1 00:00:00,000 --> 00:00:02,240 All right, so get this, we're diving into Google's AI 2 00:00:02,240 --> 00:00:03,600 co-scientist today. 3 00:00:03,600 --> 00:00:06,400 And it's not just about number crunching. 4 00:00:06,400 --> 00:00:08,400 This AI is straight up collaborating 5 00:00:08,400 --> 00:00:10,760 on some big time scientific breakthroughs. 6 00:00:10,760 --> 00:00:12,000 It's wild. 7 00:00:12,000 --> 00:00:13,720 We're talking potential game changers, 8 00:00:13,720 --> 00:00:15,640 even if you don't have a science degree. 9 00:00:15,640 --> 00:00:18,240 We've got research papers, articles, all that jazz. 10 00:00:18,240 --> 00:00:20,520 Yeah, what's really mind blowing about AI co-scientist 11 00:00:20,520 --> 00:00:23,680 is that it tackles a huge problem in science right now. 12 00:00:23,680 --> 00:00:25,960 Researchers are just drowning in information. 13 00:00:25,960 --> 00:00:26,640 Oh, absolutely. 14 00:00:26,640 --> 00:00:28,280 I mean, just look at leukemia research, 15 00:00:28,280 --> 00:00:32,480 a quick Google scholar search pops up over 2.6 million results. 16 00:00:32,480 --> 00:00:34,000 I mean, come on, how could anyone even 17 00:00:34,000 --> 00:00:35,200 begin to make sense of all that? 18 00:00:35,200 --> 00:00:35,840 Exactly. 19 00:00:35,840 --> 00:00:37,200 It's totally overwhelming. 20 00:00:37,200 --> 00:00:38,720 So while there are tools out there, 21 00:00:38,720 --> 00:00:42,520 like deep research from OpenAI to help summarize all this stuff, 22 00:00:42,520 --> 00:00:44,920 they don't actually come up with new ideas on their own. 23 00:00:44,920 --> 00:00:47,160 And that's where AI co-scientist steps in. 24 00:00:47,160 --> 00:00:50,040 So it's not just about analyzing what we already know. 25 00:00:50,040 --> 00:00:52,880 It's about generating completely new hypotheses, 26 00:00:52,880 --> 00:00:54,360 new paths for research. 27 00:00:54,360 --> 00:00:55,320 That's pretty incredible. 28 00:00:55,320 --> 00:00:55,840 It really is. 29 00:00:55,840 --> 00:00:58,720 And it does this through what's called a multi-agent system. 30 00:00:58,720 --> 00:01:00,160 OK, so break that down for me. 31 00:01:00,160 --> 00:01:02,360 Basically, imagine a bunch of AI specialists, 32 00:01:02,360 --> 00:01:05,000 each with their own specific role, all working together. 33 00:01:05,000 --> 00:01:07,960 So like, what a virtual scientific dream team. 34 00:01:07,960 --> 00:01:08,480 Exactly. 35 00:01:08,480 --> 00:01:10,040 Some are all about generating ideas. 36 00:01:10,040 --> 00:01:11,880 Others are evaluating and ranking them 37 00:01:11,880 --> 00:01:13,520 and even refining the best ones. 38 00:01:13,520 --> 00:01:15,480 Then you've got an agent dedicated to making sure 39 00:01:15,480 --> 00:01:18,560 there's no redundancy, no repeated suggestions. 40 00:01:18,560 --> 00:01:20,840 So it's got checks and balances built right in. 41 00:01:20,840 --> 00:01:21,600 Right on. 42 00:01:21,600 --> 00:01:23,120 There's even a meta review agent that 43 00:01:23,120 --> 00:01:25,920 analyzes the patterns and insights from all the data. 44 00:01:25,920 --> 00:01:28,560 And here's the kicker the whole system learns and improves 45 00:01:28,560 --> 00:01:31,160 constantly, even incorporates feedback from us, 46 00:01:31,160 --> 00:01:32,120 human scientists. 