1 00:00:00,000 --> 00:00:02,180 Welcome back everyone for another deep dive. 2 00:00:02,180 --> 00:00:04,180 You guys have been digging through a ton of stuff 3 00:00:04,180 --> 00:00:06,120 on OpenAI's O3 model. 4 00:00:06,120 --> 00:00:07,060 Definitely making waves. 5 00:00:07,060 --> 00:00:10,180 Yeah, and we are here to help you synthesize it all 6 00:00:10,180 --> 00:00:11,020 and break it down. 7 00:00:11,020 --> 00:00:11,940 So- This is absolutely- 8 00:00:11,940 --> 00:00:14,500 What seems to have everyone, especially buzzing, 9 00:00:14,500 --> 00:00:18,860 is O3's performance on the ARC AGI competition. 10 00:00:18,860 --> 00:00:21,580 Kind of an intelligence test for AI. 11 00:00:21,580 --> 00:00:24,260 Yeah, it's kind of like a benchmark, 12 00:00:24,260 --> 00:00:26,580 you know, for how general an AI is. 13 00:00:26,580 --> 00:00:27,700 Like, you know, people always talk about 14 00:00:27,700 --> 00:00:30,820 can AI beat us at chess or can it write code or whatever. 15 00:00:30,820 --> 00:00:32,680 You know, like those are very specific tasks. 16 00:00:32,680 --> 00:00:36,260 This is really about can it learn and adapt 17 00:00:36,260 --> 00:00:38,620 to new problems that it's never seen before. 18 00:00:38,620 --> 00:00:40,140 So when we're talking about ARC AGI, 19 00:00:40,140 --> 00:00:41,580 are we talking about, you know, 20 00:00:41,580 --> 00:00:44,420 robots doing Rubik's cubes or something like that? 21 00:00:44,420 --> 00:00:45,260 Yeah, no, no, no. 22 00:00:45,260 --> 00:00:47,380 So it's all about visual puzzles. 23 00:00:47,380 --> 00:00:50,260 So imagine like you've got these grids with colored squares 24 00:00:50,260 --> 00:00:52,140 and the AI has to kind of figure out like, 25 00:00:52,140 --> 00:00:53,540 what's the pattern, what's the rule, 26 00:00:53,540 --> 00:00:55,180 and complete the puzzle. 27 00:00:55,180 --> 00:00:57,300 But the really interesting thing is that these puzzles 28 00:00:57,300 --> 00:00:59,100 are designed to be solvable using only 29 00:00:59,100 --> 00:01:01,020 what they call core knowledge priors, 30 00:01:01,020 --> 00:01:02,900 which is like the basic stuff that humans 31 00:01:02,900 --> 00:01:04,260 just kind of get about the world. 32 00:01:04,260 --> 00:01:05,660 Like object permanence or something like that. 33 00:01:05,660 --> 00:01:06,500 Exactly. 34 00:01:06,500 --> 00:01:08,380 So it's like, you know, things about like objects 35 00:01:08,380 --> 00:01:10,020 and basic geometry and stuff like that. 36 00:01:10,020 --> 00:01:12,300 So no specialized knowledge needed. 37 00:01:12,300 --> 00:01:14,100 And the idea is to make it a fair comparison 38 00:01:14,100 --> 00:01:16,060 between human and artificial intelligence. 39 00:01:16,060 --> 00:01:16,900 Makes sense. 40 00:01:16,900 --> 00:01:18,220 And so yeah, for years, you know, 41 00:01:18,220 --> 00:01:20,980 AI has struggled to make much headway on these puzzles. 42 00:01:20,980 --> 00:01:24,460 Like previous ARC AGI competitions, 43 00:01:24,460 --> 00:01:26,700 they saw progress kind of incremental 44 00:01:26,700 --> 00:01:29,740 going from like 0% to about 33%. 45 00:01:29,740 --> 00:01:31,700 Over four years, it seemed like 46 00:01:31,700 --> 00:01:34,420 just scaling up existing AI models, 47 00:01:34,420 --> 00:01:36,820 like, you know, just making them bigger wasn't enough. 48 00:01:36,820 --> 00:01:37,660 Right. 