1 00:00:00,000 --> 00:00:02,600 All right, so you send over this fascinating paper. 2 00:00:02,600 --> 00:00:04,240 It's called Long Bench V2. 3 00:00:04,240 --> 00:00:07,200 And it's about testing these large language models, 4 00:00:07,200 --> 00:00:10,600 or LLMs, can they understand complex text? 5 00:00:10,600 --> 00:00:13,200 Yeah, and what's interesting about this paper, 6 00:00:13,200 --> 00:00:15,320 it goes beyond, you know those simple tests 7 00:00:15,320 --> 00:00:18,080 where AI just has to find a specific answer? 8 00:00:18,080 --> 00:00:21,320 Like Long Bench V2, it's designed to be a lot harder. 9 00:00:21,320 --> 00:00:25,880 Like giving these models a graduate level exam, 10 00:00:25,880 --> 00:00:27,640 can they really understand? 11 00:00:27,640 --> 00:00:30,440 So instead of just like skimming a news article, 12 00:00:30,440 --> 00:00:34,840 it's tackling like a legal document, like a dense textbook. 13 00:00:34,840 --> 00:00:38,280 Exactly, and the lengths they're testing are mind blowing. 14 00:00:38,280 --> 00:00:40,680 It's anywhere from 8,000 words, 15 00:00:40,680 --> 00:00:41,520 Wow. 16 00:00:41,520 --> 00:00:43,160 All the way up to 2 million words. 17 00:00:43,160 --> 00:00:45,800 That's longer than like most books out there. 18 00:00:45,800 --> 00:00:47,280 It's longer than most novels, yeah. 19 00:00:47,280 --> 00:00:49,800 Yeah, why is the length of the text so important though? 20 00:00:49,800 --> 00:00:51,160 Well, think about it this way, 21 00:00:51,160 --> 00:00:53,080 reading a short email, one thing. 22 00:00:53,080 --> 00:00:53,920 Right. 23 00:00:53,920 --> 00:00:56,240 Comprehending a full length novel, different level. 24 00:00:56,240 --> 00:00:59,120 You have to keep track of characters, plot twists, 25 00:00:59,120 --> 00:01:01,280 you know, remember things from chapters ago. 26 00:01:01,280 --> 00:01:02,120 Right. 27 00:01:02,120 --> 00:01:04,240 It's a real test of reading comprehension. 28 00:01:04,240 --> 00:01:09,240 So Long Bench V2 is basically seeing if AI can handle 29 00:01:09,760 --> 00:01:13,680 that kind of like, you know, mental marathon. 30 00:01:13,680 --> 00:01:14,920 That's a good way to put it, yeah. 31 00:01:14,920 --> 00:01:16,680 And not just spit back fat. 32 00:01:16,680 --> 00:01:19,400 Exactly, they wanna see if AI can actually learn 33 00:01:19,400 --> 00:01:21,760 and reason from what it reads. 34 00:01:21,760 --> 00:01:23,640 Can it understand the big picture, 35 00:01:23,640 --> 00:01:25,680 not just the individual pieces? 36 00:01:25,680 --> 00:01:28,000 So how did they actually create this benchmark? 37 00:01:28,000 --> 00:01:30,640 Did they just like, you know, lock a bunch of researchers 38 00:01:30,640 --> 00:01:33,520 in a room and force them to write like super long essays? 39 00:01:33,520 --> 00:01:37,960 Not quite, they actually used 97 highly-educated 40 00:01:37,960 --> 00:01:40,800 annotators from like top universities. 41 00:01:40,800 --> 00:01:41,640 97. 42 00:01:41,640 --> 00:01:43,640 And these annotators, they used real documents 43 00:01:43,640 --> 00:01:44,840 they were already familiar with. 44 00:01:44,840 --> 00:01:47,480 So academic papers, legal documents, 45 00:01:47,480 --> 00:01:49,680 financial reports, even detective novels. 46 00:01:49,680 --> 00:01:51,800 Wow, so 97 annotators. 47 00:01:51,800 --> 00:01:52,640 Yeah. 48 00:01:52,640 --> 00:01:54,760 That's a lot of brain power going into it. 49 00:01:54,760 --> 00:01:56,320 Like what were they doing with all these documents? 50 00:01:56,320 --> 00:01:58,160 They were crafting these really challenging 51 00:01:58,160 --> 00:01:59,840 multiple choice questions. 52 00:01:59,840 --> 00:02:00,680 Okay. 53 00:02:00,680 --> 00:02:02,200 To test deep understanding. 54 00:02:02,200 --> 00:02:04,760 And they made sure that these questions weren't too easy, 55 00:02:04,760 --> 00:02:07,680 so they had three different LLMs try them out first. 56 00:02:07,680 --> 00:02:10,200 So they were using AI to make sure the questions 57 00:02:10,200 --> 00:02:11,960 were tough enough for other AI. 58 00:02:11,960 --> 00:02:14,680 Right, and if all three AI models could answer 59 00:02:14,680 --> 00:02:17,560 a question easily, they threw it out. 60 00:02:17,560 --> 00:02:20,760 Then they had human experts try these same questions. 61 00:02:20,760 --> 00:02:21,600 Okay. 62 00:02:21,600 --> 00:02:26,520 And the experts only averaged about 53.7% accuracy. 