1 00:00:00,000 --> 00:00:06,720 Okay, ready to dive in. Today, we're looking at this paper. Better bench, assessing AI 2 00:00:06,720 --> 00:00:10,160 benchmarks, uncovering issues, and establishing best practices. 3 00:00:10,160 --> 00:00:12,640 Sounds pretty meta, right? Judging the judges. 4 00:00:12,640 --> 00:00:16,360 Exactly. Like, we use benchmarks to see how good AI is. 5 00:00:16,360 --> 00:00:20,320 Right. Like, how well can it translate languages or write code or whatever. 6 00:00:20,320 --> 00:00:25,400 But this paper asks, hold on, are those benchmarks actually any good? 7 00:00:25,400 --> 00:00:29,760 Whoa, yeah. Like, if the test itself is flawed... 8 00:00:29,760 --> 00:00:31,960 And the AI score doesn't mean much, does it? 9 00:00:31,960 --> 00:00:37,720 Not at all. And the better bench folks, they found that, just like AI, benchmarks need 10 00:00:37,720 --> 00:00:39,080 careful design. 11 00:00:39,080 --> 00:00:44,040 Makes sense. So they didn't just complain, they actually made a framework for, like, 12 00:00:44,040 --> 00:00:45,600 judging how good a benchmark is. 13 00:00:45,600 --> 00:00:49,120 Yeah. They looked at the whole life cycle of a benchmark, from, like, when it's first 14 00:00:49,120 --> 00:00:51,960 being designed to when it's finally retired because it's out of date. 15 00:00:51,960 --> 00:00:54,800 Oh, interesting. So what makes a benchmark good? What are they looking for? 16 00:00:54,800 --> 00:00:57,480 They came up with 46 criteria. It's pretty comprehensive. 17 00:00:57,480 --> 00:00:59,720 46? What kind of things are we talking about? 18 00:00:59,720 --> 00:01:04,640 Well, some are pretty basic. Like, does the benchmark clearly define what it's 19 00:01:04,640 --> 00:01:09,000 measuring? Is the code easy to use? Is the documentation good? 20 00:01:09,000 --> 00:01:12,080 Right. So other researchers could reproduce the results? 21 00:01:12,080 --> 00:01:15,400 Exactly. Reproducibility is a huge one. And surprisingly... 22 00:01:15,400 --> 00:01:16,840 A lot of benchmarks don't do well there. 23 00:01:16,840 --> 00:01:21,000 17 out of the 24 they looked at made it difficult to reproduce their results. 24 00:01:21,000 --> 00:01:24,840 Wow. Okay. So red flag right there. What else do they find? 25 00:01:24,840 --> 00:01:29,520 A lot of benchmarks didn't even report if their findings were statistically significant. 26 00:01:29,520 --> 00:01:33,960 It's like, is this AI really 99% accurate? Or is that just a fluke? 27 00:01:33,960 --> 00:01:37,840 Yeah. Exactly. We need that context to really know what the numbers mean. 28 00:01:37,840 --> 00:01:41,680 So this is like a call for higher standards in AI benchmarking? 29 00:01:41,680 --> 00:01:45,240 Definitely. The good news is they also offer solutions. 30 00:01:45,240 --> 00:01:48,760 Like, simple stuff. Like, better documentation. 31 00:01:48,760 --> 00:01:52,000 Sometimes the fix is easier than you think. Exactly. 32 00:01:52,000 --> 00:01:56,400 Now, they didn't just look at general principles. They also evaluated some specific benchmarks, 33 00:01:56,400 --> 00:01:59,480 like, you know, MMLU. Oh yeah, one for large language models. 34 00:01:59,480 --> 00:02:00,560 Yeah. It's pretty popular. 35 00:02:00,560 --> 00:02:03,080 Well, it actually got the lowest score in their assessment. 36 00:02:03,080 --> 00:02:07,960 Oof. That's not good. What about other benchmarks? Any standouts? 37 00:02:07,960 --> 00:02:13,760 GPQA did much better. So, you know, next time you see an AI boasting about its MMLU score... 38 00:02:13,760 --> 00:02:16,080 Me tell you with a grain of salt. Exactly. 39 00:02:16,080 --> 00:02:19,160 It's always good to be a critical consumer of information, 40 00:02:19,160 --> 00:02:22,600 especially in the world of AI where things change so fast. 41 00:02:22,600 --> 00:02:27,880 This is already so interesting. So if these benchmarks are supposed to track AI progress 42 00:02:27,880 --> 00:02:31,720 but aren't always reliable, what does that mean for the field as a whole? 43 00:02:31,720 --> 00:02:36,960 It's a big concern. If we're using these benchmarks to make decisions about which AI gets funded 44 00:02:36,960 --> 00:02:39,920 or which gets deployed... And the benchmarks themselves are flawed. 45 00:02:39,920 --> 00:02:44,000 ...then we could be making bad decisions. So this paper is really a wake-up call. 46 00:02:44,000 --> 00:02:48,360 A call for the AI community to, like, step up their benchmarking game. 47 00:02:48,360 --> 00:02:51,680 Exactly. And they've got this website, BetterBench, to help. 48 00:02:51,680 --> 00:02:54,360 It's like a consumer reports for AI benchmarks. 49 00:02:54,360 --> 00:02:57,760 Oh, cool. So you can look up a benchmark and see how it scores on all these criteria. 50 00:02:57,760 --> 00:02:59,800 Yep. Transparency is key. 51 00:02:59,800 --> 00:03:03,800 And they also encourage users to contribute their own evaluations and feedback. 52 00:03:03,800 --> 00:03:06,720 It's a community effort to improve AI benchmarking. 53 00:03:06,720 --> 00:03:10,360 That's fantastic. It sounds like this paper is really pushing the field forward. 54 00:03:10,360 --> 00:03:14,400 So we know now that not all benchmarks are created equal. 55 00:03:14,400 --> 00:03:18,320 Right. And using a bad benchmark is like using a broken ruler. 56 00:03:18,320 --> 00:03:22,680 It gives you misleading measurements. So how do we design good benchmarks? 57 00:03:22,680 --> 00:03:24,240 What are the key ingredients? 58 00:03:24,240 --> 00:03:26,400 I'm guessing it depends on what you're trying to measure, right? 59 00:03:26,400 --> 00:03:31,600 Like, a benchmark for a chatbot is going to be different from a benchmark for an AI 60 00:03:31,600 --> 00:03:33,560 that diagnoses medical images. 61 00:03:33,560 --> 00:03:38,040 Exactly. It depends on whether you're designing a benchmark for, like, general AI 62 00:03:38,040 --> 00:03:41,320 capabilities or for a very specific task. 63 00:03:41,320 --> 00:03:44,160 A general benchmark might be trying to measure something broad, 64 00:03:44,160 --> 00:03:46,320 like reasoning or problem solving. 65 00:03:46,320 --> 00:03:49,200 While a specific one focuses on a narrow skill, 66 00:03:49,200 --> 00:03:52,520 like how well an AI can translate from French to Mandarin. 67 00:03:52,520 --> 00:03:53,480 Exactly. Yeah. 68 00:03:53,480 --> 00:03:56,560 But the problem is general benchmarks are really hard to create. 69 00:03:56,560 --> 00:03:59,520 Like, how do you even measure something as broad as intelligence? 70 00:03:59,520 --> 00:04:01,600 Right. There's no single definition everyone agrees on. 71 00:04:01,600 --> 00:04:04,400 So are general benchmarks even possible? 72 00:04:04,400 --> 00:04:07,960 Or should we just stick to evaluating AI in very specific areas? 73 00:04:07,960 --> 00:04:10,120 That's a big debate in the field. 74 00:04:10,120 --> 00:04:14,040 Some people think we need general benchmarks if we want to track progress 75 00:04:14,040 --> 00:04:16,320 towards human-like AI. 76 00:04:16,320 --> 00:04:21,600 Because if an AI can't reason or solve problems generally. 77 00:04:21,600 --> 00:04:23,480 It's not really intelligent, is it? 78 00:04:23,480 --> 00:04:28,040 But others argue that for now we should focus on what we can measure reliably. 79 00:04:28,040 --> 00:04:29,640 Specific tasks. 