49 00:01:37,660 --> 00:01:38,500 And then 03 comes along 50 00:01:38,500 --> 00:01:40,100 and suddenly it's a completely different game. 51 00:01:40,100 --> 00:01:40,940 Exactly. 52 00:01:40,940 --> 00:01:42,620 That's what has everybody so excited. 53 00:01:42,620 --> 00:01:46,020 So what did open AI achieve with this new model? 54 00:01:46,020 --> 00:01:50,300 So they achieved a score of 75.7% success rate 55 00:01:50,300 --> 00:01:54,100 on the ARC AGI pub semi-private evaluation set, 56 00:01:54,100 --> 00:01:55,540 which is a mouthful. 57 00:01:55,540 --> 00:01:57,820 But basically it's a massive leap forward. 58 00:01:57,820 --> 00:01:58,660 Huge. 59 00:01:58,660 --> 00:02:00,340 Compared to the slow progress we've seen before. 60 00:02:00,340 --> 00:02:01,940 It's like, you know, finding that missing piece 61 00:02:01,940 --> 00:02:02,780 of the puzzle. 62 00:02:02,780 --> 00:02:05,620 Something that unlocks this whole new level of capability. 63 00:02:05,620 --> 00:02:08,900 75.7%, like that is a huge jump. 64 00:02:08,900 --> 00:02:12,100 It almost sounds too good to be true. 65 00:02:12,100 --> 00:02:13,940 How are they even testing 03? 66 00:02:13,940 --> 00:02:14,820 I mean, yeah. 67 00:02:14,820 --> 00:02:16,900 So they used two different configurations, 68 00:02:16,900 --> 00:02:19,980 a high efficiency setup using only six samples. 69 00:02:19,980 --> 00:02:23,940 And then a low efficiency setup requiring 2024 samples. 70 00:02:23,940 --> 00:02:26,020 So the high efficiency one shows that 03 71 00:02:26,020 --> 00:02:28,780 can actually learn pretty quickly with minimal data. 72 00:02:28,780 --> 00:02:29,620 Wow. 73 00:02:29,620 --> 00:02:31,940 Whereas the low efficiency one kind of reveals 74 00:02:31,940 --> 00:02:34,420 its potential for even higher accuracy 75 00:02:34,420 --> 00:02:35,860 when given more information. 76 00:02:35,860 --> 00:02:38,500 So I'm guessing more samples, more computing power, 77 00:02:38,500 --> 00:02:39,460 more cost. 78 00:02:39,460 --> 00:02:41,580 Yeah, you got the cost per task range 79 00:02:41,580 --> 00:02:43,380 from like 17 to 20 bucks. 80 00:02:43,380 --> 00:02:45,620 Compared to about five bucks for a human. 81 00:02:45,620 --> 00:02:47,020 But you know, you gotta remember, 82 00:02:47,020 --> 00:02:48,780 computational costs keep dropping. 83 00:02:48,780 --> 00:02:50,300 AI efficiency keeps rising. 84 00:02:50,300 --> 00:02:52,340 So this isn't really a permanent limitation. 85 00:02:52,340 --> 00:02:54,180 But more sign of where we're headed. 86 00:02:54,180 --> 00:02:56,180 Okay, so it sounds like 03's success 87 00:02:56,180 --> 00:02:59,500 isn't just about throwing more computing power at the problem. 88 00:02:59,500 --> 00:03:02,180 Right, it's not just bigger or trained on more data. 89 00:03:02,180 --> 00:03:04,500 It represents like a fundamentally different approach 90 00:03:04,500 --> 00:03:06,340 to AI architecture. 91 00:03:06,340 --> 00:03:10,740 It's about designing AI systems that can learn and adapt 92 00:03:10,740 --> 00:03:14,940 in a way that's more analogous to how human cognition works. 93 00:03:14,940 --> 00:03:17,460 Okay, so let's dive into the technical stuff for a sec. 94 00:03:17,460 --> 00:03:20,740 We all know about LLMs, these large language models, 95 00:03:20,740 --> 00:03:24,660 the brains behind chatbots and text generators and all that. 96 00:03:24,660 --> 00:03:27,740 But it seems like 03 is doing something kind of different. 