63 00:02:26,520 --> 00:02:29,000 Wait, so even humans were struggling with these questions? 64 00:02:29,000 --> 00:02:30,200 Yeah, and keep in mind, 65 00:02:30,200 --> 00:02:33,560 they gave the human experts a 15 minute time limit 66 00:02:33,560 --> 00:02:34,720 for each question. 67 00:02:34,720 --> 00:02:35,560 Okay. 68 00:02:35,560 --> 00:02:37,800 So some of these questions probably require like 69 00:02:37,800 --> 00:02:41,280 hours of reading to fully grasp the material. 70 00:02:41,280 --> 00:02:43,440 This benchmark sounds incredibly tough. 71 00:02:43,440 --> 00:02:44,280 It is. 72 00:02:44,280 --> 00:02:46,160 Okay, so beyond just being long, 73 00:02:46,160 --> 00:02:48,200 what makes these tasks so challenging? 74 00:02:48,200 --> 00:02:49,040 Wow. 75 00:02:49,040 --> 00:02:50,000 What kinds of questions are we talking about? 76 00:02:50,000 --> 00:02:51,880 They cover a wide range of tasks 77 00:02:51,880 --> 00:02:54,640 from analyzing financial reports to figuring out 78 00:02:54,640 --> 00:02:56,680 who doon it in a detective novel. 79 00:02:56,680 --> 00:02:59,440 Six main categories with 20 sub tasks. 80 00:02:59,440 --> 00:03:01,400 So it's a really comprehensive test. 81 00:03:01,400 --> 00:03:02,840 Give me some specific examples. 82 00:03:02,840 --> 00:03:05,240 Like what are these AI models up against? 83 00:03:05,240 --> 00:03:07,400 Okay, so one task is like 84 00:03:07,400 --> 00:03:09,640 single document question answering. 85 00:03:10,520 --> 00:03:13,560 So for example, the AI might have to analyze 86 00:03:13,560 --> 00:03:14,960 a financial report, 87 00:03:14,960 --> 00:03:17,880 answer questions about the company's performance. 88 00:03:17,880 --> 00:03:20,600 But it's not just finding a specific number. 89 00:03:20,600 --> 00:03:22,160 It's understanding the trends, 90 00:03:22,160 --> 00:03:23,920 drawing conclusions from the data. 91 00:03:23,920 --> 00:03:26,440 So it's like asking the AI to be a financial analyst. 92 00:03:26,440 --> 00:03:27,280 Right. 93 00:03:27,280 --> 00:03:28,120 That's impressive. 94 00:03:28,120 --> 00:03:30,960 Then there's multi-document question answering 95 00:03:30,960 --> 00:03:33,640 where the AI has to synthesize information 96 00:03:33,640 --> 00:03:36,280 from multiple sources like news articles 97 00:03:36,280 --> 00:03:37,720 or research papers. 98 00:03:37,720 --> 00:03:39,840 For example, it might have to piece together 99 00:03:39,840 --> 00:03:42,400 a timeline of events based on different accounts. 100 00:03:42,400 --> 00:03:44,800 So like a detective piecing together clues 101 00:03:44,800 --> 00:03:46,880 from like, you know, different witnesses. 102 00:03:46,880 --> 00:03:50,040 Exactly. And then there's long in context learning. 103 00:03:50,040 --> 00:03:50,880 Okay. 104 00:03:50,880 --> 00:03:53,160 The AI has to learn from a lengthy document, 105 00:03:53,160 --> 00:03:54,400 like a user manual. 106 00:03:54,400 --> 00:03:55,240 Okay. 107 00:03:55,240 --> 00:03:57,800 And then answer questions about how to use 108 00:03:57,800 --> 00:03:59,840 like a specific device or software. 109 00:03:59,840 --> 00:04:01,840 Okay, so it's not just understanding what it already knows, 110 00:04:01,840 --> 00:04:03,600 but learning new things on the fly. 111 00:04:03,600 --> 00:04:04,440 Exactly. 112 00:04:04,440 --> 00:04:06,480 Okay, so we've got financial reports. 113 00:04:06,480 --> 00:04:09,960 Detective novels, user manuals. 114 00:04:09,960 --> 00:04:10,800 What else? 115 00:04:10,800 --> 00:04:12,760 They even have tasks focused on understanding 116 00:04:12,760 --> 00:04:14,600 long dialogue histories. 117 00:04:14,600 --> 00:04:16,960 So the AI has to follow conversations 118 00:04:16,960 --> 00:04:18,360 between multiple people. 119 00:04:18,360 --> 00:04:19,200 Okay. 120 00:04:19,200 --> 00:04:21,720 And answer questions about like, what went down? 121 00:04:21,720 --> 00:04:23,960 Imagine like trying to keep up with a group chat. 122 00:04:23,960 --> 00:04:24,800 Yeah. 123 00:04:24,800 --> 00:04:25,800 Dozens of messages. 124 00:04:25,800 --> 00:04:27,240 That's what the AI is dealing with. 125 00:04:27,240 --> 00:04:29,320 That's like the AI is eavesdropping 126 00:04:29,320 --> 00:04:31,040 on a really complex conversation. 127 00:04:31,040 --> 00:04:31,880 Right. 128 00:04:31,880 --> 00:04:33,480 Trying to like figure out who said what 129 00:04:33,480 --> 00:04:34,760 and what they really meant. 