1 00:00:00,000 --> 00:00:00,960 All right, so are you ready for this? 2 00:00:00,960 --> 00:00:03,520 This deep dive, we're going to be looking at how AI is totally 3 00:00:03,520 --> 00:00:05,680 changing the game in science. 4 00:00:05,680 --> 00:00:07,560 And I think knowing you and your interest in AI, 5 00:00:07,560 --> 00:00:09,840 you're going to have some serious mind-blown moments. 6 00:00:09,840 --> 00:00:13,080 We're talking like plastic-eating enzymes, 7 00:00:13,080 --> 00:00:16,640 the possibility of AI exceeding human intelligence, 8 00:00:16,640 --> 00:00:17,800 like the whole nine yards. 9 00:00:17,800 --> 00:00:18,520 I'm excited. 10 00:00:18,520 --> 00:00:19,080 Yeah. 11 00:00:19,080 --> 00:00:20,880 So our source material today is an episode 12 00:00:20,880 --> 00:00:22,380 of the AI Uncorked podcast. 13 00:00:22,380 --> 00:00:25,960 They recorded a live episode at the AI for Science Forum. 14 00:00:25,960 --> 00:00:27,660 And it features Demis Asabas. 15 00:00:27,660 --> 00:00:29,920 He's the CEO of Google DeepMind. 16 00:00:29,920 --> 00:00:30,560 Oh, wow. 17 00:00:30,560 --> 00:00:31,320 Yeah. 18 00:00:31,320 --> 00:00:33,960 And they're talking with Professor Hannah Frye. 19 00:00:33,960 --> 00:00:34,920 But here's the kicker. 20 00:00:34,920 --> 00:00:38,200 This episode also includes a panel discussion with four, 21 00:00:38,200 --> 00:00:39,800 yes, four Nobel Prize winners. 22 00:00:39,800 --> 00:00:40,640 Wow. 23 00:00:40,640 --> 00:00:41,200 Yeah. 24 00:00:41,200 --> 00:00:43,880 So we've got the minds behind AlphaFold and CRISPR 25 00:00:43,880 --> 00:00:44,920 all in one room. 26 00:00:44,920 --> 00:00:47,280 So yeah, we're dealing with some serious brain power here. 27 00:00:47,280 --> 00:00:47,960 That's incredible. 28 00:00:47,960 --> 00:00:50,240 So let's start with Demis Asabas. 29 00:00:50,240 --> 00:00:54,560 This guy has done it all, chess prodigy, video game designer, 30 00:00:54,560 --> 00:00:58,280 neuroscientist, and now pioneer in AI. 31 00:00:58,280 --> 00:01:01,040 Seriously, is there anything this guy can't do? 32 00:01:01,040 --> 00:01:03,120 I have to suspect that he's got the blueprint 33 00:01:03,120 --> 00:01:05,640 for artificial general intelligence just 34 00:01:05,640 --> 00:01:06,760 tucked away somewhere. 35 00:01:06,760 --> 00:01:07,260 Yeah. 36 00:01:07,260 --> 00:01:09,240 Like just waiting to unleash it on the world. 37 00:01:09,240 --> 00:01:10,320 That wouldn't surprise me. 38 00:01:10,320 --> 00:01:10,880 No. 39 00:01:10,880 --> 00:01:12,920 But he also seems like a pretty down-to-earth guy. 40 00:01:12,920 --> 00:01:14,560 He shared this hilarious story about how 41 00:01:14,560 --> 00:01:16,160 he found out he won the Nobel Prize. 42 00:01:16,160 --> 00:01:17,600 It was actually his wife's computer 43 00:01:17,600 --> 00:01:20,760 that started buzzing with a Skype call. 44 00:01:20,760 --> 00:01:22,800 And it turned out to be the call from Sweden. 45 00:01:22,800 --> 00:01:24,720 And they were scrambling to find his phone number. 46 00:01:24,720 --> 00:01:25,440 Oh, wow. 47 00:01:25,440 --> 00:01:28,080 Imagine that, just like waiting, anticipation. 48 00:01:28,080 --> 00:01:30,240 And then to celebrate, he had a poker night. 49 00:01:30,240 --> 00:01:31,280 Ah. 50 00:01:31,280 --> 00:01:33,760 With a bunch of chess grandmasters, 51 00:01:33,760 --> 00:01:35,240 including Magnus Carlsen. 52 00:01:35,240 --> 00:01:36,400 Wow. 53 00:01:36,400 --> 00:01:37,960 So talk about a high stakes game. 54 00:01:37,960 --> 00:01:40,120 I'm going to wonder who walked away with the winnings. 55 00:01:40,120 --> 00:01:41,120 Yeah, it does. 56 00:01:41,120 --> 00:01:42,800 What's fascinating about Asabas, though, 57 00:01:42,800 --> 00:01:45,680 is that his work extends far beyond just winning games. 58 00:01:45,680 --> 00:01:48,440 He's truly dedicated to using AI to solve 59 00:01:48,440 --> 00:01:50,600 these fundamental scientific problems. 60 00:01:50,600 --> 00:01:51,200 Oh, totally. 61 00:01:51,200 --> 00:01:52,160 Let's get into that. 62 00:01:52,160 --> 00:01:55,400 So his creation, Alpha Fold, has completely 63 00:01:55,400 --> 00:01:58,680 revolutionized the field of protein structure prediction. 64 00:01:58,680 --> 00:02:01,520 We're talking over 28,000 citations. 65 00:02:01,520 --> 00:02:02,320 Yeah. 66 00:02:02,320 --> 00:02:04,040 But it's not just about the numbers. 67 00:02:04,040 --> 00:02:06,080 What matters is what those citations represent. 68 00:02:06,080 --> 00:02:07,160 Exactly. 