80 00:04:29,640 --> 00:04:32,920 Yeah. It's probably easier to design a good benchmark for, like, 81 00:04:32,920 --> 00:04:35,520 image recognition than for common sense. 82 00:04:35,520 --> 00:04:36,640 Exactly. 83 00:04:36,640 --> 00:04:40,240 And there's another challenge that's becoming more and more relevant. 84 00:04:40,240 --> 00:04:41,880 Benchmark saturation. 85 00:04:41,880 --> 00:04:43,200 Saturation. What does that mean? 86 00:04:43,200 --> 00:04:47,080 AI is improving so rapidly that it's hitting the ceiling of what existing 87 00:04:47,080 --> 00:04:48,840 benchmarks can measure. 88 00:04:48,840 --> 00:04:52,720 A benchmark that was challenging a year ago might be considered easy today. 89 00:04:52,720 --> 00:04:56,200 So we're constantly having to invent new, harder benchmarks. 90 00:04:56,200 --> 00:04:57,440 Yeah. It's like a moving target. 91 00:04:57,440 --> 00:05:00,320 And that's why some researchers are excited about dynamic benchmarks. 92 00:05:00,320 --> 00:05:02,040 Dynamic, meaning they can change. 93 00:05:02,040 --> 00:05:05,760 Exactly. Instead of being fixed, they can adapt to the current state of AI. 94 00:05:05,760 --> 00:05:08,200 Like, they might adjust the difficulty of the tasks. 95 00:05:08,200 --> 00:05:11,040 Or introduce new challenges as AI gets better. 96 00:05:11,040 --> 00:05:13,320 Exactly. It's a pretty new idea, 97 00:05:13,320 --> 00:05:16,000 but it has the potential to solve a lot of these problems. 98 00:05:16,000 --> 00:05:16,760 That's really interesting. 99 00:05:16,760 --> 00:05:20,160 It's like the benchmark is co-evolving with the AI. 100 00:05:20,160 --> 00:05:21,320 Exactly. 101 00:05:21,320 --> 00:05:24,320 It's kind of like an arms race, but in a good way. 102 00:05:24,320 --> 00:05:27,760 We want the benchmarks to keep pushing AI to its limits. 103 00:05:27,760 --> 00:05:31,200 So it's not enough to design a good benchmark. 104 00:05:31,200 --> 00:05:34,040 You also have to think about how to keep it relevant over time. 105 00:05:34,040 --> 00:05:36,000 Yep. It's an ongoing challenge. 106 00:05:36,000 --> 00:05:38,920 But there's another challenge that's even more fundamental. 107 00:05:38,920 --> 00:05:41,200 And that's the issue of contamination. 108 00:05:41,200 --> 00:05:43,120 Contamination. What do you mean by that? 109 00:05:43,120 --> 00:05:47,760 It's when an AI system has been trained on data that's very similar 110 00:05:47,760 --> 00:05:49,160 to the data used in the benchmark. 111 00:05:49,160 --> 00:05:52,200 Oh, so it's like the AI has already seen the test questions? 112 00:05:52,200 --> 00:05:53,120 Kind of. 113 00:05:53,120 --> 00:05:55,280 And that means it might do well on the benchmark, 114 00:05:55,280 --> 00:05:57,400 not because it's genuinely intelligent, 115 00:05:57,400 --> 00:05:59,840 but because it's essentially memorized the answers. 116 00:05:59,840 --> 00:06:01,040 Ah, that makes sense. 117 00:06:01,040 --> 00:06:02,680 So how do we prevent that? 118 00:06:02,680 --> 00:06:05,280 Researchers use techniques like canary strings. 119 00:06:05,280 --> 00:06:08,120 It's like embedding unique identifiers in the data. 120 00:06:08,120 --> 00:06:12,480 So if an AI model later shows signs of these canary strings. 121 00:06:12,480 --> 00:06:15,280 It means it's been trained on the benchmark data. 122 00:06:15,280 --> 00:06:16,840 And the results are invalid. 123 00:06:16,840 --> 00:06:19,040 It's like a cheating detection system for AI. 124 00:06:19,040 --> 00:06:22,680 Exactly. And these techniques are becoming more and more important 125 00:06:22,680 --> 00:06:25,960 as AI models are trained on these massive data sets 126 00:06:25,960 --> 00:06:27,120 scraped from the internet. 127 00:06:27,120 --> 00:06:28,800 Because who knows what's in those data sets? 128 00:06:28,800 --> 00:06:31,920 There could be all sorts of stuff from the benchmarks themselves. 129 00:06:31,920 --> 00:06:35,680 Exactly. So it's like a constant game of cat and mouse 130 00:06:35,680 --> 00:06:40,240 trying to make sure the AI is truly being tested on its own merits, 131 00:06:40,240 --> 00:06:42,680 not just its ability to memorize. 132 00:06:42,680 --> 00:06:45,480 Wow, this is a lot more complex than I realized. 133 00:06:45,480 --> 00:06:47,800 It's not just about coming up with clever tasks. 134 00:06:47,800 --> 00:06:49,760 It's about all these subtle factors 135 00:06:49,760 --> 00:06:51,840 that can affect the validity of the results. 136 00:06:51,840 --> 00:06:52,800 You've got it. 137 00:06:52,800 --> 00:06:56,360 Designing a good AI benchmark is a real art form. 138 00:06:56,360 --> 00:06:59,360 And it requires a deep understanding of both AI 139 00:06:59,360 --> 00:07:01,320 and the scientific process. 140 00:07:01,320 --> 00:07:03,000 This has been so eye-opening already. 141 00:07:03,000 --> 00:07:05,880 I'm thinking about benchmarks and a whole new light. 142 00:07:05,880 --> 00:07:08,280 But we've only scratched the surface, right? 143 00:07:08,280 --> 00:07:09,960 What other challenges and considerations 144 00:07:09,960 --> 00:07:11,240 should we be aware of? 145 00:07:11,240 --> 00:07:13,000 Well, one of the most fundamental ones, 146 00:07:13,000 --> 00:07:15,720 and it's one that philosophers have been debating for centuries, 147 00:07:15,720 --> 00:07:18,040 is the issue of construct validity. 148 00:07:18,040 --> 00:07:20,320 Construct validity remind me what that is again. 149 00:07:20,320 --> 00:07:23,360 It's basically asking, are we actually measuring 150 00:07:23,360 --> 00:07:24,840 what we think we're measuring? 151 00:07:24,840 --> 00:07:25,360 Oh, right. 152 00:07:25,360 --> 00:07:29,800 So a benchmark might claim to measure intelligence. 153 00:07:29,800 --> 00:07:33,080 But how do we know it's not just measuring the AI's ability 154 00:07:33,080 --> 00:07:37,280 to mimic human language instead of actually understanding 155 00:07:37,280 --> 00:07:38,160 the concepts? 156 00:07:38,160 --> 00:07:40,960 Right, like just because an AI can write a grammatically 157 00:07:40,960 --> 00:07:43,320 correct sentence doesn't mean it understands what it's saying. 158 00:07:43,320 --> 00:07:44,440 Exactly. 159 00:07:44,440 --> 00:07:45,800 And this gets really tricky when we're 160 00:07:45,800 --> 00:07:48,680 talking about things like intelligence, or creativity, 161 00:07:48,680 --> 00:07:49,640 or consciousness. 162 00:07:49,640 --> 00:07:51,720 Because there's no scientific definition of those things. 163 00:07:51,720 --> 00:07:54,240 Like we can't even agree on what they mean for humans, 164 00:07:54,240 --> 00:07:55,400 let alone for AI. 165 00:07:55,400 --> 00:07:58,040 So how do we even approach this problem? 166 00:07:58,040 --> 00:08:00,520 If we can't be sure our benchmarks are measuring 167 00:08:00,520 --> 00:08:03,440 the right thing, do the results even mean anything? 168 00:08:03,440 --> 00:08:05,520 Well, I think the key here is transparency 169 00:08:05,520 --> 00:08:06,720 and critical thinking. 170 00:08:06,720 --> 00:08:07,320 Meaning? 171 00:08:07,320 --> 00:08:10,960 We need to be upfront about the limitations of our benchmarks. 172 00:08:10,960 --> 00:08:12,200 So don't pretend they're perfect. 173 00:08:12,200 --> 00:08:12,840 Exactly. 174 00:08:12,840 --> 00:08:15,200 We need to be clear about the assumptions we're making 175 00:08:15,200 --> 00:08:17,920 and acknowledge that there could be biases built 176 00:08:17,920 --> 00:08:19,440 into the benchmark itself. 