1 00:00:00,000 --> 00:00:02,400 Hey everyone, ready for another deep dive? 2 00:00:02,400 --> 00:00:04,640 Today we're tackling a research paper 3 00:00:04,640 --> 00:00:06,760 that asks a huge G question. 4 00:00:06,760 --> 00:00:07,720 No! 5 00:00:07,720 --> 00:00:11,880 Can AI actually shape a whole new age for civilization? 6 00:00:11,880 --> 00:00:12,720 Wow! 7 00:00:12,720 --> 00:00:15,480 We're looking at can transformative AI 8 00:00:15,480 --> 00:00:18,160 shape a new age for our civilization? 9 00:00:18,160 --> 00:00:19,000 Okay. 10 00:00:19,000 --> 00:00:19,880 This deep dive. 11 00:00:19,880 --> 00:00:22,040 It isn't just about algorithms and code though. 12 00:00:22,040 --> 00:00:26,080 It's about how AI could be this massive force for change. 13 00:00:26,080 --> 00:00:29,160 Maybe even as big as like the invention of the wheel. 14 00:00:29,160 --> 00:00:30,800 You know, or the internet. 15 00:00:30,800 --> 00:00:31,640 Right, right. 16 00:00:31,640 --> 00:00:32,960 And it gets really interesting when the paper 17 00:00:32,960 --> 00:00:35,760 explores all the roadblocks in the way. 18 00:00:35,760 --> 00:00:37,720 Luckily we've got our AI expert here 19 00:00:37,720 --> 00:00:38,720 to break it all down with us. 20 00:00:38,720 --> 00:00:40,160 Happy to be here. 21 00:00:40,160 --> 00:00:41,640 So something that really stood out to me 22 00:00:41,640 --> 00:00:43,680 was how this paper starts. 23 00:00:43,680 --> 00:00:46,760 It compares the potential impact of AI 24 00:00:46,760 --> 00:00:49,120 to these big inventions from the past. 25 00:00:49,120 --> 00:00:53,840 Like fire the wheel, the steam engine, even penicillin. 26 00:00:53,840 --> 00:00:54,680 Interesting. 27 00:00:54,680 --> 00:00:56,080 What do you make of all those comparisons? 28 00:00:56,080 --> 00:00:57,400 Well it's definitely a fascinating way 29 00:00:57,400 --> 00:00:58,660 to get the conversation going. 30 00:00:58,660 --> 00:01:01,040 The paper is basically saying that AI 31 00:01:01,040 --> 00:01:02,680 could be the next big leap. 32 00:01:02,680 --> 00:01:05,720 You know, potentially redefining how humans progress. 33 00:01:05,720 --> 00:01:07,720 Kind of like those other inventions did. 34 00:01:07,720 --> 00:01:10,640 So you're saying AI could be as significant 35 00:01:10,640 --> 00:01:11,800 as discovering fire. 36 00:01:11,800 --> 00:01:13,480 That's what they're arguing, yeah. 37 00:01:13,480 --> 00:01:15,880 And the paper does say that not every promising tech 38 00:01:15,880 --> 00:01:17,960 lives up to the hype of course. 39 00:01:17,960 --> 00:01:19,600 But they make a pretty strong argument 40 00:01:19,600 --> 00:01:21,580 for why AI could be different. 41 00:01:21,580 --> 00:01:23,040 Okay, so let's get to the basics here. 42 00:01:23,040 --> 00:01:25,360 What kind of AI are we even talking about? 43 00:01:25,360 --> 00:01:26,200 Right. 44 00:01:26,200 --> 00:01:27,680 The paper makes a distinction between 45 00:01:27,680 --> 00:01:31,320 like narrow AI and transformative AI. 46 00:01:31,320 --> 00:01:32,720 Can you explain that difference? 47 00:01:32,720 --> 00:01:33,560 Absolutely. 48 00:01:33,560 --> 00:01:37,560 So narrow or weak AI, that's what we mostly see today. 49 00:01:37,560 --> 00:01:42,000 It's AI that's super good at one very specific task. 50 00:01:42,000 --> 00:01:45,120 Like playing chess or recommending products online. 51 00:01:45,120 --> 00:01:47,240 So AI that could beat you at Jeopardy 52 00:01:47,240 --> 00:01:48,560 but couldn't fold your laundry. 53 00:01:48,560 --> 00:01:49,400 Exactly. 54 00:01:49,400 --> 00:01:51,120 Specialized intelligence basically. 