1 00:00:00,000 --> 00:00:05,000 Okay, so today we're going to be looking at this paper about GPQA. 2 00:00:05,680 --> 00:00:09,440 And it sounds like it's a pretty interesting way to like benchmark AI. 3 00:00:09,680 --> 00:00:11,040 Yeah, yeah, it is. 4 00:00:11,040 --> 00:00:12,880 It's a really cool new benchmark. 5 00:00:12,880 --> 00:00:16,600 You see a lot of the AI benchmarks kind of focus on things that are, you know, 6 00:00:16,600 --> 00:00:20,600 kind of like easy to find information on like facts or common knowledge. 7 00:00:20,640 --> 00:00:21,000 Right. 8 00:00:21,280 --> 00:00:22,520 But GPQA is different. 9 00:00:22,520 --> 00:00:25,960 It really focuses on these questions that are what they call Google proof. 10 00:00:25,960 --> 00:00:26,520 Google proof. 11 00:00:26,520 --> 00:00:26,680 Yeah. 12 00:00:26,680 --> 00:00:29,880 So we're talking about questions that like, even if you were like the most 13 00:00:29,880 --> 00:00:32,400 hardcore internet sleuth you would have trouble with. 14 00:00:32,400 --> 00:00:33,040 Exactly. 15 00:00:33,040 --> 00:00:37,800 These are questions in like physics, chemistry and biology that are really 16 00:00:37,800 --> 00:00:41,080 meant to be challenging even for people with PhDs in those areas. 17 00:00:41,080 --> 00:00:41,520 Wow. 18 00:00:41,520 --> 00:00:44,840 There are questions that really require deep understanding and reasoning. 19 00:00:44,840 --> 00:00:47,520 So it's not like just being able to like find the answer on Wikipedia. 20 00:00:47,520 --> 00:00:50,680 It's more like actually like understanding it and like applying it. 21 00:00:50,720 --> 00:00:51,560 Exactly. 22 00:00:51,880 --> 00:00:55,520 The researchers wanted to see if AI could go beyond simple information 23 00:00:55,520 --> 00:00:59,360 retrieval and actually like solve problems that even highly skilled 24 00:00:59,360 --> 00:01:00,680 humans find difficult. 25 00:01:00,840 --> 00:01:01,600 That's pretty cool. 26 00:01:01,680 --> 00:01:01,960 Yeah. 27 00:01:02,000 --> 00:01:02,800 I'm already intrigued. 28 00:01:02,840 --> 00:01:03,120 Yeah. 29 00:01:03,360 --> 00:01:07,920 Um, so how did they even go about creating these Google proof questions? 30 00:01:08,240 --> 00:01:09,960 Well, they had a pretty intense process. 31 00:01:09,960 --> 00:01:13,920 First they recruited, you know, PhD level scientists from physics, 32 00:01:13,920 --> 00:01:16,720 chemistry and biology to actually craft the questions. 33 00:01:16,720 --> 00:01:17,000 Okay. 34 00:01:17,360 --> 00:01:21,280 But it wasn't just like those scientists coming up with their own pet questions 35 00:01:21,280 --> 00:01:21,800 or anything. 36 00:01:21,800 --> 00:01:25,480 They had like a whole four stage process to make sure these questions were legit. 37 00:01:25,640 --> 00:01:26,000 Gotcha. 38 00:01:26,000 --> 00:01:27,720 So what was like the first round like? 39 00:01:27,840 --> 00:01:30,320 The first round was all about expert validation. 40 00:01:30,320 --> 00:01:34,040 So other experts would review the questions and make sure they were actually 41 00:01:34,040 --> 00:01:35,280 accurate and challenging. 42 00:01:35,520 --> 00:01:35,960 Makes sense. 43 00:01:35,960 --> 00:01:36,200 Yeah. 44 00:01:36,240 --> 00:01:39,200 Gotta make sure the questions are actually, you know, difficult enough for 45 00:01:39,200 --> 00:01:39,840 the right people. 46 00:01:39,880 --> 00:01:40,120 Yeah. 47 00:01:40,120 --> 00:01:41,360 So what about like round two? 48 00:01:41,480 --> 00:01:45,160 Round two, they took all the feedback they got and like revised the questions 49 00:01:45,160 --> 00:01:45,800 even more. 50 00:01:46,040 --> 00:01:48,800 They really wanted to like make sure these questions were like, you know, 51 00:01:48,800 --> 00:01:52,520 bulletproof, but then around three things got really interesting. 52 00:01:52,520 --> 00:01:52,760 Oh, right. 53 00:01:52,960 --> 00:01:57,520 They took the questions and they gave them to other PhDs, but from different fields. 54 00:01:57,720 --> 00:02:01,800 Oh, so like someone who's an expert in like physics might have gotten questions 55 00:02:01,800 --> 00:02:02,720 that were in chemistry. 56 00:02:02,760 --> 00:02:03,240 Yeah. 57 00:02:03,320 --> 00:02:06,040 And even with like the entire internet at their disposal. 58 00:02:06,200 --> 00:02:06,880 That sounds right. 59 00:02:06,880 --> 00:02:08,400 These PhDs still struggled. 60 00:02:08,440 --> 00:02:12,080 Like the average time they spent per question was over 30 minutes. 61 00:02:12,440 --> 00:02:13,120 Wow. 62 00:02:13,600 --> 00:02:18,320 So that really is a good way to show that these questions were truly Google proof. 63 00:02:18,320 --> 00:02:18,880 Exactly. 64 00:02:18,880 --> 00:02:20,760 It wasn't just about like, you know, looking stuff up. 65 00:02:20,760 --> 00:02:23,240 It was about like really understanding the concepts. 