177 00:08:19,440 --> 00:08:22,520 Because the people designing the benchmarks are human, 178 00:08:22,520 --> 00:08:23,840 and humans have biases. 179 00:08:23,840 --> 00:08:24,640 Exactly. 180 00:08:24,640 --> 00:08:28,360 So it's not about pretending those biases don't exist. 181 00:08:28,360 --> 00:08:31,160 It's about being aware of them and trying to mitigate them 182 00:08:31,160 --> 00:08:32,400 as much as possible. 183 00:08:32,400 --> 00:08:34,440 And also being open to criticism, right? 184 00:08:34,440 --> 00:08:38,360 Like other researchers should be able to scrutinize the benchmark 185 00:08:38,360 --> 00:08:40,080 and point out potential flaws. 186 00:08:40,080 --> 00:08:40,800 Absolutely. 187 00:08:40,800 --> 00:08:42,080 That's how science works. 188 00:08:42,080 --> 00:08:44,760 You put your ideas out there, and you let other people 189 00:08:44,760 --> 00:08:45,800 poke holes in them. 190 00:08:45,800 --> 00:08:47,640 And through that process, hopefully, you 191 00:08:47,640 --> 00:08:49,040 get closer to the truth. 192 00:08:49,040 --> 00:08:49,720 Exactly. 193 00:08:49,720 --> 00:08:53,160 So even though we might never have a perfect benchmark 194 00:08:53,160 --> 00:08:54,800 for something like intelligence, 195 00:08:54,800 --> 00:08:58,640 we can at least try to make our benchmarks as robust and unbiased 196 00:08:58,640 --> 00:08:59,400 as possible. 197 00:08:59,400 --> 00:09:00,440 Exactly. 198 00:09:00,440 --> 00:09:02,640 And that brings us to another challenge, which 199 00:09:02,640 --> 00:09:05,840 is the lack of standardization in benchmark reporting. 200 00:09:05,840 --> 00:09:06,640 Standardization? 201 00:09:06,640 --> 00:09:07,440 What do you mean? 202 00:09:07,440 --> 00:09:10,040 Well, right now, there's no agreed upon format 203 00:09:10,040 --> 00:09:11,920 for reporting benchmark results. 204 00:09:11,920 --> 00:09:13,680 Different researchers might use different metrics, 205 00:09:13,680 --> 00:09:15,680 different data sets, even different definitions 206 00:09:15,680 --> 00:09:17,120 of the task itself. 207 00:09:17,120 --> 00:09:19,040 So it's hard to compare apples to apples. 208 00:09:19,040 --> 00:09:20,040 Exactly. 209 00:09:20,040 --> 00:09:20,920 It's a mess. 210 00:09:20,920 --> 00:09:23,400 And it makes it really difficult to get a clear picture 211 00:09:23,400 --> 00:09:25,840 of how well AI is actually doing. 212 00:09:25,840 --> 00:09:28,920 It sounds like we need some kind of international AI 213 00:09:28,920 --> 00:09:31,560 benchmarking committee to set some standards. 214 00:09:31,560 --> 00:09:34,040 Yeah, some people have suggested that. 215 00:09:34,040 --> 00:09:35,240 But it's tricky. 216 00:09:35,240 --> 00:09:36,040 Because? 217 00:09:36,040 --> 00:09:38,800 Well, on the one hand, standardization 218 00:09:38,800 --> 00:09:41,840 is important if we want to be able to compare results 219 00:09:41,840 --> 00:09:43,280 across different studies. 220 00:09:43,280 --> 00:09:45,920 But on the other hand, we don't want to stifle innovation. 221 00:09:45,920 --> 00:09:48,520 We don't want to create a situation where everyone 222 00:09:48,520 --> 00:09:50,680 is forced to use the same benchmarks, 223 00:09:50,680 --> 00:09:53,200 even if they're not the best tool for the job. 224 00:09:53,200 --> 00:09:55,680 Right, because that could stifle creativity 225 00:09:55,680 --> 00:09:58,760 and prevent new, better benchmarks from emerging. 226 00:09:58,760 --> 00:09:59,520 Exactly. 227 00:09:59,520 --> 00:10:00,720 So it's a balancing act. 228 00:10:00,720 --> 00:10:03,680 Finding a way to promote consistency and comparability 229 00:10:03,680 --> 00:10:06,920 without stifling the creativity and diversity of approaches 230 00:10:06,920 --> 00:10:08,360 in AI evaluation. 231 00:10:08,360 --> 00:10:10,160 Sounds like a tough challenge. 232 00:10:10,160 --> 00:10:12,520 But let's talk about something more optimistic. 233 00:10:12,520 --> 00:10:14,360 Like, remember those dynamic benchmarks 234 00:10:14,360 --> 00:10:16,440 we mentioned earlier, the ones that can adapt 235 00:10:16,440 --> 00:10:17,640 to the current state of AI? 236 00:10:17,640 --> 00:10:18,440 Oh, yeah. 237 00:10:18,440 --> 00:10:19,640 Those are super interesting. 238 00:10:19,640 --> 00:10:22,400 Are those really the future of AI evaluation, 239 00:10:22,400 --> 00:10:23,520 or is that just hype? 240 00:10:23,520 --> 00:10:25,080 I think they have huge potential. 241 00:10:25,080 --> 00:10:26,880 They could address a lot of the challenges we've 242 00:10:26,880 --> 00:10:27,880 been talking about. 243 00:10:27,880 --> 00:10:29,320 Like how? 244 00:10:29,320 --> 00:10:31,040 Well, for one thing, they could help us 245 00:10:31,040 --> 00:10:33,800 deal with that problem of benchmark saturation 246 00:10:33,800 --> 00:10:34,800 we talked about earlier. 247 00:10:34,800 --> 00:10:37,960 Because the benchmark would get harder as the AI gets smarter. 248 00:10:37,960 --> 00:10:38,720 Exactly. 249 00:10:38,720 --> 00:10:41,920 Instead of becoming obsolete, the benchmark 250 00:10:41,920 --> 00:10:44,120 would evolve alongside the AI. 251 00:10:44,120 --> 00:10:46,640 It's like instead of having a fixed target. 252 00:10:46,640 --> 00:10:47,480 It's a moving target. 253 00:10:47,480 --> 00:10:50,840 Right, which makes it much harder for the AI to cheat 254 00:10:50,840 --> 00:10:52,040 or to game the system. 255 00:10:52,040 --> 00:10:54,600 Because the rules of the game are constantly changing. 256 00:10:54,600 --> 00:10:55,360 Exactly. 257 00:10:55,360 --> 00:10:58,000 And another advantage is that dynamic benchmarks 258 00:10:58,000 --> 00:10:59,520 could be more personalized. 259 00:10:59,520 --> 00:11:00,280 Personalized. 260 00:11:00,280 --> 00:11:01,120 How so? 261 00:11:01,120 --> 00:11:03,160 Well, imagine a benchmark that adapts 262 00:11:03,160 --> 00:11:06,360 to the specific strengths and weaknesses of the AI system 263 00:11:06,360 --> 00:11:07,320 being evaluated. 264 00:11:07,320 --> 00:11:08,040 Oh, interesting. 265 00:11:08,040 --> 00:11:10,520 So instead of giving every AI the same test. 266 00:11:10,520 --> 00:11:12,240 You give them a test that's tailored 267 00:11:12,240 --> 00:11:14,400 to their unique capabilities. 268 00:11:14,400 --> 00:11:17,040 That would definitely make the results more meaningful. 269 00:11:17,040 --> 00:11:19,760 But how would you even design a benchmark like that? 270 00:11:19,760 --> 00:11:20,640 That's the challenge. 271 00:11:20,640 --> 00:11:23,760 It's a really active area of research right now. 272 00:11:23,760 --> 00:11:25,760 People are exploring different approaches, 273 00:11:25,760 --> 00:11:28,680 like reinforcement learning or evolutionary algorithms. 274 00:11:28,680 --> 00:11:31,280 So it's like the benchmark itself is a kind of AI. 275 00:11:31,280 --> 00:11:32,720 In a way, yes. 276 00:11:32,720 --> 00:11:34,920 And that makes things even more meta. 277 00:11:34,920 --> 00:11:37,920 Because now we need benchmarks to evaluate the benchmarks. 278 00:11:37,920 --> 00:11:38,640 Exactly. 279 00:11:38,640 --> 00:11:40,240 It's benchmarks all the way down. 280 00:11:40,240 --> 00:11:42,680 OK, I'm officially mind blown. 281 00:11:42,680 --> 00:11:45,160 But seriously, this is really exciting stuff. 282 00:11:45,160 --> 00:11:47,360 It sounds like dynamic benchmarks 283 00:11:47,360 --> 00:11:50,400 could revolutionize how we evaluate AI. 284 00:11:50,400 --> 00:11:51,880 I think so too. 285 00:11:51,880 --> 00:11:54,520 But there's still in the early stages of development. 