 513 00:16:47,200 --> 00:16:49,800 to ensure that these advancements are used for good 514 00:16:49,800 --> 00:16:51,760 and that they benefit all of humanity. 515 00:16:51,760 --> 00:16:52,760 Well said. 516 00:16:52,760 --> 00:16:56,040 It's been a fascinating deep dive into AI and V2, 517 00:16:56,040 --> 00:16:58,280 a model that's truly pushing the boundaries of what's 518 00:16:58,280 --> 00:16:59,640 possible with AI. 519 00:16:59,640 --> 00:17:02,160 And what's remarkable is that this groundbreaking research 520 00:17:02,160 --> 00:17:04,160 is coming out of Apple, a company known more 521 00:17:04,160 --> 00:17:06,720 for its consumer products than for its contributions 522 00:17:06,720 --> 00:17:08,640 to fundamental AI research. 523 00:17:08,640 --> 00:17:08,840 Right. 524 00:17:08,840 --> 00:17:09,640 You think iPhones? 525 00:17:09,640 --> 00:17:09,920 Exactly. 526 00:17:09,920 --> 00:17:13,520 It's a reminder that innovation can come from unexpected places 527 00:17:13,520 --> 00:17:16,480 and that the field of AI is constantly evolving 528 00:17:16,480 --> 00:17:19,640 with new players emerging and pushing the boundaries of what 529 00:17:19,640 --> 00:17:21,200 we thought was possible. 530 00:17:21,200 --> 00:17:22,840 And speaking of pushing boundaries, 531 00:17:22,840 --> 00:17:25,520 one aspect of AI and V2 that we haven't touched on yet 532 00:17:25,520 --> 00:17:28,640 is its potential for multimodal generation. 533 00:17:28,640 --> 00:17:30,000 Multimodal generation? 534 00:17:30,000 --> 00:17:30,560 Yeah. 535 00:17:30,560 --> 00:17:31,440 That sounds intriguing. 536 00:17:31,440 --> 00:17:32,920 What exactly does that mean? 537 00:17:32,920 --> 00:17:36,240 Well, so far we've mostly talked about AI and V2's ability 538 00:17:36,240 --> 00:17:38,200 to understand images and text. 539 00:17:38,200 --> 00:17:38,560 Right. 540 00:17:38,560 --> 00:17:40,040 But the researchers also suggest 541 00:17:40,040 --> 00:17:42,480 that it could be used to generate new content, 542 00:17:42,480 --> 00:17:45,680 combining both modalities in novel ways. 543 00:17:45,680 --> 00:17:49,240 So it's not just about analyzing existing content, 544 00:17:49,240 --> 00:17:51,440 but also about creating something entirely new. 545 00:17:51,440 --> 00:17:51,960 Exactly. 546 00:17:51,960 --> 00:17:54,720 Blending images and text in ways we haven't even imagined yet. 547 00:17:54,720 --> 00:17:56,920 Taking things to a whole other level. 548 00:17:56,920 --> 00:18:00,360 So imagine being able to give AI and V2 a text prompt, 549 00:18:00,360 --> 00:18:03,800 like create a painting of a futuristic city. 550 00:18:03,800 --> 00:18:06,840 And it could actually generate a unique and visually stunning 551 00:18:06,840 --> 00:18:08,920 image based on that description. 552 00:18:08,920 --> 00:18:09,880 That's the idea. 553 00:18:09,880 --> 00:18:11,800 Or conversely, you could show it an image 554 00:18:11,800 --> 00:18:14,960 and ask it to write a poem or a story inspired by what it sees. 555 00:18:14,960 --> 00:18:16,240 Wow, that's amazing. 556 00:18:16,240 --> 00:18:18,320 It sounds like something straight out of science fiction. 557 00:18:18,320 --> 00:18:21,120 But it's happening right now in research labs around the world. 