130 00:04:34,760 --> 00:04:35,600 Uh-huh. 131 00:04:35,600 --> 00:04:37,200 It's almost like testing its social intelligence. 132 00:04:37,200 --> 00:04:38,040 Yeah. 133 00:04:38,040 --> 00:04:38,880 Yeah. 134 00:04:38,880 --> 00:04:39,720 In a way. 135 00:04:39,720 --> 00:04:41,680 And of course there are tasks focused on code. 136 00:04:41,680 --> 00:04:42,520 Yeah. 137 00:04:42,520 --> 00:04:44,880 So the AI has to understand how a piece of code works, 138 00:04:44,880 --> 00:04:46,800 answer questions about its functionality. 139 00:04:46,800 --> 00:04:47,640 Okay. 140 00:04:47,640 --> 00:04:48,800 This is really challenging because the code 141 00:04:48,800 --> 00:04:50,840 could be really dense and abstract. 142 00:04:50,840 --> 00:04:51,680 Okay. 143 00:04:51,680 --> 00:04:53,240 So I've got finance, detective work, 144 00:04:53,240 --> 00:04:56,120 user manuals, conversations, and even code. 145 00:04:56,120 --> 00:04:56,960 Yeah. 146 00:04:56,960 --> 00:05:01,000 This long bench V2 is a real multifaceted challenge. 147 00:05:01,000 --> 00:05:01,840 It is, yeah. 148 00:05:01,840 --> 00:05:03,480 Now for the big question, 149 00:05:03,480 --> 00:05:08,080 how did the AI actually perform against this benchmark? 150 00:05:08,080 --> 00:05:10,040 Well, remember those human experts? 151 00:05:10,040 --> 00:05:10,880 Yeah. 152 00:05:10,880 --> 00:05:13,800 They averaged 53.7%. 153 00:05:13,800 --> 00:05:15,680 The best performing AI model, 154 00:05:15,680 --> 00:05:18,480 they only got 50.1% right. 155 00:05:18,480 --> 00:05:20,600 So not exactly acing the test? 156 00:05:20,600 --> 00:05:22,520 Not quite, but it's important to note 157 00:05:22,520 --> 00:05:23,920 that this model did perform 158 00:05:23,920 --> 00:05:26,040 significantly better than smaller models. 159 00:05:26,040 --> 00:05:26,880 Yeah. 160 00:05:26,880 --> 00:05:28,960 They only achieved around 30% accuracy. 161 00:05:28,960 --> 00:05:29,800 Okay. 162 00:05:29,800 --> 00:05:32,200 So this suggests that size does matter, 163 00:05:32,200 --> 00:05:34,000 at least for these complex tasks. 164 00:05:34,000 --> 00:05:34,840 Makes sense. 165 00:05:34,840 --> 00:05:36,280 Like a bigger brain can hold more information, right? 166 00:05:36,280 --> 00:05:37,600 Right, exactly. 167 00:05:37,600 --> 00:05:38,720 Didn't you mention earlier though 168 00:05:38,720 --> 00:05:41,280 that one model actually scored higher? 169 00:05:41,280 --> 00:05:44,840 Did any of the AI models outperform humans? 170 00:05:44,840 --> 00:05:46,320 Well, this is where it gets interesting. 171 00:05:46,320 --> 00:05:47,160 Okay. 172 00:05:47,160 --> 00:05:51,120 The researchers also tested a technique called O1 Preview, 173 00:05:51,120 --> 00:05:53,400 and it essentially involves giving the AI model 174 00:05:53,400 --> 00:05:57,200 more time to think and reason through the problem. 175 00:05:57,200 --> 00:05:59,000 So instead of just spinning out a quick answer, 176 00:05:59,000 --> 00:06:00,440 they're like, show your work. 177 00:06:00,440 --> 00:06:02,680 Exactly, like giving the AI scratch paper 178 00:06:02,680 --> 00:06:03,800 to work out the problem. 179 00:06:03,800 --> 00:06:05,920 And with this extra reasoning time, 180 00:06:05,920 --> 00:06:10,920 the O1 Preview model actually achieved 57.7% accuracy. 181 00:06:11,320 --> 00:06:12,160 Wow. 182 00:06:12,160 --> 00:06:13,400 Exceeding the human baseline. 183 00:06:13,400 --> 00:06:15,400 So it seems like giving AI a chance 184 00:06:15,400 --> 00:06:17,680 to really think things through, 185 00:06:17,680 --> 00:06:18,920 that makes a big difference. 186 00:06:18,920 --> 00:06:19,760 Yeah. 187 00:06:19,760 --> 00:06:21,160 What does that tell us about 188 00:06:21,160 --> 00:06:23,440 how AI processes information? 189 00:06:23,440 --> 00:06:24,600 Hmm. 190 00:06:24,600 --> 00:06:28,400 Well, it suggests that AI might not be understanding text 191 00:06:28,400 --> 00:06:30,240 in the same way that humans do. 192 00:06:30,240 --> 00:06:31,920 It seems like incorporating more of the 193 00:06:31,920 --> 00:06:34,280 step-by-step reasoning processes 194 00:06:34,280 --> 00:06:37,360 allows them to better grasp the meaning, 195 00:06:37,360 --> 00:06:38,960 come up with better conclusions. 196 00:06:38,960 --> 00:06:39,800 That's fascinating. 197 00:06:39,800 --> 00:06:40,640 Yeah. 198 00:06:40,640 --> 00:06:42,840 So size matters, reasoning helps, 199 00:06:42,840 --> 00:06:46,000 but even the best AI isn't quite human level yet. 200 00:06:46,000 --> 00:06:46,840 Right. 