69 00:02:07,160 --> 00:02:09,760 Those 28,000 citations, they translate 70 00:02:09,760 --> 00:02:12,160 to real world applications. 71 00:02:12,160 --> 00:02:13,800 Alpha Fold has helped researchers 72 00:02:13,800 --> 00:02:17,680 determine the structure of incredibly complex proteins, 73 00:02:17,680 --> 00:02:21,360 like the nuclear pore complex, which acts as a gatekeeper 74 00:02:21,360 --> 00:02:22,680 for a cell's nucleus. 75 00:02:22,680 --> 00:02:23,180 Right. 76 00:02:23,180 --> 00:02:25,880 It's like figuring out this intricate lock mechanism 77 00:02:25,880 --> 00:02:28,240 for one of the most important doors in the cell, 78 00:02:28,240 --> 00:02:29,840 and it doesn't stop there. 79 00:02:29,840 --> 00:02:31,840 Alpha Fold is also being used to develop things 80 00:02:31,840 --> 00:02:34,480 like a molecular syringe, like for delivering drugs 81 00:02:34,480 --> 00:02:36,000 with incredible precision. 82 00:02:36,000 --> 00:02:37,760 And here's where it gets really exciting. 83 00:02:37,760 --> 00:02:40,360 Researchers are using Alpha Fold to design enzymes 84 00:02:40,360 --> 00:02:42,560 that can literally break down plastic. 85 00:02:42,560 --> 00:02:45,280 Imagine the potential impact on the environment. 86 00:02:45,280 --> 00:02:47,120 This isn't just theoretical science. 87 00:02:47,120 --> 00:02:49,720 It's about developing solutions to real world problems. 88 00:02:49,720 --> 00:02:50,560 That's incredible. 89 00:02:50,560 --> 00:02:52,520 OK, so they've solved the static picture 90 00:02:52,520 --> 00:02:54,320 of proteins, as Asawa calls it. 91 00:02:54,320 --> 00:02:55,680 But where do they go from there? 92 00:02:55,680 --> 00:02:57,600 What's next on the AI for Science agenda? 93 00:02:57,600 --> 00:02:59,640 Well, one of the exciting avenues they're exploring 94 00:02:59,640 --> 00:03:03,880 is the Genome Project, which uses AI to design entirely 95 00:03:03,880 --> 00:03:04,880 new materials. 96 00:03:04,880 --> 00:03:06,840 And this ties in directly with your interest 97 00:03:06,840 --> 00:03:07,880 in sustainability. 98 00:03:07,880 --> 00:03:09,160 OK, I'm all ears. 99 00:03:09,160 --> 00:03:09,680 How so? 100 00:03:09,680 --> 00:03:12,360 Well, imagine a world with vastly improved batteries, 101 00:03:12,360 --> 00:03:15,800 capable of storing much more energy, or even room 102 00:03:15,800 --> 00:03:18,680 temperature superconductors, which could revolutionize 103 00:03:18,680 --> 00:03:21,880 energy transmission, making it incredibly efficient. 104 00:03:21,880 --> 00:03:24,000 That's the kind of potential we're talking about with AI 105 00:03:24,000 --> 00:03:24,880 design materials. 106 00:03:24,880 --> 00:03:26,880 Wow, that would be a game changer. 107 00:03:26,880 --> 00:03:28,880 So we've got plastic eating enzymes, 108 00:03:28,880 --> 00:03:30,400 super efficient batteries. 109 00:03:30,400 --> 00:03:32,440 What other scientific dreams are they cooking up 110 00:03:32,440 --> 00:03:33,600 over at DeepMind? 111 00:03:33,600 --> 00:03:37,400 One of Hasabas's long term goals is to create a virtual cell, 112 00:03:37,400 --> 00:03:39,880 a complete computer model of a living cell. 113 00:03:39,880 --> 00:03:41,160 They're starting with yeast. 114 00:03:41,160 --> 00:03:43,800 But the ultimate goal is to simulate a human cell. 115 00:03:43,800 --> 00:03:45,400 Hold on, a virtual human cell. 116 00:03:45,400 --> 00:03:47,720 That sounds like something straight out of a sci-fi movie. 117 00:03:47,720 --> 00:03:48,960 What would that even look like? 118 00:03:48,960 --> 00:03:51,080 Think of it as the ultimate simulation. 119 00:03:51,080 --> 00:03:54,200 By modeling all the complex interactions within a cell, 120 00:03:54,200 --> 00:03:57,200 we could dramatically accelerate biological research. 121 00:03:57,200 --> 00:03:59,320 We could test new drugs virtually, 122 00:03:59,320 --> 00:04:01,880 gain a deeper understanding of diseases, 123 00:04:01,880 --> 00:04:04,920 and potentially even personalize medicine. 124 00:04:04,920 --> 00:04:07,880 Based on your unique genetic makeup, 125 00:04:07,880 --> 00:04:10,440 the possibilities are truly mind boggling. 126 00:04:10,440 --> 00:04:11,080 Wow. 127 00:04:11,080 --> 00:04:11,600 Yeah. 128 00:04:11,600 --> 00:04:14,760 So a virtual cell, that's wild. 129 00:04:14,760 --> 00:04:17,960 It just makes you wonder, what can't we do with AI these days? 130 00:04:17,960 --> 00:04:19,600 Speaking of pushing boundaries, I'm 131 00:04:19,600 --> 00:04:22,840 curious about their thoughts on quantum computing. 