55 00:01:51,120 --> 00:01:55,080 Transformative AI, T-AI, that's a whole other game. 56 00:01:55,080 --> 00:01:58,520 The paper defines it as AI that could like catapult 57 00:01:58,520 --> 00:02:00,120 civilization to a new level. 58 00:02:00,120 --> 00:02:02,160 A whole new era for humanity. 59 00:02:02,160 --> 00:02:05,320 That sounds like something straight out of sci-fi, almost. 60 00:02:05,320 --> 00:02:06,160 It does, doesn't it? 61 00:02:06,160 --> 00:02:09,040 What might T-AI actually look like in the real world? 62 00:02:09,040 --> 00:02:11,080 Well, the paper dives into some possibilities. 63 00:02:11,080 --> 00:02:14,840 They talk about autonomous multi-agent systems. 64 00:02:14,840 --> 00:02:17,560 Basically, it's like a network of AI agents 65 00:02:17,560 --> 00:02:19,200 all working together. 66 00:02:19,200 --> 00:02:21,680 Each one has its own special skills, you know? 67 00:02:21,680 --> 00:02:23,480 Like a team of AI specialists 68 00:02:23,480 --> 00:02:24,840 each with their own superpower. 69 00:02:24,840 --> 00:02:25,680 Exactly. 70 00:02:25,680 --> 00:02:27,320 They can solve really complex problems 71 00:02:27,320 --> 00:02:29,440 by collaborating and sharing information. 72 00:02:29,440 --> 00:02:30,280 That's cool. 73 00:02:30,280 --> 00:02:32,520 Then the paper touches on quantum AI too. 74 00:02:32,520 --> 00:02:33,360 Oh wow. 75 00:02:33,360 --> 00:02:35,200 Mind-blowing possibilities there 76 00:02:35,200 --> 00:02:37,400 but that's probably a whole other deep dive. 77 00:02:37,400 --> 00:02:38,640 Definitely one for the future. 78 00:02:38,640 --> 00:02:39,480 Yeah. 79 00:02:39,480 --> 00:02:44,480 Okay, so if T-AI could really reshape civilization, 80 00:02:44,960 --> 00:02:46,320 what's standing in our way? 81 00:02:46,320 --> 00:02:47,840 What are the roadblocks? 82 00:02:47,840 --> 00:02:50,400 Well, the paper breaks it down into two main categories. 83 00:02:50,400 --> 00:02:52,320 You've got your human roadblocks 84 00:02:52,320 --> 00:02:53,840 and then the technical hurdles. 85 00:02:53,840 --> 00:02:55,000 Gotcha. 86 00:02:55,000 --> 00:02:56,760 Let's start with the human side. 87 00:02:56,760 --> 00:02:59,240 I think one big obstacle is risk aversion. 88 00:02:59,240 --> 00:03:00,080 Makes sense. 89 00:03:00,080 --> 00:03:02,000 People get nervous about new tech, right? 90 00:03:02,000 --> 00:03:04,080 Especially something as powerful as AI. 91 00:03:04,080 --> 00:03:07,600 Worries about jobs, AI becoming uncontrollable. 92 00:03:07,600 --> 00:03:09,480 The paper's point is that this fear, 93 00:03:09,480 --> 00:03:12,800 especially if it leads to like super strict regulations, 94 00:03:12,800 --> 00:03:14,720 that could really slow things down. 95 00:03:14,720 --> 00:03:16,640 It's all about finding the right balance 96 00:03:16,640 --> 00:03:20,080 between caution and letting exploration happen. 97 00:03:20,080 --> 00:03:22,240 So we need safeguards, 98 00:03:22,240 --> 00:03:24,640 but not so many that we stifle progress. 99 00:03:24,640 --> 00:03:25,560 Exactly. 100 00:03:25,560 --> 00:03:28,800 Now, the paper also mentions this attention economy thing 101 00:03:28,800 --> 00:03:29,760 as a roadblock. 102 00:03:29,760 --> 00:03:30,600 Oh, yeah. 103 00:03:30,600 --> 00:03:31,440 What's that all about? 104 00:03:31,440 --> 00:03:33,080 Think about how much time we all spend 105 00:03:33,080 --> 00:03:36,400 on social media and stuff designed to keep us hooked, right? 106 00:03:36,400 --> 00:03:38,640 The paper argues that those systems, 107 00:03:38,640 --> 00:03:41,480 even though they're powered by AI a lot of the time, 108 00:03:41,480 --> 00:03:42,920 they're actually distracting us 109 00:03:42,920 --> 00:03:45,840 from making more meaningful AI applications. 