66 00:02:23,320 --> 00:02:25,480 So what did they do with these questions once they had them? 67 00:02:25,640 --> 00:02:28,680 Well, they actually created three different levels of difficulty. 68 00:02:28,720 --> 00:02:29,040 Yeah. 69 00:02:29,040 --> 00:02:32,640 So there's like GPQA extended, which is like the whole enchilada. 70 00:02:32,760 --> 00:02:33,040 Yeah. 71 00:02:33,080 --> 00:02:34,960 All 546 questions. 72 00:02:35,000 --> 00:02:35,080 Okay. 73 00:02:35,080 --> 00:02:36,240 So that's like the ultimate challenge. 74 00:02:36,240 --> 00:02:36,520 Yeah. 75 00:02:36,800 --> 00:02:40,160 Then there's the main set, which is just called GPQA. 76 00:02:40,200 --> 00:02:42,920 And that one has 448 questions. 77 00:02:43,120 --> 00:02:46,640 And these are the ones where at least one of the original experts could answer it. 78 00:02:46,680 --> 00:02:47,000 Okay. 79 00:02:47,000 --> 00:02:49,240 But most of the non experts in round three couldn't. 80 00:02:49,360 --> 00:02:52,360 So this became like the main benchmark for the AI. 81 00:02:52,440 --> 00:02:52,840 Gotcha. 82 00:02:52,840 --> 00:02:55,360 So it's like challenging, but still like possible. 83 00:02:55,400 --> 00:02:56,000 Exactly. 84 00:02:56,400 --> 00:02:59,400 And then finally, there's GPQA diamond. 85 00:02:59,560 --> 00:03:00,760 Ooh, diamond. 86 00:03:01,480 --> 00:03:02,840 So this is where it gets really interesting. 87 00:03:02,880 --> 00:03:03,840 This one's intense. 88 00:03:03,920 --> 00:03:07,680 It has 198 questions that both of the experts aced. 89 00:03:07,920 --> 00:03:08,240 Okay. 90 00:03:08,240 --> 00:03:10,760 But most of the non experts couldn't answer. 91 00:03:11,000 --> 00:03:12,240 So like the super hard ones? 92 00:03:12,240 --> 00:03:12,480 Yeah. 93 00:03:12,480 --> 00:03:13,960 These are like the real brainbusters. 94 00:03:13,960 --> 00:03:18,440 Okay. So we've got like this tiered system of ultra difficult science questions. 95 00:03:18,520 --> 00:03:22,200 Can we like get into some actual examples of what these questions are like? 96 00:03:22,240 --> 00:03:22,600 Sure. 97 00:03:22,640 --> 00:03:28,280 So one of the questions in organic chemistry was about a compound called methylcyclo-pentadine. 98 00:03:28,880 --> 00:03:31,400 And it involved this whole cascade of reactions. 99 00:03:31,440 --> 00:03:31,640 Okay. 100 00:03:31,840 --> 00:03:35,960 And the challenge was to figure out how many possible isomers the final product would have. 101 00:03:36,480 --> 00:03:38,520 But you weren't allowed to count stereoisomers. 102 00:03:38,520 --> 00:03:39,400 I'm already lost. 103 00:03:39,680 --> 00:03:41,080 But I'm assuming that's the point. 104 00:03:41,120 --> 00:03:41,440 Right. 105 00:03:41,440 --> 00:03:45,880 And the answer was 16. Like there are 16 possible isomers. 106 00:03:45,880 --> 00:03:47,320 16. Wow. 107 00:03:47,560 --> 00:03:47,800 Yeah. 108 00:03:47,800 --> 00:03:49,440 That sounds pretty tricky to keep track of. 109 00:03:49,520 --> 00:03:53,000 Even if you have like a background in chemistry, I can see how that would be really challenging. 110 00:03:54,680 --> 00:03:55,640 What other examples are there? 111 00:03:56,080 --> 00:04:01,960 There was also one in molecular biology about a scientist who's trying to create a heat tolerant wheat cultivar. 112 00:04:02,080 --> 00:04:07,320 And it got into all these complex mechanisms of like gene expression and protein synthesis. 113 00:04:07,360 --> 00:04:08,040 Genetics. 114 00:04:08,120 --> 00:04:09,400 Always a fascinating topic. 115 00:04:09,400 --> 00:04:11,400 And then there was also one in astrophysics, right? 116 00:04:11,440 --> 00:04:12,000 Yeah. 117 00:04:12,000 --> 00:04:17,600 This one was about determining a star's surface gravity using the spectral lines of different elements. 118 00:04:17,640 --> 00:04:20,080 So they really covered a lot of ground with these questions. 119 00:04:20,120 --> 00:04:20,320 Yeah. 120 00:04:20,320 --> 00:04:23,040 They wanted to make sure it was like a diverse range of topics. 121 00:04:23,080 --> 00:04:23,240 Yeah. 122 00:04:23,240 --> 00:04:28,240 And all the questions were designed to be like really tricky, not something you could just Google. 123 00:04:28,280 --> 00:04:28,600 Right. 124 00:04:28,600 --> 00:04:33,360 So we've established that these questions are incredibly difficult, even for like highly educated humans. 125 00:04:33,400 --> 00:04:33,840 Yeah. 126 00:04:34,760 --> 00:04:38,200 But the real question is can AI actually solve these? 127 00:04:38,200 --> 00:04:39,520 Well, that's what they wanted to find out. 128 00:04:39,520 --> 00:04:42,120 They tested a bunch of different AI models on these questions. 129 00:04:42,160 --> 00:04:42,600 Okay. 130 00:04:42,600 --> 00:04:43,240 Like which ones? 131 00:04:43,600 --> 00:04:48,680 So they tested Lama 2, GPT 3.5 and even GPT 4. 