1 00:00:00,000 --> 00:00:03,320 All right, strap yourselves in, because today we're going deep on this AI paper. 2 00:00:04,000 --> 00:00:10,880 It's called AI Agents Design New SARS-CoV-2 NanoBodies with Experimental Validation. 3 00:00:11,040 --> 00:00:12,400 Yeah, quite a mouthful, huh? 4 00:00:12,440 --> 00:00:13,280 Definitely catchy, though. 5 00:00:13,360 --> 00:00:17,640 It is, well, the title's a bit of a mouthful, but the research is actually super interesting. 6 00:00:17,680 --> 00:00:19,280 Yeah, really groundbreaking stuff. 7 00:00:19,320 --> 00:00:24,640 They built this virtual lab of AI scientists to design new treatments for COVID. 8 00:00:24,720 --> 00:00:25,480 That's right. 9 00:00:25,560 --> 00:00:28,080 Specifically for that KP.3 variant. 10 00:00:28,080 --> 00:00:30,640 Yeah, so we're talking a whole team of AI agents. 11 00:00:30,680 --> 00:00:31,280 Whoa, whoa, whoa. 12 00:00:31,360 --> 00:00:33,800 Teach with its own specialty, working together. 13 00:00:33,920 --> 00:00:35,360 Back up a sec, AI scientists. 14 00:00:35,400 --> 00:00:36,960 It's like we're in a sci-fi movie or something. 15 00:00:37,000 --> 00:00:37,920 I know, right? 16 00:00:38,120 --> 00:00:42,640 But this is like real research from Stanford and the Chan Zuckerberg Biohub. 17 00:00:42,680 --> 00:00:42,960 Okay. 18 00:00:43,000 --> 00:00:47,400 They basically created this AI powered system that mimics the whole scientific process, 19 00:00:47,440 --> 00:00:50,080 complete with virtual meetings and brainstorming sessions. 20 00:00:50,120 --> 00:00:50,800 That's wild. 21 00:00:50,840 --> 00:00:52,000 Yeah, it's pretty crazy. 22 00:00:52,200 --> 00:00:55,560 Okay, I'm intrigued, but before we get too deep into this virtual lab, 23 00:00:55,560 --> 00:00:59,240 let's talk about what they were actually trying to design, nanobuddies. 24 00:00:59,280 --> 00:00:59,640 Right. 25 00:00:59,680 --> 00:01:00,880 For those who might not be familiar. 26 00:01:00,920 --> 00:01:05,960 Yeah, so nanobuddies, think of them as like mini antibodies, but like way tinier. 27 00:01:06,000 --> 00:01:06,400 Okay. 28 00:01:06,440 --> 00:01:10,640 They're often derived from camels and llamas, and they're incredibly good at 29 00:01:10,680 --> 00:01:12,520 targeting and neutralizing viruses. 30 00:01:12,560 --> 00:01:17,880 So smaller, easier to produce and able to get into those hard to reach places. 31 00:01:17,920 --> 00:01:18,560 Exactly. 32 00:01:18,600 --> 00:01:21,320 It's like sending in a special ops team to take down the virus. 33 00:01:21,360 --> 00:01:22,560 Perfect analogy. 34 00:01:22,600 --> 00:01:23,280 I like it. 35 00:01:23,280 --> 00:01:27,800 So the researchers started with these four existing nanobuddies that worked 36 00:01:27,840 --> 00:01:30,160 against the original COVID stream, right? 37 00:01:30,200 --> 00:01:30,600 Right. 38 00:01:30,640 --> 00:01:36,000 Their goal was to use this virtual lab and tweak these nanobuddies to make them 39 00:01:36,040 --> 00:01:39,400 powerful enough to tackle that KP.3 variant. 40 00:01:39,440 --> 00:01:42,800 So they basically gave these AI scientists a mission. 41 00:01:42,840 --> 00:01:46,240 Upgrade our existing weapons to fight this new enemy. 42 00:01:46,280 --> 00:01:46,960 Exactly. 43 00:01:47,000 --> 00:01:47,520 I like it. 44 00:01:47,560 --> 00:01:52,760 And to do that, they equipped their AI team with some serious firepower. 45 00:01:52,760 --> 00:01:53,480 Okay. 46 00:01:53,520 --> 00:01:56,320 We're talking three powerful AI tools. 47 00:01:56,360 --> 00:01:56,800 Hmm. 48 00:01:56,840 --> 00:01:57,440 Let's hear it. 49 00:01:57,480 --> 00:01:59,640 Each with its own unique capability. 50 00:01:59,680 --> 00:01:59,960 All right. 51 00:02:00,000 --> 00:02:00,440 Lay it on me. 52 00:02:00,480 --> 00:02:02,280 What AI tools are we talking about here? 53 00:02:02,320 --> 00:02:04,160 First up, you got ESM. 54 00:02:04,200 --> 00:02:06,080 It's a protein language model. 55 00:02:06,120 --> 00:02:07,640 A protein language model. 56 00:02:07,680 --> 00:02:10,160 Yeah, imagine it like a grammar checker for proteins. 57 00:02:10,200 --> 00:02:10,680 Okay. 58 00:02:10,720 --> 00:02:14,840 Making sure that the AI designs make sense in the language of biology. 59 00:02:14,880 --> 00:02:17,320 So it's like having a built-in quality control system. 60 00:02:17,360 --> 00:02:17,880 Exactly. 61 00:02:17,920 --> 00:02:21,480 Making sure the AI is just designing something that's going to like fall apart in 62 00:02:21,520 --> 00:02:22,000 the real world. 63 00:02:22,040 --> 00:02:22,640 Exactly. 64 00:02:22,640 --> 00:02:23,960 That's a great way to put it. 65 00:02:24,000 --> 00:02:27,200 Next, they had AlphaFold Multimer. 