1 00:00:00,000 --> 00:00:02,280 Okay, so get ready for this. 2 00:00:02,280 --> 00:00:03,840 We're gonna be exploring a future 3 00:00:03,840 --> 00:00:07,800 where scientific discoveries happen like crazy fast. 4 00:00:07,800 --> 00:00:09,160 All thanks to AI. 5 00:00:09,160 --> 00:00:12,120 I bet you're probably thinking AI in science. 6 00:00:12,120 --> 00:00:13,520 Yeah, is that even possible? 7 00:00:13,520 --> 00:00:14,680 Exactly. 8 00:00:14,680 --> 00:00:16,920 Well, get ready to have your mind blown 9 00:00:16,920 --> 00:00:18,360 because we're going deep into this 10 00:00:18,360 --> 00:00:21,600 leading astrophysicists video essay. 11 00:00:21,600 --> 00:00:22,440 Oh, cool. 12 00:00:22,440 --> 00:00:24,040 And he shares all these insights 13 00:00:24,040 --> 00:00:26,080 along with some other scientists too. 14 00:00:26,080 --> 00:00:28,400 And it's pretty mind blowing. 15 00:00:28,400 --> 00:00:30,160 It's not just science fiction anymore. 16 00:00:30,160 --> 00:00:31,000 Yeah. 17 00:00:31,000 --> 00:00:33,000 I mean, AI is already changing research. 18 00:00:33,000 --> 00:00:33,840 Really? 19 00:00:33,840 --> 00:00:34,680 Oh yeah. 20 00:00:34,680 --> 00:00:35,520 Like how so? 21 00:00:35,520 --> 00:00:39,080 Well, the impact is like growing super fast, 22 00:00:39,080 --> 00:00:41,600 especially in fields like astronomy, 23 00:00:41,600 --> 00:00:45,320 where researchers are dealing with these massive data sets. 24 00:00:45,320 --> 00:00:48,940 So it's like AI is giving scientists these superpowers 25 00:00:48,940 --> 00:00:50,760 to analyze information. 26 00:00:50,760 --> 00:00:52,440 That would just take humans forever. 27 00:00:52,440 --> 00:00:53,280 Exactly. 28 00:00:53,280 --> 00:00:54,120 Think about it. 29 00:00:54,120 --> 00:00:54,960 Okay. 30 00:00:54,960 --> 00:00:57,080 Telescopes are gathering mountains of data every night. 31 00:00:57,080 --> 00:00:57,920 Right. 32 00:00:57,920 --> 00:01:01,920 So AI can like sift through that data and identify patterns. 33 00:01:01,920 --> 00:01:03,960 And even make predictions way faster. 34 00:01:03,960 --> 00:01:05,240 That's crazy. 35 00:01:05,240 --> 00:01:07,600 And more efficiently than humans ever could. 36 00:01:07,600 --> 00:01:11,120 Okay, so AI is like a super powered research assistant then. 37 00:01:11,120 --> 00:01:12,320 Yeah, that's a good way to put it. 38 00:01:12,320 --> 00:01:15,800 But wait, isn't all AI basically the same? 39 00:01:16,960 --> 00:01:17,800 Not quite. 40 00:01:17,800 --> 00:01:20,520 The way AI is being used in science, 41 00:01:20,520 --> 00:01:23,640 it's like evolved over time. 42 00:01:23,640 --> 00:01:24,480 Oh, okay. 43 00:01:24,480 --> 00:01:26,120 You can think of it as three distinct waves. 44 00:01:26,120 --> 00:01:27,400 Three waves. 45 00:01:27,400 --> 00:01:28,720 Okay, I'm intrigued. 46 00:01:28,720 --> 00:01:32,720 So the first wave involved like simpler neural networks, 47 00:01:32,720 --> 00:01:36,080 mainly used for tasks like classifying objects 48 00:01:36,080 --> 00:01:37,760 or predicting outcomes. 49 00:01:37,760 --> 00:01:41,440 Then came the second wave with more sophisticated networks, 50 00:01:41,440 --> 00:01:43,640 capable of things like image recognition. 51 00:01:43,640 --> 00:01:44,480 Oh, cool. 52 00:01:44,480 --> 00:01:46,360 You know, like self-driving cars use that. 53 00:01:46,360 --> 00:01:47,200 Right, right. 54 00:01:47,200 --> 00:01:48,520 So that's the second wave. 55 00:01:48,520 --> 00:01:50,560 Okay, so we're in the third wave now. 56 00:01:50,560 --> 00:01:51,400 Yeah, we are. 57 00:01:51,400 --> 00:01:53,200 What's so special about that one? 58 00:01:53,200 --> 00:01:56,920 Well, this third wave is all about unsupervised 59 00:01:56,920 --> 00:01:58,400 and generative deep learning. 60 00:01:58,400 --> 00:01:59,560 Oh, wow. 61 00:01:59,560 --> 00:02:01,720 This is where things get really interesting. 