132 00:04:48,720 --> 00:04:49,160 Oh, wow. 133 00:04:49,160 --> 00:04:49,960 The big guns. 134 00:04:50,000 --> 00:04:50,600 Yeah. 135 00:04:50,640 --> 00:04:53,400 And they tested the AI in two different scenarios. 136 00:04:53,440 --> 00:04:57,320 One where they didn't have access to the internet and one where they did. 137 00:04:57,320 --> 00:05:00,560 So it's kind of like a closed book versus open book test. 138 00:05:00,600 --> 00:05:00,920 Yeah. 139 00:05:00,920 --> 00:05:01,800 I like that analogy. 140 00:05:01,840 --> 00:05:02,640 So what happened? 141 00:05:02,640 --> 00:05:05,280 Did the AI manage to conquer these questions? 142 00:05:05,320 --> 00:05:07,080 Well, the results were pretty interesting. 143 00:05:07,080 --> 00:05:15,080 Even the best model, which was GPT 4 with search capabilities, only got 39% accuracy on the main GPQ A set. 144 00:05:15,120 --> 00:05:15,800 Wow. 145 00:05:15,840 --> 00:05:20,040 So better than those PhDs who weren't experts in the specific field. 146 00:05:20,080 --> 00:05:20,520 Right. 147 00:05:20,520 --> 00:05:24,000 But still nowhere near as good as the experts who actually designed the questions. 148 00:05:24,040 --> 00:05:24,480 Exactly. 149 00:05:24,480 --> 00:05:26,840 And that's what makes this research so fascinating. 150 00:05:26,880 --> 00:05:31,920 It shows us that AI has come a long way, but there's still so much room for improvement. 151 00:05:31,960 --> 00:05:32,440 Yeah. 152 00:05:32,440 --> 00:05:35,960 So AI can access all this information and process it really quickly. 153 00:05:35,960 --> 00:05:41,160 But it still struggles with the kind of like deep understanding and reasoning that humans are really good at. 154 00:05:41,200 --> 00:05:41,560 Right. 155 00:05:41,560 --> 00:05:43,760 And that's one of the key takeaways from this research. 156 00:05:43,760 --> 00:05:49,800 We need AI that can not only like solve problems, but also explain how it got to the solution. 157 00:05:49,840 --> 00:05:54,160 That's a really good point, especially when we're talking about problems that even human experts find challenging. 158 00:05:54,200 --> 00:05:58,160 You want to make sure that the AI isn't just like making lucky guesses or something. 159 00:05:58,200 --> 00:05:58,760 Exactly. 160 00:05:58,760 --> 00:06:04,760 We need to know that it's actually reasoning its way to the answer and that it understands the underlying concepts. 161 00:06:04,760 --> 00:06:06,760 So explainability is really important. 162 00:06:06,800 --> 00:06:07,360 Yeah. 163 00:06:07,360 --> 00:06:08,960 And it's a big challenge right now. 164 00:06:08,960 --> 00:06:14,960 A lot of these really powerful AI models are basically like black boxes. 165 00:06:15,560 --> 00:06:18,360 We know it goes in and we know what comes out. 166 00:06:18,360 --> 00:06:19,960 We don't know what happens in between. 167 00:06:19,960 --> 00:06:23,960 So it's like having a student who gets all the answers right on a test. 168 00:06:23,960 --> 00:06:24,560 Yeah. 169 00:06:24,560 --> 00:06:26,160 But can't explain how they got them. 170 00:06:26,160 --> 00:06:26,560 Yeah. 171 00:06:26,560 --> 00:06:30,560 You start to wonder if they're actually learning or just like cheating somehow. 172 00:06:30,560 --> 00:06:31,160 Right. 173 00:06:31,160 --> 00:06:33,760 And that's not good enough, especially when we're talking about 174 00:06:33,760 --> 00:06:34,960 like scientific research. 175 00:06:34,960 --> 00:06:35,760 Exactly. 176 00:06:35,760 --> 00:06:36,360 Yeah. 177 00:06:36,360 --> 00:06:43,160 We need AI that can explain its reasoning in a way that humans can understand so that we can trust the results. 178 00:06:43,160 --> 00:06:46,760 So this kind of ties into that idea of scalable oversight that you mentioned earlier. 179 00:06:46,760 --> 00:06:47,760 It does. 180 00:06:47,760 --> 00:06:51,760 As AI gets more powerful and starts tackling more and more complex problems. 181 00:06:51,760 --> 00:06:52,760 They need to be able to keep up with it. 182 00:06:52,760 --> 00:06:53,160 Yeah. 183 00:06:53,160 --> 00:06:56,160 We need ways to like monitor and guide these systems. 184 00:06:56,160 --> 00:06:59,760 Even if we don't fully understand the problems ourselves. 185 00:06:59,760 --> 00:07:02,760 It's like sending explorers into uncharted territory. 186 00:07:02,760 --> 00:07:03,760 Right. 187 00:07:03,760 --> 00:07:09,760 We got to make sure they have the tools and the communication systems they need so they don't get lost or do something dangerous. 188 00:07:09,760 --> 00:07:10,760 Exactly. 189 00:07:10,760 --> 00:07:13,760 So it's not just about building a really smart AI. 190 00:07:13,760 --> 00:07:18,760 It's about building a smart AI that we can trust and that we can understand. 191 00:07:18,760 --> 00:07:20,760 And that we can work with. 192 00:07:20,760 --> 00:07:24,760 This research is really helping to lay the groundwork for that. 193 00:07:24,760 --> 00:07:31,760 It's not just about celebrating what AI can do, but also about being realistic about its limitations. 