286 00:11:54,520 --> 00:11:56,240 There's still challenges to overcome. 287 00:11:56,240 --> 00:11:56,880 For sure. 288 00:11:56,880 --> 00:11:59,800 Like one challenge is simply complexity. 289 00:11:59,800 --> 00:12:02,560 Designing a dynamic benchmark that can effectively 290 00:12:02,560 --> 00:12:06,960 adapt to a wide range of AI capabilities is really hard. 291 00:12:06,960 --> 00:12:09,160 It's not so you can just whip up in an afternoon. 292 00:12:09,160 --> 00:12:09,880 Definitely not. 293 00:12:09,880 --> 00:12:12,520 And then there's the challenge of ensuring fairness. 294 00:12:12,520 --> 00:12:13,160 Fairness. 295 00:12:13,160 --> 00:12:16,320 Yeah, like if the benchmark is constantly changing. 296 00:12:16,320 --> 00:12:18,440 How do we make sure that all the AI systems are 297 00:12:18,440 --> 00:12:20,600 being evaluated on a level playing field? 298 00:12:20,600 --> 00:12:21,440 Exactly. 299 00:12:21,440 --> 00:12:23,240 We don't want to create a situation where 300 00:12:23,240 --> 00:12:27,960 some AI systems are unfairly advantaged or disadvantaged 301 00:12:27,960 --> 00:12:30,440 because the benchmark keeps shifting under their feet. 302 00:12:30,440 --> 00:12:30,640 Right. 303 00:12:30,640 --> 00:12:32,160 It's like if you were taking a test 304 00:12:32,160 --> 00:12:33,960 and the questions kept changing while you were trying 305 00:12:33,960 --> 00:12:34,520 to answer them. 306 00:12:34,520 --> 00:12:35,240 Exactly. 307 00:12:35,240 --> 00:12:36,840 That wouldn't be very fair, would it? 308 00:12:36,840 --> 00:12:40,440 So we need to find ways to design dynamic benchmarks that 309 00:12:40,440 --> 00:12:42,360 are both adaptive and fair. 310 00:12:42,360 --> 00:12:45,920 Adaptive to the AI and fair to all the different AI systems 311 00:12:45,920 --> 00:12:46,880 being evaluated. 312 00:12:46,880 --> 00:12:47,840 Exactly. 313 00:12:47,840 --> 00:12:50,880 It's a tough challenge, but it's one that's worth tackling. 314 00:12:50,880 --> 00:12:53,000 Because the potential payoff is huge. 315 00:12:53,000 --> 00:12:53,880 Exactly. 316 00:12:53,880 --> 00:12:55,960 If we can get dynamic benchmarks right, 317 00:12:55,960 --> 00:12:58,160 I think they could really help us accelerate 318 00:12:58,160 --> 00:13:01,640 the progress of AI in a safe and beneficial direction. 319 00:13:01,640 --> 00:13:03,800 So it's not just about measuring AI. 320 00:13:03,800 --> 00:13:05,240 It's about shaping its future. 321 00:13:05,240 --> 00:13:06,000 Exactly. 322 00:13:06,000 --> 00:13:08,640 And that's why this whole field of AI benchmarking 323 00:13:08,640 --> 00:13:09,720 is so important. 324 00:13:09,720 --> 00:13:11,760 It's not just a technical exercise. 325 00:13:11,760 --> 00:13:14,480 It's about values, responsibility, and the kind 326 00:13:14,480 --> 00:13:15,840 of future we want to create. 327 00:13:15,840 --> 00:13:16,640 Well said. 328 00:13:16,640 --> 00:13:19,000 And it's a future that we're all creating together. 329 00:13:19,000 --> 00:13:21,040 This has been an amazing conversation so far. 330 00:13:21,040 --> 00:13:22,240 I'm learning so much. 331 00:13:22,240 --> 00:13:24,640 But I'm curious to hear more about the practical side 332 00:13:24,640 --> 00:13:25,480 of things. 333 00:13:25,480 --> 00:13:27,280 Like, what are some of the challenges 334 00:13:27,280 --> 00:13:29,480 that researchers face when they're actually 335 00:13:29,480 --> 00:13:31,520 trying to build these benchmarks? 336 00:13:31,520 --> 00:13:34,320 Once you've got the theory and the design principles down, 337 00:13:34,320 --> 00:13:37,320 what are the hurdles that you encounter in the real world? 338 00:13:37,320 --> 00:13:39,320 Well, one of the biggest hurdles, 339 00:13:39,320 --> 00:13:41,600 and it's something we've touched on already, 340 00:13:41,600 --> 00:13:43,320 is simply data. 341 00:13:43,320 --> 00:13:43,820 Data. 342 00:13:43,820 --> 00:13:45,520 What kind of data are we talking about here? 343 00:13:45,520 --> 00:13:49,720 Well, AI benchmarks need a lot of data to be effective. 344 00:13:49,720 --> 00:13:50,400 Like for what? 345 00:13:50,400 --> 00:13:51,400 For everything. 346 00:13:51,400 --> 00:13:53,960 They need data to train the AI systems that 347 00:13:53,960 --> 00:13:55,280 are being evaluated. 348 00:13:55,280 --> 00:13:58,760 They need data to evaluate the performance of those systems. 349 00:13:58,760 --> 00:14:02,240 And they even need data to validate the benchmark itself. 350 00:14:02,240 --> 00:14:03,280 Oh, wow. 351 00:14:03,280 --> 00:14:05,520 So it's not just about coming up with clever tasks. 352 00:14:05,520 --> 00:14:08,200 It's also about having the data to support those tasks. 353 00:14:08,200 --> 00:14:08,800 Exactly. 354 00:14:08,800 --> 00:14:12,000 And that data needs to be high quality, relevant, 355 00:14:12,000 --> 00:14:14,520 and representative of the real world. 356 00:14:14,520 --> 00:14:15,960 So it's not just about quantity. 357 00:14:15,960 --> 00:14:17,240 It's about quality, too. 358 00:14:17,240 --> 00:14:17,880 Absolutely. 359 00:14:17,880 --> 00:14:18,560 You know what they say? 360 00:14:18,560 --> 00:14:19,800 Garbage in, garbage out. 361 00:14:19,800 --> 00:14:22,640 Meaning if you train an AI on bad data, 362 00:14:22,640 --> 00:14:24,040 you're going to get bad results. 363 00:14:24,040 --> 00:14:24,680 Exactly. 364 00:14:24,680 --> 00:14:26,400 And the same goes for benchmarks. 365 00:14:26,400 --> 00:14:29,080 If the data used to create the benchmark is flawed, 366 00:14:29,080 --> 00:14:30,520 then the results of the benchmark 367 00:14:30,520 --> 00:14:31,440 are going to be flawed, too. 368 00:14:31,440 --> 00:14:32,680 Precisely. 369 00:14:32,680 --> 00:14:37,360 So where do benchmark developers get all this data from? 370 00:14:37,360 --> 00:14:38,760 Yeah, that's a good question. 371 00:14:38,760 --> 00:14:41,160 I mean, it's not like they can just pull it out of thin air. 372 00:14:41,160 --> 00:14:41,640 Right. 373 00:14:41,640 --> 00:14:43,520 Well, they get it from a variety of sources. 374 00:14:43,520 --> 00:14:45,720 Sometimes they use existing data sets 375 00:14:45,720 --> 00:14:47,000 that are publicly available. 376 00:14:47,000 --> 00:14:49,440 Like data sets that other researchers have collected. 377 00:14:49,440 --> 00:14:50,040 Exactly. 378 00:14:50,040 --> 00:14:51,400 There are tons of those out there. 379 00:14:51,400 --> 00:14:54,760 And reusing those data sets can save a lot of time and effort. 380 00:14:54,760 --> 00:14:55,880 Makes sense. 381 00:14:55,880 --> 00:14:58,680 But what if there's no existing data set that fits their needs? 382 00:14:58,680 --> 00:15:00,640 Then they might have to create their own. 383 00:15:00,640 --> 00:15:01,880 Whoa, really? 384 00:15:01,880 --> 00:15:02,920 Like from scratch? 385 00:15:02,920 --> 00:15:04,600 Sometimes yes. 386 00:15:04,600 --> 00:15:06,400 Especially if they're working on a benchmark 387 00:15:06,400 --> 00:15:08,880 for a very specific task or domain. 388 00:15:08,880 --> 00:15:10,400 That sounds like a lot of work. 389 00:15:10,400 --> 00:15:11,320 It can be. 390 00:15:11,320 --> 00:15:15,160 Data collection and curation are huge parts of AI benchmarking. 