1 00:00:00,000 --> 00:00:02,920 Okay, so get ready because we are diving 2 00:00:02,920 --> 00:00:06,000 into some seriously mind-bending stuff today. 3 00:00:06,000 --> 00:00:09,120 Like you have been all over this quantum computing, 4 00:00:09,120 --> 00:00:11,480 AI, even animal communication. 5 00:00:11,480 --> 00:00:15,160 And we're gonna try to like connect these seemingly 6 00:00:15,160 --> 00:00:17,200 really, really separate threads 7 00:00:17,200 --> 00:00:20,000 in a way that just might blow your mind. 8 00:00:20,000 --> 00:00:22,620 Ever wonder if we'll ever actually understand 9 00:00:22,620 --> 00:00:27,060 what dolphins are saying or what's going on in a dog's head? 10 00:00:27,060 --> 00:00:30,960 It's a question that has like captivated humans for ages, 11 00:00:30,960 --> 00:00:33,400 trying to crack the code of animal communication. 12 00:00:33,400 --> 00:00:34,240 Yeah. 13 00:00:34,240 --> 00:00:35,360 And you know that you're researching right now, 14 00:00:35,360 --> 00:00:38,580 advancements in AI, specifically algorithms 15 00:00:38,580 --> 00:00:40,720 that analyze complex patterns. 16 00:00:40,720 --> 00:00:42,100 Okay. 17 00:00:42,100 --> 00:00:44,800 It's bringing us closer than ever 18 00:00:44,800 --> 00:00:46,280 to potentially understanding. 19 00:00:46,280 --> 00:00:48,920 I'm seeing that link, this idea that we might need 20 00:00:48,920 --> 00:00:52,400 totally new kinds of tools to even begin to process 21 00:00:52,400 --> 00:00:54,520 the complexity of how animals communicate. 22 00:00:54,520 --> 00:00:56,380 It's like we've been trying to listen to an orchestra 23 00:00:56,380 --> 00:00:58,720 with earmuffs on and now we're finally starting 24 00:00:58,720 --> 00:01:00,200 to hear the nuances. 25 00:01:00,200 --> 00:01:01,560 A perfect analogy. 26 00:01:01,560 --> 00:01:03,920 And speaking of powerful tools, 27 00:01:03,920 --> 00:01:06,200 your deep dive into quantum computing 28 00:01:06,200 --> 00:01:07,440 plays a big role here. 29 00:01:07,440 --> 00:01:08,280 Okay. 30 00:01:08,280 --> 00:01:09,760 It's not just science fiction anymore. 31 00:01:09,760 --> 00:01:10,600 It's real. 32 00:01:10,600 --> 00:01:11,440 Right. 33 00:01:11,440 --> 00:01:15,520 And the implications are, well, pretty wild to consider. 34 00:01:15,520 --> 00:01:16,360 Yeah. 35 00:01:16,360 --> 00:01:17,920 No, quantum computing has definitely 36 00:01:17,920 --> 00:01:19,280 captured my imagination. 37 00:01:19,280 --> 00:01:20,120 Yeah. 38 00:01:20,120 --> 00:01:21,760 It's like the wild west out there, 39 00:01:21,760 --> 00:01:25,800 nations investing billions this race to unlock its power. 40 00:01:25,800 --> 00:01:26,640 Yeah. 41 00:01:26,640 --> 00:01:29,040 But I'll admit, sometimes I feel like I need a PhD 42 00:01:29,040 --> 00:01:31,360 in physics just to understand the basics. 43 00:01:31,360 --> 00:01:32,200 Right. 44 00:01:32,200 --> 00:01:33,020 Yeah. 45 00:01:33,020 --> 00:01:34,080 It's a whole new way of thinking about computing. 46 00:01:34,080 --> 00:01:34,920 Yeah. 47 00:01:34,920 --> 00:01:36,080 You've been digging into IBM's work, right? 48 00:01:36,080 --> 00:01:36,920 Yeah, yeah. 49 00:01:36,920 --> 00:01:40,480 Their multi-billion dollar gamble on quantum computers. 50 00:01:40,480 --> 00:01:41,560 Exactly. 51 00:01:41,560 --> 00:01:45,200 Their approach focusing on these things called quibits, 52 00:01:45,200 --> 00:01:47,760 it's completely different from the computers we're used to. 53 00:01:47,760 --> 00:01:48,680 You're right on the money. 54 00:01:48,680 --> 00:01:49,520 Okay. 55 00:01:49,520 --> 00:01:51,360 See, a normal computer bit is like a light switch. 56 00:01:51,360 --> 00:01:52,200 Oh, okay. 57 00:01:52,200 --> 00:01:53,320 It's either on or off, one or O. 58 00:01:53,320 --> 00:01:54,160 Right. 59 00:01:54,160 --> 00:01:55,000 But a quibit, 60 00:01:55,000 --> 00:01:56,800 to the wonders of quantum mechanics, 61 00:01:56,800 --> 00:01:57,640 Yeah. 62 00:01:57,640 --> 00:01:59,240 can be in a sort of blurry state 63 00:01:59,240 --> 00:02:02,440 where it's both one and O at the same time. 64 00:02:02,440 --> 00:02:03,260 What? 65 00:02:03,260 --> 00:02:04,360 It's called superposition. 66 00:02:04,360 --> 00:02:05,200 Wow. 67 00:02:05,200 --> 00:02:06,800 Imagine spinning a coin. 68 00:02:06,800 --> 00:02:07,640 Okay. 69 00:02:07,640 --> 00:02:12,240 Until it falls, it's both heads and tails simultaneously. 