201 00:06:46,840 --> 00:06:47,680 What else did they discover? 202 00:06:47,680 --> 00:06:50,400 Well, one interesting finding was that the AI models 203 00:06:50,400 --> 00:06:53,560 really struggled with understanding long-structured data. 204 00:06:53,560 --> 00:06:55,360 Huh, that's surprising. 205 00:06:55,360 --> 00:06:57,880 I would have thought AI would be great at, like, 206 00:06:57,880 --> 00:07:00,440 you know, crunching numbers, analyzing data. 207 00:07:00,440 --> 00:07:01,400 You'd think so, right? 208 00:07:01,400 --> 00:07:02,240 Yeah. 209 00:07:02,240 --> 00:07:03,360 But they think it's because these models 210 00:07:03,360 --> 00:07:05,800 haven't been trained as much on structured data, 211 00:07:05,800 --> 00:07:08,080 especially in these long-form contexts. 212 00:07:08,080 --> 00:07:08,920 Okay. 213 00:07:08,920 --> 00:07:10,440 They've been exposed to a lot more text. 214 00:07:10,440 --> 00:07:12,480 So it's back to the idea that, like, 215 00:07:12,480 --> 00:07:14,040 their understanding is only as good 216 00:07:14,040 --> 00:07:15,640 as the data they've been fed. 217 00:07:15,640 --> 00:07:17,680 If they haven't seen a lot of structured data. 218 00:07:17,680 --> 00:07:18,520 Exactly. 219 00:07:18,520 --> 00:07:20,000 There's no surprise they struggle with it. 220 00:07:20,000 --> 00:07:22,720 What about giving them more information to work with, though? 221 00:07:22,720 --> 00:07:23,960 They actually tested that. 222 00:07:23,960 --> 00:07:25,240 They tested something called 223 00:07:25,240 --> 00:07:29,120 retrieval augmented generation, or RAGI, for short. 224 00:07:29,120 --> 00:07:32,560 It's basically, like, giving the AI access to a search engine. 225 00:07:32,560 --> 00:07:34,200 Oh, so AI with Google. 226 00:07:34,200 --> 00:07:35,680 Yeah, pretty much. 227 00:07:35,680 --> 00:07:37,080 And the results were interesting. 228 00:07:37,080 --> 00:07:40,800 Some models, like Quinn 2.5, they did improve, 229 00:07:40,800 --> 00:07:42,600 but only up to a certain point. 230 00:07:42,600 --> 00:07:44,680 Oh, so, like, information overload? 231 00:07:44,680 --> 00:07:45,520 Yeah, kind of. 232 00:07:45,520 --> 00:07:46,360 Ref for AI. 233 00:07:46,360 --> 00:07:47,200 It seems so. 234 00:07:47,200 --> 00:07:50,320 However, there was one model, GPT-4O, 235 00:07:50,320 --> 00:07:52,800 that could effectively use these longer, 236 00:07:52,800 --> 00:07:54,440 retrieved contexts. 237 00:07:54,440 --> 00:07:55,280 Wow. 238 00:07:55,280 --> 00:07:57,200 So this suggests that some models are better 239 00:07:57,200 --> 00:07:59,320 at sifting through tons of information 240 00:07:59,320 --> 00:08:00,800 and picking out what's important. 241 00:08:00,800 --> 00:08:03,000 Okay, one last question before we move on. 242 00:08:03,000 --> 00:08:06,320 How do we know that the AI wasn't just, like, 243 00:08:06,320 --> 00:08:08,680 memorizing answers from its training data? 244 00:08:08,680 --> 00:08:10,560 Like, could it be cheating somehow? 245 00:08:10,560 --> 00:08:12,040 Yeah, that's a great question. 246 00:08:12,040 --> 00:08:13,920 And they were careful to address that. 247 00:08:13,920 --> 00:08:15,200 To check for memorization, 248 00:08:15,200 --> 00:08:16,680 they tested the models 249 00:08:16,680 --> 00:08:18,920 without giving them the long text at all. 250 00:08:18,920 --> 00:08:19,760 Right. 251 00:08:19,760 --> 00:08:21,560 Just the questions, the results. 252 00:08:21,560 --> 00:08:22,400 Pretty much random. 253 00:08:22,400 --> 00:08:24,200 Wow, so this is genuine comprehension. 254 00:08:24,200 --> 00:08:26,920 It's not just, like, AI regurgitating facts. 255 00:08:26,920 --> 00:08:27,960 Exactly. 256 00:08:27,960 --> 00:08:30,400 This is what makes Long Bench V2 so valuable. 257 00:08:30,400 --> 00:08:31,760 It's pushing the boundaries 258 00:08:31,760 --> 00:08:34,320 of how we evaluate AI understanding. 259 00:08:34,320 --> 00:08:35,800 It's forcing us to think differently 260 00:08:35,800 --> 00:08:38,360 about how we train and develop these systems. 261 00:08:38,360 --> 00:08:39,720 It sounds like this research is gonna have 262 00:08:39,720 --> 00:08:41,920 a huge impact on the AI field. 263 00:08:41,920 --> 00:08:42,760 Yeah. 264 00:08:42,760 --> 00:08:43,600 Like, it's already... 265 00:08:43,600 --> 00:08:45,440 It's already sparking new ideas and approaches. 