62 00:02:01,720 --> 00:02:06,720 These AI models can learn from huge amounts of data, 63 00:02:06,800 --> 00:02:09,600 unlabeled data, finding complex patterns 64 00:02:09,600 --> 00:02:12,360 and relationships that humans might miss. 65 00:02:12,360 --> 00:02:13,200 Okay. 66 00:02:13,200 --> 00:02:14,240 And they can generate new data. 67 00:02:14,240 --> 00:02:15,080 Really? 68 00:02:15,080 --> 00:02:18,200 Like images, texts, even code. 69 00:02:18,200 --> 00:02:21,360 So it's not just about analyzing existing information, 70 00:02:21,360 --> 00:02:23,520 but actually like creating new knowledge. 71 00:02:23,520 --> 00:02:24,680 Wow. 72 00:02:24,680 --> 00:02:25,640 That's pretty wild. 73 00:02:25,640 --> 00:02:26,480 It is. 74 00:02:26,480 --> 00:02:28,360 There's a lot of people here deep learning 75 00:02:28,360 --> 00:02:30,360 and they just think of chat GPT. 76 00:02:30,360 --> 00:02:31,200 Yeah. 77 00:02:31,200 --> 00:02:32,040 Is that right? 78 00:02:32,040 --> 00:02:32,880 You're right to think of it. 79 00:02:32,880 --> 00:02:33,720 Okay. 80 00:02:33,720 --> 00:02:35,680 Chat GPT is like a prime example of this third wave. 81 00:02:35,680 --> 00:02:36,520 Okay. 82 00:02:36,520 --> 00:02:38,080 It's a generative AI model. 83 00:02:38,080 --> 00:02:38,920 Uh-huh. 84 00:02:38,920 --> 00:02:41,640 That can understand and generate human language. 85 00:02:41,640 --> 00:02:43,000 So it's like super versatile. 86 00:02:43,000 --> 00:02:43,840 Super versatile. 87 00:02:43,840 --> 00:02:46,400 Okay, but chat GPT is mainly known for 88 00:02:46,400 --> 00:02:48,400 like writing essays and poems, right? 89 00:02:48,400 --> 00:02:49,240 Yeah. 90 00:02:49,240 --> 00:02:50,760 How does that apply to actual science? 91 00:02:50,760 --> 00:02:53,760 Well, imagine using chat GPT 92 00:02:53,760 --> 00:02:56,320 to summarize complex research papers. 93 00:02:56,320 --> 00:02:57,160 Okay. 94 00:02:57,160 --> 00:02:59,000 Translate scientific articles. 95 00:02:59,000 --> 00:03:00,280 Into different languages. 96 00:03:00,280 --> 00:03:01,680 Into different languages. 97 00:03:01,680 --> 00:03:04,560 Or even like help write sections of manuscripts 98 00:03:04,560 --> 00:03:05,560 or grant proposals. 99 00:03:05,560 --> 00:03:06,840 No, that's a good idea. 100 00:03:06,840 --> 00:03:08,480 And in fact, it's already happening. 101 00:03:08,480 --> 00:03:09,320 Really? 102 00:03:09,320 --> 00:03:10,160 One study found. 103 00:03:10,160 --> 00:03:11,000 Okay. 104 00:03:11,000 --> 00:03:14,120 That 30% of scientists are already using AI tools. 105 00:03:14,120 --> 00:03:15,040 What? 106 00:03:15,040 --> 00:03:15,880 30%. 107 00:03:15,880 --> 00:03:16,720 30%. 108 00:03:16,720 --> 00:03:17,560 That's a lot. 109 00:03:17,560 --> 00:03:18,800 It is, it is. 110 00:03:18,800 --> 00:03:19,640 But hold on. 111 00:03:19,640 --> 00:03:20,480 Yeah. 112 00:03:20,480 --> 00:03:22,120 If AI is helping write scientific papers. 113 00:03:22,120 --> 00:03:22,960 Yeah. 114 00:03:22,960 --> 00:03:24,800 Doesn't that raise some ethical questions? 115 00:03:24,800 --> 00:03:25,640 Of course. 116 00:03:25,640 --> 00:03:27,720 Like who gets credit for the work? 117 00:03:27,720 --> 00:03:28,560 Right. 118 00:03:28,560 --> 00:03:30,160 That's a key question being debated right now. 119 00:03:30,160 --> 00:03:31,000 Yeah. 120 00:03:31,000 --> 00:03:33,440 Should AI be listed as a co-author? 121 00:03:33,440 --> 00:03:34,280 That's so interesting. 122 00:03:34,280 --> 00:03:36,920 Some journals are even banning the use of AI. 123 00:03:36,920 --> 00:03:37,760 Really? 124 00:03:37,760 --> 00:03:38,600 Altogether. 125 00:03:38,600 --> 00:03:39,440 Wow. 126 00:03:39,440 --> 00:03:41,960 Because of concerns about plagiarism and accountability. 127 00:03:41,960 --> 00:03:43,240 Yeah, that makes sense. 