47 00:01:32,120 --> 00:01:33,960 Wow, so it's a truly dynamic process, 48 00:01:33,960 --> 00:01:36,280 a real collaboration between AI and humans. 49 00:01:36,280 --> 00:01:37,480 Pretty amazing. 50 00:01:37,480 --> 00:01:39,120 But you know what really blew my mind? 51 00:01:39,120 --> 00:01:40,520 The actual results. 52 00:01:40,520 --> 00:01:42,520 So let's talk about that leukemia research I read, 53 00:01:42,520 --> 00:01:46,400 that AI co-scientist was tasked with finding existing drugs 54 00:01:46,400 --> 00:01:48,440 that could be repurposed to treat leukemia. 55 00:01:48,440 --> 00:01:49,440 Yeah, and it did. 56 00:01:49,440 --> 00:01:52,120 The AI identified several promising drug candidates. 57 00:01:52,120 --> 00:01:54,400 And what's really wild is that these were then 58 00:01:54,400 --> 00:01:55,720 tested in a real lab. 59 00:01:55,720 --> 00:01:56,640 And did they work? 60 00:01:56,640 --> 00:01:57,800 Like actually work? 61 00:01:57,800 --> 00:01:58,200 They did. 62 00:01:58,200 --> 00:02:01,400 Scientists found that the drugs identified by AI co-scientists 63 00:02:01,400 --> 00:02:03,960 actually inhibited leukemia cell activity. 64 00:02:03,960 --> 00:02:05,600 Think about that traditional drug discovery 65 00:02:05,600 --> 00:02:08,520 can take years, even decades, AI co-scientists 66 00:02:08,520 --> 00:02:10,120 spit up the process big time. 67 00:02:10,120 --> 00:02:12,880 That's huge, especially for patients and their families 68 00:02:12,880 --> 00:02:14,040 waiting for new treatments. 69 00:02:14,040 --> 00:02:15,040 It gives me chills. 70 00:02:15,040 --> 00:02:16,400 But it doesn't stop there, right? 71 00:02:16,400 --> 00:02:18,520 There's the liver fibrosis story too, isn't there? 72 00:02:18,520 --> 00:02:19,320 Oh, absolutely. 73 00:02:19,320 --> 00:02:21,400 Liver fibrosis is a really serious condition, 74 00:02:21,400 --> 00:02:23,400 could eventually lead to liver failure. 75 00:02:23,400 --> 00:02:25,680 And even cancer AI co-scientists was 76 00:02:25,680 --> 00:02:28,440 tasked with finding new targets for treatment. 77 00:02:28,440 --> 00:02:30,600 And did it succeed? 78 00:02:30,600 --> 00:02:31,040 It did. 79 00:02:31,040 --> 00:02:31,640 It really did. 80 00:02:31,640 --> 00:02:34,040 It pinpointed several totally new targets. 81 00:02:34,040 --> 00:02:36,960 And when those were tested using human liver cells, 82 00:02:36,960 --> 00:02:39,000 they were effective in reducing fibrosis. 83 00:02:39,000 --> 00:02:39,400 Wow. 84 00:02:39,400 --> 00:02:41,560 So another potential game changer for tackling 85 00:02:41,560 --> 00:02:42,800 a really tough disease. 86 00:02:42,800 --> 00:02:44,400 OK, but this next one, this is where 87 00:02:44,400 --> 00:02:45,800 it gets really interesting for me. 88 00:02:45,800 --> 00:02:47,800 The whole antibiotic resistance thing 89 00:02:47,800 --> 00:02:50,440 that AI co-scientists just figured out on its own. 90 00:02:50,440 --> 00:02:52,880 This one really shows the power of thinking AI. 91 00:02:52,880 --> 00:02:55,000 You see, for over a decade, scientists 92 00:02:55,000 --> 00:02:58,080 have been researching this key factor in antibiotic resistance 93 00:02:58,080 --> 00:02:59,800 called CFIIs. 94 00:02:59,800 --> 00:03:01,800 They were getting close to some big conclusions, 95 00:03:01,800 --> 00:03:03,280 but hadn't published anything yet. 96 00:03:03,280 --> 00:03:06,280 And AI co-scientists cracked it independently. 