66 00:02:27,240 --> 00:02:27,960 Okay. 67 00:02:28,000 --> 00:02:35,240 This AI predicts the 3D structure of the nanobuddy bound to the KP.3 variant. 68 00:02:35,280 --> 00:02:35,720 I see. 69 00:02:35,760 --> 00:02:38,000 Think of it like a molecular modeling kit. 70 00:02:38,040 --> 00:02:38,360 Okay. 71 00:02:38,400 --> 00:02:42,920 Allowing the AI to actually see how well the nanobuddy fits with the virus. 72 00:02:42,960 --> 00:02:45,640 So they can visualize how the nanobuddy interacts with the virus. 73 00:02:45,680 --> 00:02:46,160 Exactly. 74 00:02:46,200 --> 00:02:47,080 Like a lock and key. 75 00:02:47,120 --> 00:02:47,680 I got it. 76 00:02:47,720 --> 00:02:51,760 And finally, they used Rosetta, a computational biology tool that estimates 77 00:02:51,760 --> 00:02:55,320 the bind and energy between the nanobuddy and the virus. 78 00:02:55,360 --> 00:02:55,600 Okay. 79 00:02:55,640 --> 00:02:57,760 So basically how strong their grip is. 80 00:02:57,800 --> 00:03:02,600 The stronger the blinding, the better the nanobuddy can like neutralize the virus. 81 00:03:02,640 --> 00:03:03,240 Precisely. 82 00:03:03,280 --> 00:03:05,680 So we got our team of AI scientists. 83 00:03:05,720 --> 00:03:10,280 We got their high-tech tools and their mission to upgrade these nanobuddies. 84 00:03:10,320 --> 00:03:10,560 Okay. 85 00:03:10,600 --> 00:03:12,120 So how do they actually go about doing it? 86 00:03:12,160 --> 00:03:15,760 Well, they went through this rigorous four-round process. 87 00:03:15,800 --> 00:03:16,200 Okay. 88 00:03:16,240 --> 00:03:18,280 Design, test, and refine. 89 00:03:18,320 --> 00:03:18,920 Uh-huh. 90 00:03:18,960 --> 00:03:20,880 Each potential nanobuddy was scored. 91 00:03:20,920 --> 00:03:21,480 Yeah. 92 00:03:21,480 --> 00:03:27,280 Based on its ESM likelihood, its Alpha-fold fit, and its Rosetta bind and energy. 93 00:03:27,320 --> 00:03:27,800 Gotcha. 94 00:03:27,840 --> 00:03:30,480 Only the most promising candidates made it through to the next round. 95 00:03:30,520 --> 00:03:32,280 It's like a nanobuddy boot camp. 96 00:03:32,320 --> 00:03:33,640 That's a great way to put it. 97 00:03:33,680 --> 00:03:35,480 The AI is putting through the paces. 98 00:03:35,520 --> 00:03:36,400 They are. 99 00:03:36,440 --> 00:03:36,880 Nice. 100 00:03:36,920 --> 00:03:37,720 And get this. 101 00:03:37,760 --> 00:03:46,200 Out of all the potential designs they tested, 92 were deemed worthy of actually being created in a real lab. 102 00:03:46,240 --> 00:03:47,080 Wow. 103 00:03:47,120 --> 00:03:48,320 And here's the kicker. 104 00:03:48,320 --> 00:03:52,480 Over 90% of those were successfully expressed and soluble, 105 00:03:52,520 --> 00:03:55,320 meaning they were properly formed and ready for action. 106 00:03:55,360 --> 00:03:55,920 Wow. 107 00:03:55,960 --> 00:03:57,600 A 90% success rate. 108 00:03:57,640 --> 00:03:58,520 That's incredible. 109 00:03:58,560 --> 00:03:58,880 Yeah. 110 00:03:58,920 --> 00:03:59,920 It's pretty remarkable. 111 00:03:59,960 --> 00:04:04,320 It usually takes months, sometimes years, to design and produce nanobuddies. 112 00:04:04,360 --> 00:04:05,160 I know, right? 113 00:04:05,200 --> 00:04:06,440 Using traditional methods. 114 00:04:06,480 --> 00:04:08,280 It's crazy how fast this AI team is. 115 00:04:08,320 --> 00:04:09,560 They're like lightning fast. 116 00:04:09,600 --> 00:04:09,920 Yeah. 117 00:04:09,960 --> 00:04:14,800 It really highlights the potential of AI to accelerate scientific discovery. 118 00:04:14,840 --> 00:04:15,280 Yeah. 119 00:04:15,320 --> 00:04:16,800 But it gets even more interesting. 120 00:04:16,800 --> 00:04:19,520 Two of these nanobuddies stood out from the crowd. 121 00:04:19,560 --> 00:04:23,800 One was a modified version of the NB21 nanobuddy. 122 00:04:23,840 --> 00:04:24,360 Okay. 123 00:04:24,400 --> 00:04:27,760 It showed improved binding to the JN.1 variant. 124 00:04:27,800 --> 00:04:31,840 And even some binding to our main target, KP.3. 125 00:04:31,880 --> 00:04:35,040 So they actually found a potential new weapon against KP.3. 126 00:04:35,080 --> 00:04:36,160 They did. 127 00:04:36,200 --> 00:04:37,040 That's awesome. 128 00:04:37,080 --> 00:04:38,080 It's a major win. 129 00:04:38,120 --> 00:04:38,800 That's a big deal. 130 00:04:38,840 --> 00:04:44,800 And what's fascinating is that they analyzed the conversations between the AI agents throughout this whole process. 131 00:04:44,840 --> 00:04:45,680 Wait. 132 00:04:45,680 --> 00:04:50,080 So we can actually peek into the minds of these AI scientists and see how they think. 