70 00:02:12,240 --> 00:02:13,080 Oh, right. 71 00:02:13,080 --> 00:02:14,040 It's both at the same time. 72 00:02:14,040 --> 00:02:14,880 How? 73 00:02:14,880 --> 00:02:15,700 In a way. 74 00:02:15,700 --> 00:02:18,740 So instead of processing every possibility one by one, 75 00:02:18,740 --> 00:02:21,400 a quantum computer could explore a massive number 76 00:02:21,400 --> 00:02:23,120 of solutions at the same time. 77 00:02:23,120 --> 00:02:23,940 Right. 78 00:02:23,940 --> 00:02:25,560 Talk about a game changer. 79 00:02:25,560 --> 00:02:28,080 It's the potential to tackle problems 80 00:02:28,080 --> 00:02:29,880 that are currently impossible. 81 00:02:29,880 --> 00:02:30,720 Yeah. 82 00:02:30,720 --> 00:02:32,360 Even for the most powerful supercomputers. 83 00:02:32,360 --> 00:02:33,200 Right. 84 00:02:33,200 --> 00:02:34,880 Think about drug discovery, for example. 85 00:02:34,880 --> 00:02:35,720 Okay. 86 00:02:35,720 --> 00:02:37,640 Right now, designing new medications 87 00:02:37,640 --> 00:02:40,760 at the molecular level is incredibly complex 88 00:02:40,760 --> 00:02:41,600 and time consuming. 89 00:02:41,600 --> 00:02:42,420 Yeah. 90 00:02:42,420 --> 00:02:44,280 But a quantum computer could analyze 91 00:02:44,280 --> 00:02:47,000 the intricate interactions of molecules 92 00:02:47,000 --> 00:02:49,200 in ways we can barely imagine, 93 00:02:49,200 --> 00:02:51,680 potentially leading to cures for diseases 94 00:02:51,680 --> 00:02:53,640 we currently have no answers for. 95 00:02:53,640 --> 00:02:55,760 It's like having a super powered microscope 96 00:02:55,760 --> 00:02:57,560 that can see and manipulate matter 97 00:02:57,560 --> 00:02:59,080 at its most fundamental level. 98 00:02:59,080 --> 00:02:59,920 Yeah. 99 00:02:59,920 --> 00:03:01,840 It's amazing, but also a bit daunting, isn't it? 100 00:03:01,840 --> 00:03:02,680 Yeah, it's true. 101 00:03:02,680 --> 00:03:05,000 This kind of power has the potential 102 00:03:05,000 --> 00:03:07,060 to be incredibly beneficial, 103 00:03:07,060 --> 00:03:10,340 but also, well, maybe a bit scary. 104 00:03:10,340 --> 00:03:11,680 You're absolutely right to be thinking 105 00:03:11,680 --> 00:03:12,920 about the other side of the coin. 106 00:03:12,920 --> 00:03:13,760 Yeah. 107 00:03:13,760 --> 00:03:14,600 Because with this kind of power 108 00:03:14,600 --> 00:03:16,400 comes serious responsibility. 109 00:03:16,400 --> 00:03:17,240 Yeah. 110 00:03:17,240 --> 00:03:18,760 The same technology that could lead 111 00:03:18,760 --> 00:03:20,600 to medical breakthroughs could also be used 112 00:03:20,600 --> 00:03:25,160 to crack the encryption that protects our online data. 113 00:03:25,160 --> 00:03:26,000 Right. 114 00:03:26,000 --> 00:03:28,400 Our financial systems, even national secrets. 115 00:03:28,400 --> 00:03:30,160 It's like we're talking about handing someone 116 00:03:30,160 --> 00:03:32,720 the master key to the entire digital world. 117 00:03:32,720 --> 00:03:33,560 It is, yeah. 118 00:03:33,560 --> 00:03:35,500 It makes you wonder who we can trust 119 00:03:35,500 --> 00:03:36,640 with that kind of power. 120 00:03:36,640 --> 00:03:37,480 Right. 121 00:03:37,480 --> 00:03:38,580 And based on my reading, 122 00:03:38,580 --> 00:03:41,240 there's already a global race going on 123 00:03:41,240 --> 00:03:43,720 to see who will control this technology. 124 00:03:43,720 --> 00:03:45,120 You hit on a crucial point. 125 00:03:45,120 --> 00:03:45,960 Okay. 126 00:03:45,960 --> 00:03:48,520 The geopolitical implications are enormous. 127 00:03:48,520 --> 00:03:49,360 Yeah. 128 00:03:49,360 --> 00:03:52,080 I'm seeing the US-China rivalry in this space, I see. 129 00:03:52,080 --> 00:03:52,920 Right. 130 00:03:52,920 --> 00:03:53,960 It's not just about economic advantage, 131 00:03:53,960 --> 00:03:55,200 it's about national security. 132 00:03:55,200 --> 00:03:56,040 Yeah. 133 00:03:56,040 --> 00:03:57,840 And who gets to shape the future of global power. 134 00:03:57,840 --> 00:03:59,960 It's fascinating and a bit unnerving. 135 00:03:59,960 --> 00:04:01,920 On the one hand, we have the potential 136 00:04:01,920 --> 00:04:04,640 for incredible advancements in medicine, 137 00:04:04,640 --> 00:04:07,920 material science, or understanding of the universe itself. 