266 00:08:45,440 --> 00:08:47,800 Well, I can't wait to, like, 267 00:08:47,800 --> 00:08:50,320 dive deeper into these implications 268 00:08:50,320 --> 00:08:52,480 and explore, like, what the future holds. 269 00:08:52,480 --> 00:08:53,320 Me too. 270 00:08:53,320 --> 00:08:54,360 There's a lot more to unpack here. 271 00:08:54,360 --> 00:08:56,880 It's really fascinating, like, to see, you know, 272 00:08:56,880 --> 00:08:58,440 how these models are being challenged 273 00:08:58,440 --> 00:09:00,040 and where they still need to improve. 274 00:09:00,040 --> 00:09:02,160 So let's talk about the big picture here. 275 00:09:02,160 --> 00:09:07,040 What does Long Bench V2 tell us about the future of AI? 276 00:09:07,040 --> 00:09:09,080 Are we, like, you know, on the verge 277 00:09:09,080 --> 00:09:12,680 of having, like, AI lawyers or novelists? 278 00:09:12,680 --> 00:09:14,080 Well, not quite yet. 279 00:09:14,080 --> 00:09:16,120 While, you know, we are seeing 280 00:09:16,120 --> 00:09:18,680 these incredible advancements in AI, 281 00:09:18,680 --> 00:09:20,680 Long Bench V2, it highlights 282 00:09:20,680 --> 00:09:22,240 that there's still a lot of work to do. 283 00:09:22,240 --> 00:09:25,880 So is it, like, back to the drawing board for researchers, 284 00:09:25,880 --> 00:09:27,400 are we headed in the wrong direction? 285 00:09:27,400 --> 00:09:28,720 No, not at all. 286 00:09:28,720 --> 00:09:32,520 Long Bench V2, it's not about showcasing AI's failures. 287 00:09:32,520 --> 00:09:34,320 It's about providing a roadmap. 288 00:09:34,320 --> 00:09:35,160 Okay. 289 00:09:35,160 --> 00:09:36,480 It gives researchers this new tool 290 00:09:36,480 --> 00:09:39,120 to evaluate their models, identify weak spots, 291 00:09:39,120 --> 00:09:40,600 and develop new techniques. 292 00:09:40,600 --> 00:09:43,120 So instead of a roadblock, it's more like, you know, 293 00:09:43,120 --> 00:09:44,680 a really challenging obstacle course. 294 00:09:44,680 --> 00:09:46,800 It helps push AI development forward. 295 00:09:46,800 --> 00:09:48,120 Exactly, yeah. 296 00:09:48,120 --> 00:09:50,800 And by, like, studying how different models 297 00:09:50,800 --> 00:09:52,960 handle these challenges, we can learn a lot about 298 00:09:52,960 --> 00:09:54,600 how they learn, how they reason, 299 00:09:54,600 --> 00:09:56,880 how they interact with information. 300 00:09:56,880 --> 00:09:59,480 You mentioned earlier that AI models 301 00:09:59,480 --> 00:10:01,520 struggle with structured data. 302 00:10:01,520 --> 00:10:04,320 Like, is that something researchers are, like, 303 00:10:04,320 --> 00:10:05,880 actively trying to address? 304 00:10:05,880 --> 00:10:06,880 Yeah, absolutely. 305 00:10:06,880 --> 00:10:08,960 There's a growing focus on developing AI 306 00:10:08,960 --> 00:10:10,800 that can handle that effectively. 307 00:10:10,800 --> 00:10:14,040 We might see new training methods, specialized architectures, 308 00:10:14,040 --> 00:10:16,280 or even new ways of representing information. 309 00:10:16,280 --> 00:10:17,680 So we're not just talking about 310 00:10:17,680 --> 00:10:19,280 expanding their training data sets. 311 00:10:19,280 --> 00:10:21,880 We're talking about fundamentally changing, 312 00:10:21,880 --> 00:10:24,200 like how AI processes information. 313 00:10:24,200 --> 00:10:27,080 Yeah, it's not just about teaching AI what to think, 314 00:10:27,080 --> 00:10:28,640 but how to think. 315 00:10:28,640 --> 00:10:30,840 And we're learning that the way AI thinks, 316 00:10:30,840 --> 00:10:32,320 it might be fundamentally different 317 00:10:32,320 --> 00:10:33,520 from how humans think. 318 00:10:33,520 --> 00:10:35,080 That's a mind-blowing thought. 319 00:10:35,080 --> 00:10:38,320 And the researchers specifically called for more research 320 00:10:38,320 --> 00:10:42,600 into scaling inference time compute. 321 00:10:42,600 --> 00:10:43,720 What does that even mean? 322 00:10:43,720 --> 00:10:44,640 Good question. 323 00:10:44,640 --> 00:10:48,040 It basically means giving AI more processing power, 324 00:10:48,040 --> 00:10:49,960 more time to think things through. 325 00:10:49,960 --> 00:10:51,840 Right, so, like, you know, 326 00:10:51,840 --> 00:10:53,160 give them a chance to show their work. 327 00:10:53,160 --> 00:10:54,120 Exactly, yeah. 328 00:10:54,120 --> 00:10:55,800 And this has some interesting implications 329 00:10:55,800 --> 00:11:00,320 for the future of, like, AI, hardware, and infrastructure. 