97 00:03:27,740 --> 00:03:29,580 Yeah, traditional LLMs, they're great 98 00:03:29,580 --> 00:03:31,940 at recognizing patterns and applying what they've learned. 99 00:03:31,940 --> 00:03:35,380 But throw them a curve ball, like a truly new task, 100 00:03:35,380 --> 00:03:37,580 and they often stumble. 101 00:03:37,580 --> 00:03:40,860 03 on the other hand, works more like a massive library 102 00:03:40,860 --> 00:03:42,940 of what they call vector programs. 103 00:03:42,940 --> 00:03:45,500 Think of these as abstract representations 104 00:03:45,500 --> 00:03:49,660 of concepts and procedures that 03 can access and execute. 105 00:03:49,660 --> 00:03:51,700 So it's not just memorizing a bunch of solutions. 106 00:03:51,700 --> 00:03:54,020 Exactly, the key innovation is that it can generate 107 00:03:54,020 --> 00:03:56,420 and execute its own programs at test time. 108 00:03:56,420 --> 00:03:57,380 Hmm, interesting. 109 00:03:57,380 --> 00:03:58,900 So it's like it's thinking on its feet, 110 00:03:58,900 --> 00:04:00,500 it's working through the problem, 111 00:04:00,500 --> 00:04:02,140 coming up with new solutions. 112 00:04:02,140 --> 00:04:02,980 Learning to learn. 113 00:04:02,980 --> 00:04:05,260 It is, that's the really exciting part here. 114 00:04:05,260 --> 00:04:06,900 It's not just applying what it knows, 115 00:04:06,900 --> 00:04:09,740 but actually generating new knowledge 116 00:04:09,740 --> 00:04:11,660 to solve novel problems. 117 00:04:11,660 --> 00:04:14,180 So does that mean 03 is thinking like a human? 118 00:04:14,180 --> 00:04:15,900 Well, that is the million dollar question, isn't it? 119 00:04:15,900 --> 00:04:18,260 To understand how it achieves this, 120 00:04:18,260 --> 00:04:20,420 we need to look at how it combines deep learning 121 00:04:20,420 --> 00:04:22,580 with a powerful search strategy. 122 00:04:22,580 --> 00:04:23,420 Okay. 123 00:04:23,420 --> 00:04:25,340 Like it has this giant toolbox of programs, 124 00:04:25,340 --> 00:04:28,180 and it knows which tool to use for the job, 125 00:04:28,180 --> 00:04:30,780 even if it's never seen that particular job before. 126 00:04:30,780 --> 00:04:32,380 Wow, so it's learning to learn, 127 00:04:32,380 --> 00:04:35,140 this feels like a huge step forward in AI, 128 00:04:35,140 --> 00:04:39,140 but is it really thinking, have we achieved AGI with 03? 129 00:04:39,140 --> 00:04:40,260 That's what we're here to discuss. 130 00:04:40,260 --> 00:04:43,060 I think that's a question that has been debated for decades, 131 00:04:43,060 --> 00:04:45,020 and there's no easy answer. 132 00:04:45,020 --> 00:04:48,340 03's performance on ARC AGI is remarkable, 133 00:04:48,340 --> 00:04:51,180 but AGI is about more than just solving puzzles. 134 00:04:51,180 --> 00:04:53,940 Yeah, I was gonna say, just because an AI can solve 135 00:04:53,940 --> 00:04:56,700 complex puzzles, it doesn't mean it's as intelligent 136 00:04:56,700 --> 00:04:57,860 as a human in every way. 137 00:04:57,860 --> 00:04:58,980 Precisely. 138 00:04:58,980 --> 00:05:00,580 Humans excel at so many things, 139 00:05:00,580 --> 00:05:03,340 language, creativity, social interaction, 140 00:05:03,340 --> 00:05:04,980 understanding emotions. 