97 00:03:06,280 --> 00:03:06,680 It did. 98 00:03:06,680 --> 00:03:07,920 And here's the crazy part. 99 00:03:07,920 --> 00:03:10,720 It reached the same conclusions in just two days 100 00:03:10,720 --> 00:03:13,240 compared to like a decade of human research. 101 00:03:13,240 --> 00:03:14,320 Two days? 102 00:03:14,320 --> 00:03:16,600 Seriously, that's just, I don't even know what to say. 103 00:03:16,600 --> 00:03:19,520 It's like having a research assistant that never sleeps 104 00:03:19,520 --> 00:03:22,360 and processes information at warp speed. 105 00:03:22,360 --> 00:03:23,520 It is pretty incredible. 106 00:03:23,520 --> 00:03:25,040 It really makes you wonder, though, 107 00:03:25,040 --> 00:03:26,960 about this whole idea of thinking AI. 108 00:03:26,960 --> 00:03:29,120 It sounds like something straight out of, I don't know, 109 00:03:29,120 --> 00:03:29,800 Star Trek. 110 00:03:29,800 --> 00:03:30,800 Can you break that down a little? 111 00:03:30,800 --> 00:03:31,320 Sure. 112 00:03:31,320 --> 00:03:33,440 It all comes down to what's called test time compute. 113 00:03:33,440 --> 00:03:36,760 So traditional AI models, like, say, GPT-4, 114 00:03:36,760 --> 00:03:38,520 they give you an answer almost instantly, 115 00:03:38,520 --> 00:03:40,680 super fast at processing, but they don't really 116 00:03:40,680 --> 00:03:42,400 like think things through. 117 00:03:42,400 --> 00:03:45,040 So they're all reacting, then contemplating. 118 00:03:45,040 --> 00:03:45,880 Exactly. 119 00:03:45,880 --> 00:03:48,640 Reasoning AI models, like AI co-scientists, 120 00:03:48,640 --> 00:03:52,560 they actually take their time to think before giving an answer. 121 00:03:52,560 --> 00:03:54,280 And the more compute power you give them, 122 00:03:54,280 --> 00:03:56,560 the longer they can think and the better their results. 123 00:03:56,560 --> 00:03:58,480 So like giving them more brain power. 124 00:03:58,480 --> 00:03:59,200 Exactly. 125 00:03:59,200 --> 00:04:01,000 And get this AI co-scientist has actually 126 00:04:01,000 --> 00:04:05,000 been shown to outperform even Google's own top models, 127 00:04:05,000 --> 00:04:08,200 like Gemini 2.0 Pro and Gemini 2.0 Flash Thinking, 128 00:04:08,200 --> 00:04:09,480 at least in certain tasks. 129 00:04:09,480 --> 00:04:10,080 Wait, hold on. 130 00:04:10,080 --> 00:04:11,960 It's outperforming Google's own AI. 131 00:04:11,960 --> 00:04:13,080 Come on, that's insane. 132 00:04:13,080 --> 00:04:13,840 It's pretty remarkable. 133 00:04:13,840 --> 00:04:16,440 And in some cases, it's actually surpassing human experts, 134 00:04:16,440 --> 00:04:16,880 too. 135 00:04:16,880 --> 00:04:20,120 Wow, so they measured the novelty of the ideas, right? 136 00:04:20,120 --> 00:04:21,720 And the actual impact. 137 00:04:21,720 --> 00:04:24,840 And AI co-scientists is consistently coming out on top. 138 00:04:24,840 --> 00:04:25,640 That's right. 139 00:04:25,640 --> 00:04:26,840 I mean, that's mind-blowing. 140 00:04:26,840 --> 00:04:28,920 We're at a point where AI can actually 141 00:04:28,920 --> 00:04:32,200 exceed human's capabilities in scientific discovery. 142 00:04:32,200 --> 00:04:33,120 That's incredible. 