110 00:03:45,840 --> 00:03:48,600 So like we're using AI to create distractions 111 00:03:48,600 --> 00:03:50,600 instead of harnessing its real power. 112 00:03:50,600 --> 00:03:51,720 Exactly. 113 00:03:51,720 --> 00:03:54,400 And that ties into another human challenge, 114 00:03:54,400 --> 00:03:58,000 the lack of a unified vision for AI development. 115 00:03:58,000 --> 00:04:00,640 The paper calls it the diluted AI effect. 116 00:04:00,640 --> 00:04:02,600 Meaning instead of working together 117 00:04:02,600 --> 00:04:04,240 towards some shared goal, 118 00:04:04,240 --> 00:04:05,720 we've got all these different countries, 119 00:04:05,720 --> 00:04:08,320 companies, research groups, all doing their own thing. 120 00:04:08,320 --> 00:04:09,600 That's the issue. 121 00:04:09,600 --> 00:04:12,640 The paper says a more coordinated approach is key. 122 00:04:12,640 --> 00:04:15,760 Okay, so we've got fear holding us back, 123 00:04:15,760 --> 00:04:17,880 distractions pulling us away, 124 00:04:17,880 --> 00:04:20,280 and a lack of global unity. 125 00:04:20,280 --> 00:04:21,920 What about those technical challenges? 126 00:04:21,920 --> 00:04:22,840 Right, right. 127 00:04:22,840 --> 00:04:24,320 The paper gets pretty complex there. 128 00:04:24,320 --> 00:04:25,160 It does. 129 00:04:25,160 --> 00:04:26,560 I think one of the biggest hurdles 130 00:04:26,560 --> 00:04:30,120 is what they call detecting new abilities. 131 00:04:30,120 --> 00:04:32,200 As AI systems get more complex, 132 00:04:32,200 --> 00:04:35,160 they might develop what we call emergent properties, 133 00:04:35,160 --> 00:04:38,400 capabilities that we didn't program into them. 134 00:04:38,400 --> 00:04:41,120 So the AI could surprise us with new skills 135 00:04:41,120 --> 00:04:42,640 or even new ways of thinking. 136 00:04:42,640 --> 00:04:43,520 Exactly. 137 00:04:43,520 --> 00:04:46,280 It's both exciting and a little unnerving, right? 138 00:04:46,280 --> 00:04:47,200 Definitely. 139 00:04:47,200 --> 00:04:49,200 We wouldn't want to be blindsided by an AI 140 00:04:49,200 --> 00:04:51,280 that suddenly gets a lot smarter than we expected. 141 00:04:51,280 --> 00:04:52,120 Exactly. 142 00:04:52,120 --> 00:04:54,120 We need ways to monitor and understand 143 00:04:54,120 --> 00:04:56,640 these emerging abilities as AI evolves. 144 00:04:56,640 --> 00:04:59,160 Okay, what other technical challenges are there? 145 00:04:59,160 --> 00:05:00,680 Well, there's the data paradox. 146 00:05:00,680 --> 00:05:01,520 The what? 147 00:05:01,520 --> 00:05:02,360 The data paradox. 148 00:05:02,360 --> 00:05:03,680 AI needs data to learn, right? 149 00:05:03,680 --> 00:05:04,520 The more the better. 150 00:05:04,520 --> 00:05:05,360 Right. 151 00:05:05,360 --> 00:05:07,040 But to train really advanced AI, 152 00:05:07,040 --> 00:05:08,840 we need a huge amount of data, 153 00:05:08,840 --> 00:05:10,600 and that creates problems. 154 00:05:10,600 --> 00:05:12,320 I was like, too much of a good thing. 155 00:05:12,320 --> 00:05:13,320 How so? 156 00:05:13,320 --> 00:05:16,080 Well, all that data needs to be stored and processed. 157 00:05:16,080 --> 00:05:18,800 That takes massive computing power and energy. 158 00:05:18,800 --> 00:05:19,640 Right. 159 00:05:19,640 --> 00:05:21,200 The paper even suggests we might need to look at 160 00:05:21,200 --> 00:05:24,240 nuclear power to meet those energy demands. 161 00:05:24,240 --> 00:05:27,280 Wow, nuclear reactors to train AI. 162 00:05:27,280 --> 00:05:29,200 That really shows the scale of this. 163 00:05:29,200 --> 00:05:30,040 It does. 164 00:05:30,040 --> 00:05:32,240 And then there's the whole issue of Moore's law, 165 00:05:32,240 --> 00:05:34,840 which says basically that computing power 166 00:05:34,840 --> 00:05:36,800 doubles every two years or so. 167 00:05:36,800 --> 00:05:37,640 Right. 