132 00:04:22,840 --> 00:04:24,440 You'd think that quantum computing would 133 00:04:24,440 --> 00:04:27,000 be a natural fit for a company like DeepMind, right? 134 00:04:27,000 --> 00:04:28,000 Yeah, for sure. 135 00:04:28,000 --> 00:04:30,920 But Hasabas actually has kind of a surprising perspective on it. 136 00:04:30,920 --> 00:04:31,280 Really? 137 00:04:31,280 --> 00:04:31,720 Yeah. 138 00:04:31,720 --> 00:04:32,440 Tell me more. 139 00:04:32,440 --> 00:04:34,400 So he believes that traditional computers, 140 00:04:34,400 --> 00:04:37,600 like the kind we use every day, still have untapped potential. 141 00:04:37,600 --> 00:04:39,800 He thinks that we can push them further, maybe even 142 00:04:39,800 --> 00:04:42,880 to the point of modeling quantum systems themselves. 143 00:04:42,880 --> 00:04:45,040 He's been in discussions with some of the leading minds 144 00:04:45,040 --> 00:04:46,080 in quantum science. 145 00:04:46,080 --> 00:04:48,660 And it seems like we might be on the verge of a major shift 146 00:04:48,660 --> 00:04:50,280 in our understanding of computing. 147 00:04:50,280 --> 00:04:52,960 So maybe we don't need to rush into quantum computing just 148 00:04:52,960 --> 00:04:53,520 yet. 149 00:04:53,520 --> 00:04:55,280 Maybe we can just squeeze a bit more out 150 00:04:55,280 --> 00:04:56,480 of our current technology. 151 00:04:56,480 --> 00:04:57,240 Exactly. 152 00:04:57,240 --> 00:04:59,360 It's a reminder that we should never underestimate 153 00:04:59,360 --> 00:05:01,440 the power of human ingenuity, even 154 00:05:01,440 --> 00:05:04,320 when it comes to designing and utilizing computers. 155 00:05:04,320 --> 00:05:06,600 And speaking of ingenuity, Hasabas 156 00:05:06,600 --> 00:05:10,400 has also founded Isomorphic Labs, a company specifically 157 00:05:10,400 --> 00:05:13,160 focused on using AI for drug discovery. 158 00:05:13,160 --> 00:05:15,040 Oh, yeah, I've heard of Isomorphic Labs. 159 00:05:15,040 --> 00:05:17,360 They've partnered with some major pharmaceutical companies, 160 00:05:17,360 --> 00:05:17,520 right? 161 00:05:17,520 --> 00:05:18,560 They have, yeah. 162 00:05:18,560 --> 00:05:19,760 What's their approach? 163 00:05:19,760 --> 00:05:23,600 They're using AI to streamline the drug discovery process 164 00:05:23,600 --> 00:05:26,760 with the goal of dramatically reducing the time and cost 165 00:05:26,760 --> 00:05:28,840 it takes to develop new medicines. 166 00:05:28,840 --> 00:05:29,880 That's huge. 167 00:05:29,880 --> 00:05:31,520 We all know how long and expensive 168 00:05:31,520 --> 00:05:34,220 it can be to bring a new drug to market. 169 00:05:34,220 --> 00:05:36,080 If they can really speed things up, 170 00:05:36,080 --> 00:05:37,880 it could have a massive impact on the lives 171 00:05:37,880 --> 00:05:38,920 of millions of people. 172 00:05:38,920 --> 00:05:39,640 Absolutely. 173 00:05:39,640 --> 00:05:42,120 And it's not just about speed and cost. 174 00:05:42,120 --> 00:05:43,880 AI could also lead to the development 175 00:05:43,880 --> 00:05:47,400 of entirely new types of drugs, targeting diseases 176 00:05:47,400 --> 00:05:49,240 that were previously untreatable. 177 00:05:49,240 --> 00:05:51,960 It's a really exciting time for medical research. 178 00:05:51,960 --> 00:05:55,280 It sounds like Demis Hasabas has his fingers in a lot of pies. 179 00:05:55,280 --> 00:05:57,160 Leading the charge in AI research, 180 00:05:57,160 --> 00:05:59,840 exploring new frontiers, and computing, 181 00:05:59,840 --> 00:06:03,000 revolutionizing drug discovery. 182 00:06:03,000 --> 00:06:04,020 Does he ever sleep? 183 00:06:04,020 --> 00:06:05,680 He's certainly a busy guy. 184 00:06:05,680 --> 00:06:07,800 But what's remarkable is that he manages 185 00:06:07,800 --> 00:06:10,280 to balance all of this with a deep commitment 186 00:06:10,280 --> 00:06:12,280 to ethical considerations. 187 00:06:12,280 --> 00:06:15,200 He's not just interested in building cool technology. 188 00:06:15,200 --> 00:06:18,600 He wants to ensure that it's used for the benefit of humanity. 189 00:06:18,600 --> 00:06:20,120 That's refreshing to hear. 190 00:06:20,120 --> 00:06:22,520 It's easy to get caught up in all the hype of AI. 191 00:06:22,520 --> 00:06:25,080 But it's important to remember that these technologies have 192 00:06:25,080 --> 00:06:28,280 the potential to impact our lives in profound ways. 193 00:06:28,280 --> 00:06:31,120 We need people like Hasabas who are thinking carefully 194 00:06:31,120 --> 00:06:32,680 about the ethical implications. 