143 00:04:33,120 --> 00:04:35,160 If this is what's possible today, 144 00:04:35,160 --> 00:04:36,520 what does the future even look like? 145 00:04:36,520 --> 00:04:37,840 It really makes you think. 146 00:04:37,840 --> 00:04:38,440 It really does. 147 00:04:38,440 --> 00:04:40,000 And that's exactly what we'll be exploring 148 00:04:40,000 --> 00:04:41,240 in the next part of our deep dive. 149 00:04:41,240 --> 00:04:43,200 We'll talk about what this thinking AI could 150 00:04:43,200 --> 00:04:46,920 mean for the future of scientific discovery and beyond. 151 00:04:46,920 --> 00:04:47,360 I can't wait. 152 00:04:47,360 --> 00:04:50,480 OK, so we just finished talking about how AI co-scientists 153 00:04:50,480 --> 00:04:53,360 is even beating human experts in some areas. 154 00:04:53,360 --> 00:04:55,320 It's really kind of crazy to think about what 155 00:04:55,320 --> 00:04:57,400 this whole thinking AI thing could 156 00:04:57,400 --> 00:04:59,520 mean for the future of scientific discovery. 157 00:04:59,520 --> 00:05:02,920 I mean, where do you even begin to imagine the possibilities? 158 00:05:02,920 --> 00:05:03,560 Oh, right. 159 00:05:03,560 --> 00:05:05,160 It's a total game changer. 160 00:05:05,160 --> 00:05:07,120 We could be talking about speeding up breakthroughs 161 00:05:07,120 --> 00:05:10,520 in pretty much every field, from medicine to materials 162 00:05:10,520 --> 00:05:12,240 to climate change research. 163 00:05:12,240 --> 00:05:13,160 It makes you wonder, right? 164 00:05:13,160 --> 00:05:16,200 Like, if AI co-scientists is doing this much already, 165 00:05:16,200 --> 00:05:19,320 what's it'll be capable of in like five, 10, 20 years? 166 00:05:19,320 --> 00:05:21,760 The potential is, I mean, it's mind blowing. 167 00:05:21,760 --> 00:05:24,760 Imagine a world where AI is working with scientists 168 00:05:24,760 --> 00:05:27,920 to cure diseases like Alzheimer's or Parkinson's, 169 00:05:27,920 --> 00:05:30,600 or even figure out how to slow down aging. 170 00:05:30,600 --> 00:05:31,200 Think about that. 171 00:05:31,200 --> 00:05:31,840 Whoa. 172 00:05:31,840 --> 00:05:33,480 OK, yeah, it's almost like we're about to enter 173 00:05:33,480 --> 00:05:36,680 this whole new era of science and AI is leading the way. 174 00:05:36,680 --> 00:05:37,560 But hold on a sec. 175 00:05:37,560 --> 00:05:40,120 This kind of rapid progress, it kind of raises some questions 176 00:05:40,120 --> 00:05:40,680 too, doesn't it? 177 00:05:40,680 --> 00:05:42,800 Like, what happens to the role of scientists 178 00:05:42,800 --> 00:05:45,200 if AI can do some things better than they can? 179 00:05:45,200 --> 00:05:45,720 Right. 180 00:05:45,720 --> 00:05:46,760 And that's something we definitely 181 00:05:46,760 --> 00:05:48,120 need to think about carefully. 182 00:05:48,120 --> 00:05:48,720 But you know what? 183 00:05:48,720 --> 00:05:50,880 I think AI, just like all the other big tech 184 00:05:50,880 --> 00:05:53,800 revolutions before it, it's going to create more opportunities 185 00:05:53,800 --> 00:05:54,720 than it eliminates. 186 00:05:54,720 --> 00:05:56,440 So instead of replacing scientists, 187 00:05:56,440 --> 00:05:58,560 AI will become like a really powerful tool that 188 00:05:58,560 --> 00:05:59,800 helps them do even more. 189 00:05:59,800 --> 00:06:00,560 Exactly. 