194 00:07:31,760 --> 00:07:33,760 And figuring out how to overcome them. 195 00:07:33,760 --> 00:07:34,760 I think that's a great way to put it. 196 00:07:34,760 --> 00:07:39,760 This research really paints a nuanced picture of AI's potential in science. 197 00:07:39,760 --> 00:07:44,760 It's exciting to see how these systems are already being used to tackle really tough problems. 198 00:07:44,760 --> 00:07:47,760 But it's also clear that we need to be careful. 199 00:07:47,760 --> 00:07:52,760 And we need to make sure that we're developing and using AI in a way that benefits everyone. 200 00:07:52,760 --> 00:07:53,760 Absolutely. 201 00:07:53,760 --> 00:07:54,760 It's a collaborative effort. 202 00:07:54,760 --> 00:07:57,760 It's not just up to the AI researchers to figure this out. 203 00:07:57,760 --> 00:08:01,760 We need scientists, ethicists, policymakers all working together. 204 00:08:01,760 --> 00:08:02,760 That's a good point. 205 00:08:02,760 --> 00:08:04,760 This isn't just a technological challenge. 206 00:08:04,760 --> 00:08:05,760 It's a societal one. 207 00:08:05,760 --> 00:08:07,760 We need to be having these conversations now. 208 00:08:07,760 --> 00:08:12,760 So that we're prepared for a future where AI plays an even bigger role in our lives. 209 00:08:12,760 --> 00:08:20,760 But before we go too far down that road, I want to come back to something you mentioned earlier about the different versions of the GPQA benchmark. 210 00:08:20,760 --> 00:08:21,760 Oh, yeah. 211 00:08:21,760 --> 00:08:26,760 Why did they decide to create three different versions with varying levels of difficulty? 212 00:08:26,760 --> 00:08:34,760 Well, it's a really smart approach, actually, because it allows researchers to test different AI models and different approaches as they become more sophisticated. 213 00:08:34,760 --> 00:08:37,760 So you can start with the easier questions. 214 00:08:37,760 --> 00:08:40,760 And then as the AI gets better, you can move up to the harder ones. 215 00:08:40,760 --> 00:08:45,760 It's like having a series of increasingly difficult obstacle courses for our AI athletes. 216 00:08:45,760 --> 00:08:47,760 They can see how far they can get. 217 00:08:47,760 --> 00:08:50,760 And we can figure out what they're good at and what they need to work on. 218 00:08:50,760 --> 00:08:56,760 And it's also important to remember that this benchmark doesn't just test AI's overall problem-solving abilities. 219 00:08:56,760 --> 00:09:01,760 It also looks at how AI performs across different scientific domains. 220 00:09:01,760 --> 00:09:06,760 So did they find that the AI was better at some types of questions than others? 221 00:09:06,760 --> 00:09:10,760 Like, were the chemistry questions easier than the physics questions or vice versa? 222 00:09:10,760 --> 00:09:12,760 Yeah, they did find some variation. 223 00:09:12,760 --> 00:09:19,760 Even with a really powerful model like GP24, the performance varied depending on things like the specific prompting technique, 224 00:09:19,760 --> 00:09:26,760 whether it had access to internet search, and, yeah, even the scientific domain of the question. 225 00:09:26,760 --> 00:09:28,760 So it's not just about how smart the AI is. 226 00:09:28,760 --> 00:09:32,760 It's also about how we interact with it and how we set it up for success. 227 00:09:32,760 --> 00:09:33,760 Exactly. 228 00:09:33,760 --> 00:09:35,760 And that's one of the most exciting things about this research. 229 00:09:35,760 --> 00:09:39,760 It shows us that there's still so much we can do to improve how we use AI. 230 00:09:39,760 --> 00:09:41,760 And to tailor it to specific tasks. 231 00:09:41,760 --> 00:09:42,760 Yeah. 232 00:09:42,760 --> 00:09:44,760 It's like realizing that AI isn't just one thing. 233 00:09:44,760 --> 00:09:46,760 It's a whole set of tools. 234 00:09:46,760 --> 00:09:49,760 And we need different AI specialists for different jobs. 235 00:09:49,760 --> 00:09:56,760 And this research gives us valuable insights into how we can create those specialists and how we can train them to excel in their fields. 236 00:09:56,760 --> 00:09:57,760 That's awesome. 237 00:09:57,760 --> 00:10:00,760 It also highlights the importance of evaluating AI performance carefully. 238 00:10:00,760 --> 00:10:01,760 Yeah, definitely. 239 00:10:01,760 --> 00:10:04,760 It's not enough to just look at the overall accuracy score. 240 00:10:04,760 --> 00:10:10,760 We need to really dive deep and understand where the AI is succeeding, where it's failing, and why. 241 00:10:10,760 --> 00:10:14,760 Right, because even the most advanced AI can still make mistakes. 242 00:10:14,760 --> 00:10:16,760 Especially when we're talking about complex scientific concepts. 243 00:10:16,760 --> 00:10:17,760 Exactly. 244 00:10:17,760 --> 00:10:23,760 If we're going to trust these systems to help us with groundbreaking research, we need to be aware of their limitations. 