168 00:05:37,640 --> 00:05:39,080 But the paper points out that we might be hitting 169 00:05:39,080 --> 00:05:40,360 the limits of that law. 170 00:05:40,360 --> 00:05:43,120 Meaning our hardware might not be able to keep up 171 00:05:43,120 --> 00:05:44,320 with AI's needs. 172 00:05:44,320 --> 00:05:45,480 That's the worry. 173 00:05:45,480 --> 00:05:47,960 If we can't make chips more powerful, 174 00:05:47,960 --> 00:05:50,000 it could hold AI development back. 175 00:05:50,000 --> 00:05:52,160 Okay, that's a lot to take in. 176 00:05:52,160 --> 00:05:56,360 Fear, distractions, no unity, AI developing new abilities, 177 00:05:56,360 --> 00:05:59,680 this data paradox, the limits of computing power. 178 00:05:59,680 --> 00:06:01,520 Sounds like a tough road to TAI. 179 00:06:01,520 --> 00:06:02,680 It's definitely not easy, 180 00:06:02,680 --> 00:06:04,800 but the paper doesn't just focus on the challenges, 181 00:06:04,800 --> 00:06:05,640 you know. 182 00:06:05,640 --> 00:06:06,480 Oh. 183 00:06:06,480 --> 00:06:07,320 There are some really interesting possibilities 184 00:06:07,320 --> 00:06:09,360 and reasons to be optimistic, too. 185 00:06:09,360 --> 00:06:10,480 Well, that's good to hear. 186 00:06:10,480 --> 00:06:12,880 After all those roadblocks, I could use some good news. 187 00:06:12,880 --> 00:06:15,360 One of the most exciting ideas is that we might be able 188 00:06:15,360 --> 00:06:18,760 to skip a step on the path to TAI. 189 00:06:18,760 --> 00:06:19,600 What do you mean? 190 00:06:19,600 --> 00:06:22,320 Well, a lot of experts think we need to create an AI 191 00:06:22,320 --> 00:06:25,640 that's basically as smart as a human first. 192 00:06:25,640 --> 00:06:26,480 Uh-huh. 193 00:06:26,480 --> 00:06:29,160 What they call BCAI, like a stepping stone to TAI. 194 00:06:29,160 --> 00:06:30,000 Right. 195 00:06:30,000 --> 00:06:32,480 But the paper suggests we might not need that step. 196 00:06:32,480 --> 00:06:35,560 Yeah, they used this analogy of submarines and swimming. 197 00:06:35,560 --> 00:06:36,400 Okay. 198 00:06:36,400 --> 00:06:38,240 Submarines don't swim, right? 199 00:06:38,240 --> 00:06:40,240 But they still explore the ocean depths. 200 00:06:40,240 --> 00:06:41,080 It's true. 201 00:06:41,080 --> 00:06:44,160 The point is, AI doesn't have to think exactly 202 00:06:44,160 --> 00:06:46,120 like a human to be transformative. 203 00:06:46,120 --> 00:06:46,960 Ah, they see. 204 00:06:46,960 --> 00:06:49,960 It might find its own unique ways to do incredible things, 205 00:06:49,960 --> 00:06:52,560 just like a submarine navigating underwater 206 00:06:52,560 --> 00:06:54,040 without mimicking a fish. 207 00:06:54,040 --> 00:06:55,360 That's a fascinating thought. 208 00:06:55,360 --> 00:06:57,000 So even with all these challenges, 209 00:06:57,000 --> 00:06:59,280 we might get to TAI sooner than expected. 210 00:06:59,280 --> 00:07:00,280 It's not about chance. 211 00:07:00,280 --> 00:07:01,720 It's about deliberate progress. 212 00:07:01,720 --> 00:07:02,560 Right. 213 00:07:02,560 --> 00:07:03,440 And speaking of progress, 214 00:07:03,440 --> 00:07:05,560 the paper highlights some promising trends. 215 00:07:05,560 --> 00:07:06,400 Oh, good. 216 00:07:06,400 --> 00:07:07,400 Tell me more. 217 00:07:07,400 --> 00:07:09,960 Well, for one, investment in AI research 218 00:07:09,960 --> 00:07:11,560 is exploding right now. 219 00:07:11,560 --> 00:07:13,400 That's fueling all kinds of innovation, 220 00:07:13,400 --> 00:07:15,120 bringing in the best minds. 