128 00:03:43,240 --> 00:03:44,280 But on the flip side. 129 00:03:44,280 --> 00:03:45,120 Yeah. 130 00:03:45,120 --> 00:03:48,640 Imagine a future where AI can analyze tons of data. 131 00:03:48,640 --> 00:03:49,480 Okay. 132 00:03:49,480 --> 00:03:51,480 And generate draft sections of a paper. 133 00:03:51,480 --> 00:03:52,320 Wow. 134 00:03:52,320 --> 00:03:54,680 Freeing up scientists to focus on the big picture. 135 00:03:54,680 --> 00:03:56,200 Okay. I see both sides of that. 136 00:03:56,200 --> 00:03:57,040 Yeah. 137 00:03:57,040 --> 00:04:00,120 On one hand, it seems unfair to give AI authorship. 138 00:04:00,120 --> 00:04:00,960 Right. 139 00:04:00,960 --> 00:04:02,520 If it's just a tool. 140 00:04:02,520 --> 00:04:05,480 But on the other hand, if AI can help scientists 141 00:04:05,480 --> 00:04:07,680 be more efficient and productive. 142 00:04:07,680 --> 00:04:08,520 Yeah. 143 00:04:08,520 --> 00:04:09,360 And productive. 144 00:04:09,360 --> 00:04:11,200 That could lead to more breakthroughs, right? 145 00:04:11,200 --> 00:04:12,040 Exactly. 146 00:04:12,040 --> 00:04:12,880 Yeah. 147 00:04:12,880 --> 00:04:13,880 It's a complex issue. 148 00:04:13,880 --> 00:04:15,240 And there are no easy answers. 149 00:04:15,240 --> 00:04:16,080 Yeah. 150 00:04:16,080 --> 00:04:19,160 What's clear is that AI is already impacting research. 151 00:04:19,160 --> 00:04:20,000 Right. 152 00:04:20,000 --> 00:04:21,080 In profound ways. 153 00:04:21,080 --> 00:04:21,920 Yeah. 154 00:04:21,920 --> 00:04:23,080 And it's not just about writing, right? 155 00:04:23,080 --> 00:04:23,920 Right. 156 00:04:23,920 --> 00:04:25,000 It's about other parts of research. 157 00:04:25,000 --> 00:04:25,840 Yeah. Like what? 158 00:04:25,840 --> 00:04:29,120 Like collecting data, designing experiments, 159 00:04:29,120 --> 00:04:31,760 or even peer review. 160 00:04:31,760 --> 00:04:32,600 Yeah. 161 00:04:32,600 --> 00:04:33,800 Can AI play a role there too? 162 00:04:33,800 --> 00:04:34,640 Absolutely. 163 00:04:34,640 --> 00:04:35,480 Yeah. 164 00:04:35,480 --> 00:04:38,560 Let's take the example of data analysis in astronomy. 165 00:04:38,560 --> 00:04:39,400 Okay. 166 00:04:39,400 --> 00:04:42,160 AI can help identify patterns. 167 00:04:42,160 --> 00:04:43,000 Uh-huh. 168 00:04:43,000 --> 00:04:45,200 In the massive amounts of data collected by telescopes. 169 00:04:45,200 --> 00:04:46,040 Right. 170 00:04:46,040 --> 00:04:47,760 Helping researchers find new objects. 171 00:04:47,760 --> 00:04:48,600 Oh. 172 00:04:48,600 --> 00:04:49,440 Or events. 173 00:04:49,440 --> 00:04:52,040 So AI is like a super powered detective. 174 00:04:52,040 --> 00:04:52,880 Yeah. 175 00:04:52,880 --> 00:04:54,000 It includes in space. 176 00:04:54,000 --> 00:04:54,840 That's a great analogy. 177 00:04:54,840 --> 00:04:56,760 And think about designing experiments. 178 00:04:56,760 --> 00:04:57,600 Yeah. 179 00:04:57,600 --> 00:05:01,080 AI can help scientists optimize their setups. 180 00:05:01,080 --> 00:05:01,920 Right. 181 00:05:01,920 --> 00:05:03,520 By analyzing previous data. 182 00:05:03,520 --> 00:05:04,360 Exactly. 183 00:05:04,360 --> 00:05:05,680 And suggesting parameters. 184 00:05:05,680 --> 00:05:06,520 That's right. 185 00:05:06,520 --> 00:05:07,760 That are most likely to yield results. 186 00:05:07,760 --> 00:05:08,920 Yeah. Meaningful results. 187 00:05:08,920 --> 00:05:09,760 Right. 188 00:05:09,760 --> 00:05:11,520 This can save a lot of time and resources. 189 00:05:11,520 --> 00:05:12,360 That's amazing. 190 00:05:12,360 --> 00:05:15,080 Allowing scientists to conduct more experiments. 191 00:05:15,080 --> 00:05:16,320 That's fascinating. 192 00:05:16,320 --> 00:05:17,160 Yeah. 193 00:05:17,160 --> 00:05:21,120 So AI can actually make science more efficient 194 00:05:21,120 --> 00:05:22,080 and innovative. 195 00:05:22,080 --> 00:05:23,120 Yeah. More efficient. 196 00:05:23,120 --> 00:05:23,960 More innovative. 197 00:05:23,960 --> 00:05:25,640 What about peer review? 