138 00:04:07,920 --> 00:04:08,760 Right. 139 00:04:08,760 --> 00:04:11,840 And on the other, we have the very real possibility 140 00:04:11,840 --> 00:04:15,000 of a world where privacy is a distant memory 141 00:04:15,000 --> 00:04:16,800 and the balance of global power 142 00:04:16,800 --> 00:04:18,720 shifts in unpredictable ways. 143 00:04:18,720 --> 00:04:21,360 It's unpredictable, it's a tightrope walk, isn't it? 144 00:04:21,360 --> 00:04:22,200 Yeah. 145 00:04:22,200 --> 00:04:25,560 Balancing the immense potential of these technologies 146 00:04:25,560 --> 00:04:28,600 with the very real risks. 147 00:04:28,600 --> 00:04:29,440 Yeah. 148 00:04:29,440 --> 00:04:31,240 And as we delve deeper into your research. 149 00:04:31,240 --> 00:04:32,080 Okay. 150 00:04:32,080 --> 00:04:33,080 It's clear that quantum computing 151 00:04:33,080 --> 00:04:34,580 is just one piece of the puzzle. 152 00:04:34,580 --> 00:04:35,420 Right. 153 00:04:35,420 --> 00:04:37,520 It's pushing us to confront some even bigger questions 154 00:04:37,520 --> 00:04:39,720 about the nature of intelligence itself. 155 00:04:39,720 --> 00:04:40,560 Right. 156 00:04:40,560 --> 00:04:42,840 Especially with the rise of artificial intelligence. 157 00:04:42,840 --> 00:04:43,680 It's interesting you say that 158 00:04:43,680 --> 00:04:44,740 because I've been thinking a lot 159 00:04:44,740 --> 00:04:47,200 about how we define intelligence. 160 00:04:47,200 --> 00:04:48,040 Yeah. 161 00:04:48,040 --> 00:04:50,400 Especially as AI becomes more sophisticated. 162 00:04:50,400 --> 00:04:51,240 Right. 163 00:04:51,240 --> 00:04:53,960 We're already surrounded by AI in our daily lives. 164 00:04:53,960 --> 00:04:54,800 We are. 165 00:04:54,800 --> 00:04:56,520 But it seems like we're on the verge of something 166 00:04:56,520 --> 00:04:57,720 even more profound. 167 00:04:57,720 --> 00:04:58,560 Right. 168 00:04:58,560 --> 00:04:59,400 Something that could fundamentally 169 00:04:59,400 --> 00:05:00,960 challenge our place in the world. 170 00:05:00,960 --> 00:05:03,840 It's that leap from narrow AI machines 171 00:05:03,840 --> 00:05:06,160 that are really good at specific tasks. 172 00:05:06,160 --> 00:05:07,000 Right. 173 00:05:07,000 --> 00:05:09,480 To general AI, which is what really gets people talking. 174 00:05:09,480 --> 00:05:10,320 Okay. 175 00:05:10,320 --> 00:05:11,240 And maybe a little nervous. 176 00:05:11,240 --> 00:05:12,080 Right. 177 00:05:12,080 --> 00:05:14,160 Narrow AI is like having a calculator 178 00:05:14,160 --> 00:05:16,520 that can solve complex equations in seconds. 179 00:05:16,520 --> 00:05:17,360 Right. 180 00:05:17,360 --> 00:05:19,280 It's useful, but still just a tool. 181 00:05:19,280 --> 00:05:20,120 It is. 182 00:05:20,120 --> 00:05:20,960 But general AI. 183 00:05:20,960 --> 00:05:21,800 Yeah. 184 00:05:21,800 --> 00:05:22,620 That's like creating a machine 185 00:05:22,620 --> 00:05:24,720 that could potentially outthink us 186 00:05:24,720 --> 00:05:26,480 in every way imaginable. 187 00:05:26,480 --> 00:05:27,320 Exactly. 188 00:05:27,320 --> 00:05:28,160 Yeah. 189 00:05:28,160 --> 00:05:29,740 General AI aims to create machines 190 00:05:29,740 --> 00:05:31,720 that don't just follow instructions. 191 00:05:31,720 --> 00:05:32,560 Right. 192 00:05:32,560 --> 00:05:35,600 But can actually learn, adapt, and reason 193 00:05:35,600 --> 00:05:37,280 at a level comparable to 194 00:05:37,280 --> 00:05:40,120 or even exceeding human capabilities. 195 00:05:40,120 --> 00:05:40,960 Wow. 196 00:05:40,960 --> 00:05:43,640 It's the kind of AI that could write a bestselling novel. 197 00:05:43,640 --> 00:05:44,480 Right. 198 00:05:44,480 --> 00:05:46,260 Compose a symphony, or even design 199 00:05:46,260 --> 00:05:48,680 a groundbreaking scientific experiment. 200 00:05:48,680 --> 00:05:49,520 Why? 201 00:05:49,520 --> 00:05:52,440 All things we currently consider uniquely human pursuits. 202 00:05:52,440 --> 00:05:55,320 It makes you wonder if we're even close to understanding 203 00:05:55,320 --> 00:05:57,880 what true artificial intelligence would look like. 204 00:05:57,880 --> 00:05:58,720 You wanna? 205 00:05:58,720 --> 00:05:59,540 Let alone how to create it. 206 00:05:59,540 --> 00:06:00,380 Right. 