330 00:11:00,320 --> 00:11:03,200 If we want to unlock AI's full potential, 331 00:11:03,200 --> 00:11:05,880 we might need to develop specialized processors, 332 00:11:05,880 --> 00:11:09,080 computing systems, designed for these reasoning tasks. 333 00:11:09,080 --> 00:11:10,360 Wow, so this paper, 334 00:11:10,360 --> 00:11:13,600 it's not just impacting, like, you know, the AI algorithms. 335 00:11:13,600 --> 00:11:15,720 It's influencing the development of the hardware 336 00:11:15,720 --> 00:11:16,960 that powers those algorithms. 337 00:11:16,960 --> 00:11:17,520 Right. 338 00:11:17,520 --> 00:11:18,560 It's like a ripple effect. 339 00:11:18,560 --> 00:11:20,320 What about the software side of things? 340 00:11:20,320 --> 00:11:22,080 Yeah, that's another crucial aspect. 341 00:11:22,080 --> 00:11:25,720 As we build AI that can handle massive amounts of data, 342 00:11:25,720 --> 00:11:28,800 we need to develop better tools and interfaces 343 00:11:28,800 --> 00:11:31,280 so that humans can interact with them effectively. 344 00:11:31,280 --> 00:11:33,600 We need to understand how their reasoning 345 00:11:33,600 --> 00:11:36,920 guide their learning and ensure they're used responsibly. 346 00:11:36,920 --> 00:11:38,400 So we need a translator, right? 347 00:11:38,400 --> 00:11:41,000 Like, between human thinking and AI thinking. 348 00:11:41,000 --> 00:11:41,760 Exactly. 349 00:11:41,760 --> 00:11:45,280 And this is where the field of human-computer interaction 350 00:11:45,280 --> 00:11:46,520 becomes important. 351 00:11:46,520 --> 00:11:47,960 This is all incredibly exciting, 352 00:11:47,960 --> 00:11:52,240 but are there any, like, potential downsides, 353 00:11:52,240 --> 00:11:54,280 concerns we should be aware of? 354 00:11:54,280 --> 00:11:56,600 Of course, like with any powerful technology, 355 00:11:56,600 --> 00:11:58,360 there are risks and challenges. 356 00:11:58,360 --> 00:12:01,840 We need to ensure that AI is developed and used ethically 357 00:12:01,840 --> 00:12:04,200 in a way that benefits society as a whole. 358 00:12:04,200 --> 00:12:04,800 Absolutely. 359 00:12:04,800 --> 00:12:08,240 But, like, did the researchers identify any limitations of? 360 00:12:08,240 --> 00:12:10,240 Like, long bench V2 itself? 361 00:12:10,240 --> 00:12:10,800 They did. 362 00:12:10,800 --> 00:12:11,600 They mentioned a few. 363 00:12:11,600 --> 00:12:13,480 One is the size of the benchmark. 364 00:12:13,480 --> 00:12:13,880 Okay. 365 00:12:13,880 --> 00:12:16,200 With only 503 questions, 366 00:12:16,200 --> 00:12:19,080 it's not as comprehensive as some other benchmarks. 367 00:12:19,080 --> 00:12:21,440 The results might be more sensitive to randomness 368 00:12:21,440 --> 00:12:24,280 and may not fully represent AI's capabilities. 369 00:12:24,280 --> 00:12:27,160 So, like, a larger, more diverse benchmark 370 00:12:27,160 --> 00:12:28,880 would give us a more accurate picture. 371 00:12:28,880 --> 00:12:29,560 Exactly. 372 00:12:29,560 --> 00:12:32,240 They also acknowledge that the current data set 373 00:12:32,240 --> 00:12:33,680 is limited to English. 374 00:12:33,680 --> 00:12:36,840 Obviously, this doesn't reflect the global nature of language. 375 00:12:36,840 --> 00:12:39,280 So, expanding to other languages 376 00:12:39,280 --> 00:12:41,880 is a major challenge for future research. 377 00:12:41,880 --> 00:12:42,400 That makes sense. 378 00:12:42,400 --> 00:12:44,480 Like, translating those tasks and making sure 379 00:12:44,480 --> 00:12:45,520 they're equally challenging. 380 00:12:45,520 --> 00:12:46,840 That's no easy feat. 381 00:12:46,840 --> 00:12:49,720 And, you know, language is so nuanced. 382 00:12:49,720 --> 00:12:50,200 Yeah. 383 00:12:50,200 --> 00:12:52,320 Small differences can change the meaning. 384 00:12:52,320 --> 00:12:56,040 So, developing AI that can truly understand 385 00:12:56,040 --> 00:12:58,880 and reason across languages, it's huge. 386 00:12:58,880 --> 00:13:00,920 It's like we're trying to teach AI, 387 00:13:00,920 --> 00:13:03,080 not just different languages, but different ways of thinking. 388 00:13:03,080 --> 00:13:03,760 Exactly. 389 00:13:03,760 --> 00:13:05,720 Okay, we've covered a lot of ground here. 390 00:13:05,720 --> 00:13:07,760 We've talked about the benchmark, the results, 391 00:13:07,760 --> 00:13:09,480 the impact, the limitations. 