141 00:05:04,980 --> 00:05:07,980 03 might be a whiz at visual puzzles, 142 00:05:07,980 --> 00:05:09,700 but it still struggles with tasks 143 00:05:09,700 --> 00:05:12,020 that humans find relatively easy. 144 00:05:12,020 --> 00:05:14,060 Like saying someone's a master chess player 145 00:05:14,060 --> 00:05:17,660 doesn't automatically make them a genius at everything. 146 00:05:17,660 --> 00:05:18,860 Right, exactly. 147 00:05:18,860 --> 00:05:20,660 They might be brilliant in that domain, 148 00:05:20,660 --> 00:05:22,540 but struggle with other things. 149 00:05:22,540 --> 00:05:24,140 So what is the secret sauce 150 00:05:24,140 --> 00:05:27,540 behind 03's puzzle solving prowess? 151 00:05:27,540 --> 00:05:29,940 The article mentioned this thing called 152 00:05:29,940 --> 00:05:32,300 Deep Learning Guided Program Search. 153 00:05:32,300 --> 00:05:34,020 Yeah, that's the heart of it. 154 00:05:34,020 --> 00:05:36,820 Instead of just applying pre-learned patterns, 155 00:05:36,820 --> 00:05:40,380 03 actively searches for the best solution 156 00:05:40,380 --> 00:05:42,820 by generating and testing its own programs. 157 00:05:42,820 --> 00:05:44,260 So it's coming up with its own solutions. 158 00:05:44,260 --> 00:05:47,140 Yeah, imagine it like having a giant toolbox, 159 00:05:47,140 --> 00:05:48,380 knowing which tool to use, 160 00:05:48,380 --> 00:05:50,820 even for a task you've never seen before. 161 00:05:50,820 --> 00:05:52,380 Wow, so it's not just memorizing. 162 00:05:52,380 --> 00:05:53,500 It's not just memorization, 163 00:05:53,500 --> 00:05:57,260 it's really about this ability to reason and problem solve. 164 00:05:57,260 --> 00:05:59,180 That sounds like a massive search problem. 165 00:05:59,180 --> 00:06:01,020 How does it do that efficiently? 166 00:06:01,020 --> 00:06:03,100 Well, one way is through what they call 167 00:06:03,100 --> 00:06:05,420 chains of thought or co-teas. 168 00:06:05,420 --> 00:06:07,260 Instead of just spitting out an answer, 169 00:06:07,260 --> 00:06:10,740 03 generates a series of steps, like a thought process. 170 00:06:10,740 --> 00:06:11,580 Interesting. 171 00:06:11,580 --> 00:06:12,620 To arrive at a solution, 172 00:06:12,620 --> 00:06:15,460 it's like the AI is showing its work, 173 00:06:15,460 --> 00:06:17,460 letting us peek into its decision-making. 174 00:06:17,460 --> 00:06:19,180 Oh, so it's not just randomly trying things. 175 00:06:19,180 --> 00:06:22,100 Exactly, it's actually reasoning through the problem. 176 00:06:22,100 --> 00:06:22,940 Okay. 177 00:06:22,940 --> 00:06:25,620 And there's some evidence that it might also be 178 00:06:25,620 --> 00:06:29,180 using something similar to Monte Carlo Tree Search, 179 00:06:29,180 --> 00:06:31,940 which is a technique commonly used in game-playing AI, 180 00:06:31,940 --> 00:06:33,420 like AlphaGo and stuff. 181 00:06:33,420 --> 00:06:35,420 It's a way to efficiently explore 182 00:06:35,420 --> 00:06:37,620 a vast number of possibilities 183 00:06:37,620 --> 00:06:40,300 by focusing on the most promising options. 184 00:06:40,300 --> 00:06:41,140 Gotcha. 185 00:06:41,140 --> 00:06:45,340 And dealing with a massive number of potential solutions, 186 00:06:45,340 --> 00:06:47,980 you need a smart way to narrow down the search. 187 00:06:47,980 --> 00:06:48,820 Exactly. 188 00:06:48,820 --> 00:06:51,900 So it sounds like 03 combines this deep learning 189 00:06:51,900 --> 00:06:54,900 with a really powerful search strategy 190 00:06:54,900 --> 00:06:56,300 to achieve this breakthrough. 