207 00:06:00,380 --> 00:06:01,840 It seems like every time we think we're getting close, 208 00:06:01,840 --> 00:06:03,300 the goalposts move. 209 00:06:03,300 --> 00:06:04,960 You've hit on a fundamental challenge. 210 00:06:04,960 --> 00:06:06,200 Okay. 211 00:06:06,200 --> 00:06:08,960 Defining intelligence is surprisingly difficult. 212 00:06:08,960 --> 00:06:09,840 Right. 213 00:06:09,840 --> 00:06:13,120 Is it the ability to process information quickly? 214 00:06:13,120 --> 00:06:13,960 Right. 215 00:06:13,960 --> 00:06:15,360 To solve complex problems. 216 00:06:15,360 --> 00:06:16,200 Uh-huh. 217 00:06:16,200 --> 00:06:17,040 To be more aware. 218 00:06:17,040 --> 00:06:17,880 Right. 219 00:06:17,880 --> 00:06:19,320 Or is it something even more elusive? 220 00:06:19,320 --> 00:06:20,160 Yeah. 221 00:06:20,160 --> 00:06:21,800 Something we haven't even begun to comprehend. 222 00:06:21,800 --> 00:06:24,320 It's like trying to define what it means to be human. 223 00:06:24,320 --> 00:06:25,160 Right. 224 00:06:25,160 --> 00:06:26,600 It's a question that philosophers have been wrestling with 225 00:06:26,600 --> 00:06:27,440 for centuries. 226 00:06:27,440 --> 00:06:28,260 Exactly. 227 00:06:28,260 --> 00:06:31,080 But I do think there are some key aspects of intelligence 228 00:06:31,080 --> 00:06:33,760 that most researchers seem to agree on. 229 00:06:33,760 --> 00:06:34,600 Okay. 230 00:06:34,600 --> 00:06:35,640 Like learning, for example. 231 00:06:35,640 --> 00:06:36,480 Okay. 232 00:06:36,480 --> 00:06:37,800 A truly intelligent system, 233 00:06:37,800 --> 00:06:40,120 whether it's biological or artificial, 234 00:06:40,120 --> 00:06:43,120 needs to be able to learn from its experiences 235 00:06:43,120 --> 00:06:44,840 and adapt to new information. 236 00:06:44,840 --> 00:06:45,680 Absolutely. 237 00:06:45,680 --> 00:06:47,360 It's not just about rote memorization. 238 00:06:47,360 --> 00:06:48,180 Okay. 239 00:06:48,180 --> 00:06:51,320 It's about understanding the why behind the what. 240 00:06:51,320 --> 00:06:52,160 Okay. 241 00:06:52,160 --> 00:06:53,700 About making connections between 242 00:06:53,700 --> 00:06:56,040 seemingly disparate pieces of information. 243 00:06:56,040 --> 00:06:56,880 Okay. 244 00:06:56,880 --> 00:07:01,240 It's about using past experiences to solve new problems. 245 00:07:01,240 --> 00:07:02,080 Right. 246 00:07:02,080 --> 00:07:03,800 And navigate unfamiliar situations. 247 00:07:03,800 --> 00:07:04,640 Yeah. 248 00:07:04,640 --> 00:07:07,320 So it's not just knowing that a tomato is a fruit. 249 00:07:07,320 --> 00:07:08,160 Right. 250 00:07:08,160 --> 00:07:10,880 It's understanding why that matters in a botanical sense. 251 00:07:10,880 --> 00:07:11,720 Right. 252 00:07:11,720 --> 00:07:14,000 But also maybe knowing when to use that knowledge. 253 00:07:14,000 --> 00:07:14,840 Yeah. 254 00:07:14,840 --> 00:07:17,720 Like doing a trivia contest or explaining to your friend 255 00:07:17,720 --> 00:07:20,560 why they shouldn't put tomatoes in their fruit salad. 256 00:07:20,560 --> 00:07:21,400 Precisely. 257 00:07:21,400 --> 00:07:22,220 Yeah. 258 00:07:22,220 --> 00:07:23,620 It's about applying knowledge 259 00:07:23,620 --> 00:07:26,000 in a flexible and creative way. 260 00:07:26,000 --> 00:07:26,840 Okay. 261 00:07:26,840 --> 00:07:28,880 And that brings us to another hallmark of intelligence. 262 00:07:28,880 --> 00:07:29,720 Yeah. 263 00:07:29,720 --> 00:07:30,560 Reasoning. 264 00:07:30,560 --> 00:07:31,400 Right. 265 00:07:31,400 --> 00:07:33,200 It's the ability to think abstractly. 266 00:07:33,200 --> 00:07:34,020 Okay. 267 00:07:34,020 --> 00:07:34,860 To see patterns, 268 00:07:34,860 --> 00:07:37,680 to draw logical conclusions from available information. 269 00:07:37,680 --> 00:07:38,520 Right. 270 00:07:38,520 --> 00:07:40,320 It's what allows us to plan for the future. 271 00:07:40,320 --> 00:07:41,160 Right. 272 00:07:41,160 --> 00:07:43,320 To make predictions, to understand cause and effect. 273 00:07:43,320 --> 00:07:46,440 And if you're talking about AI that can truly reason, 274 00:07:46,440 --> 00:07:47,900 you have to consider the possibility 275 00:07:47,900 --> 00:07:50,040 that it might need more than just lines of code 276 00:07:50,040 --> 00:07:50,880 and algorithms. 277 00:07:50,880 --> 00:07:51,720 Yeah. 278 00:07:51,720 --> 00:07:54,760 Some researchers believe that AI might need a physical body 279 00:07:54,760 --> 00:07:57,920 to fully interact with and understand the world. 