221 00:07:15,120 --> 00:07:16,080 That's encouraging. 222 00:07:17,160 --> 00:07:17,920 Anything else? 223 00:07:17,920 --> 00:07:20,520 There's also a growing focus on AI ethics. 224 00:07:20,520 --> 00:07:21,720 So people are really thinking 225 00:07:21,720 --> 00:07:23,640 about the moral implications of all this. 226 00:07:23,640 --> 00:07:24,560 Exactly. 227 00:07:24,560 --> 00:07:26,880 More and more organizations and governments 228 00:07:26,880 --> 00:07:30,040 are creating frameworks and guidelines 229 00:07:30,040 --> 00:07:32,600 for responsible AI development. 230 00:07:32,600 --> 00:07:36,720 So we're trying to build ethics into AI from the ground up. 231 00:07:36,720 --> 00:07:38,960 So it doesn't become some sci-fi nightmare. 232 00:07:38,960 --> 00:07:40,000 Exactly. 233 00:07:40,000 --> 00:07:43,200 The paper even mentions things like the EU's AI Act. 234 00:07:43,200 --> 00:07:45,720 California's AB-3030. 235 00:07:45,720 --> 00:07:46,560 Right. 236 00:07:46,560 --> 00:07:48,200 The OECD AI principles. 237 00:07:48,200 --> 00:07:49,040 Gotcha. 238 00:07:49,040 --> 00:07:51,000 All signs that we're taking this stuff seriously. 239 00:07:51,000 --> 00:07:52,320 That's good to hear. 240 00:07:52,320 --> 00:07:54,960 So final thoughts from this first part of our deep dive. 241 00:07:54,960 --> 00:07:57,560 Well, I'd say, yeah, there are hurdles for sure. 242 00:07:57,560 --> 00:07:58,400 Definitely. 243 00:07:58,400 --> 00:08:00,520 But there's real momentum behind AI too. 244 00:08:00,520 --> 00:08:01,360 You know? 245 00:08:01,360 --> 00:08:02,200 Right. 246 00:08:02,200 --> 00:08:04,080 It's not about if TAI will happen, but when. 247 00:08:04,080 --> 00:08:05,200 And how we prepare for it. 248 00:08:05,200 --> 00:08:06,040 Exactly. 249 00:08:06,040 --> 00:08:07,600 Okay, well, we've covered a lot of ground here. 250 00:08:07,600 --> 00:08:08,440 We have. 251 00:08:08,440 --> 00:08:10,520 We've explored the potential of transformative AI 252 00:08:10,520 --> 00:08:14,000 to reshape civilization. 253 00:08:14,000 --> 00:08:15,880 Uncovered a bunch of challenges and looked 254 00:08:15,880 --> 00:08:17,600 at some reasons for hope. 255 00:08:17,600 --> 00:08:19,760 Don't go anywhere, because when we come back, 256 00:08:19,760 --> 00:08:22,560 we'll be diving into even more of this paper's insights. 257 00:08:22,560 --> 00:08:23,400 Sounds good. 258 00:08:23,400 --> 00:08:24,240 Stay tuned. 259 00:08:24,240 --> 00:08:26,360 And preparing for TAI means we really 260 00:08:26,360 --> 00:08:29,760 need to grasp just how big this change could be. 261 00:08:29,760 --> 00:08:31,960 Remember how the paper compared AI's impact 262 00:08:31,960 --> 00:08:33,920 to those huge historical moments? 263 00:08:33,920 --> 00:08:37,240 Yeah, fire, the wheel, agriculture, even penicillin. 264 00:08:37,240 --> 00:08:37,520 Right. 265 00:08:37,520 --> 00:08:39,600 It wasn't just about the tech itself, though. 266 00:08:39,600 --> 00:08:39,840 Right. 267 00:08:39,840 --> 00:08:41,840 It's how those things completely changed 268 00:08:41,840 --> 00:08:43,480 the way civilization worked. 269 00:08:43,480 --> 00:08:44,040 Like how? 270 00:08:44,040 --> 00:08:45,960 Well, take fire, for example. 271 00:08:45,960 --> 00:08:46,520 Okay. 272 00:08:46,520 --> 00:08:47,520 It's so basic now. 273 00:08:47,520 --> 00:08:48,560 We don't even think about it. 274 00:08:48,560 --> 00:08:48,760 Right. 275 00:08:48,760 --> 00:08:51,360 But the paper points out, like controlling fire, 276 00:08:51,360 --> 00:08:53,120 that wasn't just warmth or cooking. 277 00:08:53,120 --> 00:08:53,320 Right. 