391 00:15:15,160 --> 00:15:17,920 So benchmark developers are kind of like data scientists, 392 00:15:17,920 --> 00:15:18,640 too. 393 00:15:18,640 --> 00:15:20,000 In a way, yes. 394 00:15:20,000 --> 00:15:23,000 They need to understand how to collect data, clean it, 395 00:15:23,000 --> 00:15:25,200 label it, and organize it in a way that 396 00:15:25,200 --> 00:15:27,440 makes it usable for AI benchmarks. 397 00:15:27,440 --> 00:15:28,800 It sounds like a whole other skill 398 00:15:28,800 --> 00:15:30,880 set on top of everything else they need to know. 399 00:15:30,880 --> 00:15:31,680 It is. 400 00:15:31,680 --> 00:15:34,080 And it's often the most time-consuming and expensive 401 00:15:34,080 --> 00:15:35,280 part of the process. 402 00:15:35,280 --> 00:15:37,320 Wow, I never realized how much work goes 403 00:15:37,320 --> 00:15:39,480 into creating these benchmarks. 404 00:15:39,480 --> 00:15:42,760 Yeah, it's a lot more than just coming up with clever tasks. 405 00:15:42,760 --> 00:15:43,640 But it's worth it. 406 00:15:43,640 --> 00:15:44,120 Because? 407 00:15:44,120 --> 00:15:46,680 Because data is the foundation of AI. 408 00:15:46,680 --> 00:15:50,120 If we want to build AI systems that can solve real-world problems, 409 00:15:50,120 --> 00:15:52,640 we need to make sure they're trained on high-quality data. 410 00:15:52,640 --> 00:15:53,920 Exactly. 411 00:15:53,920 --> 00:15:56,000 And that means we need high-quality data 412 00:15:56,000 --> 00:15:57,640 for our benchmarks, too. 413 00:15:57,640 --> 00:15:58,920 So it's all connected. 414 00:15:58,920 --> 00:15:59,240 It is. 415 00:15:59,240 --> 00:16:00,480 It all comes back to data. 416 00:16:00,480 --> 00:16:02,320 OK, so let's say we've got the tasks. 417 00:16:02,320 --> 00:16:03,400 We've got the data. 418 00:16:03,400 --> 00:16:04,800 What's next? 419 00:16:04,800 --> 00:16:08,600 How do we actually know that the benchmark is working? 420 00:16:08,600 --> 00:16:10,800 How do we know that it's measuring what 421 00:16:10,800 --> 00:16:13,040 it's supposed to be measuring? 422 00:16:13,040 --> 00:16:15,520 That's the million-dollar question. 423 00:16:15,520 --> 00:16:18,040 And that's where benchmark validation comes in. 424 00:16:18,040 --> 00:16:18,600 Validation. 425 00:16:18,600 --> 00:16:19,240 What's that? 426 00:16:19,240 --> 00:16:22,040 It's basically a process of testing the benchmark 427 00:16:22,040 --> 00:16:24,320 to make sure it's doing what it's supposed to be doing. 428 00:16:24,320 --> 00:16:27,520 So it's like a quality control check for the benchmark itself? 429 00:16:27,520 --> 00:16:28,200 Exactly. 430 00:16:28,200 --> 00:16:31,040 You wouldn't release a new car without testing it first, right? 431 00:16:31,040 --> 00:16:33,280 You'd want to make sure it's safe, reliable, 432 00:16:33,280 --> 00:16:35,440 and performs as expected. 433 00:16:35,440 --> 00:16:36,400 Makes sense. 434 00:16:36,400 --> 00:16:39,880 So what are some of the things that developers look for when 435 00:16:39,880 --> 00:16:41,680 they're validating a benchmark? 436 00:16:41,680 --> 00:16:44,640 Well, one of the most important things is reliability. 437 00:16:44,640 --> 00:16:45,680 Reliability. 438 00:16:45,680 --> 00:16:50,040 Meaning it means that the benchmark produces consistent results. 439 00:16:50,040 --> 00:16:52,640 So if you run the benchmark multiple times, 440 00:16:52,640 --> 00:16:54,600 you should get similar results each time. 441 00:16:54,600 --> 00:16:56,440 No matter who's running it or when they're running it. 442 00:16:56,440 --> 00:16:57,480 Exactly. 443 00:16:57,480 --> 00:16:59,680 If a benchmark isn't reliable, it's 444 00:16:59,680 --> 00:17:02,960 not a very useful tool for evaluating AI systems. 445 00:17:02,960 --> 00:17:03,440 Right. 446 00:17:03,440 --> 00:17:05,440 Because if the results are all over the place, 447 00:17:05,440 --> 00:17:07,280 then you don't know if the differences you're seeing 448 00:17:07,280 --> 00:17:10,280 are due to actual differences in the AI systems 449 00:17:10,280 --> 00:17:13,200 or just random noise in the benchmark itself. 450 00:17:13,200 --> 00:17:16,400 So how do benchmark developers test for reliability? 451 00:17:16,400 --> 00:17:19,600 Well, one common approach is to have multiple people run 452 00:17:19,600 --> 00:17:22,680 the benchmark independently and then compare their results. 453 00:17:22,680 --> 00:17:23,680 Oh, that's interesting. 454 00:17:23,680 --> 00:17:26,000 So it's like a peer review process for benchmarks. 455 00:17:26,000 --> 00:17:26,600 Exactly. 456 00:17:26,600 --> 00:17:28,280 If everyone is getting similar results, 457 00:17:28,280 --> 00:17:30,680 that's a good sign that the benchmark is reliable. 458 00:17:30,680 --> 00:17:32,240 Makes sense. 459 00:17:32,240 --> 00:17:34,320 What else do they look for? 460 00:17:34,320 --> 00:17:37,280 Another crucial thing is validity. 461 00:17:37,280 --> 00:17:39,120 Wait, didn't we already talk about validity, 462 00:17:39,120 --> 00:17:40,440 like construct validity? 463 00:17:40,440 --> 00:17:41,280 We did. 464 00:17:41,280 --> 00:17:44,080 But construct validity is just one type of validity. 465 00:17:44,080 --> 00:17:45,600 Oh, right. 466 00:17:45,600 --> 00:17:46,760 So what are the other types? 467 00:17:46,760 --> 00:17:48,400 Well, there's also content validity. 468 00:17:48,400 --> 00:17:48,920 It is. 469 00:17:48,920 --> 00:17:51,680 It's basically asking whether the benchmark is actually 470 00:17:51,680 --> 00:17:54,280 measuring the content it's supposed to be measuring. 471 00:17:54,280 --> 00:17:55,160 Can you give an example? 472 00:17:55,160 --> 00:17:55,560 Sure. 473 00:17:55,560 --> 00:17:57,320 Imagine you're designing a benchmark 474 00:17:57,320 --> 00:18:00,560 to measure an AI's ability to translate from English 475 00:18:00,560 --> 00:18:01,280 to French. 476 00:18:01,280 --> 00:18:01,840 OK. 477 00:18:01,840 --> 00:18:04,680 Content validity would ask whether the benchmark is actually 478 00:18:04,680 --> 00:18:08,560 testing the AI's knowledge of French grammar and vocabulary. 479 00:18:08,560 --> 00:18:11,880 As opposed to its ability to guess the meaning of words 480 00:18:11,880 --> 00:18:12,880 from context. 481 00:18:12,880 --> 00:18:13,640 Exactly. 482 00:18:13,640 --> 00:18:16,360 Or its ability to simply copy and paste text 483 00:18:16,360 --> 00:18:18,040 from an online translator. 484 00:18:18,040 --> 00:18:20,040 So how do you test for content validity? 485 00:18:20,040 --> 00:18:23,920 One way is to have human experts review the benchmark tasks 486 00:18:23,920 --> 00:18:25,680 and make sure that they're actually testing 487 00:18:25,680 --> 00:18:27,320 the relevant skills and knowledge. 488 00:18:27,320 --> 00:18:30,160 So it's like having a panel of French teachers 489 00:18:30,160 --> 00:18:32,560 check the test to make sure it's actually testing French. 490 00:18:32,560 --> 00:18:33,280 Exactly. 491 00:18:33,280 --> 00:18:35,480 And there's also criterion validity. 492 00:18:35,480 --> 00:18:36,320 OK, what's that one? 493 00:18:36,320 --> 00:18:39,400 It's asking whether the benchmark results correlate 494 00:18:39,400 --> 00:18:41,720 with other measures of the same thing. 495 00:18:41,720 --> 00:18:44,280 So like if you have a benchmark that 496 00:18:44,280 --> 00:18:46,320 claims to measure intelligence. 497 00:18:46,320 --> 00:18:48,960 Criterion validity would ask whether the results of that 498 00:18:48,960 --> 00:18:51,760 benchmark correlate with the results of other intelligence 499 00:18:51,760 --> 00:18:52,240 tests. 