191 00:06:56,300 --> 00:06:57,340 That's a great way to put it. 192 00:06:57,340 --> 00:07:00,140 And while we're still unraveling the intricacies 193 00:07:00,140 --> 00:07:05,140 of how 03 works, its success on ARC-AGI is undeniable. 194 00:07:05,380 --> 00:07:06,940 It's a significant step forward 195 00:07:06,940 --> 00:07:08,980 in the pursuit of artificial general intelligence. 196 00:07:08,980 --> 00:07:10,420 But are there any skeptics out there, 197 00:07:10,420 --> 00:07:12,140 people who aren't convinced 198 00:07:12,140 --> 00:07:13,740 that this is really a breakthrough? 199 00:07:13,740 --> 00:07:15,940 Well, there are always skeptics and that's healthy. 200 00:07:15,940 --> 00:07:18,140 We've seen these AI hype cycles come and go, 201 00:07:18,140 --> 00:07:19,860 but 03 is different. 202 00:07:19,860 --> 00:07:22,380 It's not just a faster, bigger version 203 00:07:22,380 --> 00:07:23,740 of what we had before. 204 00:07:23,740 --> 00:07:25,380 It represents a fundamental shift 205 00:07:25,380 --> 00:07:27,420 in how we approach AI development. 206 00:07:27,420 --> 00:07:31,220 It's not just about more data or more computing power, 207 00:07:31,220 --> 00:07:33,620 but about new ideas, new algorithms, 208 00:07:33,620 --> 00:07:35,620 new ways of thinking about AI. 209 00:07:35,620 --> 00:07:36,460 Precisely. 210 00:07:36,460 --> 00:07:38,540 And that's what makes it so exciting. 211 00:07:38,540 --> 00:07:41,340 It's not the finish line, but it's a signpost. 212 00:07:41,340 --> 00:07:44,700 Pointing towards the future where AI can truly learn, 213 00:07:44,700 --> 00:07:47,900 adapt and solve problems in ways we can only imagine today. 214 00:07:47,900 --> 00:07:49,420 So back to the big question, 215 00:07:49,420 --> 00:07:51,940 have we achieved AGI with 03? 216 00:07:51,940 --> 00:07:53,860 Is this the artificial general intelligence 217 00:07:53,860 --> 00:07:55,020 we've been waiting for? 218 00:07:55,020 --> 00:07:56,980 It's a question that's been debated for decades 219 00:07:56,980 --> 00:07:58,700 and there's no easy answer. 220 00:07:58,700 --> 00:08:02,220 03's performance on ARC-AGI is remarkable, 221 00:08:02,220 --> 00:08:05,580 but AGI is about more than just solving puzzles. 222 00:08:05,580 --> 00:08:07,540 It's about having the same breadth and depth 223 00:08:07,540 --> 00:08:09,020 of intelligence as a human. 224 00:08:09,020 --> 00:08:11,420 So things like language creativity, 225 00:08:11,420 --> 00:08:13,860 social intelligence, emotional understanding, 226 00:08:13,860 --> 00:08:15,140 all that kind of stuff that 03 227 00:08:15,140 --> 00:08:16,500 hasn't necessarily mastered yet. 228 00:08:16,500 --> 00:08:17,340 Exactly. 229 00:08:17,340 --> 00:08:21,220 While 03 can solve problems that stump traditional AI, 230 00:08:21,220 --> 00:08:22,900 it's still fall short in areas 231 00:08:22,900 --> 00:08:25,700 that humans find effortless, for example. 232 00:08:25,700 --> 00:08:27,580 It struggles with common sense reasoning 233 00:08:27,580 --> 00:08:29,420 and understanding social nuances. 234 00:08:29,420 --> 00:08:30,660 It's like that old saying, 235 00:08:30,660 --> 00:08:32,700 if it walks like a duck and quacks like a duck, 236 00:08:32,700 --> 00:08:34,060 it must be a duck. 237 00:08:34,060 --> 00:08:36,300 Just because an AI can solve these complex puzzles 238 00:08:36,300 --> 00:08:38,500 doesn't mean it has the full range 239 00:08:38,500 --> 00:08:39,740 of human-like intelligence. 