195 00:06:32,680 --> 00:06:33,440 Absolutely. 196 00:06:33,440 --> 00:06:35,280 And that brings us to the panel discussion 197 00:06:35,280 --> 00:06:37,240 with the Nobel laureates, which was 198 00:06:37,240 --> 00:06:38,400 a highlight of the podcast. 199 00:06:38,400 --> 00:06:39,200 OK, let's hear it. 200 00:06:39,200 --> 00:06:42,040 What were some of the key takeaways from that discussion? 201 00:06:42,040 --> 00:06:44,060 One of the central themes was the importance 202 00:06:44,060 --> 00:06:47,080 of scientific rigor in the age of AI. 203 00:06:47,080 --> 00:06:49,080 Hasabas and the other panelists emphasized 204 00:06:49,080 --> 00:06:52,960 that we can't just blindly trust the results of AI algorithms. 205 00:06:52,960 --> 00:06:55,280 We need to apply the same level of skepticism 206 00:06:55,280 --> 00:06:57,160 and critical thinking that we would 207 00:06:57,160 --> 00:06:59,160 to any scientific finding. 208 00:06:59,160 --> 00:06:59,880 That makes sense. 209 00:06:59,880 --> 00:07:00,240 Yeah. 210 00:07:00,240 --> 00:07:01,920 Just because an AI comes up with a result 211 00:07:01,920 --> 00:07:03,800 doesn't mean it's automatically correct. 212 00:07:03,800 --> 00:07:05,960 We still need to test it, verify it, 213 00:07:05,960 --> 00:07:07,320 understand the reasoning behind it. 214 00:07:07,320 --> 00:07:08,320 Exactly. 215 00:07:08,320 --> 00:07:11,480 It's about ensuring that AI is a tool that enhances 216 00:07:11,480 --> 00:07:13,460 our scientific understanding. 217 00:07:13,460 --> 00:07:15,840 Not a shortcut that undermines it. 218 00:07:15,840 --> 00:07:18,680 And this ties into another important point that came up. 219 00:07:18,680 --> 00:07:21,520 The need for AI systems that can explain themselves. 220 00:07:21,520 --> 00:07:22,680 Oh, that's an interesting one. 221 00:07:22,680 --> 00:07:25,460 So it's not enough for an AI to just spit out an answer. 222 00:07:25,460 --> 00:07:27,640 We need to know how it got there, right? 223 00:07:27,640 --> 00:07:28,140 Right. 224 00:07:28,140 --> 00:07:30,640 If we can understand how an AI system arrives 225 00:07:30,640 --> 00:07:33,560 at its conclusions, we can gain deeper insights 226 00:07:33,560 --> 00:07:35,480 into the problem we're trying to solve. 227 00:07:35,480 --> 00:07:37,600 It's like having a conversation with the AI 228 00:07:37,600 --> 00:07:40,240 where it can teach us as well as answer our questions. 229 00:07:40,240 --> 00:07:42,040 That would be incredible. 230 00:07:42,040 --> 00:07:43,240 But is that even possible? 231 00:07:43,240 --> 00:07:46,080 Can AI really explain its thought process in a way 232 00:07:46,080 --> 00:07:47,680 that humans can understand? 233 00:07:47,680 --> 00:07:49,600 It's definitely a challenge, but researchers 234 00:07:49,600 --> 00:07:51,120 are making progress in this area. 235 00:07:51,120 --> 00:07:53,520 They're developing techniques to make AI systems more 236 00:07:53,520 --> 00:07:55,960 transparent and interpretable. 237 00:07:55,960 --> 00:07:58,080 And Hasabas believes that we'll eventually 238 00:07:58,080 --> 00:08:01,080 have AI systems that can communicate their reasoning, 239 00:08:01,080 --> 00:08:03,520 perhaps even in the language of mathematics. 240 00:08:03,520 --> 00:08:05,120 Imagine the possibilities. 241 00:08:05,120 --> 00:08:05,760 Wow. 242 00:08:05,760 --> 00:08:07,280 That would be a game changer. 243 00:08:07,280 --> 00:08:10,080 It'd be like having an AI tutor that can not only solve 244 00:08:10,080 --> 00:08:12,840 problems, but also explain the concepts behind them. 245 00:08:12,840 --> 00:08:13,600 Exactly. 246 00:08:13,600 --> 00:08:15,760 It would be a powerful tool for education 247 00:08:15,760 --> 00:08:17,640 and scientific discovery. 248 00:08:17,640 --> 00:08:20,160 And speaking of pushing the boundaries of what's possible, 249 00:08:20,160 --> 00:08:22,820 someone in the audience raised a fascinating question. 250 00:08:22,820 --> 00:08:26,020 Can AI be applied to the social sciences? 251 00:08:26,020 --> 00:08:28,960 Can we use algorithms to understand human behavior? 252 00:08:28,960 --> 00:08:30,040 Ooh. 253 00:08:30,040 --> 00:08:31,320 Well, that is a tricky one. 254 00:08:31,320 --> 00:08:35,560 Human behavior is so complex, influenced by so many factors. 255 00:08:35,560 --> 00:08:37,760 Can we really capture all of that in an algorithm? 256 00:08:37,760 --> 00:08:38,960 It's definitely a challenge. 257 00:08:38,960 --> 00:08:43,240 Human behavior is not as predictable as the physical world. 