500 00:18:52,240 --> 00:18:53,640 Like IQ tests or something. 501 00:18:53,640 --> 00:18:54,600 Exactly. 502 00:18:54,600 --> 00:18:56,800 If they do correlate, that's a good sign 503 00:18:56,800 --> 00:18:59,560 that the benchmark is actually measuring intelligence. 504 00:18:59,560 --> 00:19:00,680 But if they don't correlate. 505 00:19:00,680 --> 00:19:02,600 Then it might mean that the benchmark is 506 00:19:02,600 --> 00:19:04,680 measuring something else entirely. 507 00:19:04,680 --> 00:19:06,240 Or that it's biased in some way. 508 00:19:06,240 --> 00:19:06,920 Right. 509 00:19:06,920 --> 00:19:10,080 So testing for validity is a really important part 510 00:19:10,080 --> 00:19:11,480 of benchmark development. 511 00:19:11,480 --> 00:19:14,520 It's not enough to just come up with tasks and collect data. 512 00:19:14,520 --> 00:19:16,840 You also have to make sure that the benchmark is actually 513 00:19:16,840 --> 00:19:18,840 measuring what it's supposed to be measuring. 514 00:19:18,840 --> 00:19:20,480 Otherwise the results are meaningless. 515 00:19:20,480 --> 00:19:21,160 Exactly. 516 00:19:21,160 --> 00:19:23,640 It's all about making sure that we're using the right tools 517 00:19:23,640 --> 00:19:25,360 to evaluate AI. 518 00:19:25,360 --> 00:19:28,160 So we've talked about reliability and validity. 519 00:19:28,160 --> 00:19:29,920 What else do benchmark developers 520 00:19:29,920 --> 00:19:32,760 need to consider when they're validating a benchmark? 521 00:19:32,760 --> 00:19:35,120 Another important factor is usability. 522 00:19:35,120 --> 00:19:35,640 Usability. 523 00:19:35,640 --> 00:19:36,140 Yeah. 524 00:19:36,140 --> 00:19:39,320 Basically it's asking how easy the benchmark is to use. 525 00:19:39,320 --> 00:19:39,560 Oh. 526 00:19:39,560 --> 00:19:41,840 So it's not just about being scientifically sound. 527 00:19:41,840 --> 00:19:43,520 It's also about being user friendly. 528 00:19:43,520 --> 00:19:44,200 Exactly. 529 00:19:44,200 --> 00:19:47,000 If a benchmark is too complex or too difficult to use. 530 00:19:47,000 --> 00:19:48,120 People aren't going to want to use it. 531 00:19:48,120 --> 00:19:49,040 Exactly. 532 00:19:49,040 --> 00:19:51,200 So developers need to make sure that the benchmark is 533 00:19:51,200 --> 00:19:54,080 well documented, that the code is easy to understand, 534 00:19:54,080 --> 00:19:57,160 and that the results are presented in a clear and concise way. 535 00:19:57,160 --> 00:20:00,480 So it's like the benchmark needs to have a good user 536 00:20:00,480 --> 00:20:01,280 interface. 537 00:20:01,280 --> 00:20:01,920 Kind of. 538 00:20:01,920 --> 00:20:03,800 It needs to be something that people can actually use 539 00:20:03,800 --> 00:20:05,040 without pulling their hair out. 540 00:20:05,040 --> 00:20:05,680 Makes sense. 541 00:20:05,680 --> 00:20:08,840 So let's say a benchmark has been validated. 542 00:20:08,840 --> 00:20:09,920 It's reliable. 543 00:20:09,920 --> 00:20:10,600 It's valid. 544 00:20:10,600 --> 00:20:11,720 And it's usable. 545 00:20:11,720 --> 00:20:12,360 Are we done? 546 00:20:12,360 --> 00:20:14,120 Can we just sit back and relax? 547 00:20:14,120 --> 00:20:15,840 I have a feeling the answer is no. 548 00:20:15,840 --> 00:20:16,640 You're right. 549 00:20:16,640 --> 00:20:18,680 Benchmarking is an ongoing process. 550 00:20:18,680 --> 00:20:19,280 Meaning. 551 00:20:19,280 --> 00:20:21,440 Even after a benchmark has been validated, 552 00:20:21,440 --> 00:20:24,280 it still needs to be maintained and updated over time. 553 00:20:24,280 --> 00:20:25,560 Why is that? 554 00:20:25,560 --> 00:20:29,520 Well, for one thing, AI is constantly evolving. 555 00:20:29,520 --> 00:20:32,440 So a benchmark that was challenging a few years ago 556 00:20:32,440 --> 00:20:34,480 might be considered trivial today. 557 00:20:34,480 --> 00:20:35,040 Exactly. 558 00:20:35,040 --> 00:20:38,000 So if we want our benchmarks to stay relevant, 559 00:20:38,000 --> 00:20:39,520 we need to keep updating them. 560 00:20:39,520 --> 00:20:40,680 Update them how? 561 00:20:40,680 --> 00:20:44,360 Well, we might need to add new tasks, update the data sets, 562 00:20:44,360 --> 00:20:46,600 or even change the evaluation metrics. 563 00:20:46,600 --> 00:20:48,000 So it's like a textbook that needs 564 00:20:48,000 --> 00:20:49,960 to be revised every few years to keep up 565 00:20:49,960 --> 00:20:52,040 with the latest scientific discoveries. 566 00:20:52,040 --> 00:20:53,240 Exactly. 567 00:20:53,240 --> 00:20:55,680 Benchmarking is a dynamic field. 568 00:20:55,680 --> 00:20:58,880 It's not something you can just do once and then forget about. 569 00:20:58,880 --> 00:21:01,360 It's a constant process of improvement and adaptation. 570 00:21:01,360 --> 00:21:02,040 Precisely. 571 00:21:02,040 --> 00:21:05,040 And it requires a lot of ongoing effort from the AI community. 572 00:21:05,040 --> 00:21:05,920 But it's worth it, right? 573 00:21:05,920 --> 00:21:06,880 Absolutely. 574 00:21:06,880 --> 00:21:10,040 Because AI benchmarking is essential for guiding 575 00:21:10,040 --> 00:21:13,680 the development of AI in a safe and beneficial direction. 576 00:21:13,680 --> 00:21:16,400 This whole conversation about benchmark maintenance 577 00:21:16,400 --> 00:21:18,840 is really making me think about the long-term vision that's 578 00:21:18,840 --> 00:21:19,800 needed in this field. 579 00:21:19,800 --> 00:21:20,320 Yeah. 580 00:21:20,320 --> 00:21:22,800 It's not just about creating a benchmark 581 00:21:22,800 --> 00:21:24,240 and then moving on to the next thing. 582 00:21:24,240 --> 00:21:27,400 It's about nurturing those benchmarks over time, 583 00:21:27,400 --> 00:21:31,000 making sure they stay relevant, reliable, and unbiased. 584 00:21:31,000 --> 00:21:32,240 It's like tending a garden. 585 00:21:32,240 --> 00:21:33,120 Exactly. 586 00:21:33,120 --> 00:21:35,120 You can't just plant the seeds and then walk away. 587 00:21:35,120 --> 00:21:37,480 You have to keep watering them, weeding them, 588 00:21:37,480 --> 00:21:40,400 and making sure they have the nutrients they need to thrive. 589 00:21:40,400 --> 00:21:42,880 And the same goes for AI benchmarks. 590 00:21:42,880 --> 00:21:44,160 Precisely. 591 00:21:44,160 --> 00:21:47,720 They need care and attention if we want them to bear fruit. 592 00:21:47,720 --> 00:21:50,160 This has been such a fascinating conversation. 593 00:21:50,160 --> 00:21:52,560 I'm really starting to appreciate the complexity 594 00:21:52,560 --> 00:21:55,520 and the importance of AI benchmarking. 595 00:21:55,520 --> 00:21:58,120 It's definitely a field that's full of challenges. 596 00:21:58,120 --> 00:21:59,760 But it's also full of opportunity. 597 00:21:59,760 --> 00:22:00,480 Absolutely. 598 00:22:00,480 --> 00:22:02,600 And I think that's what makes it so exciting. 599 00:22:02,600 --> 00:22:04,560 So what are some of those opportunities? 600 00:22:04,560 --> 00:22:07,080 What are the things that you're most excited about 601 00:22:07,080 --> 00:22:09,480 in the field of AI benchmarking? 602 00:22:09,480 --> 00:22:11,880 Well, one thing I'm really excited about 603 00:22:11,880 --> 00:22:14,320 is the development of dynamic benchmarks, which 604 00:22:14,320 --> 00:22:15,320 we talked about earlier. 605 00:22:15,320 --> 00:22:15,800 Right. 606 00:22:15,800 --> 00:22:17,600 The benchmarks that can adapt to the changing 607 00:22:17,600 --> 00:22:18,800 capabilities of AI. 608 00:22:18,800 --> 00:22:19,720 Exactly. 