240 00:08:39,740 --> 00:08:42,940 So what's the next step in this AGI journey? 241 00:08:42,940 --> 00:08:44,100 Where do we go from here? 242 00:08:44,100 --> 00:08:45,900 Well, the creators of ARC-AGI 243 00:08:45,900 --> 00:08:48,140 are already developing RSAGI2. 244 00:08:48,140 --> 00:08:49,100 Oh, wow. 245 00:08:49,100 --> 00:08:50,980 A more challenging version of the competition 246 00:08:50,980 --> 00:08:54,620 designed to push the boundaries of AI even further. 247 00:08:54,620 --> 00:08:57,300 Early testing suggests that RSAGI2 248 00:08:57,300 --> 00:09:00,860 will be a significant hurdle for 03 and other AI systems. 249 00:09:00,860 --> 00:09:02,260 So they're raising the bar, 250 00:09:02,260 --> 00:09:04,780 making it even harder for AI to succeed. 251 00:09:04,780 --> 00:09:06,380 So what will that tell us 252 00:09:06,380 --> 00:09:09,100 about the state of artificial general intelligence 253 00:09:09,100 --> 00:09:12,020 if an AI manages to crack ARC-AGI2? 254 00:09:12,020 --> 00:09:14,260 Does that mean we've finally reached AGI? 255 00:09:14,260 --> 00:09:15,900 It would certainly be a major accomplishment 256 00:09:15,900 --> 00:09:18,980 showing that we're making significant progress towards AGI. 257 00:09:18,980 --> 00:09:21,180 But it's important to remember that ARC-AGI, 258 00:09:21,180 --> 00:09:23,620 even in its more challenging form, 259 00:09:23,620 --> 00:09:25,460 is still just one benchmark. 260 00:09:25,460 --> 00:09:28,380 It's like saying, okay, this AI aced the SATs. 261 00:09:28,380 --> 00:09:29,780 So it must be ready for college. 262 00:09:29,780 --> 00:09:30,620 Exactly. 263 00:09:30,620 --> 00:09:32,100 You all know that academic success 264 00:09:32,100 --> 00:09:33,660 is just one piece of the puzzle. 265 00:09:33,660 --> 00:09:34,500 Exactly. 266 00:09:34,500 --> 00:09:37,900 And that comes to being a well-rounded individual. 267 00:09:37,900 --> 00:09:41,060 So what else needs to be done to really assess AGI? 268 00:09:41,060 --> 00:09:43,820 We need a diverse set of benchmarks 269 00:09:43,820 --> 00:09:46,860 that test a wide range of cognitive abilities. 270 00:09:46,860 --> 00:09:50,020 We need to go beyond puzzle solving and challenge AI 271 00:09:50,020 --> 00:09:53,220 to understand language, navigate social situations, 272 00:09:53,220 --> 00:09:55,220 even exhibit creativity. 273 00:09:55,220 --> 00:09:58,540 It's like we need a comprehensive IQ test for AI, 274 00:09:58,540 --> 00:10:01,100 one that measures not just problem solving, 275 00:10:01,100 --> 00:10:03,820 but also the ability to learn, adapt, 276 00:10:03,820 --> 00:10:07,500 and interact with the world in a truly human-like way. 277 00:10:07,500 --> 00:10:10,420 It sounds like ARC-AGI II is gonna be the real test 278 00:10:10,420 --> 00:10:12,260 for these AI systems like O3. 279 00:10:12,260 --> 00:10:14,420 What are some of the specific challenges 280 00:10:14,420 --> 00:10:16,060 that it's expected to pose? 281 00:10:16,060 --> 00:10:17,900 Well, one key area is what they call 282 00:10:17,900 --> 00:10:19,620 compositional generalization, 283 00:10:19,620 --> 00:10:22,140 which is the ability to kind of take pieces of knowledge 284 00:10:22,140 --> 00:10:25,820 and combine them in new ways to solve unfamiliar problems. 