245 00:10:23,760 --> 00:10:24,760 And potential biases. 246 00:10:24,760 --> 00:10:25,760 Yeah, for sure. 247 00:10:25,760 --> 00:10:29,760 This paper does a really good job of encouraging a balanced perspective on AI. 248 00:10:29,760 --> 00:10:30,760 I think so too. 249 00:10:30,760 --> 00:10:42,760 It acknowledges the incredible potential of AI, but it also emphasizes the importance of responsible development, rigorous evaluation, and continuous oversight. 250 00:10:42,760 --> 00:10:43,760 Absolutely. 251 00:10:43,760 --> 00:10:48,760 It's like saying, hey, AI is an amazing tool, but let's not get carried away. 252 00:10:48,760 --> 00:10:49,760 We need to use it wiser. 253 00:10:49,760 --> 00:10:54,760 We need to make sure that it remains a force for good in the scientific world. 254 00:10:54,760 --> 00:10:55,760 I couldn't agree more. 255 00:10:55,760 --> 00:10:58,760 So this brings up a really important question. 256 00:10:58,760 --> 00:11:01,760 What does all of this mean for the average person? 257 00:11:01,760 --> 00:11:02,760 That's a great question. 258 00:11:02,760 --> 00:11:08,760 Like if you're not a scientist and you're not an AI researcher, how does this research impact your life? 259 00:11:08,760 --> 00:11:10,760 Well, even if you're not directly involved in those fields. 260 00:11:10,760 --> 00:11:13,760 AI is becoming more and more a part of everyone's lives. 261 00:11:13,760 --> 00:11:14,760 It is. 262 00:11:14,760 --> 00:11:27,760 And this research raises some really crucial questions about the future of knowledge, the role of AI in society, and the importance of critical thinking in a world where technology is becoming increasingly powerful. 263 00:11:27,760 --> 00:11:33,760 So it's like saying that AI is becoming so important that everyone needs to understand it, not just the experts. 264 00:11:33,760 --> 00:11:34,760 Exactly. 265 00:11:34,760 --> 00:11:36,760 We need to be informed citizens. 266 00:11:36,760 --> 00:11:37,760 Right. 267 00:11:37,760 --> 00:11:42,760 We need to participate in these conversations about AI and help shape the future in a way that benefits everyone. 268 00:11:42,760 --> 00:11:43,760 I completely agree. 269 00:11:43,760 --> 00:11:45,760 We all have a stake in this. 270 00:11:45,760 --> 00:11:49,760 We need to make sure that AI is developed and used ethically and responsibly. 271 00:11:49,760 --> 00:11:50,760 Yes. 272 00:11:50,760 --> 00:11:52,760 And in a way that aligns with our values as a society. 273 00:11:52,760 --> 00:11:56,760 This research is a good reminder that we're living in a really exciting time. 274 00:11:56,760 --> 00:11:57,760 We are. 275 00:11:57,760 --> 00:11:58,760 Technology is advancing so rapidly. 276 00:11:58,760 --> 00:11:59,760 Yeah. 277 00:11:59,760 --> 00:12:03,760 And it's up to us to make sure that those advancements lead to a better world. 278 00:12:03,760 --> 00:12:05,760 A more just and equitable world. 279 00:12:05,760 --> 00:12:06,760 Exactly. 280 00:12:06,760 --> 00:12:10,760 But I think we need to pause here and come back for the third and final part of our deep dive. 281 00:12:10,760 --> 00:12:11,760 It's a really good point. 282 00:12:11,760 --> 00:12:19,760 It's like saying that even if AI can access all this information and process it really fast, it still struggles with that deep understanding. 283 00:12:19,760 --> 00:12:20,760 Right. 284 00:12:20,760 --> 00:12:22,760 Like humans are still much better at that. 285 00:12:22,760 --> 00:12:23,760 Yeah. 286 00:12:23,760 --> 00:12:30,760 And that actually leads to one of the other really big takeaways from this research, which is this idea of explainability in AI. 287 00:12:30,760 --> 00:12:32,760 Explainability. 288 00:12:32,760 --> 00:12:37,760 So like even if the AI gets the right answer, we need to be able to understand how it got there. 289 00:12:37,760 --> 00:12:38,760 Exactly. 290 00:12:38,760 --> 00:12:43,760 Especially when we're dealing with problems that even human experts find challenging. 291 00:12:43,760 --> 00:12:49,760 We don't want the AI just making lucky guesses or, you know, taking advantage of some weird quirk in the data. 292 00:12:49,760 --> 00:12:50,760 Right. 293 00:12:50,760 --> 00:12:52,760 We need to know that it actually understands what it's doing. 294 00:12:52,760 --> 00:12:53,760 Exactly. 295 00:12:53,760 --> 00:12:58,760 And that's a big challenge right now because a lot of these really powerful AI models are essentially black boxes. 296 00:12:58,760 --> 00:12:59,760 Black boxes. 297 00:12:59,760 --> 00:13:01,760 So we can see what goes in and what comes out. 298 00:13:01,760 --> 00:13:02,760 Right. 299 00:13:02,760 --> 00:13:04,760 And we really know what's happening in between. 300 00:13:04,760 --> 00:13:05,760 Yeah. 301 00:13:05,760 --> 00:13:10,760 It's like having this brilliant student who aces every test but can't explain how they got the answers. 