609 00:22:19,720 --> 00:22:22,560 I think those have the potential to revolutionize 610 00:22:22,560 --> 00:22:24,520 how we value AI. 611 00:22:24,520 --> 00:22:27,640 Because they can keep pace with the rapid progress of AI. 612 00:22:27,640 --> 00:22:28,200 Exactly. 613 00:22:28,200 --> 00:22:29,640 And they can also help us to address 614 00:22:29,640 --> 00:22:33,360 some of the limitations of traditional static benchmarks. 615 00:22:33,360 --> 00:22:33,800 Like what? 616 00:22:33,800 --> 00:22:36,200 Well, for one thing, dynamic benchmarks 617 00:22:36,200 --> 00:22:39,080 can help to prevent benchmark saturation, which 618 00:22:39,080 --> 00:22:41,960 is the problem of AI systems hitting the ceiling of what 619 00:22:41,960 --> 00:22:43,680 a benchmark can measure. 620 00:22:43,680 --> 00:22:45,520 Because the benchmark keeps getting harder 621 00:22:45,520 --> 00:22:46,840 as the AI gets smarter. 622 00:22:46,840 --> 00:22:47,440 Exactly. 623 00:22:47,440 --> 00:22:49,760 It's like a moving target, which makes it much harder 624 00:22:49,760 --> 00:22:51,680 for the AI to game the system. 625 00:22:51,680 --> 00:22:53,800 And it also makes the results more meaningful. 626 00:22:53,800 --> 00:22:54,360 Right. 627 00:22:54,360 --> 00:22:56,720 Because we're not just measuring how well the AI can 628 00:22:56,720 --> 00:22:58,400 do on a fixed set of tasks. 629 00:22:58,400 --> 00:23:01,480 We're measuring how well it can adapt and learn new things. 630 00:23:01,480 --> 00:23:03,680 Which is a much better indicator of true intelligence. 631 00:23:03,680 --> 00:23:03,800 Right. 632 00:23:03,800 --> 00:23:04,440 Absolutely. 633 00:23:04,440 --> 00:23:07,080 So I'm really excited to see how dynamic benchmarks 634 00:23:07,080 --> 00:23:08,320 evolve in the coming years. 635 00:23:08,320 --> 00:23:09,600 Me too. 636 00:23:09,600 --> 00:23:11,000 What else are you excited about? 637 00:23:11,000 --> 00:23:13,680 Another thing I'm excited about is the increasing focus 638 00:23:13,680 --> 00:23:16,120 on human in the loop evaluation. 639 00:23:16,120 --> 00:23:17,000 Human in the loop. 640 00:23:17,000 --> 00:23:17,840 What does that mean? 641 00:23:17,840 --> 00:23:21,000 It means that instead of just relying on automated metrics 642 00:23:21,000 --> 00:23:23,400 to evaluate AI systems, we're also 643 00:23:23,400 --> 00:23:26,640 incorporating human judgment into the process. 644 00:23:26,640 --> 00:23:30,720 So like having humans actually interact with the AI 645 00:23:30,720 --> 00:23:32,000 and assess its performance. 646 00:23:32,000 --> 00:23:32,800 Exactly. 647 00:23:32,800 --> 00:23:35,040 And there are lots of different ways to do this. 648 00:23:35,040 --> 00:23:37,680 Well, one common approach is to have humans rate 649 00:23:37,680 --> 00:23:40,120 the quality of the AI's output. 650 00:23:40,120 --> 00:23:42,920 So for example, if the AI is writing a poem. 651 00:23:42,920 --> 00:23:47,600 You might have humans rate how creative or how well written 652 00:23:47,600 --> 00:23:48,560 the poem is. 653 00:23:48,560 --> 00:23:49,600 Interesting. 654 00:23:49,600 --> 00:23:51,600 And that human feedback would then 655 00:23:51,600 --> 00:23:54,200 be used to evaluate the AI's performance. 656 00:23:54,200 --> 00:23:55,000 Exactly. 657 00:23:55,000 --> 00:23:58,240 And this kind of human in the loop evaluation 658 00:23:58,240 --> 00:24:01,360 is becoming more and more important as we develop AI systems 659 00:24:01,360 --> 00:24:03,520 for tasks that are inherently subjective. 660 00:24:03,520 --> 00:24:05,600 Like writing or art or music? 661 00:24:05,600 --> 00:24:06,240 Exactly. 662 00:24:06,240 --> 00:24:08,040 Because for those kinds of tasks, 663 00:24:08,040 --> 00:24:09,600 there's no single right answer. 664 00:24:09,600 --> 00:24:11,520 It's all about human perception and judgment. 665 00:24:11,520 --> 00:24:12,440 Precisely. 666 00:24:12,440 --> 00:24:15,200 So if we want to develop AI systems that can truly 667 00:24:15,200 --> 00:24:17,680 excel at those kinds of tasks, we 668 00:24:17,680 --> 00:24:20,240 need to involve humans in the evaluation process. 669 00:24:20,240 --> 00:24:21,200 That makes a lot of sense. 670 00:24:21,200 --> 00:24:23,240 It's like you wouldn't judge a painting competition 671 00:24:23,240 --> 00:24:24,760 without having human judges, right? 672 00:24:24,760 --> 00:24:25,360 Exactly. 673 00:24:25,360 --> 00:24:28,040 So I'm excited to see how human in the loop evaluation 674 00:24:28,040 --> 00:24:30,160 continues to develop and how it shapes 675 00:24:30,160 --> 00:24:32,280 the future of AI benchmarking. 676 00:24:32,280 --> 00:24:33,560 Me too. 677 00:24:33,560 --> 00:24:36,400 This whole conversation has been incredibly insightful. 678 00:24:36,400 --> 00:24:39,040 It's clear that AI benchmarking is a field that's 679 00:24:39,040 --> 00:24:41,040 constantly evolving and that there 680 00:24:41,040 --> 00:24:43,360 are a lot of exciting challenges and opportunities ahead. 681 00:24:43,360 --> 00:24:44,400 Absolutely. 682 00:24:44,400 --> 00:24:46,600 And I think the key to success in this field 683 00:24:46,600 --> 00:24:49,680 is to approach it with a spirit of collaboration, 684 00:24:49,680 --> 00:24:52,840 open-mindedness, and a commitment to building 685 00:24:52,840 --> 00:24:54,880 AI that benefits humanity. 686 00:24:54,880 --> 00:24:55,840 Well said. 687 00:24:55,840 --> 00:24:58,040 I'm really looking forward to seeing what the future holds 688 00:24:58,040 --> 00:24:59,200 for AI benchmarking. 689 00:24:59,200 --> 00:25:00,360 Me too. 690 00:25:00,360 --> 00:25:02,520 It's an exciting time to be working in this field. 691 00:25:02,520 --> 00:25:04,320 This has been a fantastic conversation. 692 00:25:04,320 --> 00:25:05,640 I feel like I've learned so much. 693 00:25:05,640 --> 00:25:07,200 Thanks for taking the time to chat with me. 694 00:25:07,200 --> 00:25:07,720 My pleasure. 695 00:25:07,720 --> 00:25:09,120 It's been great talking to you too. 696 00:25:09,120 --> 00:25:10,840 And to our listeners, we hope you've 697 00:25:10,840 --> 00:25:14,880 enjoyed this deep dive into the world of AI benchmarking. 698 00:25:14,880 --> 00:25:17,080 It's a complex and fascinating topic, 699 00:25:17,080 --> 00:25:19,040 and we've only just scratched the surface. 700 00:25:19,040 --> 00:25:21,080 But we hope that this conversation has given you 701 00:25:21,080 --> 00:25:23,440 a better understanding of the challenges and opportunities 702 00:25:23,440 --> 00:25:25,480 in this field and the crucial role 703 00:25:25,480 --> 00:25:28,760 that benchmarking plays in shaping the future of AI. 704 00:25:28,760 --> 00:25:34,200 And remember, AI benchmarking is an ongoing process. 705 00:25:34,200 --> 00:25:36,600 It's a collaborative effort that requires input 706 00:25:36,600 --> 00:25:39,160 from researchers, developers, and users alike. 707 00:25:39,160 --> 00:25:41,760 So stay curious, stay engaged, and keep 708 00:25:41,760 --> 00:25:43,280 asking those tough questions. 709 00:25:43,280 --> 00:25:45,320 Until next time, happy learning. 710 00:25:45,320 --> 00:25:47,280 You know, all this talk about making benchmarks 711 00:25:47,280 --> 00:25:50,600 fair and unbiased, it makes you think about how we actually 712 00:25:50,600 --> 00:25:52,320 run these things in the real world. 