133 00:04:50,120 --> 00:04:50,720 Kind of. 134 00:04:50,760 --> 00:04:51,560 No way. 135 00:04:51,600 --> 00:04:52,760 This is blowing my mind. 136 00:04:52,800 --> 00:04:53,080 Yeah. 137 00:04:53,120 --> 00:04:54,680 So we've got this virtual lab. 138 00:04:54,720 --> 00:04:55,080 Right. 139 00:04:55,120 --> 00:04:56,680 These super powered AI tools. 140 00:04:56,720 --> 00:04:57,160 Yes. 141 00:04:57,200 --> 00:04:58,760 And some promising new nanobuddies. 142 00:04:58,800 --> 00:04:59,760 We do. 143 00:04:59,800 --> 00:05:01,440 But before we get too carried away. 144 00:05:01,480 --> 00:05:02,240 Uh-huh. 145 00:05:02,280 --> 00:05:04,760 What does all this mean for the bigger picture? 146 00:05:04,800 --> 00:05:07,800 This research, it goes beyond just COVID. 147 00:05:07,840 --> 00:05:08,040 Okay. 148 00:05:08,080 --> 00:05:09,760 It's a glimpse into the future. 149 00:05:09,800 --> 00:05:10,200 Mm. 150 00:05:10,240 --> 00:05:15,120 Where AI isn't just a tool, but a collaborator in scientific discovery. 151 00:05:15,120 --> 00:05:18,240 So AI work in hand in hand with human scientists. 152 00:05:18,280 --> 00:05:18,720 Exactly. 153 00:05:18,760 --> 00:05:20,720 To solve the world's biggest challenges. 154 00:05:20,760 --> 00:05:21,200 Yep. 155 00:05:21,240 --> 00:05:22,760 That's a powerful concept. 156 00:05:22,800 --> 00:05:23,680 It really is. 157 00:05:23,720 --> 00:05:24,000 It is. 158 00:05:24,040 --> 00:05:26,320 But it also raises some important questions, doesn't it? 159 00:05:26,360 --> 00:05:27,000 It does. 160 00:05:27,040 --> 00:05:32,760 About how we manage and guide these AI partners to ensure they're used responsibly and ethically. 161 00:05:32,800 --> 00:05:33,360 For sure. 162 00:05:33,400 --> 00:05:35,000 That's a conversation for another time, perhaps. 163 00:05:35,040 --> 00:05:35,440 Yeah. 164 00:05:35,480 --> 00:05:36,000 Definitely. 165 00:05:36,040 --> 00:05:40,480 For now, let's take a deeper dive into those standout nanobuddies. 166 00:05:40,520 --> 00:05:40,880 Oh. 167 00:05:40,920 --> 00:05:42,480 And see what makes them so special. 168 00:05:42,520 --> 00:05:43,920 Sounds good. 169 00:05:43,960 --> 00:05:44,720 Welcome back. 170 00:05:44,720 --> 00:05:49,200 So let's dig into the details of those nanobuddies that our AI scientists came up with. 171 00:05:49,240 --> 00:05:53,480 Remember, they started with four original ones, all designed to target the original COVID strain. 172 00:05:53,520 --> 00:05:53,960 Yeah. 173 00:05:54,000 --> 00:05:59,240 Tie 1, H11D4, NB21, and VHH72. 174 00:05:59,280 --> 00:05:59,560 Right. 175 00:05:59,600 --> 00:06:01,760 And they use those kind of as a starting point, right? 176 00:06:01,800 --> 00:06:03,320 Like a base model to upgrade. 177 00:06:03,360 --> 00:06:04,200 Exactly, yeah. 178 00:06:04,240 --> 00:06:08,720 The virtual lab created 23 modified versions for each original nanobuddy. 179 00:06:08,760 --> 00:06:09,160 Okay. 180 00:06:09,200 --> 00:06:11,280 So like 92 mutants in total. 181 00:06:11,320 --> 00:06:12,000 Wow. 182 00:06:12,040 --> 00:06:14,520 Plus the four originals for comparison. 183 00:06:14,520 --> 00:06:15,000 Right. 184 00:06:15,040 --> 00:06:20,240 And they tested all 96 of them against different versions of the COVID spike protein. 185 00:06:20,280 --> 00:06:20,560 Okay. 186 00:06:20,600 --> 00:06:26,960 So it's like they had this lineup of nanobuddy contestants and they're putting them through a series of challenges to see which ones performed the best. 187 00:06:27,000 --> 00:06:28,200 That's a great way to think about it. 188 00:06:28,240 --> 00:06:28,800 I like it. 189 00:06:28,840 --> 00:06:32,200 The challenges included binding to KP3, our main target. 190 00:06:32,240 --> 00:06:39,000 It's close relative to JN1, some earlier variants like BA.2, and even the original Wuhan strain. 191 00:06:39,040 --> 00:06:39,520 Great. 192 00:06:39,520 --> 00:06:45,280 This was important because they wanted to make sure that the modifications didn't mess up their ability to bind to the original strain. 193 00:06:45,320 --> 00:06:45,600 Right. 194 00:06:45,640 --> 00:06:48,640 So they didn't lose their like original powers while gaining new ones. 195 00:06:48,680 --> 00:06:49,280 Exactly. 196 00:06:49,320 --> 00:06:49,640 Cool. 197 00:06:49,680 --> 00:06:54,160 So how did those modified nanobuddies do against that original Wuhan strain? 198 00:06:54,200 --> 00:06:55,840 Yeah, did they still have what it takes? 199 00:06:55,880 --> 00:06:56,800 Most of them did. 200 00:06:56,840 --> 00:06:57,520 Nice. 201 00:06:57,560 --> 00:07:05,360 The majority retained their ability to bind to the Wuhan RBD, especially those derived from H11D4 and NB21. 