190 00:06:00,560 --> 00:06:02,520 Think of it as like a shift in skill sets. 191 00:06:02,520 --> 00:06:04,200 You know, the scientists of the future, 192 00:06:04,200 --> 00:06:06,280 they're going to need to know how to work with AI, 193 00:06:06,280 --> 00:06:08,320 understand its strengths and weaknesses, 194 00:06:08,320 --> 00:06:10,160 and guide its research in the right direction. 195 00:06:10,160 --> 00:06:10,480 Gotcha. 196 00:06:10,480 --> 00:06:12,160 So it's not AI versus humans. 197 00:06:12,160 --> 00:06:14,800 It's AI and humans teaming up to achieve 198 00:06:14,800 --> 00:06:16,560 like crazy, amazing things. 199 00:06:16,560 --> 00:06:17,240 Exactly. 200 00:06:17,240 --> 00:06:19,440 And who knows what breakthroughs this partnership could 201 00:06:19,440 --> 00:06:21,720 lead to, like personalized medicine based 202 00:06:21,720 --> 00:06:25,880 on your unique DNA or totally sustainable energy solutions 203 00:06:25,880 --> 00:06:27,880 that change how we power the whole planet? 204 00:06:27,880 --> 00:06:28,280 OK. 205 00:06:28,280 --> 00:06:31,000 Yeah, those are some seriously exciting possibilities. 206 00:06:31,000 --> 00:06:33,320 Seems like AI could help us tackle some of humanity's 207 00:06:33,320 --> 00:06:34,560 biggest problems. 208 00:06:34,560 --> 00:06:37,120 But let's get back to AI co-scientists for a minute. 209 00:06:37,120 --> 00:06:40,160 We talked about drug discovery and antibiotic resistance. 210 00:06:40,160 --> 00:06:43,080 But is it showing promise anywhere else? 211 00:06:43,080 --> 00:06:44,080 Oh, totally. 212 00:06:44,080 --> 00:06:46,960 Researchers are exploring its potential in material science. 213 00:06:46,960 --> 00:06:48,120 OK, how so? 214 00:06:48,120 --> 00:06:50,280 Imagine AI designing brand new materials 215 00:06:50,280 --> 00:06:52,840 with crazy properties, like way lighter, way 216 00:06:52,840 --> 00:06:55,640 stronger, more sustainable than anything we've got now. 217 00:06:55,640 --> 00:06:56,200 Oh, I see. 218 00:06:56,200 --> 00:06:58,800 That would change everything from buildings to cars 219 00:06:58,800 --> 00:07:00,440 to like everyday stuff. 220 00:07:00,440 --> 00:07:00,920 Right. 221 00:07:00,920 --> 00:07:03,240 And that's just one area AI co-scientists 222 00:07:03,240 --> 00:07:07,000 is also being used to explore new avenues in renewable energy, 223 00:07:07,000 --> 00:07:10,000 environmental science, even space exploration. 224 00:07:10,000 --> 00:07:12,320 Wow, the possibilities are kind of endless, aren't they? 225 00:07:12,320 --> 00:07:14,600 Feels like we're on the edge of a scientific revolution. 226 00:07:14,600 --> 00:07:15,200 We are. 227 00:07:15,200 --> 00:07:18,400 And it's a revolution driven by collaboration, humans 228 00:07:18,400 --> 00:07:21,000 and machines working together, different scientific fields 229 00:07:21,000 --> 00:07:24,400 coming together, researchers all over the world sharing ideas. 230 00:07:24,400 --> 00:07:26,000 It's this collaborative spirit that's 231 00:07:26,000 --> 00:07:29,240 going to allow us to really unlock the potential of AI 232 00:07:29,240 --> 00:07:30,320 to benefit everyone. 233 00:07:30,320 --> 00:07:32,000 It's pretty exciting to think about all that. 234 00:07:32,000 --> 00:07:34,080 But we can't forget that any new tech comes 235 00:07:34,080 --> 00:07:35,480 with responsibility, right? 