278 00:08:53,320 --> 00:08:54,680 It led to metalworking. 279 00:08:54,680 --> 00:08:55,160 Oh. 280 00:08:55,160 --> 00:08:58,640 Which then gave us tools, weapons, all this new tech. 281 00:08:58,640 --> 00:09:01,800 So something simple had these huge ripple effects. 282 00:09:01,800 --> 00:09:02,720 Exactly. 283 00:09:02,720 --> 00:09:05,280 And the paper goes on to talk about, like, 284 00:09:05,280 --> 00:09:07,080 domesticating animals and plants. 285 00:09:07,080 --> 00:09:07,400 Right. 286 00:09:07,400 --> 00:09:09,040 The development of agriculture. 287 00:09:09,040 --> 00:09:10,480 That was a game changer, too, wasn't it? 288 00:09:10,480 --> 00:09:11,600 Huge. 289 00:09:11,600 --> 00:09:14,840 That meant we could stay in one place, grow crops, raise 290 00:09:14,840 --> 00:09:15,640 animals. 291 00:09:15,640 --> 00:09:17,720 Led to villages, towns, cities. 292 00:09:17,720 --> 00:09:18,120 Yeah. 293 00:09:18,120 --> 00:09:19,800 Basically how we live now. 294 00:09:19,800 --> 00:09:21,040 And then there's the wheel. 295 00:09:21,040 --> 00:09:21,560 Oh, yeah. 296 00:09:21,560 --> 00:09:22,720 Seems obvious, but. 297 00:09:22,720 --> 00:09:24,240 But imagine no wheels. 298 00:09:24,240 --> 00:09:25,600 Everything carrier dragged. 299 00:09:25,600 --> 00:09:26,320 Exactly. 300 00:09:26,320 --> 00:09:28,800 The paper really emphasizes how the wheel 301 00:09:28,800 --> 00:09:30,280 changed trade and travel. 302 00:09:30,280 --> 00:09:30,440 Right. 303 00:09:30,440 --> 00:09:33,760 Made it so much easier to move stuff and people long distances. 304 00:09:33,760 --> 00:09:36,320 Which led to more economic and cultural exchange. 305 00:09:36,320 --> 00:09:36,960 Exactly. 306 00:09:36,960 --> 00:09:39,200 So each of those innovations, they triggered this, 307 00:09:39,200 --> 00:09:43,400 like, cascade of changes shaped civilization, basically. 308 00:09:43,400 --> 00:09:46,000 And the argument is that transformative AI 309 00:09:46,000 --> 00:09:48,600 could be that next big trigger. 310 00:09:48,600 --> 00:09:49,600 That's what they're saying. 311 00:09:49,600 --> 00:09:51,840 And just like with those past innovations, 312 00:09:51,840 --> 00:09:53,960 we can't predict every single way 313 00:09:53,960 --> 00:09:55,720 TAI might change things. 314 00:09:55,720 --> 00:09:56,280 Right. 315 00:09:56,280 --> 00:09:59,040 But the paper does give us some interesting points 316 00:09:59,040 --> 00:09:59,800 to think about. 317 00:09:59,800 --> 00:10:00,320 OK. 318 00:10:00,320 --> 00:10:02,960 One thing they talk about is the need 319 00:10:02,960 --> 00:10:07,080 to navigate what they call potholes on the road to TAI. 320 00:10:07,080 --> 00:10:07,800 Potholes. 321 00:10:07,800 --> 00:10:08,600 I like that analogy. 322 00:10:08,600 --> 00:10:09,120 Yeah, right. 323 00:10:09,120 --> 00:10:11,720 So what are some of the potholes we need to watch out for? 324 00:10:11,720 --> 00:10:14,800 Well, we've already talked about some human-caused ones, 325 00:10:14,800 --> 00:10:14,960 right? 326 00:10:14,960 --> 00:10:15,680 We have. 327 00:10:15,680 --> 00:10:19,320 The fear, the distractions, the lack of global coordination. 328 00:10:19,320 --> 00:10:19,640 Right. 329 00:10:19,640 --> 00:10:21,400 But the paper dives deeper. 330 00:10:21,400 --> 00:10:25,160 One that really stood out to me was information overload. 331 00:10:25,160 --> 00:10:27,800 We're bombarded with data every day, though. 332 00:10:27,800 --> 00:10:29,400 It's not just the volume, though. 333 00:10:29,400 --> 00:10:29,800 What else? 334 00:10:29,800 --> 00:10:34,320 It's the fact that so much of it is just noise or misinformation 335 00:10:34,320 --> 00:10:35,880 or just irrelevant stuff. 