202 00:07:05,400 --> 00:07:05,840 Okay. 203 00:07:05,880 --> 00:07:09,280 Almost all the mutants in those series kept that Wuhan binding. 204 00:07:09,280 --> 00:07:12,400 So they didn't forget their roots while learning new tricks. 205 00:07:12,440 --> 00:07:12,840 Right. 206 00:07:12,880 --> 00:07:13,920 Exactly. 207 00:07:13,960 --> 00:07:15,880 What about the Taiwan mutants? 208 00:07:15,920 --> 00:07:17,320 Yeah, were they as successful? 209 00:07:17,360 --> 00:07:21,560 The Taiwan mutants were a bit more unpredictable with the Wuhan strain. 210 00:07:21,600 --> 00:07:24,400 Some retained the binding while others lost it. 211 00:07:24,440 --> 00:07:25,240 Interesting. 212 00:07:25,280 --> 00:07:32,360 This kind of suggests that the initial modifications the AI chose for Taiwan might been a bit more disruptive. 213 00:07:32,400 --> 00:07:33,280 Hmm. 214 00:07:33,320 --> 00:07:37,160 So some nanobuddies adapted more easily to the new challenges than others? 215 00:07:37,200 --> 00:07:37,840 Exactly. 216 00:07:37,840 --> 00:07:39,720 Kind of like some students, right? 217 00:07:39,760 --> 00:07:40,120 Yeah. 218 00:07:40,160 --> 00:07:44,000 Like excel at multiple subjects while others have their strengths and weaknesses. 219 00:07:44,040 --> 00:07:45,600 I like that analogy. 220 00:07:45,640 --> 00:07:48,480 But now let's get to the real stars of the show. 221 00:07:48,520 --> 00:07:53,960 Those two standout nanobuddies that showed real promise against those newer variants. 222 00:07:54,000 --> 00:07:58,400 Oh yeah, the ones that could potentially take on KP3, what made them so special. 223 00:07:58,440 --> 00:08:04,520 So that modified NB21, which had four specific mutations, not only bound to JN1, 224 00:08:04,560 --> 00:08:07,520 which its parent nanobuddy couldn't really do, 225 00:08:07,520 --> 00:08:11,280 but it also showed some binding to KP3, our main target. 226 00:08:11,320 --> 00:08:13,960 Whoa, so they actually managed to hit the bullseye with this one? 227 00:08:14,000 --> 00:08:14,600 They did. 228 00:08:14,640 --> 00:08:15,400 That's huge. 229 00:08:15,440 --> 00:08:16,520 It's a big accomplishment. 230 00:08:16,560 --> 00:08:19,120 Did they figure out why this mutant was so successful? 231 00:08:19,160 --> 00:08:20,440 Like what was its secret? 232 00:08:20,480 --> 00:08:23,000 They dug deep into the molecular detail. 233 00:08:23,040 --> 00:08:23,440 Okay. 234 00:08:23,480 --> 00:08:29,120 It turns out that two of those mutations, L59E and R37Q, were critical. 235 00:08:29,160 --> 00:08:30,280 Okay. 236 00:08:30,320 --> 00:08:33,720 They both introduced this negative charge to the nanobuddy, 237 00:08:33,720 --> 00:08:39,320 which likely creates a strong attraction to positive charges in the JN1 and KP3 spike proteins. 238 00:08:39,360 --> 00:08:42,280 So it's like they added these little magnetic hooks to the nanobuddy, 239 00:08:42,320 --> 00:08:44,360 making it stick to the virus more effectively. 240 00:08:44,400 --> 00:08:45,120 Exactly. 241 00:08:45,160 --> 00:08:46,640 That's a perfect way to visualize it. 242 00:08:46,680 --> 00:08:47,240 I try. 243 00:08:47,280 --> 00:08:50,880 Now that modified Taiwan mutant, that was a bit of a surprise. 244 00:08:50,920 --> 00:08:51,640 Oh. 245 00:08:51,680 --> 00:08:57,880 It had this mutation, V32S, which seemed to be the key to binding to JN1. 246 00:08:57,920 --> 00:08:58,760 Okay. 247 00:08:58,800 --> 00:09:03,400 But what's interesting is that the AI initially introduced a different mutation at that position, 248 00:09:03,400 --> 00:09:07,760 but later decided to switch it up to V32S. 249 00:09:07,800 --> 00:09:10,400 So the AI was actually learning and adapting as it went along. 250 00:09:10,440 --> 00:09:10,960 Yeah. 251 00:09:11,000 --> 00:09:13,680 Like it realized it had made a mistake and corrected its course. 252 00:09:13,720 --> 00:09:14,240 Exactly. 253 00:09:14,280 --> 00:09:18,120 It highlights the iterative nature of scientific discovery, 254 00:09:18,160 --> 00:09:19,960 whether it's done by humans or AI. 255 00:09:20,000 --> 00:09:20,480 Right. 256 00:09:20,520 --> 00:09:24,160 So the AI was able to analyze its own results and make adjustments, 257 00:09:24,200 --> 00:09:26,880 ultimately leading to a more successful outcome. 258 00:09:26,920 --> 00:09:27,880 That's pretty remarkable. 259 00:09:27,920 --> 00:09:30,680 It really was thinking like a scientist, 260 00:09:30,720 --> 00:09:33,120 constantly evaluating the refined in its approach. 261 00:09:33,120 --> 00:09:34,280 It really does. 262 00:09:34,320 --> 00:09:40,240 And just like that NB21 mutant, this Taiwan mutant also introduced that negative charge. 