236 00:07:35,480 --> 00:07:38,520 Like, how can we be sure that something like AI co-scientists 237 00:07:38,520 --> 00:07:41,680 gets used for good and not for bad stuff? 238 00:07:41,680 --> 00:07:43,200 That's a million dollar question and one 239 00:07:43,200 --> 00:07:44,520 that's being debated right now. 240 00:07:44,520 --> 00:07:48,080 We need clear ethical guidelines for developing and using AI. 241 00:07:48,080 --> 00:07:50,800 Make sure it's transparent, accountable, and fair. 242 00:07:50,800 --> 00:07:52,800 So it's not just about pushing boundaries. 243 00:07:52,800 --> 00:07:55,520 It's about making sure those possibilities are actually 244 00:07:55,520 --> 00:07:56,640 good for society. 245 00:07:56,640 --> 00:07:57,320 Exactly. 246 00:07:57,320 --> 00:07:59,560 And that takes a careful and thoughtful approach, 247 00:07:59,560 --> 00:08:02,800 one that involves scientists, ethicists, policymakers, 248 00:08:02,800 --> 00:08:05,080 and everyday people all working together. 249 00:08:05,080 --> 00:08:08,080 Sounds like the conversation about AI is just getting started. 250 00:08:08,080 --> 00:08:10,360 And it's a conversation we all need to be a part of. 251 00:08:10,360 --> 00:08:11,840 We've covered a lot of ground today. 252 00:08:11,840 --> 00:08:14,000 The inner workings of AI co-scientists, 253 00:08:14,000 --> 00:08:16,200 its accomplishments, its potential. 254 00:08:16,200 --> 00:08:18,400 Is there anything else you want to add before we wrap up 255 00:08:18,400 --> 00:08:19,200 this deep dive? 256 00:08:19,200 --> 00:08:20,760 I think the big takeaway here is 257 00:08:20,760 --> 00:08:22,840 that we're living in a truly amazing time. 258 00:08:22,840 --> 00:08:24,680 AI isn't science fiction anymore. 259 00:08:24,680 --> 00:08:25,320 It's here. 260 00:08:25,320 --> 00:08:26,200 It's now. 261 00:08:26,200 --> 00:08:27,840 It's already changing things. 262 00:08:27,840 --> 00:08:30,280 And as AI keeps evolving, it's crew you 263 00:08:30,280 --> 00:08:32,600 that we're both enthusiastic and responsible about how 264 00:08:32,600 --> 00:08:33,560 we develop and use it. 265 00:08:33,560 --> 00:08:34,400 Well said. 266 00:08:34,400 --> 00:08:37,640 This has been an incredible look at AI co-scientists 267 00:08:37,640 --> 00:08:38,720 and all its implications. 268 00:08:38,720 --> 00:08:40,320 It feels like we're just scratching the surface 269 00:08:40,320 --> 00:08:41,520 of what's possible. 270 00:08:41,520 --> 00:08:44,160 And the future, well, it's full of incredible potential 271 00:08:44,160 --> 00:08:45,240 and big challenges. 272 00:08:45,240 --> 00:08:46,160 It sure is. 273 00:08:46,160 --> 00:08:47,760 Thanks for being with us on this journey. 274 00:08:47,760 --> 00:08:49,320 We hope you've enjoyed this deep dive 275 00:08:49,320 --> 00:08:51,440 into the world of AI co-scientists. 276 00:08:51,440 --> 00:08:53,560 But before we go, I want to leave our listeners 277 00:08:53,560 --> 00:08:55,480 with a little something to think about. 278 00:08:55,480 --> 00:08:58,720 If AI can help us figure out diseases, aging, even 279 00:08:58,720 --> 00:09:02,080 the universe itself, what role are we as humans going 280 00:09:02,080 --> 00:09:03,800 to play in shaping this future? 281 00:09:03,800 --> 00:09:05,840 What kind of world do we want to create 282 00:09:05,840 --> 00:09:07,720 with this awesome power in our hands? 