198 00:05:25,640 --> 00:05:26,720 Oh, that's interesting. 199 00:05:26,720 --> 00:05:28,440 That crucial process. 200 00:05:28,440 --> 00:05:29,720 Yeah. Checking research. 201 00:05:29,720 --> 00:05:30,800 For its published. 202 00:05:30,800 --> 00:05:31,640 Right. 203 00:05:31,640 --> 00:05:33,840 Can AI really contribute there? 204 00:05:33,840 --> 00:05:35,720 That's where things get really interesting. 205 00:05:35,720 --> 00:05:36,560 Okay. 206 00:05:36,560 --> 00:05:37,800 Imagine an AI system. 207 00:05:37,800 --> 00:05:38,640 Yeah. 208 00:05:38,640 --> 00:05:40,720 That can analyze a manuscript. 209 00:05:40,720 --> 00:05:41,560 Mm-hmm. 210 00:05:41,560 --> 00:05:44,240 Identify its key findings and contributions. 211 00:05:44,240 --> 00:05:45,080 Right. 212 00:05:45,080 --> 00:05:47,520 And even suggest potential reviewers. 213 00:05:47,520 --> 00:05:48,360 Wow. 214 00:05:48,360 --> 00:05:49,720 Or experts in the field. 215 00:05:49,720 --> 00:05:51,320 Wait. So instead of journals struggling 216 00:05:51,320 --> 00:05:52,280 to find reviewers. 217 00:05:52,280 --> 00:05:53,120 Yeah. 218 00:05:53,120 --> 00:05:54,880 AI could streamline the whole process. 219 00:05:54,880 --> 00:05:55,720 Exactly. 220 00:05:55,720 --> 00:05:57,360 And speed up the publication timeline. 221 00:05:57,360 --> 00:05:58,200 Exactly. 222 00:05:58,200 --> 00:06:02,520 AI could even help with tasks like checking for plagiarism. 223 00:06:02,520 --> 00:06:03,360 Oh. 224 00:06:03,360 --> 00:06:04,200 Okay. 225 00:06:04,200 --> 00:06:05,040 Identifying statistical errors 226 00:06:05,040 --> 00:06:07,400 or highlighting ethical concerns. 227 00:06:07,400 --> 00:06:08,240 Yeah. Yeah. 228 00:06:08,240 --> 00:06:10,320 Of course, human oversight would still be essential. 229 00:06:10,320 --> 00:06:11,160 Right. 230 00:06:11,160 --> 00:06:12,920 But AI could reduce the workload 231 00:06:12,920 --> 00:06:14,600 for editors and reviewers. 232 00:06:14,600 --> 00:06:15,600 That makes a lot of sense. 233 00:06:15,600 --> 00:06:16,440 Yeah. 234 00:06:16,440 --> 00:06:19,240 But what about the concern that journals 235 00:06:19,240 --> 00:06:22,720 might start selling their archives of. 236 00:06:22,720 --> 00:06:23,560 Oh, yeah. 237 00:06:23,560 --> 00:06:25,680 Reviewer comments and author responses. 238 00:06:25,680 --> 00:06:26,520 Right. 239 00:06:26,520 --> 00:06:27,840 To train these AI models. 240 00:06:27,840 --> 00:06:28,680 Yeah. 241 00:06:28,680 --> 00:06:29,520 It seems a little sketchy. 242 00:06:29,520 --> 00:06:30,480 It's a valid concern. 243 00:06:30,480 --> 00:06:31,320 Yeah. 244 00:06:31,320 --> 00:06:34,320 As AI becomes more integrated into research. 245 00:06:34,320 --> 00:06:35,160 Right. 246 00:06:35,160 --> 00:06:37,440 We need to be mindful of the potential consequences. 247 00:06:37,440 --> 00:06:38,280 Yeah. 248 00:06:38,280 --> 00:06:39,720 And make sure that ethical considerations 249 00:06:39,720 --> 00:06:40,560 are front and center. 250 00:06:40,560 --> 00:06:41,400 For sure. 251 00:06:41,400 --> 00:06:42,240 Yeah. 252 00:06:42,240 --> 00:06:44,160 It sounds like we're on the verge of a major shift. 253 00:06:44,160 --> 00:06:45,000 We are. 254 00:06:45,000 --> 00:06:45,840 In how science is done. 255 00:06:45,840 --> 00:06:46,680 Totally. 256 00:06:46,680 --> 00:06:48,720 But where do humans fit into all of this? 257 00:06:48,720 --> 00:06:49,560 Right. 258 00:06:49,560 --> 00:06:51,440 If AI can do so much. 259 00:06:51,440 --> 00:06:52,280 Uh-huh. 260 00:06:52,280 --> 00:06:55,720 Does that mean the end of the human scientist? 261 00:06:55,720 --> 00:06:57,920 Well, that's the big question, right? 262 00:06:57,920 --> 00:06:58,760 Yeah. 263 00:06:58,760 --> 00:07:02,000 This astrophysicist, he's really grappling with this. 264 00:07:02,000 --> 00:07:02,840 Yeah. 265 00:07:02,840 --> 00:07:05,120 He talks about how for a lot of scientists, 266 00:07:05,120 --> 00:07:06,960 research is driven by this. 267 00:07:06,960 --> 00:07:08,560 Like this curiosity. 