280 00:07:57,920 --> 00:07:59,400 You're thinking about the work of researchers 281 00:07:59,400 --> 00:08:00,520 like Sergey Levine, right? 282 00:08:00,520 --> 00:08:01,360 Yes. 283 00:08:01,360 --> 00:08:03,240 They're building robots that learn by interacting 284 00:08:03,240 --> 00:08:04,680 with the physical world. 285 00:08:04,680 --> 00:08:05,520 Yeah. 286 00:08:05,520 --> 00:08:08,160 Much like a child learns through play and exploration. 287 00:08:08,160 --> 00:08:09,360 Exactly. 288 00:08:09,360 --> 00:08:13,240 I was blown away by that video I saw of Sergey's robot. 289 00:08:13,240 --> 00:08:15,840 It was learning how to manipulate objects, 290 00:08:15,840 --> 00:08:17,880 like figuring out how to use a spoon 291 00:08:17,880 --> 00:08:19,400 to skip something into a bowl. 292 00:08:19,400 --> 00:08:20,520 Wow. 293 00:08:20,520 --> 00:08:23,240 It was incredible because it wasn't just following 294 00:08:23,240 --> 00:08:25,000 pre-programmed instructions. 295 00:08:25,000 --> 00:08:27,960 It seemed like it was actually experimenting, 296 00:08:27,960 --> 00:08:29,640 learning from its mistakes. 297 00:08:29,640 --> 00:08:30,480 Yeah. 298 00:08:30,480 --> 00:08:31,960 And developing its own understanding 299 00:08:31,960 --> 00:08:34,480 of how the physical world works. 300 00:08:34,480 --> 00:08:37,040 That's what's so intriguing about this line of research. 301 00:08:37,040 --> 00:08:37,880 Yeah. 302 00:08:37,880 --> 00:08:41,480 The idea that AI might need a body, 303 00:08:41,480 --> 00:08:43,120 a physical presence in the world. 304 00:08:43,120 --> 00:08:43,960 Yeah. 305 00:08:43,960 --> 00:08:46,080 To develop the kind of common sense understanding 306 00:08:46,080 --> 00:08:48,040 that we humans take for granted. 307 00:08:48,040 --> 00:08:50,560 It's like that saying, you don't really know something 308 00:08:50,560 --> 00:08:52,240 until you've experienced it firsthand. 309 00:08:52,240 --> 00:08:53,080 Right. 310 00:08:53,080 --> 00:08:53,920 Maybe it's the same for AI. 311 00:08:53,920 --> 00:08:54,760 Right. 312 00:08:54,760 --> 00:08:57,160 Maybe to truly understand what it means to be intelligent. 313 00:08:57,160 --> 00:08:58,000 Yeah. 314 00:08:58,000 --> 00:08:59,480 AI needs to interact with the world 315 00:08:59,480 --> 00:09:01,760 in a way that's more than just theoretical. 316 00:09:01,760 --> 00:09:02,600 Yeah. 317 00:09:02,600 --> 00:09:04,200 It needs to experience the world 318 00:09:04,200 --> 00:09:06,040 through its own senses, so to speak. 319 00:09:06,040 --> 00:09:07,320 It's a fascinating concept. 320 00:09:07,320 --> 00:09:08,160 Yeah. 321 00:09:08,160 --> 00:09:09,400 And it raises a whole host of questions 322 00:09:09,400 --> 00:09:11,720 about the nature of consciousness, embodiment, 323 00:09:11,720 --> 00:09:13,360 what it truly means to be intelligent. 324 00:09:13,360 --> 00:09:14,200 Right. 325 00:09:14,200 --> 00:09:17,840 But it also highlights the potential risks involved 326 00:09:17,840 --> 00:09:19,360 in creating something 327 00:09:19,360 --> 00:09:21,360 that could be more intelligent than ourselves. 328 00:09:21,360 --> 00:09:23,560 That's the million dollar question, isn't it? 329 00:09:23,560 --> 00:09:26,800 Humans develop larger brains, more complex language. 330 00:09:26,800 --> 00:09:27,640 Right. 331 00:09:27,640 --> 00:09:29,600 And the ability to use tools 332 00:09:29,600 --> 00:09:31,760 in increasingly sophisticated ways. 333 00:09:31,760 --> 00:09:32,600 Okay. 334 00:09:32,600 --> 00:09:35,320 As a result, we have come to dominate the planet. 335 00:09:35,320 --> 00:09:36,160 Right. 336 00:09:36,160 --> 00:09:39,320 While gorillas are confined to shrinking habitats, 337 00:09:39,320 --> 00:09:42,960 their survival often dependent on our goodwill. 338 00:09:42,960 --> 00:09:46,240 So the analogy being that humans could become the gorillas 339 00:09:46,240 --> 00:09:49,920 in a future dominated by super-intelligent AI. 340 00:09:49,920 --> 00:09:50,760 That's the question. 341 00:09:50,760 --> 00:09:52,680 Relegated to the sidelines, 342 00:09:52,680 --> 00:09:54,920 our fate in the hands of something we created 343 00:09:54,920 --> 00:09:56,640 but can no longer control. 344 00:09:56,640 --> 00:09:58,000 It's a sobering thought. 