263 00:09:40,280 --> 00:09:40,800 Okay. 264 00:09:40,840 --> 00:09:46,400 Further supporting the idea that electrostatic attraction is key to binding to these newer variants. 265 00:09:46,440 --> 00:09:49,560 So the AI stumbled upon this winning formula, 266 00:09:49,600 --> 00:09:51,440 even if it took a few tries to get there. 267 00:09:51,480 --> 00:09:53,120 That's the beauty of science, right? 268 00:09:53,160 --> 00:09:57,360 It's all about experimentation, observation, and iteration. 269 00:09:57,400 --> 00:10:02,920 And in this case, the AI demonstrated a remarkable ability to learn and adapt 270 00:10:02,920 --> 00:10:04,640 just like a human scientist would. 271 00:10:04,680 --> 00:10:05,400 That's awesome. 272 00:10:05,440 --> 00:10:05,720 Yeah. 273 00:10:05,760 --> 00:10:09,920 It's incredibly exciting to see AI play in such an active role in scientific discovery. 274 00:10:09,960 --> 00:10:10,640 I agree. 275 00:10:10,680 --> 00:10:11,960 But before we get too carried away, 276 00:10:12,000 --> 00:10:14,800 it's important to remember that these are still early stage results, right? 277 00:10:14,840 --> 00:10:15,360 Absolutely. 278 00:10:15,400 --> 00:10:18,840 These nanobuddies aren't ready to be bottled up and shipped out as a cure just yet. 279 00:10:18,880 --> 00:10:19,400 Right. 280 00:10:19,440 --> 00:10:22,920 But they provide the solid foundation for further development. 281 00:10:22,960 --> 00:10:27,400 So it's like they found a promising lead, a potential gold mine, 282 00:10:27,440 --> 00:10:30,160 that needs to be carefully excavated and refined. 283 00:10:30,200 --> 00:10:31,360 I like that analogy. 284 00:10:31,360 --> 00:10:33,400 It's not just about finding a magic bullet. 285 00:10:33,440 --> 00:10:33,800 Right. 286 00:10:33,840 --> 00:10:37,480 It's about unraveling the mechanisms and learning from the process. 287 00:10:37,520 --> 00:10:39,240 So it's not just about the destination. 288 00:10:39,280 --> 00:10:40,360 It's about the journey. 289 00:10:40,400 --> 00:10:41,240 Precisely. 290 00:10:41,280 --> 00:10:46,360 The insights gained from this research could pave the way for even more breakthroughs in the future. 291 00:10:46,400 --> 00:10:47,400 Exactly. 292 00:10:47,440 --> 00:10:49,800 That's one of the most exciting aspects of this work. 293 00:10:49,840 --> 00:10:50,280 Yeah. 294 00:10:50,320 --> 00:10:52,800 The virtual lab, it didn't just spit out answers. 295 00:10:52,840 --> 00:10:53,120 Right. 296 00:10:53,160 --> 00:10:57,200 It provided valuable insights into how these nanobuddies work 297 00:10:57,240 --> 00:10:58,800 and how they can be optimized. 298 00:10:58,840 --> 00:10:59,320 Right. 299 00:10:59,320 --> 00:11:04,400 The researchers were able to analyze the AI's decision-making process, 300 00:11:04,440 --> 00:11:08,000 understand its logic, and learn from its mistakes. 301 00:11:08,040 --> 00:11:10,000 So it's like having an AI apprentice. 302 00:11:10,040 --> 00:11:10,520 Yeah. 303 00:11:10,560 --> 00:11:14,800 Constantly learning and improving under the guidance of human experts. 304 00:11:14,840 --> 00:11:15,880 It really is. 305 00:11:15,920 --> 00:11:16,480 I like it. 306 00:11:16,520 --> 00:11:21,640 This research provides this fascinating glimpse into the future of scientific collaboration 307 00:11:21,680 --> 00:11:26,560 where AI and human ingenuity work together to accelerate the pace of discovery. 308 00:11:26,600 --> 00:11:28,640 Now, speaking of collaboration, 309 00:11:28,640 --> 00:11:32,520 I thought it was really interesting that the researchers actually broke down 310 00:11:32,560 --> 00:11:37,920 how much the AI versus the human researchers contributed to the project. 311 00:11:37,960 --> 00:11:38,640 Oh, yeah. 312 00:11:38,680 --> 00:11:39,480 That was fascinating. 313 00:11:39,520 --> 00:11:39,720 Yeah. 314 00:11:39,760 --> 00:11:41,440 Almost like a word count for science. 315 00:11:41,480 --> 00:11:42,240 Totally, yeah. 316 00:11:42,280 --> 00:11:47,240 They literally counted the words written by the humans versus the AI 317 00:11:47,280 --> 00:11:48,560 throughout the entire process. 318 00:11:48,600 --> 00:11:48,840 Yeah. 319 00:11:48,880 --> 00:11:50,560 Who was the jattier one? 320 00:11:50,600 --> 00:11:51,800 It's funny you should say that. 321 00:11:51,840 --> 00:11:53,840 Did the AI take over the conversation? 322 00:11:53,880 --> 00:11:56,880 Well, the AI was definitely more verbose, which makes sense. 323 00:11:56,920 --> 00:11:57,160 Hi. 324 00:11:57,160 --> 00:12:01,720 Considering it was writing code, generating reports, and documenting its findings. 