268 00:07:08,560 --> 00:07:09,400 Right. 269 00:07:09,400 --> 00:07:11,440 You know, this need to understand the universe. 270 00:07:11,440 --> 00:07:12,280 What they understand. 271 00:07:12,280 --> 00:07:13,640 It's like this fundamental. 272 00:07:13,640 --> 00:07:14,960 Yeah, like a human drive. 273 00:07:14,960 --> 00:07:15,880 Human drive. 274 00:07:15,880 --> 00:07:16,720 Totally. 275 00:07:16,720 --> 00:07:17,560 Right. 276 00:07:17,560 --> 00:07:18,400 Yeah. 277 00:07:18,400 --> 00:07:22,560 It draws a lot of us to like science podcasts, 278 00:07:22,560 --> 00:07:24,520 documentaries, all that stuff. 279 00:07:24,520 --> 00:07:25,600 Yeah, we crave that. 280 00:07:25,600 --> 00:07:26,920 Yeah, you crave that. 281 00:07:26,920 --> 00:07:27,760 Uh-huh. 282 00:07:27,760 --> 00:07:28,600 Moment. 283 00:07:28,600 --> 00:07:29,440 Uh-huh. 284 00:07:29,440 --> 00:07:30,280 I'm all over. 285 00:07:30,280 --> 00:07:31,840 A feeling of understanding something new about the world. 286 00:07:31,840 --> 00:07:32,960 Exactly. 287 00:07:32,960 --> 00:07:34,680 And that's where things get interesting. 288 00:07:34,680 --> 00:07:35,520 Okay. 289 00:07:35,520 --> 00:07:39,080 What happens if AI starts making all these ground breaking 290 00:07:39,080 --> 00:07:42,520 discoveries that are so complex, 291 00:07:42,520 --> 00:07:45,240 so beyond human comprehension, 292 00:07:45,240 --> 00:07:47,280 that we can't even grasp them? 293 00:07:47,280 --> 00:07:49,240 So it's like, is it even a discovery? 294 00:07:49,240 --> 00:07:52,600 If humans can't understand it, does it even matter? 295 00:07:52,600 --> 00:07:53,440 Right. 296 00:07:53,440 --> 00:07:54,480 It raises some deep questions. 297 00:07:54,480 --> 00:07:55,320 Yeah. 298 00:07:55,320 --> 00:07:57,520 About like the nature of knowledge and discovery. 299 00:07:57,520 --> 00:07:58,360 Yeah, for sure. 300 00:07:58,360 --> 00:08:00,680 And it makes you think about the role of scientists. 301 00:08:00,680 --> 00:08:01,520 Yeah. 302 00:08:01,520 --> 00:08:03,920 If AI can churn out all this research, 303 00:08:03,920 --> 00:08:06,320 what makes a human scientist valuable? 304 00:08:06,320 --> 00:08:07,160 Right. 305 00:08:07,160 --> 00:08:08,720 And what about science communication? 306 00:08:08,720 --> 00:08:11,240 Like all those amazing science communicators out there 307 00:08:11,240 --> 00:08:13,640 who can explain these complex topics 308 00:08:13,640 --> 00:08:15,760 in a way that's both accurate, 309 00:08:15,760 --> 00:08:17,720 but also engaging. 310 00:08:17,720 --> 00:08:20,200 Could an AI ever do that? 311 00:08:20,200 --> 00:08:21,400 Would we even want it to? 312 00:08:21,400 --> 00:08:24,360 That's where the scientists in the video 313 00:08:24,360 --> 00:08:26,160 had some really different opinions. 314 00:08:26,160 --> 00:08:31,160 Some were skeptical that AI could ever truly replicate 315 00:08:31,600 --> 00:08:34,480 human intuition and creativity, 316 00:08:34,480 --> 00:08:36,520 especially in fields like physics, 317 00:08:36,520 --> 00:08:40,000 where so much is based on physical experience 318 00:08:40,000 --> 00:08:40,960 and experimentation. 319 00:08:40,960 --> 00:08:41,800 Right. 320 00:08:41,800 --> 00:08:43,840 Like one scientist brought up 321 00:08:43,840 --> 00:08:45,400 Einstein's thought experiment. 322 00:08:45,400 --> 00:08:46,240 Oh, yeah? 323 00:08:46,240 --> 00:08:47,960 About writing in a falling elevator. 324 00:08:47,960 --> 00:08:48,800 Yeah. 325 00:08:48,800 --> 00:08:52,000 He argued that an AI without a physical body 326 00:08:52,000 --> 00:08:54,560 could never truly grasp 327 00:08:54,560 --> 00:08:56,720 what it feels like to be weightless. 