713 00:25:52,320 --> 00:25:53,960 Yeah, we've talked a lot about the theory, 714 00:25:53,960 --> 00:25:57,160 but what about the practical challenges of actually implementing 715 00:25:57,160 --> 00:25:57,800 these benchmarks? 716 00:25:57,800 --> 00:25:58,520 Exactly. 717 00:25:58,520 --> 00:26:00,640 And one of the biggest challenges is infrastructure. 718 00:26:00,640 --> 00:26:01,440 Infrastructure. 719 00:26:01,440 --> 00:26:02,600 Like roads and bridges. 720 00:26:02,600 --> 00:26:04,480 What does that have to do with AI benchmarks? 721 00:26:04,480 --> 00:26:06,200 Not that kind of infrastructure. 722 00:26:06,200 --> 00:26:08,360 I'm talking about computing power. 723 00:26:08,360 --> 00:26:11,560 AI benchmarks, these days, they require a lot of it. 724 00:26:11,560 --> 00:26:13,360 So powerful computers. 725 00:26:13,360 --> 00:26:17,360 Yeah, like servers, high speed networks, specialized software, 726 00:26:17,360 --> 00:26:18,200 all that stuff. 727 00:26:18,200 --> 00:26:20,800 So it's not something you can just run on your laptop at home. 728 00:26:20,800 --> 00:26:23,480 Not unless you've got a supercomputer in your basement. 729 00:26:23,480 --> 00:26:25,880 We're talking about training and evaluating 730 00:26:25,880 --> 00:26:28,040 these massive AI models. 731 00:26:28,040 --> 00:26:32,280 It can take days, even weeks, to run some of these benchmarks. 732 00:26:32,280 --> 00:26:33,120 Wow, OK. 733 00:26:33,120 --> 00:26:35,840 So access to these computing resources 734 00:26:35,840 --> 00:26:37,480 is a major bottleneck. 735 00:26:37,480 --> 00:26:38,800 It can be, yeah. 736 00:26:38,800 --> 00:26:40,480 Especially for smaller research groups 737 00:26:40,480 --> 00:26:43,320 or individuals who don't have the same kind of funding 738 00:26:43,320 --> 00:26:45,960 as Google or Stanford. 739 00:26:45,960 --> 00:26:46,200 Right. 740 00:26:46,200 --> 00:26:48,240 So it creates a kind of inequality in the field. 741 00:26:48,240 --> 00:26:49,200 Yeah, unfortunately. 742 00:26:49,200 --> 00:26:52,440 Like if you don't have access to the best hardware and software, 743 00:26:52,440 --> 00:26:54,000 you're kind of at a disadvantage. 744 00:26:54,000 --> 00:26:55,720 So how do we solve that? 745 00:26:55,720 --> 00:26:59,520 How do we make AI benchmarking more accessible to everyone? 746 00:26:59,520 --> 00:27:01,680 Well, one solution is cloud computing. 747 00:27:01,680 --> 00:27:02,720 Oh, right. 748 00:27:02,720 --> 00:27:05,280 Like instead of everyone having to buy their own expensive servers. 749 00:27:05,280 --> 00:27:07,320 You can just rent computing power from a company 750 00:27:07,320 --> 00:27:08,600 like Amazon or Google. 751 00:27:08,600 --> 00:27:09,560 Makes sense. 752 00:27:09,560 --> 00:27:11,480 So that could really level the playing field. 753 00:27:11,480 --> 00:27:12,240 Yeah, I think so. 754 00:27:12,240 --> 00:27:14,520 It would allow researchers from all over the world 755 00:27:14,520 --> 00:27:16,480 to participate in AI benchmarking, 756 00:27:16,480 --> 00:27:17,640 regardless of their budget. 757 00:27:17,640 --> 00:27:18,360 That's awesome. 758 00:27:18,360 --> 00:27:19,680 Are there other solutions? 759 00:27:19,680 --> 00:27:22,560 Another approach is to focus on developing more efficient 760 00:27:22,560 --> 00:27:23,880 benchmarking methods. 761 00:27:23,880 --> 00:27:24,400 Right. 762 00:27:24,400 --> 00:27:28,080 Like finding ways to reduce the computational requirements 763 00:27:28,080 --> 00:27:29,600 of the benchmarks themselves. 764 00:27:29,600 --> 00:27:32,520 So instead of needing a supercomputer to run the benchmark, 765 00:27:32,520 --> 00:27:34,800 you could maybe run it on a regular computer 766 00:27:34,800 --> 00:27:36,080 or even a smartphone. 767 00:27:36,080 --> 00:27:38,200 Whoa, that would be amazing. 768 00:27:38,200 --> 00:27:39,840 But is that even possible? 769 00:27:39,840 --> 00:27:42,160 It's definitely an active area of research. 770 00:27:42,160 --> 00:27:44,240 There are a lot of smart people working on it. 771 00:27:44,240 --> 00:27:45,760 I hope they succeed. 772 00:27:45,760 --> 00:27:48,640 It would be amazing to democratize AI benchmarking 773 00:27:48,640 --> 00:27:49,480 like that. 774 00:27:49,480 --> 00:27:50,200 I agree. 775 00:27:50,200 --> 00:27:52,040 It would be huge for the field. 776 00:27:52,040 --> 00:27:54,200 It would allow more people to contribute 777 00:27:54,200 --> 00:27:56,680 and would make the results more representative 778 00:27:56,680 --> 00:27:59,720 of the diversity of thought in the AI community. 779 00:27:59,720 --> 00:28:01,680 This whole conversation about infrastructure 780 00:28:01,680 --> 00:28:03,640 has really opened my eyes. 781 00:28:03,640 --> 00:28:06,400 It makes me realize that AI benchmarking isn't just 782 00:28:06,400 --> 00:28:08,320 about the algorithms and the data. 783 00:28:08,320 --> 00:28:11,360 It's also about access to resources and the power 784 00:28:11,360 --> 00:28:12,760 dynamics that come with that. 785 00:28:12,760 --> 00:28:14,120 That's a really important point. 786 00:28:14,120 --> 00:28:15,760 And it's something that we need to be mindful of 787 00:28:15,760 --> 00:28:16,680 as we move forward. 788 00:28:16,680 --> 00:28:19,840 We don't want to create a situation where only a select 789 00:28:19,840 --> 00:28:22,760 few have the ability to shape the future of AI. 790 00:28:22,760 --> 00:28:23,320 Right. 791 00:28:23,320 --> 00:28:25,000 AI is going to impact everyone. 792 00:28:25,000 --> 00:28:26,840 So everyone should have a voice in how 793 00:28:26,840 --> 00:28:28,400 it's developed and evaluated. 794 00:28:28,400 --> 00:28:29,240 Exactly. 795 00:28:29,240 --> 00:28:31,920 And that's why AI benchmarking is so important. 796 00:28:31,920 --> 00:28:34,680 It gives us a way to measure progress, identify 797 00:28:34,680 --> 00:28:37,560 potential risks, and ensure that AI is developed in a way 798 00:28:37,560 --> 00:28:39,680 that benefits all of humanity. 799 00:28:39,680 --> 00:28:42,280 Well, this deep dive has been an incredible journey. 800 00:28:42,280 --> 00:28:45,040 We've explored so many fascinating aspects of AI 801 00:28:45,040 --> 00:28:47,480 benchmarking, from the fundamental principles 802 00:28:47,480 --> 00:28:50,120 of benchmark design to the practical challenges 803 00:28:50,120 --> 00:28:52,920 of implementation and the ethical considerations 804 00:28:52,920 --> 00:28:54,960 of bias and accessibility. 805 00:28:54,960 --> 00:28:58,440 It's clear that AI benchmarking is a complex and multifaceted 806 00:28:58,440 --> 00:29:02,200 field that plays a crucial role in shaping the future of AI. 807 00:29:02,200 --> 00:29:04,760 It's been an absolute pleasure discussing this with you. 808 00:29:04,760 --> 00:29:07,200 I hope our listeners have gained a new appreciation 809 00:29:07,200 --> 00:29:10,240 for the challenges and the importance of AI benchmarking. 810 00:29:10,240 --> 00:29:12,600 As AI continues to evolve, the need 811 00:29:12,600 --> 00:29:15,960 for robust, transparent, and inclusive AI benchmarking 812 00:29:15,960 --> 00:29:17,480 will only become more important. 813 00:29:17,480 --> 00:29:20,480 It's a field that requires collaboration, ingenuity, 814 00:29:20,480 --> 00:29:23,720 and a deep commitment to building a better future with AI. 815 00:29:23,720 --> 00:29:25,920 To our listeners, stay curious, stay informed, 816 00:29:25,920 --> 00:29:41,960 and as always, happy learning.