302 00:13:10,760 --> 00:13:11,760 Oh, I see what you mean. 303 00:13:11,760 --> 00:13:15,760 Like are they actually learning or are they just really good at finding shortcuts? 304 00:13:15,760 --> 00:13:16,760 Exactly. 305 00:13:16,760 --> 00:13:19,760 And in the world of scientific research, that's not good enough. 306 00:13:19,760 --> 00:13:20,760 Right. 307 00:13:20,760 --> 00:13:21,760 We need to be able to trust the results. 308 00:13:21,760 --> 00:13:22,760 Yeah. 309 00:13:22,760 --> 00:13:28,760 We need AI that can not only solve the problems but also explain its reasoning in a way that humans can understand. 310 00:13:28,760 --> 00:13:30,760 So that we can trust it. 311 00:13:30,760 --> 00:13:33,760 And that all ties back to that idea of scalable oversight. 312 00:13:33,760 --> 00:13:34,760 It does. 313 00:13:34,760 --> 00:13:40,760 As these AI systems become more powerful and we start giving them these more and more complicated problems to solve, 314 00:13:40,760 --> 00:13:42,760 we need to make sure that we can keep up. 315 00:13:42,760 --> 00:13:48,760 Like we don't want to get to a point where AI is so advanced that we can't even understand what it's doing anymore. 316 00:13:48,760 --> 00:13:49,760 Exactly. 317 00:13:49,760 --> 00:13:55,760 We need to be able to monitor and guide these systems even if we don't fully understand all the details ourselves. 318 00:13:55,760 --> 00:13:59,760 It's kind of like sending explorers into uncharted territory. 319 00:13:59,760 --> 00:14:03,760 We need to make sure they have the tools and the communication systems they need to stay on track. 320 00:14:03,760 --> 00:14:04,760 Exactly. 321 00:14:04,760 --> 00:14:07,760 So it's not just about building a really smart AI. 322 00:14:07,760 --> 00:14:13,760 It's about building a smart AI that we can trust and that we can understand and that we can collaborate with. 323 00:14:13,760 --> 00:14:16,760 So this research is really laying the groundwork for that. 324 00:14:16,760 --> 00:14:23,760 It's not just about celebrating what AI can do but also about recognizing its limitations and figuring out how to address them. 325 00:14:23,760 --> 00:14:24,760 Absolutely. 326 00:14:24,760 --> 00:14:27,760 And I think that's a really important message to take away from all of this. 327 00:14:27,760 --> 00:14:28,760 It is. 328 00:14:28,760 --> 00:14:33,760 Research paints a really interesting picture of AI's potential in science. 329 00:14:33,760 --> 00:14:38,760 It's exciting to see how these systems are already being used to tackle some really complex problems. 330 00:14:38,760 --> 00:14:45,760 But it's also a reminder that we need to be cautious and make sure that we're developing and using AI in a way that ultimately benefits humanity. 331 00:14:45,760 --> 00:14:46,760 I agree. 332 00:14:46,760 --> 00:14:47,760 It's a collaborative effort. 333 00:14:47,760 --> 00:14:49,760 It's not just up to the AI researchers. 334 00:14:49,760 --> 00:14:56,760 We need scientists, ethicists, policymakers, everyone working together to make sure that AI is used responsibly. 335 00:14:56,760 --> 00:14:57,760 That's a really important point. 336 00:14:57,760 --> 00:14:59,760 This isn't just a technological challenge. 337 00:14:59,760 --> 00:15:00,760 It's a societal challenge. 338 00:15:00,760 --> 00:15:01,760 It is. 339 00:15:01,760 --> 00:15:08,760 And we need to be having these conversations now so that we're prepared for a future where AI plays an even bigger role in our lives. 340 00:15:08,760 --> 00:15:09,760 Right. 341 00:15:09,760 --> 00:15:14,760 We need to think about the ethical implications of AI and develop guidelines for its responsible development. 342 00:15:14,760 --> 00:15:17,760 And make sure that human oversight remains a top priority. 343 00:15:17,760 --> 00:15:18,760 Exactly. 344 00:15:18,760 --> 00:15:22,760 AI should be a tool that empowers humans, not a tool that replaces them. 345 00:15:22,760 --> 00:15:24,760 It's about collaboration, not competition. 346 00:15:24,760 --> 00:15:25,760 I agree. 347 00:15:25,760 --> 00:15:32,760 So before we get too philosophical here, I want to come back to something you mentioned earlier about the different versions of the GPQA benchmark. 348 00:15:32,760 --> 00:15:33,760 Oh, yeah. 349 00:15:33,760 --> 00:15:38,760 The fact that they created three different versions with varying levels of difficulty. 350 00:15:38,760 --> 00:15:40,760 Can you talk a little bit more about why they did that? 351 00:15:40,760 --> 00:15:41,760 Sure. 352 00:15:41,760 --> 00:15:50,760 It's actually a really clever approach because it allows researchers to test different AI models and different approaches as the AI gets more sophisticated. 353 00:15:50,760 --> 00:15:51,760 Okay. 354 00:15:51,760 --> 00:15:56,760 So you can start with the easier questions and then as the AI improves, you can move up to the harder ones. 355 00:15:56,760 --> 00:15:59,760 So it's like having different levels of difficulty for our AI athletes. 