283 00:09:07,720 --> 00:09:09,760 Yeah, those are some big questions we're thinking about. 284 00:09:09,760 --> 00:09:12,600 Because at the end of the day, the future of AI is up to us. 285 00:09:12,600 --> 00:09:14,880 We've spent a lot of time talking about the amazing things 286 00:09:14,880 --> 00:09:16,080 AI can do for science. 287 00:09:16,080 --> 00:09:18,760 And it's obvious that AI co-scientists is already 288 00:09:18,760 --> 00:09:20,240 making big changes. 289 00:09:20,240 --> 00:09:22,240 But as we wrap up our deep dive, I 290 00:09:22,240 --> 00:09:23,920 kind of want to switch gears a bit 291 00:09:23,920 --> 00:09:27,400 and talk about what all this means for us, for society, 292 00:09:27,400 --> 00:09:28,360 the big picture. 293 00:09:28,360 --> 00:09:30,080 Yeah, I think that's important to AI like this. 294 00:09:30,080 --> 00:09:31,920 It's not just a tool for scientists, right? 295 00:09:31,920 --> 00:09:34,240 It has the potential to change the world in some really big 296 00:09:34,240 --> 00:09:34,600 ways. 297 00:09:34,600 --> 00:09:35,840 OK, I'm listening. 298 00:09:35,840 --> 00:09:37,200 Just think about health care. 299 00:09:37,200 --> 00:09:41,240 Imagine AI helping doctors diagnose diseases way earlier 300 00:09:41,240 --> 00:09:44,000 or coming up with better treatments that actually work, 301 00:09:44,000 --> 00:09:46,160 or even personalizing health care based 302 00:09:46,160 --> 00:09:47,480 on each person's needs. 303 00:09:47,480 --> 00:09:49,120 That would be huge for so many people. 304 00:09:49,120 --> 00:09:51,520 But wouldn't something like that make health care even more 305 00:09:51,520 --> 00:09:52,280 expensive? 306 00:09:52,280 --> 00:09:54,200 I mean, we're already struggling with access 307 00:09:54,200 --> 00:09:55,200 and affordability. 308 00:09:55,200 --> 00:09:55,920 That's a good point. 309 00:09:55,920 --> 00:09:58,040 And it's something we definitely need to think about. 310 00:09:58,040 --> 00:10:00,240 As AI gets better and better at health care, 311 00:10:00,240 --> 00:10:02,640 we've got to make sure everyone can benefit, not just 312 00:10:02,640 --> 00:10:04,040 the wealthy. 313 00:10:04,040 --> 00:10:06,280 It's got to be accessible and affordable for everybody. 314 00:10:06,280 --> 00:10:07,080 Makes sense. 315 00:10:07,080 --> 00:10:09,560 But it's not just health care that could be disrupted, right? 316 00:10:09,560 --> 00:10:11,880 What about all the jobs that might be lost because of AI? 317 00:10:11,880 --> 00:10:14,520 I mean, lots of people are worried about AI taking over tasks 318 00:10:14,520 --> 00:10:16,120 that humans do now. 319 00:10:16,120 --> 00:10:16,760 Yeah, I get that. 320 00:10:16,760 --> 00:10:18,000 It's a natural worry. 321 00:10:18,000 --> 00:10:18,760 Think about this way. 322 00:10:18,760 --> 00:10:21,320 Every time there's a big technological shift, 323 00:10:21,320 --> 00:10:23,080 the job market changes too. 324 00:10:23,080 --> 00:10:26,960 But instead of seeing AI as this job killing monster, 325 00:10:26,960 --> 00:10:28,760 maybe we should think of it as a chance 326 00:10:28,760 --> 00:10:33,120 to rethink what work even means and create new jobs that 327 00:10:33,120 --> 00:10:35,680 use our creativity and problem solving skills. 