392 00:13:09,480 --> 00:13:10,920 Is there anything else we should highlight 393 00:13:10,920 --> 00:13:13,120 before we, you know, move on? 394 00:13:13,120 --> 00:13:15,000 I think it's worth emphasizing that 395 00:13:15,000 --> 00:13:18,280 while Longbench V2 focuses on long-form text, 396 00:13:18,280 --> 00:13:21,600 it also has implications for other areas of AI research. 397 00:13:21,600 --> 00:13:21,880 Okay. 398 00:13:21,880 --> 00:13:24,360 For example, it could inform AI assistants 399 00:13:24,360 --> 00:13:26,440 that understand more complex requests 400 00:13:26,440 --> 00:13:28,840 or maybe help us create educational tools. 401 00:13:28,840 --> 00:13:31,400 So, the insights from this research 402 00:13:31,400 --> 00:13:34,560 could, like, ripple out into different applications. 403 00:13:34,560 --> 00:13:35,200 Absolutely. 404 00:13:35,200 --> 00:13:36,120 Yeah, it's all connected. 405 00:13:36,120 --> 00:13:39,160 The ability to process and understand information, 406 00:13:39,160 --> 00:13:42,440 it's fundamental to so many areas. 407 00:13:42,440 --> 00:13:45,600 This has been, like, an incredible deep dive. 408 00:13:45,600 --> 00:13:48,240 I feel like we've only scratched the surface. 409 00:13:48,240 --> 00:13:50,200 It has been a thought-provoking discussion. 410 00:13:50,200 --> 00:13:52,640 And for those who want to explore further, 411 00:13:52,640 --> 00:13:56,200 the researchers have made Longbench V2 publicly available. 412 00:13:56,200 --> 00:13:56,640 That's great. 413 00:13:56,640 --> 00:13:56,880 Yeah. 414 00:13:56,880 --> 00:13:59,080 It's fantastic that they're promoting, like, you know, 415 00:13:59,080 --> 00:14:01,040 open science and encouraging collaboration. 416 00:14:01,040 --> 00:14:02,800 And then science is really important, yeah. 417 00:14:02,800 --> 00:14:05,200 But before we sign off, is there one final thought, 418 00:14:05,200 --> 00:14:08,320 something that, like, wasn't explicitly mentioned in the paper 419 00:14:08,320 --> 00:14:11,440 that you think our listeners should, like, ponder? 420 00:14:11,440 --> 00:14:11,960 Let me see. 421 00:14:11,960 --> 00:14:13,880 You know, we've talked a lot about the technical aspects, 422 00:14:13,880 --> 00:14:15,480 how well it can understand information. 423 00:14:15,480 --> 00:14:17,240 But I think it's important to remember 424 00:14:17,240 --> 00:14:19,800 that AI is still a tool. 425 00:14:19,800 --> 00:14:20,640 OK. 426 00:14:20,640 --> 00:14:22,760 It can be used for incredible things, 427 00:14:22,760 --> 00:14:25,840 but it's only as good as the humans who are developing it, 428 00:14:25,840 --> 00:14:26,680 using it. 429 00:14:26,680 --> 00:14:27,320 That's a great point. 430 00:14:27,320 --> 00:14:32,000 We can't just, like, blindly trust AI to solve all our problems. 431 00:14:32,000 --> 00:14:34,560 We need to be thoughtful about, like, how we design it. 432 00:14:34,560 --> 00:14:34,840 Right. 433 00:14:34,840 --> 00:14:35,560 How we train it. 434 00:14:35,560 --> 00:14:35,920 Yeah. 435 00:14:35,920 --> 00:14:36,680 How we use it. 436 00:14:36,680 --> 00:14:38,760 We need to be aware of its limitations, 437 00:14:38,760 --> 00:14:41,920 its potential biases, its impact on society. 438 00:14:41,920 --> 00:14:42,440 Hmm. 439 00:14:42,440 --> 00:14:44,400 We need to be asking the tough questions 440 00:14:44,400 --> 00:14:47,040 and making sure that AI is aligned with our values 441 00:14:47,040 --> 00:14:47,840 and our goals. 442 00:14:47,840 --> 00:14:48,800 Absolutely. 443 00:14:48,800 --> 00:14:53,120 We have a responsibility to steer AI in the right direction, 444 00:14:53,120 --> 00:14:54,880 use it to make the world a better place. 445 00:14:54,880 --> 00:14:55,440 Well said. 446 00:14:55,440 --> 00:14:57,680 I think that's a perfect note to end on. 447 00:14:57,680 --> 00:15:00,600 This has been a truly mind-expanding deep dive 448 00:15:00,600 --> 00:15:04,280 into, you know, Longbench V2 and the future of AI. 449 00:15:04,280 --> 00:15:04,960 It has been fun. 450 00:15:04,960 --> 00:15:07,080 A huge thanks to you for, like, you know, 451 00:15:07,080 --> 00:15:09,120 sharing your expertise and insights. 452 00:15:09,120 --> 00:15:10,760 It really has been fascinating. 453 00:15:10,760 --> 00:15:12,120 And I think, you know, our listeners 454 00:15:12,120 --> 00:15:15,000 will be buzzing with all these new ideas and questions, too. 455 00:15:15,000 --> 00:15:15,920 I hope so. 456 00:15:15,920 --> 00:15:18,720 And for those, you know, who are eager to kind of dive 457 00:15:18,720 --> 00:15:22,080 even deeper, the researchers made Longbench V2 458 00:15:22,080 --> 00:15:23,320 publicly available. 