258 00:08:43,240 --> 00:08:46,200 But that doesn't mean we should dismiss the potential of AI 259 00:08:46,200 --> 00:08:47,720 in the social sciences. 260 00:08:47,720 --> 00:08:49,840 So how could AI be used in this context? 261 00:08:49,840 --> 00:08:51,440 What are some potential applications? 262 00:08:51,440 --> 00:08:54,680 One possibility is using AI to analyze large data 263 00:08:54,680 --> 00:08:57,840 sets of human behavior, like social media posts 264 00:08:57,840 --> 00:08:59,480 or online interactions. 265 00:08:59,480 --> 00:09:01,960 This could help us identify trends patterns 266 00:09:01,960 --> 00:09:04,160 and even predict future behavior. 267 00:09:04,160 --> 00:09:06,480 That sounds a bit big brother-ish. 268 00:09:06,480 --> 00:09:08,040 Are there any ethical concerns we 269 00:09:08,040 --> 00:09:09,360 should be thinking about here? 270 00:09:09,360 --> 00:09:10,320 Absolutely. 271 00:09:10,320 --> 00:09:12,840 Data privacy is paramount. 272 00:09:12,840 --> 00:09:15,680 We need to ensure that any data used for social science 273 00:09:15,680 --> 00:09:17,400 research is collected and analyzed 274 00:09:17,400 --> 00:09:19,720 in an ethical and responsible way. 275 00:09:19,720 --> 00:09:22,360 And we need to be mindful of the potential for bias 276 00:09:22,360 --> 00:09:23,200 in these algorithms. 277 00:09:23,200 --> 00:09:26,160 Right, because if the data we feed into the AI is biased, 278 00:09:26,160 --> 00:09:28,120 then the results will be biased too. 279 00:09:28,120 --> 00:09:29,740 And that could have serious consequences 280 00:09:29,740 --> 00:09:31,760 for how we understand and interact with each other. 281 00:09:31,760 --> 00:09:32,360 Exactly. 282 00:09:32,360 --> 00:09:34,200 We need to be very careful about how we use 283 00:09:34,200 --> 00:09:36,600 AI to study human behavior. 284 00:09:36,600 --> 00:09:39,920 It's a powerful tool, but it needs to be used responsibly. 285 00:09:39,920 --> 00:09:43,160 So it's not just about the technical challenges of AI, 286 00:09:43,160 --> 00:09:45,960 but also about the ethical and societal implications. 287 00:09:45,960 --> 00:09:46,760 Precisely. 288 00:09:46,760 --> 00:09:48,840 And that brings us to another important theme that 289 00:09:48,840 --> 00:09:50,920 emerged from the panel discussion, the importance 290 00:09:50,920 --> 00:09:52,720 of public trust in AI. 291 00:09:52,720 --> 00:09:53,840 OK, that's a big one. 292 00:09:53,840 --> 00:09:55,960 If people don't trust AI, they're 293 00:09:55,960 --> 00:09:58,560 less likely to accept the scientific breakthroughs 294 00:09:58,560 --> 00:09:59,400 it enables, right? 295 00:09:59,400 --> 00:10:00,600 Exactly. 296 00:10:00,600 --> 00:10:03,440 We need to ensure that AI is developed and used 297 00:10:03,440 --> 00:10:05,720 in a way that aligns with our values. 298 00:10:05,720 --> 00:10:08,160 And that benefits society as a whole. 299 00:10:08,160 --> 00:10:11,320 If people see AI as a force for good, 300 00:10:11,320 --> 00:10:13,920 they'll be more likely to embrace its potential. 301 00:10:13,920 --> 00:10:15,160 So how do we build that trust? 302 00:10:15,160 --> 00:10:16,360 What needs to happen? 303 00:10:16,360 --> 00:10:19,560 It's about transparency, communication, and education. 304 00:10:19,560 --> 00:10:23,200 We need to demystify AI to explain how it works 305 00:10:23,200 --> 00:10:26,320 and to highlight its potential benefits in a way that resonates 306 00:10:26,320 --> 00:10:28,000 with people's everyday lives. 307 00:10:28,000 --> 00:10:30,400 So it's not just about publishing research papers 308 00:10:30,400 --> 00:10:31,680 and scientific journals. 309 00:10:31,680 --> 00:10:34,100 It's about engaging with the public, telling stories, 310 00:10:34,100 --> 00:10:35,200 and building relationships. 311 00:10:35,200 --> 00:10:35,760 Exactly. 312 00:10:35,760 --> 00:10:38,080 And it's about involving the public in the conversation 313 00:10:38,080 --> 00:10:41,640 about AI, seeking their input and addressing their concerns. 314 00:10:41,640 --> 00:10:42,200 I like that. 315 00:10:42,200 --> 00:10:44,720 It's about making AI more human-centered, 316 00:10:44,720 --> 00:10:47,200 more focused on the needs and values of people. 317 00:10:47,200 --> 00:10:48,040 Precisely. 318 00:10:48,040 --> 00:10:49,800 And that brings us to the big question that's 319 00:10:49,800 --> 00:10:53,480 on everyone's mind, artificial general intelligence, or AGI. 320 00:10:53,480 --> 00:10:57,240 Ah, yes, the holy grail of AI research, 321 00:10:57,240 --> 00:11:01,720 the idea of an AI that can think and reason like a human being. 