558 00:18:21,120 --> 00:18:24,880 And as AI and V2 and other similar models continue to evolve, 559 00:18:24,880 --> 00:18:28,200 we can expect to see even more mind-blowing applications 560 00:18:28,200 --> 00:18:30,920 emerge, transforming industries, and challenging 561 00:18:30,920 --> 00:18:33,080 our understanding of what's possible with AI. 562 00:18:33,080 --> 00:18:34,680 It's an exciting time to be alive. 563 00:18:34,680 --> 00:18:35,360 It really is. 564 00:18:35,360 --> 00:18:39,400 We're witnessing the birth of a new era of intelligent machines 565 00:18:39,400 --> 00:18:42,120 capable of understanding and interacting with the world 566 00:18:42,120 --> 00:18:43,720 in ways we never thought possible. 567 00:18:43,720 --> 00:18:45,640 And as with any powerful technology, 568 00:18:45,640 --> 00:18:48,440 it's crucial that we approach its development and deployment 569 00:18:48,440 --> 00:18:51,640 with both enthusiasm and a healthy dose of caution. 570 00:18:51,640 --> 00:18:53,160 We need that balance, right? 571 00:18:53,160 --> 00:18:53,680 We do. 572 00:18:53,680 --> 00:18:54,520 Absolutely. 573 00:18:54,520 --> 00:18:56,560 We need to ensure that these technologies are used 574 00:18:56,560 --> 00:18:59,920 ethically and responsibly, promoting fairness, transparency, 575 00:18:59,920 --> 00:19:00,760 and accountability. 576 00:19:00,760 --> 00:19:03,240 And we need to have those open and honest conversations 577 00:19:03,240 --> 00:19:06,280 about the potential societal impact of these advancements, 578 00:19:06,280 --> 00:19:08,520 involving not just AI experts, but also 579 00:19:08,520 --> 00:19:11,160 policymakers, ethicists, and the general public. 580 00:19:11,160 --> 00:19:13,840 Yeah, those conversations are so important. 581 00:19:13,840 --> 00:19:17,200 It's clear that AI is not just a technological revolution, 582 00:19:17,200 --> 00:19:19,640 but also a societal one. 583 00:19:19,640 --> 00:19:22,720 And it's up to all of us to shape its trajectory 584 00:19:22,720 --> 00:19:25,280 and ensure that it leads to a better future for all. 585 00:19:25,280 --> 00:19:26,400 Well said. 586 00:19:26,400 --> 00:19:27,920 And as we racked up this deep dive, 587 00:19:27,920 --> 00:19:29,680 I want to encourage all of you listening 588 00:19:29,680 --> 00:19:33,760 to continue exploring the fascinating world of AI 589 00:19:33,760 --> 00:19:35,920 and to engage in these thoughtful discussions about its 590 00:19:35,920 --> 00:19:38,200 potential and its implications. 591 00:19:38,200 --> 00:19:40,800 The future of AI is being written right now. 592 00:19:40,800 --> 00:19:41,160 It is. 593 00:19:41,160 --> 00:19:42,400 And your voice matters. 594 00:19:42,400 --> 00:19:42,920 Absolutely. 595 00:19:42,920 --> 00:19:43,560 Get involved. 596 00:19:43,560 --> 00:19:44,320 Stay informed. 597 00:19:44,320 --> 00:19:46,400 That's a great message to end on. 598 00:19:46,400 --> 00:19:48,040 Thank you for joining us on this deep dive 599 00:19:48,040 --> 00:19:51,920 into the world of AIMV2 and multimodal AI. 600 00:19:51,920 --> 00:19:54,280 We hope you found it informative and thought provoking, 601 00:19:54,280 --> 00:19:56,760 and perhaps even a little bit mind blowing. 602 00:19:56,760 --> 00:19:59,120 Until next time, keep exploring, keep questioning, 603 00:19:59,120 --> 00:19:59,920 and keep learning. 604 00:19:59,920 --> 00:20:08,560 See you next time.