336 00:10:35,880 --> 00:10:39,560 So how does that hinder AI development? 337 00:10:39,560 --> 00:10:42,480 Well, imagine trying to train an AI system. 338 00:10:42,480 --> 00:10:44,000 You need good data for that. 339 00:10:44,000 --> 00:10:44,320 Right. 340 00:10:44,320 --> 00:10:47,200 But finding the right data in all that mess. 341 00:10:47,200 --> 00:10:48,760 Like finding a needle in a haystack, 342 00:10:48,760 --> 00:10:50,160 but the haystack keeps growing. 343 00:10:50,160 --> 00:10:50,840 Exactly. 344 00:10:50,840 --> 00:10:52,640 And then there's the issue of misalignment. 345 00:10:52,640 --> 00:10:52,960 OK. 346 00:10:52,960 --> 00:10:55,680 Making sure AI's goals are actually aligned with ours. 347 00:10:55,680 --> 00:10:58,400 So AI working for us, not against us. 348 00:10:58,400 --> 00:10:59,160 Exactly. 349 00:10:59,160 --> 00:11:02,240 But the paper points out aligning those values, 350 00:11:02,240 --> 00:11:03,800 it's trickier than it sounds. 351 00:11:03,800 --> 00:11:04,480 How so? 352 00:11:04,480 --> 00:11:06,160 Well, whose values are we talking about? 353 00:11:06,160 --> 00:11:08,560 How do we account for different perspectives, cultures, 354 00:11:08,560 --> 00:11:09,160 all that? 355 00:11:09,160 --> 00:11:10,680 It's a tough ethical question, for sure. 356 00:11:10,680 --> 00:11:11,440 It is. 357 00:11:11,440 --> 00:11:15,520 The paper also mentions overreliance on human expertise 358 00:11:15,520 --> 00:11:17,920 as a potential roadblock. 359 00:11:17,920 --> 00:11:18,840 What does that mean? 360 00:11:18,840 --> 00:11:21,560 It's basically saying we shouldn't limit AI 361 00:11:21,560 --> 00:11:24,240 by trying to force it to think exactly like us. 362 00:11:24,240 --> 00:11:25,800 Like the submarine analogy. 363 00:11:25,800 --> 00:11:26,200 Right. 364 00:11:26,200 --> 00:11:29,400 We need to be open to AI finding its own ways 365 00:11:29,400 --> 00:11:30,800 to solve problems. 366 00:11:30,800 --> 00:11:33,280 Even if those ways are different from how humans would do it. 367 00:11:33,280 --> 00:11:34,160 Exactly. 368 00:11:34,160 --> 00:11:37,240 And then there's that diluted AI effect again. 369 00:11:37,240 --> 00:11:37,600 Right. 370 00:11:37,600 --> 00:11:39,280 The lack of global teamwork. 371 00:11:39,280 --> 00:11:39,600 Yeah. 372 00:11:39,600 --> 00:11:43,080 Everyone working in silos, which slows things down. 373 00:11:43,080 --> 00:11:45,040 The paper really emphasizes that, doesn't it? 374 00:11:45,040 --> 00:11:45,760 It does. 375 00:11:45,760 --> 00:11:48,240 They argue for a more unified approach focused 376 00:11:48,240 --> 00:11:50,240 on shared goals for everyone. 377 00:11:50,240 --> 00:11:51,160 Makes sense. 378 00:11:51,160 --> 00:11:55,480 So we've covered some big human-caused potholes 379 00:11:55,480 --> 00:11:57,200 on this road to TAI. 380 00:11:57,200 --> 00:11:57,960 We have. 381 00:11:57,960 --> 00:11:59,240 What about the technical ones? 382 00:11:59,240 --> 00:11:59,640 Oh, yeah. 383 00:11:59,640 --> 00:12:00,560 There are plenty of those. 384 00:12:00,560 --> 00:12:00,840 OK. 385 00:12:00,840 --> 00:12:01,560 Like what? 386 00:12:01,560 --> 00:12:04,680 Well, one key challenge is what they call world modeling. 387 00:12:04,680 --> 00:12:05,560 Meaning? 388 00:12:05,560 --> 00:12:07,720 Basically, it's about AI systems being 389 00:12:07,720 --> 00:12:11,840 able to build these accurate and complex models of the world. 390 00:12:11,840 --> 00:12:14,560 So not just processing data, but really understanding 391 00:12:14,560 --> 00:12:15,320 how things work. 392 00:12:15,320 --> 00:12:16,400 Exactly. 