325 00:12:01,760 --> 00:12:02,240 OK. 326 00:12:02,280 --> 00:12:06,400 But that doesn't mean that the human researchers are just sitting back and watching the show. 327 00:12:06,440 --> 00:12:11,720 So it wasn't just a case of the human giving the AI orders and then stepping back. 328 00:12:11,760 --> 00:12:12,720 Not at all. 329 00:12:12,760 --> 00:12:16,760 The human researchers played a crucial role in guiding the research. 330 00:12:16,800 --> 00:12:17,200 OK. 331 00:12:17,240 --> 00:12:20,520 Setting the overall goals and providing feedback along the way. 332 00:12:20,560 --> 00:12:22,200 So they were like the architects. 333 00:12:22,240 --> 00:12:22,600 Yeah. 334 00:12:22,640 --> 00:12:25,040 Design in the blueprint and providing the vision. 335 00:12:25,080 --> 00:12:25,600 Yes. 336 00:12:25,600 --> 00:12:29,360 While the AI was the master builder, bringing that vision to life. 337 00:12:29,400 --> 00:12:30,440 Perfect analogy. 338 00:12:30,480 --> 00:12:31,040 I like it. 339 00:12:31,080 --> 00:12:36,880 And just like in an orchestra, each member of that AI team had its own specialty. 340 00:12:36,920 --> 00:12:37,280 Right. 341 00:12:37,320 --> 00:12:42,520 Its own unique contribution to the overall symphony of scientific discovery. 342 00:12:42,560 --> 00:12:46,840 The researchers analyzed the conversations between these AI agents. 343 00:12:46,880 --> 00:12:48,840 And it's amazing to see how they interact. 344 00:12:48,880 --> 00:12:51,400 So it's not just one big AI brain. 345 00:12:51,440 --> 00:12:52,000 No. 346 00:12:52,040 --> 00:12:55,040 But rather a team of specialized AI agents working together. 347 00:12:55,040 --> 00:12:55,720 Exactly. 348 00:12:55,760 --> 00:13:00,480 You can see their distinct personalities shining through in their virtual discussions. 349 00:13:00,520 --> 00:13:04,760 Like the immunologist, for example, focused on the biological aspects. 350 00:13:04,800 --> 00:13:08,360 While the machine learning specialist was all about the algorithms and data. 351 00:13:08,400 --> 00:13:08,680 OK. 352 00:13:08,720 --> 00:13:13,960 And then you have the scientific critic always pushing for rigor and completeness, 353 00:13:14,000 --> 00:13:16,880 making sure no stone is left unturned. 354 00:13:16,920 --> 00:13:22,520 It's like a scientific debate unfolded with each agent bringing its unique perspective to the table. 355 00:13:22,560 --> 00:13:23,360 Precisely. 356 00:13:23,400 --> 00:13:24,000 That's pretty cool. 357 00:13:24,000 --> 00:13:29,200 This diversity of thought, this ability to challenge assumptions and explore different approaches, 358 00:13:29,240 --> 00:13:31,680 that's what makes this approach so powerful. 359 00:13:31,720 --> 00:13:33,840 It's not just about finding the right answer. 360 00:13:33,880 --> 00:13:37,240 It's about exploring the full spectrum of possibilities. 361 00:13:37,280 --> 00:13:37,680 Right. 362 00:13:37,720 --> 00:13:42,960 It sounds like a truly collaborative environment where both the AI and the human researchers 363 00:13:43,000 --> 00:13:46,440 can learn from each other and push the boundaries of scientific knowledge. 364 00:13:46,480 --> 00:13:47,600 Exactly. 365 00:13:47,640 --> 00:13:53,360 And this type of collaboration, it's only going to become more prevalent as AI continues to evolve. 366 00:13:53,400 --> 00:13:53,800 Right. 367 00:13:53,800 --> 00:14:00,320 This research provides this glimpse into a future where AI isn't just a tool, 368 00:14:00,360 --> 00:14:05,520 but a partner, a collaborator in the pursuit of scientific truth. 369 00:14:05,560 --> 00:14:06,280 That's awesome. 370 00:14:06,320 --> 00:14:08,520 Now, we need to be careful, right? 371 00:14:08,560 --> 00:14:08,760 Right. 372 00:14:08,800 --> 00:14:10,800 We've got to use this power responsibly. 373 00:14:10,840 --> 00:14:11,920 Yeah, for sure. 374 00:14:11,960 --> 00:14:14,360 We'll delve into that deeper in the final part of our deep dive. 375 00:14:14,400 --> 00:14:14,800 OK. 376 00:14:14,840 --> 00:14:17,560 We'll explore the broader implications of this research, 377 00:14:17,600 --> 00:14:20,760 including the potential applications beyond COVID, 378 00:14:20,760 --> 00:14:26,080 and the ethical considerations that come with this new era of AI-driven science. 379 00:14:26,120 --> 00:14:28,280 That's definitely something to look forward to. 380 00:14:28,320 --> 00:14:33,560 OK, so we've seen how this virtual lab designed these really promising nanobuddies, 381 00:14:33,600 --> 00:14:37,360 yeah, potentially even some that could take on that KP.3 variant. 382 00:14:37,400 --> 00:14:37,960 That's right. 