345 00:09:58,000 --> 00:09:58,840 It is. 346 00:09:58,840 --> 00:10:01,040 And it highlights the ethical imperative 347 00:10:01,040 --> 00:10:04,960 to approach AI development with caution and foresight. 348 00:10:04,960 --> 00:10:05,800 Right. 349 00:10:05,800 --> 00:10:07,160 We need to be asking ourselves 350 00:10:07,160 --> 00:10:10,040 not just if we can create super-intelligent AI. 351 00:10:10,040 --> 00:10:10,880 Yeah. 352 00:10:10,880 --> 00:10:11,720 But should we? 353 00:10:11,720 --> 00:10:12,540 Right. 354 00:10:12,540 --> 00:10:14,560 And if so, how do we ensure that it shares our values, 355 00:10:14,560 --> 00:10:15,760 our goals for the future? 356 00:10:15,760 --> 00:10:16,600 Yeah. 357 00:10:16,600 --> 00:10:19,720 How do we create AI that is aligned with human wellbeing? 358 00:10:19,720 --> 00:10:20,560 Right. 359 00:10:20,560 --> 00:10:21,800 Rather than something that could potentially see us 360 00:10:21,800 --> 00:10:23,160 as a threat or an obstacle. 361 00:10:23,160 --> 00:10:25,200 These aren't just hypothetical questions anymore, are they? 362 00:10:25,200 --> 00:10:26,040 We're not, no. 363 00:10:26,040 --> 00:10:27,160 We're talking about decisions 364 00:10:27,160 --> 00:10:29,000 that could determine the fate of humanity. 365 00:10:29,000 --> 00:10:29,840 We are. 366 00:10:29,840 --> 00:10:31,920 It's a lot of responsibility to bear. 367 00:10:31,920 --> 00:10:32,760 It is indeed. 368 00:10:32,760 --> 00:10:33,600 Wow. 369 00:10:33,600 --> 00:10:35,440 And as we delve further into your research. 370 00:10:35,440 --> 00:10:36,280 Okay. 371 00:10:36,280 --> 00:10:37,920 We'll see how these questions about the nature 372 00:10:37,920 --> 00:10:40,520 of intelligence and the ethics of AI 373 00:10:40,520 --> 00:10:42,080 are playing out in a field that's 374 00:10:42,080 --> 00:10:45,400 captivated humans for centuries. 375 00:10:45,400 --> 00:10:46,400 Yeah. 376 00:10:46,400 --> 00:10:47,960 Animal communication. 377 00:10:47,960 --> 00:10:48,800 Okay. 378 00:10:48,800 --> 00:10:52,520 Could the key to coexisting with artificial intelligence 379 00:10:52,520 --> 00:10:55,720 lie in unlocking the secrets of the natural world? 380 00:10:55,720 --> 00:10:56,560 Okay. 381 00:10:56,560 --> 00:10:58,240 We'll explore that right after this. 382 00:10:58,240 --> 00:11:00,240 It's fascinating to think that like the key 383 00:11:00,240 --> 00:11:02,840 to navigating the future of intelligence. 384 00:11:02,840 --> 00:11:03,680 Right. 385 00:11:03,680 --> 00:11:05,020 Both artificial and our own 386 00:11:05,020 --> 00:11:07,680 might lie in like deciphering the languages 387 00:11:07,680 --> 00:11:08,840 of other species. 388 00:11:08,840 --> 00:11:09,680 Yeah. 389 00:11:09,680 --> 00:11:11,560 We've been talking about the potential of AI. 390 00:11:11,560 --> 00:11:12,400 Right. 391 00:11:12,400 --> 00:11:13,960 But it seems like our own attempts to understand 392 00:11:13,960 --> 00:11:16,960 how animals communicate go back centuries, right? 393 00:11:16,960 --> 00:11:18,080 It's an age old pursuit. 394 00:11:18,080 --> 00:11:18,920 Right. 395 00:11:18,920 --> 00:11:21,360 Fueled by a fundamental curiosity about the natural world. 396 00:11:21,360 --> 00:11:22,200 Yeah. 397 00:11:22,200 --> 00:11:25,840 For centuries we've observed the intricate dances of bees, 398 00:11:25,840 --> 00:11:28,480 listened to the haunting songs of whales, 399 00:11:28,480 --> 00:11:31,920 and marveled at the complex social structures of ants, 400 00:11:31,920 --> 00:11:34,560 all while wondering, what are they saying? 401 00:11:34,560 --> 00:11:36,240 What's going on in those brains? 402 00:11:36,240 --> 00:11:38,960 It's like we've been staring at a giant jigsaw puzzle. 403 00:11:38,960 --> 00:11:39,800 Right. 404 00:11:39,800 --> 00:11:41,080 But missing most of the pieces. 405 00:11:41,080 --> 00:11:41,920 Yeah. 406 00:11:41,920 --> 00:11:43,440 But now with the power of AI, 407 00:11:43,440 --> 00:11:45,360 it feels like we might finally have a chance 408 00:11:45,360 --> 00:11:47,120 to start putting those pieces together. 409 00:11:47,120 --> 00:11:48,120 You're spot on. 410 00:11:48,120 --> 00:11:50,820 What you've been digging into, 411 00:11:50,820 --> 00:11:54,240 the use of AI to analyze animal communication. 412 00:11:54,240 --> 00:11:55,080 Right. 