328 00:10:35,680 --> 00:10:39,000 So instead of replacing jobs, AI will change what those jobs 329 00:10:39,000 --> 00:10:41,640 look like and lead to new jobs that we can't even 330 00:10:41,640 --> 00:10:42,560 imagine right now. 331 00:10:42,560 --> 00:10:43,080 You got it. 332 00:10:43,080 --> 00:10:44,480 Look at the internet, for example. 333 00:10:44,480 --> 00:10:45,600 Changed everything, right? 334 00:10:45,600 --> 00:10:47,160 Lots of industries were disrupted, 335 00:10:47,160 --> 00:10:48,640 but a ton of new ones popped up. 336 00:10:48,640 --> 00:10:50,440 And a whole bunch of new jobs were created too. 337 00:10:50,440 --> 00:10:52,640 We might see the same thing happen with AI. 338 00:10:52,640 --> 00:10:55,720 As AI takes over the boring and repetitive stuff, 339 00:10:55,720 --> 00:10:57,280 it frees us up to do the things that 340 00:10:57,280 --> 00:11:01,240 require real human thinking and innovation. 341 00:11:01,240 --> 00:11:02,560 I like that way of looking at it 342 00:11:02,560 --> 00:11:04,280 instead of being afraid of AI, we 343 00:11:04,280 --> 00:11:07,080 should be figuring out how to adapt and evolve with it. 344 00:11:07,080 --> 00:11:09,080 It's about preparing for the future 345 00:11:09,080 --> 00:11:10,720 and making sure everyone benefits 346 00:11:10,720 --> 00:11:12,360 from all this new technology. 347 00:11:12,360 --> 00:11:12,880 Absolutely. 348 00:11:12,880 --> 00:11:15,040 We need to focus on education and training, 349 00:11:15,040 --> 00:11:17,440 give people the skills they need to thrive in a world where 350 00:11:17,440 --> 00:11:18,800 AI is everywhere. 351 00:11:18,800 --> 00:11:21,320 And we also need to have some real, honest conversations 352 00:11:21,320 --> 00:11:22,320 about the ethics of it all. 353 00:11:22,320 --> 00:11:24,200 Make sure it's developed and used in a way that 354 00:11:24,200 --> 00:11:25,400 aligns with human values. 355 00:11:25,400 --> 00:11:27,080 So it's not just about the tech itself. 356 00:11:27,080 --> 00:11:29,720 It's about how we use it and what kind of future 357 00:11:29,720 --> 00:11:30,720 we want to build. 358 00:11:30,720 --> 00:11:31,960 Couldn't have said it better myself. 359 00:11:31,960 --> 00:11:36,200 AI has this incredible potential to do so much good in the world, 360 00:11:36,200 --> 00:11:38,800 help us solve problems, make things more sustainable 361 00:11:38,800 --> 00:11:39,720 and equitable. 362 00:11:39,720 --> 00:11:42,120 But it's up to us to steer it in the right direction. 363 00:11:42,120 --> 00:11:42,720 Well said. 364 00:11:42,720 --> 00:11:44,080 This deep dive has been amazing. 365 00:11:44,080 --> 00:11:46,960 It's been fascinating to explore AI coscientists 366 00:11:46,960 --> 00:11:49,040 and all the possibilities and challenges that come with it. 367 00:11:49,040 --> 00:11:51,680 We're just starting to understand what AI can really do. 368 00:11:51,680 --> 00:11:53,440 And the future is looking pretty exciting. 369 00:11:53,440 --> 00:11:54,200 For sure. 370 00:11:54,200 --> 00:11:55,680 Thanks for joining us on this journey. 371 00:11:55,680 --> 00:11:57,320 We hope you enjoyed this deep dive 372 00:11:57,320 --> 00:11:59,080 into the world of AI coscientists. 373 00:11:59,080 --> 00:12:02,280 And until next time, keep exploring, keep asking questions, 374 00:12:02,280 --> 00:12:30,680 and keep imagining what's possible.