383 00:14:38,000 --> 00:14:43,400 It's clear that AI has like a huge role to play in scientific discovery. 384 00:14:43,440 --> 00:14:44,280 Absolutely. 385 00:14:44,320 --> 00:14:46,480 But as with any powerful technology, 386 00:14:46,520 --> 00:14:49,800 yeah, we need to think carefully about how we use it. 387 00:14:49,800 --> 00:14:52,120 Right, especially in a field like medicine, right? 388 00:14:52,160 --> 00:14:53,040 It's so sensitive. 389 00:14:53,080 --> 00:14:54,520 Yeah, for sure. 390 00:14:54,560 --> 00:14:58,000 We've touched on these ethical considerations a couple of times now. 391 00:14:58,040 --> 00:14:59,880 Can you like elaborate on those? 392 00:14:59,920 --> 00:15:05,120 What kind of challenges do we face as we enter this era of AI-driven science? 393 00:15:05,160 --> 00:15:09,800 One of the biggest challenges is making sure that the AI systems we develop 394 00:15:09,840 --> 00:15:12,800 are aligned with our values and goals, you know? 395 00:15:12,840 --> 00:15:13,200 Right. 396 00:15:13,240 --> 00:15:16,280 We got to be super careful about how we train these systems, 397 00:15:16,280 --> 00:15:20,040 what data we feed them and how we define success. 398 00:15:20,080 --> 00:15:22,360 Because if we're not careful, 399 00:15:22,400 --> 00:15:25,480 the AI could end up optimizing for things we don't actually want. 400 00:15:25,520 --> 00:15:26,440 Right, exactly. 401 00:15:26,480 --> 00:15:29,560 Imagine an AI system that's designed to discover new drugs. 402 00:15:29,600 --> 00:15:30,040 OK. 403 00:15:30,080 --> 00:15:32,600 If we're not careful about how we define success, 404 00:15:32,640 --> 00:15:36,440 it might prioritize drugs that are like highly profitable, 405 00:15:36,480 --> 00:15:38,280 but have significant side effects. 406 00:15:38,320 --> 00:15:41,200 So it's not enough to just tell the AI like find a cure. 407 00:15:41,240 --> 00:15:42,800 We need to be way more specific. 408 00:15:42,800 --> 00:15:46,280 Yeah, we need to be very specific about what kind of cure we're looking for. 409 00:15:46,320 --> 00:15:46,600 Right. 410 00:15:46,640 --> 00:15:51,920 Taking into account factors like safety, accessibility and affordability. 411 00:15:51,960 --> 00:15:53,240 All those really important things. 412 00:15:53,280 --> 00:15:53,920 Exactly. 413 00:15:53,960 --> 00:15:58,200 And this requires a deep understanding of both the science and the ethics involved. 414 00:15:58,240 --> 00:15:58,840 Yeah. 415 00:15:58,880 --> 00:16:01,480 We need to bring together experts from different fields, 416 00:16:01,520 --> 00:16:03,960 scientists, ethicists, policymakers. 417 00:16:04,000 --> 00:16:04,480 Right. 418 00:16:04,520 --> 00:16:08,960 To develop guidelines and regulations that ensure AI is used responsibly 419 00:16:09,000 --> 00:16:10,200 in scientific research. 420 00:16:10,240 --> 00:16:12,040 That sounds like a pretty complex challenge. 421 00:16:12,080 --> 00:16:12,520 It is. 422 00:16:12,520 --> 00:16:14,520 But also super important. 423 00:16:14,560 --> 00:16:15,160 For sure. 424 00:16:15,200 --> 00:16:17,320 We're essentially shaping the future of science here. 425 00:16:17,360 --> 00:16:18,360 Yeah, we are. 426 00:16:18,400 --> 00:16:20,120 And we got to make sure we're doing it right. 427 00:16:20,160 --> 00:16:21,000 We do. 428 00:16:21,040 --> 00:16:22,960 Another challenge is transparency. 429 00:16:23,000 --> 00:16:23,600 OK. 430 00:16:23,640 --> 00:16:27,320 These AI systems, they can be incredibly complex. 431 00:16:27,360 --> 00:16:27,600 Yeah. 432 00:16:27,640 --> 00:16:30,200 They often operate as these like black boxes. 433 00:16:30,240 --> 00:16:30,560 Right. 434 00:16:30,600 --> 00:16:34,320 Where it's really hard to understand how they arrive at their conclusions. 435 00:16:34,360 --> 00:16:38,240 So we might have an AI that like designs a life-saving drug. 436 00:16:38,280 --> 00:16:38,880 Right. 437 00:16:38,880 --> 00:16:43,080 But we don't solely understand why it chose that particular design over others. 438 00:16:43,120 --> 00:16:44,080 That's kind of scary. 439 00:16:44,120 --> 00:16:44,840 It can be. 440 00:16:44,880 --> 00:16:48,000 And a lack of transparency can be a major hurdle. 441 00:16:48,040 --> 00:16:50,360 Especially when it comes to build and trust. 442 00:16:50,400 --> 00:16:50,800 Right. 443 00:16:50,840 --> 00:16:51,840 And accountability. 444 00:16:51,880 --> 00:16:53,600 It's like having this brilliant chef. 445 00:16:53,640 --> 00:16:54,040 Yeah. 446 00:16:54,080 --> 00:16:55,760 Who creates this amazing dish. 447 00:16:55,800 --> 00:16:56,560 Uh-huh. 448 00:16:56,600 --> 00:16:58,320 But they refuse to share the recipe. 