413 00:11:55,080 --> 00:11:56,560 It's revolutionizing the field. 414 00:11:56,560 --> 00:11:57,400 Okay. 415 00:11:57,400 --> 00:11:59,780 We're going beyond simple observation 416 00:11:59,780 --> 00:12:02,040 and starting to uncover the hidden patterns, 417 00:12:02,040 --> 00:12:03,920 instructions within those animal sounds. 418 00:12:03,920 --> 00:12:04,760 Okay. 419 00:12:04,760 --> 00:12:06,280 Those chirps and whistles and clicks 420 00:12:06,280 --> 00:12:10,000 that might hold the key to understanding their meaning. 421 00:12:10,000 --> 00:12:12,280 So instead of just recording a bird call and saying, 422 00:12:12,280 --> 00:12:13,320 well, that's pretty. 423 00:12:13,320 --> 00:12:14,160 Right. 424 00:12:14,160 --> 00:12:16,960 Scientists are using AI to analyze thousands, 425 00:12:16,960 --> 00:12:18,620 even millions of those calls. 426 00:12:18,620 --> 00:12:20,800 Looking for patterns that our human ears 427 00:12:20,800 --> 00:12:22,480 might completely miss. 428 00:12:22,480 --> 00:12:23,300 Exactly. 429 00:12:23,300 --> 00:12:24,520 Think about those transformer models 430 00:12:24,520 --> 00:12:26,280 that power things like chat GPT. 431 00:12:26,280 --> 00:12:27,120 Okay. 432 00:12:27,120 --> 00:12:29,040 The ones that can generate human quality attacks. 433 00:12:29,040 --> 00:12:29,880 Right. 434 00:12:29,880 --> 00:12:32,840 Researchers are now applying those same algorithms 435 00:12:32,840 --> 00:12:34,720 to analyze animal communication. 436 00:12:34,720 --> 00:12:35,760 Wow. 437 00:12:35,760 --> 00:12:38,640 These AI systems can process massive data sets 438 00:12:38,640 --> 00:12:40,560 of animal sounds, 439 00:12:40,560 --> 00:12:44,200 identifying subtle variations in pitch rhythm, 440 00:12:44,200 --> 00:12:46,960 even the emotional context of those sounds. 441 00:12:46,960 --> 00:12:49,060 Things that would be impossible for humans to do 442 00:12:49,060 --> 00:12:50,680 on such a large scale. 443 00:12:50,680 --> 00:12:53,480 It's like having a super powered ear for animal language. 444 00:12:53,480 --> 00:12:54,320 It is. 445 00:12:54,320 --> 00:12:55,440 But I imagine it's not as simple 446 00:12:55,440 --> 00:12:57,320 as just feeding these sounds into a computer 447 00:12:57,320 --> 00:12:59,080 and getting a direct translation right. 448 00:12:59,080 --> 00:12:59,920 You're right. 449 00:12:59,920 --> 00:13:01,000 It's much more nuanced than that. 450 00:13:01,000 --> 00:13:01,840 Yeah. 451 00:13:01,840 --> 00:13:03,680 The biggest challenges is context. 452 00:13:03,680 --> 00:13:04,520 Okay. 453 00:13:04,520 --> 00:13:06,200 Take the humble dog bark for example. 454 00:13:06,200 --> 00:13:07,040 Right. 455 00:13:07,040 --> 00:13:08,360 A bark can mean so many different things. 456 00:13:08,360 --> 00:13:09,200 Sure. 457 00:13:09,200 --> 00:13:11,680 Hello, play with me, danger. 458 00:13:11,680 --> 00:13:12,520 Right. 459 00:13:12,520 --> 00:13:15,440 Depending on the situation, the dog's body language, 460 00:13:15,440 --> 00:13:17,040 even the tone of the bark itself. 461 00:13:17,040 --> 00:13:18,440 And that's just one species 462 00:13:18,440 --> 00:13:20,880 with a relatively limited vocal range. 463 00:13:20,880 --> 00:13:21,720 Right. 464 00:13:21,720 --> 00:13:23,740 Imagine trying to decode the communication 465 00:13:23,740 --> 00:13:24,960 of a humpback whale. 466 00:13:24,960 --> 00:13:25,800 Right. 467 00:13:25,800 --> 00:13:27,560 With its complex songs that can last for hours. 468 00:13:27,560 --> 00:13:28,440 Precisely. 469 00:13:28,440 --> 00:13:30,240 The sheer complexity and diversity 470 00:13:30,240 --> 00:13:33,320 of animal communication systems is sounding. 471 00:13:33,320 --> 00:13:34,160 Yeah. 472 00:13:34,160 --> 00:13:36,760 And each species perceives the world in a unique way. 473 00:13:36,760 --> 00:13:37,600 Right. 474 00:13:37,600 --> 00:13:40,440 Whales for example, use echolocation 475 00:13:40,440 --> 00:13:41,960 to navigate and find food. 476 00:13:41,960 --> 00:13:42,800 Right. 477 00:13:42,800 --> 00:13:45,480 Their acoustic world is vastly different from ours. 478 00:13:45,480 --> 00:13:47,700 So it's not just about translating sounds. 479 00:13:47,700 --> 00:13:51,600 It's about understanding the entire sensory experience 480 00:13:51,600 --> 00:13:52,840 of another species. 