285 00:10:25,820 --> 00:10:27,780 Humans do this effortlessly, 286 00:10:27,780 --> 00:10:30,380 but it's a major hurdle for a lot of AI systems. 287 00:10:30,380 --> 00:10:32,980 So if you had a box of Legos, 288 00:10:32,980 --> 00:10:34,380 a human can look at those bricks 289 00:10:34,380 --> 00:10:37,020 and see countless ways to build something new. 290 00:10:37,020 --> 00:10:37,860 Exactly. 291 00:10:37,860 --> 00:10:40,020 But an AI might struggle to go beyond 292 00:10:40,020 --> 00:10:41,900 the instructions in the manual or whatever. 293 00:10:41,900 --> 00:10:43,580 That's a perfect analogy. 294 00:10:43,580 --> 00:10:48,100 ARC-AGI II is designed to test this compositional ability 295 00:10:48,100 --> 00:10:50,220 by presenting puzzles that require the AI 296 00:10:50,220 --> 00:10:53,220 to combine familiar concepts in these unfamiliar ways. 297 00:10:53,220 --> 00:10:54,700 It sounds like a real brain teaser, 298 00:10:54,700 --> 00:10:57,220 even for an advanced AI like O3. 299 00:10:57,220 --> 00:11:00,220 What else makes ARC-AGI II more challenging? 300 00:11:00,220 --> 00:11:01,700 Well, the puzzles themselves 301 00:11:01,700 --> 00:11:05,060 are expected to be much more intricate, 302 00:11:05,060 --> 00:11:08,140 requiring more steps and more abstract reasoning. 303 00:11:08,140 --> 00:11:08,980 Oh, wow. 304 00:11:08,980 --> 00:11:09,820 To solve. 305 00:11:09,820 --> 00:11:12,260 It's not just about making the puzzles harder, 306 00:11:12,260 --> 00:11:14,260 but more conceptually challenging. 307 00:11:14,260 --> 00:11:16,380 Demanding a deeper level of understanding 308 00:11:16,380 --> 00:11:17,220 and problem solving. 309 00:11:17,220 --> 00:11:18,820 It's like we're really testing the limits 310 00:11:18,820 --> 00:11:21,260 of our current understanding of intelligence. 311 00:11:21,260 --> 00:11:22,100 Absolutely. 312 00:11:22,100 --> 00:11:23,060 Both human and artificial. 313 00:11:23,060 --> 00:11:25,100 And that's what makes this whole field so fascinating. 314 00:11:25,100 --> 00:11:27,220 Yeah, by pushing the boundaries of AI, 315 00:11:27,220 --> 00:11:28,900 we're also deepening our understanding 316 00:11:28,900 --> 00:11:30,780 of our own cognitive abilities. 317 00:11:30,780 --> 00:11:33,180 So let's imagine for a moment that O3, 318 00:11:33,180 --> 00:11:38,180 or some future AI, manages to crack ARC-AGI II. 319 00:11:39,380 --> 00:11:40,220 What would that tell us 320 00:11:40,220 --> 00:11:43,180 about the state of artificial general intelligence? 321 00:11:43,180 --> 00:11:45,380 It would certainly be a major accomplishment. 322 00:11:45,380 --> 00:11:46,220 Yeah. 323 00:11:46,220 --> 00:11:48,300 A strong indication that we're on the right track, 324 00:11:48,300 --> 00:11:51,060 but even ARC-AGI II is just one benchmark. 325 00:11:51,060 --> 00:11:51,900 Right. 326 00:11:51,900 --> 00:11:55,340 It's like saying, okay, this AI aced the SATs. 327 00:11:55,340 --> 00:11:56,180 Right. 328 00:11:56,180 --> 00:11:57,140 So it must be ready for college. 329 00:11:57,140 --> 00:11:57,980 Exactly. 330 00:11:57,980 --> 00:12:00,300 And you and I both know that academic success 331 00:12:00,300 --> 00:12:01,740 is just one piece of the puzzle. 332 00:12:01,740 --> 00:12:02,580 Exactly. 