356 00:15:59,760 --> 00:16:00,760 Exactly. 357 00:16:00,760 --> 00:16:04,760 You can see how far they can go and identify their strengths and weaknesses at each level. 358 00:16:04,760 --> 00:16:05,760 I like that analogy. 359 00:16:05,760 --> 00:16:11,760 And it's important to remember that this benchmark isn't just testing AI's overall problem-solving abilities. 360 00:16:11,760 --> 00:16:15,760 It's also looking at how well it performs across different scientific domains. 361 00:16:15,760 --> 00:16:16,760 Right. 362 00:16:16,760 --> 00:16:21,760 So we find that the AI was better at certain types of questions than others. 363 00:16:21,760 --> 00:16:25,760 Like were the chemistry questions easier than the physics questions, for example? 364 00:16:25,760 --> 00:16:28,760 Yeah, they actually did find some variation in performance. 365 00:16:28,760 --> 00:16:35,760 Even with a really powerful model like GPT-4, the accuracy vary depending on things like the prompting technique they use, 366 00:16:35,760 --> 00:16:40,760 whether it had access to internet search and even the specific scientific domain of the question. 367 00:16:40,760 --> 00:16:43,760 So it's not just about how smart the AI is. 368 00:16:43,760 --> 00:16:46,760 It's about how we use it and how we set it up for success. 369 00:16:46,760 --> 00:16:47,760 Yeah. 370 00:16:47,760 --> 00:16:48,760 And that's one of the exciting things about this research. 371 00:16:48,760 --> 00:16:54,760 It shows us that there's still so much we can do to improve how we use AI and how we tailor it to specific tasks. 372 00:16:54,760 --> 00:16:57,760 It's like realizing that AI isn't just one thing. 373 00:16:57,760 --> 00:16:59,760 It's a whole toolkit. 374 00:16:59,760 --> 00:17:00,760 Exactly. 375 00:17:00,760 --> 00:17:02,760 And we need different AI specialists for different jobs. 376 00:17:02,760 --> 00:17:03,760 Right. 377 00:17:03,760 --> 00:17:09,760 And this research is helping us figure out how to create those specialists and how to train them to excel in their respective fields. 378 00:17:09,760 --> 00:17:13,760 It also highlights the importance of carefully evaluating AI performance. 379 00:17:13,760 --> 00:17:14,760 Yeah, for sure. 380 00:17:14,760 --> 00:17:18,760 It's not enough to just look at the overall accuracy score. 381 00:17:18,760 --> 00:17:24,760 We need to dig deeper and understand where the AI is succeeding, where it's failing, and why. 382 00:17:24,760 --> 00:17:32,760 Exactly, because even the most advanced AI can still make mistakes, especially when it's dealing with really complex scientific concepts. 383 00:17:32,760 --> 00:17:37,760 And if we're going to rely on these systems to help us with important research, we need to be aware of those limitations. 384 00:17:37,760 --> 00:17:38,760 And potential biases. 385 00:17:38,760 --> 00:17:42,760 This paper really emphasizes the need for a balanced perspective on AI. 386 00:17:42,760 --> 00:17:43,760 It does. 387 00:17:43,760 --> 00:17:45,760 It acknowledges the incredible potential. 388 00:17:45,760 --> 00:17:51,760 But it also stresses the importance of responsible development, rigorous evaluation, and continuous oversight. 389 00:17:51,760 --> 00:17:53,760 I think that's a really important message. 390 00:17:53,760 --> 00:17:57,760 It's like saying, hey, AI is an amazing tool, but let's not get carried away. 391 00:17:57,760 --> 00:18:02,760 We need to use it wisely and make sure it remains a force for good in the scientific world. 392 00:18:02,760 --> 00:18:03,760 I agree. 393 00:18:03,760 --> 00:18:11,760 It's about finding that balance between pushing the boundaries of what's possible and making sure that we're doing it in a way that benefits humanity as a whole. 394 00:18:11,760 --> 00:18:14,760 So all of this leads to a really important question. 395 00:18:14,760 --> 00:18:16,760 What does this research mean for the average person? 396 00:18:16,760 --> 00:18:17,760 That's a great question. 397 00:18:17,760 --> 00:18:23,760 Like if you're not a scientist or an AI researcher, how did this research impact your life? 398 00:18:23,760 --> 00:18:29,760 Well, even if you're not directly involved in those fields, AI is becoming increasingly integrated into our lives. 399 00:18:29,760 --> 00:18:30,760 It's everywhere. 400 00:18:30,760 --> 00:18:31,760 It is. 401 00:18:31,760 --> 00:18:42,760 And this research raises some really important questions about the future of knowledge, the role of AI in society, the importance of critical thinking in a world where technology is becoming more and more powerful. 402 00:18:42,760 --> 00:18:46,760 So it's like saying that AI is becoming so important that we all need to understand it. 403 00:18:46,760 --> 00:18:47,760 Exactly. 404 00:18:47,760 --> 00:18:48,760 Not just the experts. 