481 00:13:52,840 --> 00:13:53,680 Yes. 482 00:13:53,680 --> 00:13:55,800 It's about trying to see the world through their eyes. 483 00:13:55,800 --> 00:13:56,640 Right. 484 00:13:56,640 --> 00:13:58,040 Or in the case of a bat, through their ears. 485 00:13:58,040 --> 00:13:59,920 It's a humbling endeavor, really. 486 00:13:59,920 --> 00:14:00,760 Yeah. 487 00:14:00,760 --> 00:14:03,560 It forces us to confront the limits of our own perception 488 00:14:03,560 --> 00:14:05,840 and to appreciate the incredible diversity 489 00:14:05,840 --> 00:14:07,520 of intelligence on this planet. 490 00:14:07,520 --> 00:14:10,080 It makes you wonder what we might discover 491 00:14:10,080 --> 00:14:11,400 about ourselves in the process. 492 00:14:11,400 --> 00:14:15,000 But even if we never achieve perfect translation. 493 00:14:15,000 --> 00:14:15,820 Right. 494 00:14:15,820 --> 00:14:18,760 Even a basic understanding of animal communication 495 00:14:18,760 --> 00:14:20,120 could have a huge impact, 496 00:14:20,120 --> 00:14:21,720 especially when it comes to conservation. 497 00:14:21,720 --> 00:14:22,800 Absolutely. 498 00:14:22,800 --> 00:14:25,320 You've been looking into the work of Lucy King, haven't you? 499 00:14:25,320 --> 00:14:26,160 Yes. 500 00:14:26,160 --> 00:14:27,560 Her research on elephant communication 501 00:14:27,560 --> 00:14:31,320 is a fantastic example of how even a partial understanding 502 00:14:31,320 --> 00:14:34,760 of animal language can have real world benefits. 503 00:14:34,760 --> 00:14:35,880 Her work is incredible. 504 00:14:35,880 --> 00:14:38,440 It's amazing how she was able to identify 505 00:14:38,440 --> 00:14:40,560 that specific rumble elephants use 506 00:14:40,560 --> 00:14:42,360 to warn each other about bees. 507 00:14:42,360 --> 00:14:43,200 Right. 508 00:14:43,200 --> 00:14:45,600 And then use that knowledge to develop a practical solution 509 00:14:45,600 --> 00:14:47,440 to human elephant conflict. 510 00:14:47,440 --> 00:14:50,200 It's a testament to the power of curiosity driven research. 511 00:14:50,200 --> 00:14:51,040 Yeah. 512 00:14:51,040 --> 00:14:53,920 Lucy's initial observation that elephants seem to have 513 00:14:53,920 --> 00:14:55,320 an aversion to bees. 514 00:14:55,320 --> 00:14:56,160 Right. 515 00:14:56,160 --> 00:14:58,120 Had really been dismissed as a quirky footnote. 516 00:14:58,120 --> 00:14:58,960 Right. 517 00:14:58,960 --> 00:14:59,800 But she dug deeper. 518 00:14:59,800 --> 00:15:01,080 She listened to the elephants. 519 00:15:01,080 --> 00:15:03,600 And in doing so, she discovered a new way 520 00:15:03,600 --> 00:15:06,040 to protect both elephants and humans. 521 00:15:06,040 --> 00:15:09,600 It's a powerful reminder that we still have so much to learn 522 00:15:09,600 --> 00:15:10,800 from the natural world. 523 00:15:10,800 --> 00:15:11,640 We do. 524 00:15:11,640 --> 00:15:14,760 If we can find a way to listen, really listen, 525 00:15:14,760 --> 00:15:17,360 to what other species are saying, 526 00:15:17,360 --> 00:15:19,120 who knows what we might discover. 527 00:15:19,120 --> 00:15:19,960 It's true. 528 00:15:19,960 --> 00:15:23,320 Maybe the key to coexisting with artificial intelligence 529 00:15:23,320 --> 00:15:25,800 to ensuring a future where both humans and AI 530 00:15:25,800 --> 00:15:29,400 can thrive lies in deciphering the languages 531 00:15:29,400 --> 00:15:30,560 of the natural world. 532 00:15:30,560 --> 00:15:31,400 Yeah. 533 00:15:31,400 --> 00:15:34,800 In recognizing that intelligence takes many forms. 534 00:15:34,800 --> 00:15:35,280 It does. 535 00:15:35,280 --> 00:15:37,640 And that our own understanding of that concept 536 00:15:37,640 --> 00:15:38,720 is still evolving. 537 00:15:38,720 --> 00:15:40,280 You've hit on a profound point. 538 00:15:40,280 --> 00:15:40,920 Yeah. 539 00:15:40,920 --> 00:15:44,240 As we delve deeper into the realms of quantum computing, 540 00:15:44,240 --> 00:15:47,920 artificial intelligence, and animal communication, 541 00:15:47,920 --> 00:15:50,080 we're ultimately on a quest to understand 542 00:15:50,080 --> 00:15:52,360 the nature of intelligence itself. 543 00:15:52,360 --> 00:15:52,880 Right. 544 00:15:52,880 --> 00:15:54,960 In all its breathtaking complexity and diversity. 545 00:15:54,960 --> 00:15:55,760 Yeah. 546 00:15:55,760 --> 00:15:59,720 And that, perhaps, is the most exciting discovery of all.