459 00:15:23,320 --> 00:15:24,320 That's fantastic. 460 00:15:24,320 --> 00:15:26,440 Anyone can, you know, explore the data, 461 00:15:26,440 --> 00:15:28,800 see how different models perform. 462 00:15:28,800 --> 00:15:31,720 This kind of open access, it's so important 463 00:15:31,720 --> 00:15:33,920 because it allows researchers around the world 464 00:15:33,920 --> 00:15:39,400 to collaborate, compare results, and contribute to, like, 465 00:15:39,400 --> 00:15:40,680 you know, the progress 466 00:15:40,680 --> 00:15:41,320 of the field. 467 00:15:41,320 --> 00:15:44,440 So it's not just about, like, one team racing 468 00:15:44,440 --> 00:15:45,400 to the finish line. 469 00:15:45,400 --> 00:15:47,920 It's about a global community working together. 470 00:15:47,920 --> 00:15:48,680 Exactly. 471 00:15:48,680 --> 00:15:51,240 And by sharing these resources, we 472 00:15:51,240 --> 00:15:53,600 can ensure that AI development, it's 473 00:15:53,600 --> 00:15:55,720 guided by ethical considerations, 474 00:15:55,720 --> 00:15:58,480 benefits humanity as a whole, and it doesn't become something 475 00:15:58,480 --> 00:16:01,280 that's just controlled by, like, you know, 476 00:16:01,280 --> 00:16:02,960 a few powerful entities. 477 00:16:02,960 --> 00:16:03,680 That's a great point. 478 00:16:03,680 --> 00:16:06,680 It's about, like, democratizing AI 479 00:16:06,680 --> 00:16:08,400 and making sure that it's used for good. 480 00:16:08,400 --> 00:16:11,240 It benefits everyone, not just, like, a select few. 481 00:16:11,240 --> 00:16:12,440 Right, right. 482 00:16:12,440 --> 00:16:14,600 OK, before we wrap up this deep dive, 483 00:16:14,600 --> 00:16:16,280 is there, like, one final takeaway, 484 00:16:16,280 --> 00:16:18,920 something you think our listeners should really ponder? 485 00:16:18,920 --> 00:16:22,000 Hmm, let me see. 486 00:16:22,000 --> 00:16:24,000 You know, we've talked a lot about, like, 487 00:16:24,000 --> 00:16:26,000 the technical aspects of AI, how well it 488 00:16:26,000 --> 00:16:27,480 can understand information. 489 00:16:27,480 --> 00:16:28,920 But I think it's important to remember 490 00:16:28,920 --> 00:16:30,600 that AI is still a tool. 491 00:16:30,600 --> 00:16:33,640 You know, it can be used for incredible things, 492 00:16:33,640 --> 00:16:36,240 but it's only as good as the humans who are developing it 493 00:16:36,240 --> 00:16:37,000 and using it. 494 00:16:37,000 --> 00:16:37,920 That's a great point. 495 00:16:37,920 --> 00:16:38,400 Yeah. 496 00:16:38,400 --> 00:16:41,600 We can't just, like, blindly trust AI 497 00:16:41,600 --> 00:16:42,960 to solve all our problems. 498 00:16:42,960 --> 00:16:46,440 We need to be thoughtful about, like, how we design it, 499 00:16:46,440 --> 00:16:48,000 how we train it, how we use it. 500 00:16:48,000 --> 00:16:52,840 We need to be aware of the limitations, potential biases, 501 00:16:52,840 --> 00:16:54,320 impact on society. 502 00:16:54,320 --> 00:16:57,320 Right, we need to be asking, like, the tough questions 503 00:16:57,320 --> 00:16:59,760 and making sure that AI is, like, you know, 504 00:16:59,760 --> 00:17:01,280 aligned with our values and our goals. 505 00:17:01,280 --> 00:17:01,960 Absolutely, yeah. 506 00:17:01,960 --> 00:17:05,680 We have a responsibility to steer AI in the right direction, 507 00:17:05,680 --> 00:17:07,800 to use it to make the world a better place. 508 00:17:07,800 --> 00:17:08,300 Well said. 509 00:17:08,300 --> 00:17:10,800 I think that's, like, a perfect note to end on. 510 00:17:10,800 --> 00:17:13,720 This has been a truly mind-expanding deep dive 511 00:17:13,720 --> 00:17:16,200 into the world of Longbench V2 and, you know, 512 00:17:16,200 --> 00:17:17,560 the future of AI. 513 00:17:17,560 --> 00:17:20,200 A huge thank you to you for, like, sharing 514 00:17:20,200 --> 00:17:21,960 your expertise and insights with us. 515 00:17:21,960 --> 00:17:23,960 It's always a pleasure to dive into these topics, 516 00:17:23,960 --> 00:17:26,280 and I can't wait to see what breakthroughs emerge 517 00:17:26,280 --> 00:17:27,480 in the coming years. 518 00:17:27,480 --> 00:17:28,760 To you, our listeners out there. 519 00:17:28,760 --> 00:17:29,960 Keep those brains buzzing. 520 00:17:29,960 --> 00:17:32,120 We'll be back soon with another deep dive 521 00:17:32,120 --> 00:17:34,240 into the latest developments in AI. 522 00:17:34,240 --> 00:17:38,000 Until then, happy learning.