405 00:18:48,760 --> 00:18:55,760 We all need to be informed citizens so we can participate in these conversations and help shape the future of AI in a way that benefits everyone. 406 00:18:55,760 --> 00:18:56,760 I couldn't agree more. 407 00:18:56,760 --> 00:18:57,760 We all have a stake in this. 408 00:18:57,760 --> 00:19:05,760 We need to make sure that AI is developed and used ethically and responsibly and in a way that aligns with our values as a society. 409 00:19:05,760 --> 00:19:06,760 I agree. 410 00:19:06,760 --> 00:19:09,760 This research is a reminder that we're living in a really exciting time. 411 00:19:09,760 --> 00:19:17,760 Technology is advancing so rapidly and it's up to us to make sure that those advancements lead to a more just and equitable and sustainable world. 412 00:19:17,760 --> 00:19:18,760 Absolutely. 413 00:19:18,760 --> 00:19:22,760 It's up to all of us to guide these advancements in a positive direction. 414 00:19:22,760 --> 00:19:25,760 I think that's a perfect place to pause for now. 415 00:19:25,760 --> 00:19:29,760 We'll be back for the third and final part of our deep dive after a quick break. 416 00:19:29,760 --> 00:19:33,760 Yeah, it really is amazing to think about how quickly this field is moving. 417 00:19:33,760 --> 00:19:40,760 It wasn't that long ago that the idea of AI solving complex scientific problems seems like pure science fiction. 418 00:19:40,760 --> 00:19:41,760 Right. 419 00:19:41,760 --> 00:19:43,760 It's incredible to see how far we've come. 420 00:19:43,760 --> 00:19:49,760 And this research really gives us a glimpse into what might be possible in the future as AI continues to evolve. 421 00:19:49,760 --> 00:19:56,760 Yeah, I mean imagine a future where AI is helping us unlock the mysteries of dark matter or designing life-saving drugs. 422 00:19:56,760 --> 00:20:01,760 Or even coming up with like whole new theories of physics that like change our understanding of the universe. 423 00:20:01,760 --> 00:20:02,760 Exactly. 424 00:20:02,760 --> 00:20:04,760 The possibilities are really mind-blowing. 425 00:20:04,760 --> 00:20:07,760 But of course with all this potential comes a huge responsibility. 426 00:20:07,760 --> 00:20:08,760 Oh, definitely. 427 00:20:08,760 --> 00:20:09,760 Yeah. 428 00:20:09,760 --> 00:20:14,760 We have to make sure that we're developing and using AI in a way that benefits humanity, not in a way that harms us. 429 00:20:14,760 --> 00:20:15,760 Absolutely. 430 00:20:15,760 --> 00:20:19,760 We need to be having open and honest conversations about the ethical implications of AI. 431 00:20:19,760 --> 00:20:23,760 And we need to establish clear guidelines for its development and use. 432 00:20:23,760 --> 00:20:26,760 And we can't forget about human oversight. 433 00:20:26,760 --> 00:20:28,760 Right. Human oversight is crucial. 434 00:20:28,760 --> 00:20:31,760 AI should be a tool that empowers us. 435 00:20:31,760 --> 00:20:33,760 Not a tool that replaces us. 436 00:20:33,760 --> 00:20:36,760 It's about collaboration, not competition. 437 00:20:36,760 --> 00:20:37,760 Exactly. 438 00:20:37,760 --> 00:20:43,760 And that's something we need to keep in mind as we continue to explore the potential of AI in scientific discovery. 439 00:20:43,760 --> 00:20:50,760 Well, this has been a really fascinating deep dive into the world of AI and its potential to revolutionize scientific research. 440 00:20:50,760 --> 00:20:51,760 It has. 441 00:20:51,760 --> 00:20:54,760 It's clear that we're just at the beginning of this journey. 442 00:20:54,760 --> 00:20:56,760 But it's an exciting and challenging one. 443 00:20:56,760 --> 00:20:58,760 And I'm really curious to see where it takes us. 444 00:20:58,760 --> 00:20:59,760 Me too. 445 00:20:59,760 --> 00:21:03,760 I think this research is a great reminder that AI has incredible potential. 446 00:21:03,760 --> 00:21:10,760 But it's up to us to guide its development and ensure that it's used responsibly to benefit humanity and to expand the frontiers of knowledge. 447 00:21:10,760 --> 00:21:13,760 So to wrap things up, I want to leave you with one final thought. 448 00:21:13,760 --> 00:21:18,760 If AI can help us solve problems that we're currently struggling with, what new questions might it help us ask? 449 00:21:18,760 --> 00:21:20,760 Hmm. That's a really interesting question. 450 00:21:20,760 --> 00:21:23,760 What mysteries might it uncover that we haven't even thought of yet? 451 00:21:23,760 --> 00:21:27,760 I think that's a question that will drive a lot of research and exploration in the years to come. 452 00:21:27,760 --> 00:21:30,760 And I, for one, am excited to see what we discover. 453 00:21:30,760 --> 00:21:33,760 And on that note, we'll leave you to ponder that thought. 454 00:21:33,760 --> 00:21:37,760 Thanks for joining us for this deep dive into the world of AI. 455 00:21:37,760 --> 00:21:42,760 Until next time, keep exploring, keep learning, and keep asking those big questions.