328 00:08:56,720 --> 00:08:57,560 Yeah. 329 00:08:57,560 --> 00:08:59,400 To experience gravity in that way. 330 00:08:59,400 --> 00:09:01,040 It's a really thought provoking point. 331 00:09:01,040 --> 00:09:01,880 Yeah. 332 00:09:01,880 --> 00:09:05,160 How can an AI without those experiences. 333 00:09:05,160 --> 00:09:06,000 Yeah. 334 00:09:06,000 --> 00:09:07,480 Develop this deep understanding. 335 00:09:07,480 --> 00:09:08,320 Right. 336 00:09:08,320 --> 00:09:10,320 Of the physical laws that govern our universe. 337 00:09:10,320 --> 00:09:11,680 But this is where I get tripped up. 338 00:09:11,680 --> 00:09:12,520 Yeah. 339 00:09:12,520 --> 00:09:15,080 Even if AI can't have those aha moments. 340 00:09:15,080 --> 00:09:16,680 Like those original insights. 341 00:09:16,680 --> 00:09:17,520 Right. 342 00:09:17,520 --> 00:09:22,520 What if it's really good at connecting existing ideas 343 00:09:22,560 --> 00:09:24,840 in new and surprising ways? 344 00:09:24,840 --> 00:09:25,680 Yeah, that's interesting. 345 00:09:25,680 --> 00:09:27,920 Couldn't that lead to huge advances? 346 00:09:27,920 --> 00:09:28,760 Absolutely. 347 00:09:28,760 --> 00:09:29,600 Yeah. 348 00:09:29,600 --> 00:09:32,160 Like AI could be incredible at spotting patterns 349 00:09:32,160 --> 00:09:35,520 and connections across massive data sets. 350 00:09:35,520 --> 00:09:36,360 Right. 351 00:09:36,360 --> 00:09:38,080 Things that humans might miss. 352 00:09:38,080 --> 00:09:38,920 Right. 353 00:09:38,920 --> 00:09:41,640 It could even combine insights from 354 00:09:41,640 --> 00:09:43,600 different fields of science. 355 00:09:43,600 --> 00:09:45,680 In ways we wouldn't even imagine. 356 00:09:45,680 --> 00:09:46,680 Wow. 357 00:09:46,680 --> 00:09:47,720 Like you know that saying. 358 00:09:47,720 --> 00:09:48,560 Yeah. 359 00:09:48,560 --> 00:09:49,920 Creativity is just connecting things. 360 00:09:49,920 --> 00:09:50,760 Right. 361 00:09:50,760 --> 00:09:51,600 Exactly. 362 00:09:51,600 --> 00:09:54,560 So maybe it's not about AI replacing scientists. 363 00:09:54,560 --> 00:09:58,320 But about AI becoming this powerful tool. 364 00:09:58,320 --> 00:09:59,160 Yeah. 365 00:09:59,160 --> 00:10:00,720 That helps us see things in new ways. 366 00:10:00,720 --> 00:10:02,600 Make those connections faster. 367 00:10:02,600 --> 00:10:03,440 Exactly. 368 00:10:03,440 --> 00:10:05,320 And ultimately accelerate discovery. 369 00:10:05,320 --> 00:10:06,160 Totally. 370 00:10:06,160 --> 00:10:07,920 And this is where the human element 371 00:10:07,920 --> 00:10:09,840 becomes even more critical. 372 00:10:09,840 --> 00:10:10,680 Okay. 373 00:10:10,680 --> 00:10:11,520 Think about it. 374 00:10:11,520 --> 00:10:12,340 Okay. 375 00:10:12,340 --> 00:10:13,960 Interpret those findings. 376 00:10:13,960 --> 00:10:14,800 Right. 377 00:10:14,800 --> 00:10:16,000 Explain what they mean. 378 00:10:16,000 --> 00:10:16,840 Right. 379 00:10:16,840 --> 00:10:17,680 Communicate that knowledge. 380 00:10:17,680 --> 00:10:22,040 It's like even if AI is making the discoveries. 381 00:10:22,040 --> 00:10:22,880 Yeah. 382 00:10:22,880 --> 00:10:26,240 We still need humans to make sense of it all. 383 00:10:26,240 --> 00:10:27,200 Make sense of it all. 384 00:10:27,200 --> 00:10:29,000 Tell the story of science in a way 385 00:10:29,000 --> 00:10:30,560 that's compelling and meaningful. 386 00:10:30,560 --> 00:10:33,800 And to teach the next generation of scientists. 387 00:10:33,800 --> 00:10:34,640 Yeah. 388 00:10:34,640 --> 00:10:35,480 How to work with these tools. 389 00:10:35,480 --> 00:10:36,320 That's a good point. 390 00:10:36,320 --> 00:10:37,160 Right. 391 00:10:37,160 --> 00:10:37,980 Yeah. 392 00:10:37,980 --> 00:10:41,040 Makes you wonder if universities will evolve. 393 00:10:41,040 --> 00:10:41,880 Yeah. 394 00:10:41,880 --> 00:10:45,280 This is places where we mainly focus on training humans. 