322 00:11:01,720 --> 00:11:04,560 It's both exciting and a little bit terrifying, isn't it? 323 00:11:04,560 --> 00:11:05,720 It certainly is. 324 00:11:05,720 --> 00:11:07,640 And it's a topic that sparked a lot of debate 325 00:11:07,640 --> 00:11:08,640 among the panelists. 326 00:11:08,640 --> 00:11:11,080 Some believe that AGI is just around the corner, 327 00:11:11,080 --> 00:11:12,600 while others are more skeptical. 328 00:11:12,600 --> 00:11:14,680 So is it even possible? 329 00:11:14,680 --> 00:11:17,320 Will we ever achieve true AGI? 330 00:11:17,320 --> 00:11:18,920 It's hard to say for sure. 331 00:11:18,920 --> 00:11:20,120 But one thing's for certain. 332 00:11:20,120 --> 00:11:23,120 The pursuit of AGI is driving incredible innovation 333 00:11:23,120 --> 00:11:24,120 in the field of AI. 334 00:11:24,120 --> 00:11:25,920 Even if we don't achieve true AGI, 335 00:11:25,920 --> 00:11:28,480 we're still developing amazing tools and technologies 336 00:11:28,480 --> 00:11:29,080 along the way. 337 00:11:29,080 --> 00:11:29,720 Exactly. 338 00:11:29,720 --> 00:11:32,000 And those tools are already having a profound impact 339 00:11:32,000 --> 00:11:34,140 on science, from drug discovery to material 340 00:11:34,140 --> 00:11:35,640 science to climate modeling. 341 00:11:35,640 --> 00:11:38,600 So even if AGI remains elusive, the journey itself 342 00:11:38,600 --> 00:11:40,000 is incredibly valuable. 343 00:11:40,000 --> 00:11:42,280 OK, so AGI is a big unknown. 344 00:11:42,280 --> 00:11:44,120 But there are some more concrete challenges 345 00:11:44,120 --> 00:11:46,160 we need to address right now, like that question 346 00:11:46,160 --> 00:11:48,240 about public trust in AI. 347 00:11:48,240 --> 00:11:51,640 How do we ensure that people embrace these breakthroughs 348 00:11:51,640 --> 00:11:54,880 instead of rejecting them out of fear? 349 00:11:54,880 --> 00:11:57,360 OK, so we're back for the final part of our deep dive 350 00:11:57,360 --> 00:11:58,640 into AI for science. 351 00:11:58,640 --> 00:12:00,560 And I have to say, throughout this whole thing, 352 00:12:00,560 --> 00:12:02,320 I keep coming back to this question of, 353 00:12:02,320 --> 00:12:05,420 if AI can do so much, like analyze data, generate 354 00:12:05,420 --> 00:12:07,960 hypotheses, design experiments, what 355 00:12:07,960 --> 00:12:09,720 does that mean for the future of scientists? 356 00:12:09,720 --> 00:12:11,680 Are we all going to be replaced by robots? 357 00:12:11,680 --> 00:12:13,080 You know, that's a question a lot of people 358 00:12:13,080 --> 00:12:14,040 are asking these days. 359 00:12:14,040 --> 00:12:16,720 But I think it's important to remember that AI, at its core, 360 00:12:16,720 --> 00:12:17,560 it's a tool. 361 00:12:17,560 --> 00:12:19,640 And like any tool, its effectiveness 362 00:12:19,640 --> 00:12:21,800 depends on the skill of the person using it. 363 00:12:21,800 --> 00:12:23,880 Right, so like a hammer can build a house 364 00:12:23,880 --> 00:12:26,240 or it can cause destruction, depending on who's holding it. 365 00:12:26,240 --> 00:12:26,740 Exactly. 366 00:12:26,740 --> 00:12:29,720 AI can be an incredible force for scientific progress. 367 00:12:29,720 --> 00:12:32,200 But it's up to us, the humans, to guide its development 368 00:12:32,200 --> 00:12:33,400 in the application. 369 00:12:33,400 --> 00:12:34,920 We need to ask the right questions, 370 00:12:34,920 --> 00:12:37,400 design the right experiments, and interpret the results 371 00:12:37,400 --> 00:12:39,240 in a meaningful way. 372 00:12:39,240 --> 00:12:41,200 So it's not about scientists becoming obsolete. 373 00:12:41,200 --> 00:12:43,600 It's about scientists evolving and learning 374 00:12:43,600 --> 00:12:46,000 to work alongside AI as a partner. 375 00:12:46,000 --> 00:12:47,040 Precisely. 376 00:12:47,040 --> 00:12:50,280 AI can handle the heavy lifting, analyzing massive data 377 00:12:50,280 --> 00:12:53,800 sets, and identifying patterns that would take humans years 378 00:12:53,800 --> 00:12:55,080 to uncover this. 379 00:12:55,080 --> 00:12:57,960 Freeze up scientists to focus on the big picture, 380 00:12:57,960 --> 00:13:00,160 asking the truly creative questions, 381 00:13:00,160 --> 00:13:02,760 developing innovative research strategies, 382 00:13:02,760 --> 00:13:04,800 and making those crucial leaps of insight 383 00:13:04,800 --> 00:13:06,760 that drive scientific breakthroughs. 