333 00:12:02,580 --> 00:12:04,780 When it comes to doing a well-rounded individual. 334 00:12:04,780 --> 00:12:05,620 Right. 335 00:12:05,620 --> 00:12:08,020 AGI is about more than just performing well on a test. 336 00:12:08,020 --> 00:12:08,860 Right. 337 00:12:08,860 --> 00:12:10,300 It's about having that same breadth and depth 338 00:12:10,300 --> 00:12:12,140 of intelligence we see in humans. 339 00:12:12,140 --> 00:12:15,380 So even if an AI can solve these complex puzzles 340 00:12:15,380 --> 00:12:18,380 and even outperform humans on certain tasks, 341 00:12:18,380 --> 00:12:20,340 doesn't necessarily mean that it's achieved 342 00:12:20,340 --> 00:12:22,460 true human-like intelligence. 343 00:12:22,460 --> 00:12:23,300 Exactly. 344 00:12:23,300 --> 00:12:25,140 That's why we need a variety of benchmarks 345 00:12:25,140 --> 00:12:27,780 that test a wide range of cognitive abilities. 346 00:12:27,780 --> 00:12:30,020 We need to challenge AI to understand language, 347 00:12:30,020 --> 00:12:33,580 navigate social situations, even be creative. 348 00:12:33,580 --> 00:12:36,340 It's like we need to create a comprehensive IQ test. 349 00:12:36,340 --> 00:12:37,180 Yeah. 350 00:12:37,180 --> 00:12:39,220 For AI measuring, not just problem solving, 351 00:12:39,220 --> 00:12:42,220 but learning adaptation and interaction with the world 352 00:12:42,220 --> 00:12:43,740 in a truly human-like way. 353 00:12:43,740 --> 00:12:44,860 That's a great way to put it. 354 00:12:44,860 --> 00:12:47,340 And as we develop more sophisticated benchmarks, 355 00:12:47,340 --> 00:12:49,420 we'll get a clearer picture of what it truly means 356 00:12:49,420 --> 00:12:51,860 to create artificial general intelligence. 357 00:12:51,860 --> 00:12:53,740 Well, it seems like we've covered a lot of ground today 358 00:12:53,740 --> 00:12:57,620 from the intricacies of the ARC-AGI competition 359 00:12:57,620 --> 00:13:02,500 to the impressive capabilities of OpenAI's O3 model. 360 00:13:02,500 --> 00:13:04,940 We're living in a time of extraordinary advancements 361 00:13:04,940 --> 00:13:08,340 in AI, and the pursuit of artificial general intelligence 362 00:13:08,340 --> 00:13:10,660 is more captivating than ever. 363 00:13:10,660 --> 00:13:11,300 Absolutely. 364 00:13:11,300 --> 00:13:13,780 And the most exciting part is that this field is constantly 365 00:13:13,780 --> 00:13:14,820 evolving. 366 00:13:14,820 --> 00:13:17,700 What seems groundbreaking today might be commonplace tomorrow 367 00:13:17,700 --> 00:13:19,860 as researchers continue to push the boundaries of what's 368 00:13:19,860 --> 00:13:20,620 possible. 369 00:13:20,620 --> 00:13:22,860 As we wrap up this deep dive, one question lingers. 370 00:13:22,860 --> 00:13:26,060 If O3 can learn to solve complex visual puzzles, 371 00:13:26,060 --> 00:13:28,060 what other domains might be revolutionized 372 00:13:28,060 --> 00:13:30,340 by this type of adaptive AI? 373 00:13:30,340 --> 00:13:32,660 How might it impact feels like scientific discovery 374 00:13:32,660 --> 00:13:33,780 creative arts? 375 00:13:33,780 --> 00:13:35,660 Or even everyday problem solving? 376 00:13:35,660 --> 00:13:38,420 It's a thrilling time to follow these AI developments, 377 00:13:38,420 --> 00:13:40,220 and we encourage you to stay curious. 378 00:13:40,220 --> 00:13:56,780 Who knows what breakthroughs await us just around the corner?