395 00:10:45,280 --> 00:10:46,120 Okay. 396 00:10:46,120 --> 00:10:47,200 To collaborate with AI. 397 00:10:47,200 --> 00:10:48,040 Right. 398 00:10:48,040 --> 00:10:49,760 Rather than trying to compete with it. 399 00:10:49,760 --> 00:10:51,800 That's a really wild thought. 400 00:10:51,800 --> 00:10:52,640 It is. 401 00:10:52,640 --> 00:10:55,600 If AI can outperform humans. 402 00:10:55,600 --> 00:10:56,440 Yeah. 403 00:10:56,440 --> 00:10:58,400 In so many research tasks. 404 00:10:58,400 --> 00:11:01,000 What does that mean for the future of universities? 405 00:11:01,000 --> 00:11:05,120 Will they become more about teaching critical thinking. 406 00:11:05,120 --> 00:11:06,560 Creativity. 407 00:11:06,560 --> 00:11:08,280 Commutation skills. 408 00:11:08,280 --> 00:11:10,040 It's certainly a possibility. 409 00:11:10,040 --> 00:11:10,880 Yeah. 410 00:11:10,880 --> 00:11:12,240 It raises another question. 411 00:11:12,240 --> 00:11:13,080 Okay. 412 00:11:13,080 --> 00:11:15,600 What happens to the traditional research university. 413 00:11:15,600 --> 00:11:16,440 Right. 414 00:11:16,440 --> 00:11:20,280 If AI becomes the primary driver of discovery. 415 00:11:20,280 --> 00:11:21,920 Does it become obsolete. 416 00:11:21,920 --> 00:11:22,760 Yeah. 417 00:11:22,760 --> 00:11:23,960 Or does it transform into something new. 418 00:11:23,960 --> 00:11:24,800 Yeah. 419 00:11:24,800 --> 00:11:26,440 This is really making me rethink. 420 00:11:26,440 --> 00:11:27,280 Uh huh. 421 00:11:27,280 --> 00:11:29,000 The whole purpose of higher education. 422 00:11:29,000 --> 00:11:29,840 Yeah. 423 00:11:29,840 --> 00:11:31,600 And that's what's so exciting about this topic. 424 00:11:31,600 --> 00:11:32,440 Mm hmm. 425 00:11:32,440 --> 00:11:35,040 It forces us to confront these questions. 426 00:11:35,040 --> 00:11:35,880 Yeah. 427 00:11:35,880 --> 00:11:37,080 About the nature of science. 428 00:11:37,080 --> 00:11:37,920 Right. 429 00:11:37,920 --> 00:11:39,040 The role of technology. 430 00:11:39,040 --> 00:11:39,880 Yeah. 431 00:11:39,880 --> 00:11:41,320 And what it means to be human. 432 00:11:41,320 --> 00:11:42,160 Right. 433 00:11:42,160 --> 00:11:44,000 In a world increasingly shaped by AI. 434 00:11:44,000 --> 00:11:44,840 Yeah. 435 00:11:44,840 --> 00:11:45,680 That's so true. 436 00:11:45,680 --> 00:11:47,120 It feels like we're at this crossroads right. 437 00:11:47,120 --> 00:11:48,200 Like on one path. 438 00:11:48,200 --> 00:11:49,040 Yeah. 439 00:11:49,040 --> 00:11:51,680 We have AI revolutionizing science. 440 00:11:51,680 --> 00:11:52,520 Everly. 441 00:11:52,520 --> 00:11:55,360 Leading to all these discoveries we never could have imagined. 442 00:11:55,360 --> 00:11:56,200 Yeah. 443 00:11:56,200 --> 00:11:57,520 But then on the other path. 444 00:11:57,520 --> 00:11:58,360 Yeah. 445 00:11:58,360 --> 00:12:01,360 There's this fear of losing that human connection. 446 00:12:01,360 --> 00:12:02,920 That makes science meaningful. 447 00:12:02,920 --> 00:12:03,880 It's a tough one. 448 00:12:03,880 --> 00:12:04,700 Yeah. 449 00:12:04,700 --> 00:12:06,800 And the astrophysicist ends his video 450 00:12:06,800 --> 00:12:09,000 with this really inspiring message. 451 00:12:09,000 --> 00:12:12,680 He says, the future of science isn't predetermined. 452 00:12:12,680 --> 00:12:13,520 Right. 453 00:12:13,520 --> 00:12:15,680 It's something we create. 454 00:12:15,680 --> 00:12:16,640 Oh, I like that. 455 00:12:16,640 --> 00:12:17,480 Yeah. 456 00:12:17,480 --> 00:12:19,640 It's not about letting AI dictate the future. 457 00:12:19,640 --> 00:12:20,480 Yeah. 458 00:12:20,480 --> 00:12:21,320 It's about us. 459 00:12:21,320 --> 00:12:22,140 Yeah. 460 00:12:22,140 --> 00:12:24,200 Making choices about how we want to integrate AI. 461 00:12:24,200 --> 00:12:25,040 Exactly. 462 00:12:25,040 --> 00:12:26,280 What role we want it to play. 463 00:12:26,280 --> 00:12:27,880 We need to have these conversations. 