384 00:13:06,760 --> 00:13:09,000 So instead of replacing human ingenuity, 385 00:13:09,000 --> 00:13:10,720 AI actually amplifies it. 386 00:13:10,720 --> 00:13:11,200 Exactly. 387 00:13:11,200 --> 00:13:13,160 It's like having a super-powered research 388 00:13:13,160 --> 00:13:16,800 assistant capable of processing information at lightning speed 389 00:13:16,800 --> 00:13:19,280 and presenting you with a wealth of possibilities. 390 00:13:19,280 --> 00:13:21,360 But it's still the scientists with their curiosity, 391 00:13:21,360 --> 00:13:23,320 intuition, and deep domain knowledge 392 00:13:23,320 --> 00:13:25,960 who ultimately guides the direction of the research. 393 00:13:25,960 --> 00:13:28,720 And thinking back to that Nobel Laureate panel discussion, 394 00:13:28,720 --> 00:13:30,240 it seemed like they shared this view. 395 00:13:30,240 --> 00:13:32,920 They really emphasized the importance of scientific rigor, 396 00:13:32,920 --> 00:13:34,280 even in the age of AI. 397 00:13:34,280 --> 00:13:35,160 Absolutely. 398 00:13:35,160 --> 00:13:38,280 They stressed that we can't just blindly accept 399 00:13:38,280 --> 00:13:40,440 the output of an AI algorithm. 400 00:13:40,440 --> 00:13:42,880 We need to apply the same level of critical thinking, 401 00:13:42,880 --> 00:13:44,920 skepticism, and rigorous analysis 402 00:13:44,920 --> 00:13:47,880 that we would to any scientific finding. 403 00:13:47,880 --> 00:13:49,680 AI is a powerful tool, but it's not 404 00:13:49,680 --> 00:13:51,760 a substitute for good science. 405 00:13:51,760 --> 00:13:54,120 Right, and I remember one panelist even raised the question 406 00:13:54,120 --> 00:13:57,400 of whether AI could hinder our understanding of how things 407 00:13:57,400 --> 00:13:57,920 work. 408 00:13:57,920 --> 00:13:58,400 Yeah. 409 00:13:58,400 --> 00:14:01,800 If we become too reliant on AI to provide answers, 410 00:14:01,800 --> 00:14:04,680 do we risk losing the ability to ask the right questions 411 00:14:04,680 --> 00:14:05,360 ourselves? 412 00:14:05,360 --> 00:14:07,000 That's a really valid concern. 413 00:14:07,000 --> 00:14:09,800 It's like relying too heavily on a GPS navigation system. 414 00:14:09,800 --> 00:14:11,240 You might get to your destination, 415 00:14:11,240 --> 00:14:13,640 but you won't necessarily understand how you got there 416 00:14:13,640 --> 00:14:15,160 or what other routes might exist. 417 00:14:15,160 --> 00:14:18,320 So it's crucial to maintain that balance between leveraging 418 00:14:18,320 --> 00:14:21,000 the power of AI and nurturing our own scientific skills 419 00:14:21,000 --> 00:14:21,560 and intuition. 420 00:14:21,560 --> 00:14:22,080 Exactly. 421 00:14:22,080 --> 00:14:25,040 We need to use AI as a tool to enhance our understanding, 422 00:14:25,040 --> 00:14:25,960 not replace it. 423 00:14:25,960 --> 00:14:27,420 And that's where education comes in. 424 00:14:27,420 --> 00:14:29,720 We need to train the next generation of scientists 425 00:14:29,720 --> 00:14:32,100 to be both AI savvy and deeply grounded 426 00:14:32,100 --> 00:14:33,640 in scientific principles. 427 00:14:33,640 --> 00:14:37,000 It's about creating a new breed of scientists, 428 00:14:37,000 --> 00:14:39,520 one who can seamlessly navigate both the digital 429 00:14:39,520 --> 00:14:41,440 and the physical worlds of research. 430 00:14:41,440 --> 00:14:42,120 Precisely. 431 00:14:42,120 --> 00:14:44,480 And this brings us back to that final thought-provoking 432 00:14:44,480 --> 00:14:45,080 question. 433 00:14:45,080 --> 00:14:48,240 If AI can help us solve the mysteries of biology materials, 434 00:14:48,240 --> 00:14:50,680 and even the universe itself, what role 435 00:14:50,680 --> 00:14:53,080 will human curiosity and creativity 436 00:14:53,080 --> 00:14:55,960 play in shaping the future of scientific discovery? 437 00:14:55,960 --> 00:14:57,520 Yeah, what a question. 438 00:14:57,520 --> 00:14:59,080 It's almost like, what will it mean 439 00:14:59,080 --> 00:15:02,280 to be a scientist in a world where AI can do so much? 440 00:15:02,280 --> 00:15:04,560 I don't think anyone has a definitive answer yet. 441 00:15:04,560 --> 00:15:07,080 But I believe that human curiosity, our innate desire 442 00:15:07,080 --> 00:15:08,400 to understand the world around us, 443 00:15:08,400 --> 00:15:12,120 will always be the driving force behind scientific progress. 