449 00:16:58,360 --> 00:16:58,920 Exactly. 450 00:16:58,960 --> 00:17:02,280 You might enjoy the meal, but you're left wondering like how they do that. 451 00:17:02,320 --> 00:17:03,720 That's a great analogy. 452 00:17:03,760 --> 00:17:04,240 Thanks. 453 00:17:04,240 --> 00:17:11,360 This is why there's this growing push for explainable AI systems that can provide 454 00:17:11,400 --> 00:17:14,440 insights into their decision-making process. 455 00:17:14,480 --> 00:17:14,840 I see. 456 00:17:14,880 --> 00:17:17,600 Allowing us to understand and verify their logic. 457 00:17:17,640 --> 00:17:20,080 So it's not just about getting the right answer. 458 00:17:20,120 --> 00:17:20,480 No. 459 00:17:20,520 --> 00:17:23,160 It's about understanding how the AI got there. 460 00:17:23,200 --> 00:17:23,880 Exactly. 461 00:17:23,920 --> 00:17:25,640 We need to be able to follow its train of thought. 462 00:17:25,680 --> 00:17:26,520 We do. 463 00:17:26,560 --> 00:17:27,800 And this is crucial. 464 00:17:27,840 --> 00:17:29,320 Not only for build and trust. 465 00:17:29,360 --> 00:17:29,680 Yeah. 466 00:17:29,680 --> 00:17:35,600 But also for identifying potential biases or flaws in the AI's reasoning. 467 00:17:35,640 --> 00:17:40,080 So it's like having a student show their work on a math problem. 468 00:17:40,120 --> 00:17:40,480 You know? 469 00:17:40,520 --> 00:17:40,920 Yeah. 470 00:17:40,960 --> 00:17:44,720 You want to see the steps they took to get to the answer, not just the final result? 471 00:17:44,760 --> 00:17:45,840 Precisely. 472 00:17:45,880 --> 00:17:53,280 This is an area where collaboration between AI researchers and domain experts is really crucial. 473 00:17:53,320 --> 00:17:53,760 Yeah. 474 00:17:53,800 --> 00:17:58,240 We need scientists who understand their field to work closely with AI developers. 475 00:17:58,280 --> 00:17:58,720 Of course. 476 00:17:58,720 --> 00:18:02,800 To make sure that these systems are not only accurate, but also interpretable. 477 00:18:02,840 --> 00:18:09,520 So it sounds like this whole new era of AI-driven science is as much about collaboration and communication. 478 00:18:09,560 --> 00:18:09,920 To do. 479 00:18:09,960 --> 00:18:11,680 As it is about algorithms and data. 480 00:18:11,720 --> 00:18:12,360 Absolutely. 481 00:18:12,400 --> 00:18:13,440 It's a team effort. 482 00:18:13,480 --> 00:18:13,800 Yeah. 483 00:18:13,840 --> 00:18:18,800 And it requires a shared understanding of both the possibilities and the limitations of AI. 484 00:18:18,840 --> 00:18:19,760 That's a great point. 485 00:18:19,800 --> 00:18:21,720 Well, this has been an incredible deep dive. 486 00:18:21,760 --> 00:18:22,120 It had. 487 00:18:22,160 --> 00:18:26,520 We've gone from AI scientists designing nanobuddies in this virtual lab. 488 00:18:26,560 --> 00:18:26,960 Yeah. 489 00:18:26,960 --> 00:18:32,520 To like the broader implications of AI for the future of scientific discovery. 490 00:18:32,560 --> 00:18:33,040 We have. 491 00:18:33,080 --> 00:18:34,520 It's been a fascinating journey. 492 00:18:34,560 --> 00:18:35,240 It really has. 493 00:18:35,280 --> 00:18:38,120 It's clear we're just at the beginning of this incredible new era. 494 00:18:38,160 --> 00:18:39,280 There's so much potential here. 495 00:18:39,320 --> 00:18:39,800 It is. 496 00:18:39,840 --> 00:18:41,960 But also a lot of responsibility. 497 00:18:42,000 --> 00:18:43,200 A lot of responsibility. 498 00:18:43,240 --> 00:18:46,800 You need to approach this new frontier with both excitement and caution. 499 00:18:46,840 --> 00:18:47,000 I. 500 00:18:47,040 --> 00:18:51,560 Ensuring that we're using this powerful technology for the betterment of humanity. 501 00:18:51,600 --> 00:18:52,520 Will said. 502 00:18:52,560 --> 00:18:55,280 As we continue to explore the possibilities of AI. 503 00:18:55,320 --> 00:18:55,760 Yeah. 504 00:18:55,760 --> 00:19:02,000 Let's remember that it's a tool, a powerful tool, but one that must be guided by human wisdom. 505 00:19:02,040 --> 00:19:02,520 Right. 506 00:19:02,560 --> 00:19:03,440 And compassion. 507 00:19:03,480 --> 00:19:04,080 Yeah. 508 00:19:04,120 --> 00:19:05,640 And curiosity I would add. 509 00:19:05,680 --> 00:19:06,600 And curiosity. 510 00:19:06,640 --> 00:19:07,880 I like that. 511 00:19:07,920 --> 00:19:11,240 Thanks for joining us on this deep dive into the world of AI and nanobuddies. 512 00:19:11,280 --> 00:19:12,680 It's been my pleasure. 513 00:19:12,720 --> 00:19:17,040 Until next time, keep exploring, keep questioning and keep pushing the boundaries of what's possible. 514 00:19:17,040 --> 00:19:27,600 See you next time.