464 00:12:27,880 --> 00:12:30,000 Think about the ethical implications. 465 00:12:30,000 --> 00:12:30,840 Right. 466 00:12:30,840 --> 00:12:32,080 And create guidelines. 467 00:12:32,080 --> 00:12:32,920 Yeah. 468 00:12:32,920 --> 00:12:35,000 For how AI is used in research. 469 00:12:35,000 --> 00:12:37,120 So like what kind of question should we be asking? 470 00:12:37,120 --> 00:12:40,360 Well, like how do we ensure accountability 471 00:12:40,360 --> 00:12:41,960 when AI is involved? 472 00:12:41,960 --> 00:12:42,800 I think it's a good one. 473 00:12:42,800 --> 00:12:44,520 How do we protect against bias? 474 00:12:44,520 --> 00:12:45,360 Right. 475 00:12:45,360 --> 00:12:48,120 And how do we preserve the human element of discovery? 476 00:12:48,120 --> 00:12:49,600 Yeah, we were just talking about that. 477 00:12:49,600 --> 00:12:50,920 So it's like. 478 00:12:50,920 --> 00:12:52,240 It's like we're writing the rules 479 00:12:52,240 --> 00:12:54,680 for this whole new era of science. 480 00:12:54,680 --> 00:12:55,520 Wow. 481 00:12:55,520 --> 00:12:57,040 And it's up to us to get it right. 482 00:12:57,040 --> 00:12:58,720 That's a lot of pressure. 483 00:12:58,720 --> 00:12:59,560 It is. 484 00:12:59,560 --> 00:13:00,380 It is. 485 00:13:00,380 --> 00:13:02,000 But OK, let's get back to that core question. 486 00:13:02,000 --> 00:13:05,680 Can AI really empower scientists? 487 00:13:05,680 --> 00:13:06,520 Right. 488 00:13:06,520 --> 00:13:09,000 Still preserving the joy of discovery. 489 00:13:09,000 --> 00:13:10,200 That's the question, isn't it? 490 00:13:10,200 --> 00:13:10,540 Yeah. 491 00:13:10,540 --> 00:13:12,080 And it's not an easy one to answer. 492 00:13:12,080 --> 00:13:12,720 I guess not. 493 00:13:12,720 --> 00:13:16,040 It's going to require a lot of thought, collaboration 494 00:13:16,040 --> 00:13:21,880 between scientists, ethicists, policymakers, the public, 495 00:13:21,880 --> 00:13:22,600 everyone. 496 00:13:22,600 --> 00:13:24,520 And that's where you come in. 497 00:13:24,520 --> 00:13:25,240 You got it. 498 00:13:25,240 --> 00:13:26,000 Our listeners. 499 00:13:26,000 --> 00:13:27,440 Yeah, we want to hear from you. 500 00:13:27,440 --> 00:13:30,720 What are your hopes and concerns about AI in science? 501 00:13:30,720 --> 00:13:31,720 Yeah, what do you think? 502 00:13:31,720 --> 00:13:33,840 What kind of future do you envision? 503 00:13:33,840 --> 00:13:37,360 Do you think AI will enhance human understanding? 504 00:13:37,360 --> 00:13:37,920 Yeah. 505 00:13:37,920 --> 00:13:41,760 Or lead to a more detached, less human-centered approach? 506 00:13:41,760 --> 00:13:43,080 So share your thoughts. 507 00:13:43,080 --> 00:13:43,720 Yeah. 508 00:13:43,720 --> 00:13:44,800 Join the conversation. 509 00:13:44,800 --> 00:13:47,240 Because the future of science isn't something happening to us. 510 00:13:47,240 --> 00:13:47,720 Right. 511 00:13:47,720 --> 00:13:49,560 It's something we're creating together. 512 00:13:49,560 --> 00:13:50,280 Exactly. 513 00:13:50,280 --> 00:13:52,880 Well, on that note, I think we've reached the end. 514 00:13:52,880 --> 00:13:54,000 The end of our deep dive. 515 00:13:54,000 --> 00:13:57,120 Of our deep dive into AI and the future of science. 516 00:13:57,120 --> 00:13:58,240 It's been a journey. 517 00:13:58,240 --> 00:13:59,160 It really has. 518 00:13:59,160 --> 00:14:00,800 Hopefully you learned something new. 519 00:14:00,800 --> 00:14:02,400 Yeah, gained a new perspective. 520 00:14:02,400 --> 00:14:04,480 Maybe even felt the spark of curiosity. 521 00:14:04,480 --> 00:14:05,560 For sure. 522 00:14:05,560 --> 00:14:07,920 Keep asking those big questions. 523 00:14:07,920 --> 00:14:09,160 Stay curious. 524 00:14:09,160 --> 00:14:12,840 And remember, the future of science is in our hands. 525 00:14:12,840 --> 00:14:14,320 Until next time. 526 00:14:14,320 --> 00:14:34,280 Keep exploring.