444 00:15:12,120 --> 00:15:13,960 AI may be able to provide us with answers, 445 00:15:13,960 --> 00:15:16,160 but it's the questions, the why and the how, 446 00:15:16,160 --> 00:15:17,960 that truly drive us forward. 447 00:15:17,960 --> 00:15:20,960 And those questions often come from the most unexpected places, 448 00:15:20,960 --> 00:15:23,600 from a spark of inspiration, a sudden connection 449 00:15:23,600 --> 00:15:26,880 between seemingly unrelated ideas, a moment of pure, 450 00:15:26,880 --> 00:15:28,840 unadulterated awe at the complexity 451 00:15:28,840 --> 00:15:30,200 and beauty of the universe. 452 00:15:30,200 --> 00:15:31,080 Exactly. 453 00:15:31,080 --> 00:15:34,640 And those moments, those flashes of insight and inspiration, 454 00:15:34,640 --> 00:15:36,520 are at the heart of what it means to be human. 455 00:15:36,520 --> 00:15:39,080 They're what drive us to explore, to experiment, 456 00:15:39,080 --> 00:15:40,840 to push the boundaries of knowledge, 457 00:15:40,840 --> 00:15:42,960 and to create new things. 458 00:15:42,960 --> 00:15:44,680 AI can help us along the way, but it 459 00:15:44,680 --> 00:15:47,600 can't replace that fundamental human spark. 460 00:15:47,600 --> 00:15:50,240 So in a sense, the more powerful AI becomes, 461 00:15:50,240 --> 00:15:52,920 the more important human ingenuity becomes. 462 00:15:52,920 --> 00:15:55,000 And I think that's a brilliant way to put it. 463 00:15:55,000 --> 00:15:56,560 AI is not the end of science. 464 00:15:56,560 --> 00:15:57,520 It's a new beginning. 465 00:15:57,520 --> 00:15:59,200 It's an opportunity to redefine what's 466 00:15:59,200 --> 00:16:01,600 possible, to expand our horizons, 467 00:16:01,600 --> 00:16:04,200 and to tackle scientific challenges that were previously 468 00:16:04,200 --> 00:16:05,600 beyond our reach. 469 00:16:05,600 --> 00:16:08,800 But it's up to us to ensure that AI is used wisely, ethically, 470 00:16:08,800 --> 00:16:10,920 and in a way that benefits all of humanity. 471 00:16:10,920 --> 00:16:12,720 This deep dive has been incredible. 472 00:16:12,720 --> 00:16:14,960 We've explored the cutting edge of AI for science, 473 00:16:14,960 --> 00:16:17,280 from the intricate workings of protein folding, 474 00:16:17,280 --> 00:16:19,560 to the grand vision of a virtual cell. 475 00:16:19,560 --> 00:16:21,840 We've grappled with some of the biggest questions facing 476 00:16:21,840 --> 00:16:23,840 science in the 21st century. 477 00:16:23,840 --> 00:16:26,840 The role of human ingenuity in an AI-driven world, 478 00:16:26,840 --> 00:16:29,880 the ethical considerations of powerful new technologies, 479 00:16:29,880 --> 00:16:31,320 and the importance of public trust 480 00:16:31,320 --> 00:16:33,720 in shaping the future of scientific progress. 481 00:16:33,720 --> 00:16:36,600 It's been a privilege to share this exploration with you. 482 00:16:36,600 --> 00:16:40,520 And I hope it's left you feeling inspired, curious, and perhaps 483 00:16:40,520 --> 00:16:44,080 even a little bit awestruck by the incredible possibilities 484 00:16:44,080 --> 00:16:45,080 that lie ahead. 485 00:16:45,080 --> 00:16:45,960 I know I am. 486 00:16:45,960 --> 00:16:48,440 And for our listener out there, this deep dive 487 00:16:48,440 --> 00:16:50,800 has given us a glimpse into a future 488 00:16:50,800 --> 00:16:54,000 where science and technology are transforming our world 489 00:16:54,000 --> 00:16:56,320 at an unprecedented pace. 490 00:16:56,320 --> 00:16:58,000 It's a future filled with challenges, 491 00:16:58,000 --> 00:16:59,840 but also with incredible opportunities 492 00:16:59,840 --> 00:17:02,480 for discovery, innovation, and positive change. 493 00:17:02,480 --> 00:17:04,960 And as we move forward into this exciting new era, 494 00:17:04,960 --> 00:17:06,960 it's important to remember that the future is not 495 00:17:06,960 --> 00:17:07,800 predetermined. 496 00:17:07,800 --> 00:17:09,880 It's something we create collectively 497 00:17:09,880 --> 00:17:11,880 through our choices, our actions, 498 00:17:11,880 --> 00:17:14,400 and our unwavering commitment to using knowledge 499 00:17:14,400 --> 00:17:16,520 for the betterment of humankind. 500 00:17:16,520 --> 00:17:17,520 Beautifully said so. 501 00:17:17,520 --> 00:17:18,920 To our listener out there, we leave you 502 00:17:18,920 --> 00:17:20,840 with this final thought as AI continues 503 00:17:20,840 --> 00:17:22,800 to reshape the landscape of science. 504 00:17:22,800 --> 00:17:24,240 What role will you play in shaping 505 00:17:24,240 --> 00:17:50,400 the future you want to see?