393 00:12:16,400 --> 00:12:19,560 The paper says that current AI is good at narrow tasks, 394 00:12:19,560 --> 00:12:21,280 but struggles with that bigger picture. 395 00:12:21,280 --> 00:12:23,000 And then there's that sustainability question 396 00:12:23,000 --> 00:12:23,840 we touched on before. 397 00:12:23,840 --> 00:12:24,340 Right. 398 00:12:24,340 --> 00:12:24,840 The energy demand. 399 00:12:24,840 --> 00:12:25,120 Yeah. 400 00:12:25,120 --> 00:12:27,160 Training and running these advanced AI systems, 401 00:12:27,160 --> 00:12:28,240 it takes a lot of power. 402 00:12:28,240 --> 00:12:28,740 A lot. 403 00:12:28,740 --> 00:12:30,920 The paper really stresses that point, 404 00:12:30,920 --> 00:12:34,120 even suggesting we might need to look at nuclear power. 405 00:12:34,120 --> 00:12:35,000 Wow, seriously. 406 00:12:35,000 --> 00:12:36,160 It's in there. 407 00:12:36,160 --> 00:12:38,760 And then, of course, there's the whole Moore's Law issue. 408 00:12:38,760 --> 00:12:39,040 Right. 409 00:12:39,040 --> 00:12:40,320 The limits of computing power. 410 00:12:40,320 --> 00:12:40,840 Yeah. 411 00:12:40,840 --> 00:12:43,520 And as we said, if we hit those limits, 412 00:12:43,520 --> 00:12:45,880 it could really slow down AI development. 413 00:12:45,880 --> 00:12:46,160 OK. 414 00:12:46,160 --> 00:12:47,920 So lots of technical potholes, too. 415 00:12:47,920 --> 00:12:49,080 Anything else? 416 00:12:49,080 --> 00:12:52,360 Well, the paper gets into some pretty deep theoretical stuff. 417 00:12:52,360 --> 00:12:52,960 Uh-oh. 418 00:12:52,960 --> 00:12:54,760 Here comes the brain-melting part. 419 00:12:54,760 --> 00:12:55,840 I'll try to keep it simple. 420 00:12:55,840 --> 00:12:57,160 Please do. 421 00:12:57,160 --> 00:12:59,080 Basically, there are different ways 422 00:12:59,080 --> 00:13:01,560 to think about computation and intelligence, right? 423 00:13:01,560 --> 00:13:02,080 Right. 424 00:13:02,080 --> 00:13:04,840 The paper explores how some of those frameworks, 425 00:13:04,840 --> 00:13:09,160 like Turing completeness and the halting problem, 426 00:13:09,160 --> 00:13:11,400 they both support the idea of TAI, 427 00:13:11,400 --> 00:13:15,120 but also hint at some potential limitations. 428 00:13:15,120 --> 00:13:17,040 Even on a theoretical level, there 429 00:13:17,040 --> 00:13:20,120 are still a lot of questions about what TAI can really do. 430 00:13:20,120 --> 00:13:21,240 Exactly. 431 00:13:21,240 --> 00:13:24,480 It shows how much we still have to learn about intelligence 432 00:13:24,480 --> 00:13:27,120 and what it even means for something to be intelligent. 433 00:13:27,120 --> 00:13:27,400 OK. 434 00:13:27,400 --> 00:13:29,160 We've covered a lot of ground exploring 435 00:13:29,160 --> 00:13:33,160 both the human and technical challenges on this road to TAI. 436 00:13:33,160 --> 00:13:33,960 We have. 437 00:13:33,960 --> 00:13:35,880 But I'm curious, what does the paper 438 00:13:35,880 --> 00:13:39,680 say about the good stuff, the potential benefits of all this? 439 00:13:39,680 --> 00:13:41,360 Oh, that's a great question. 440 00:13:41,360 --> 00:13:42,560 Any reasons for optimism? 441 00:13:42,560 --> 00:13:43,040 Definitely. 442 00:13:43,040 --> 00:13:45,360 Remember those green shoots we mentioned earlier? 443 00:13:45,360 --> 00:13:47,120 The promising areas of AI development. 444 00:13:47,120 --> 00:13:48,080 Right. 445 00:13:48,080 --> 00:13:49,840 That's where we're heading next. 446 00:13:49,840 --> 00:13:50,160 All right. 447 00:13:50,160 --> 00:13:52,680 We're back for the final part of our deep dive 448 00:13:52,680 --> 00:13:54,480 into transformative AI. 449 00:13:54,480 --> 00:13:55,640 Back at it. 450 00:13:55,640 --> 00:13:58,240 We've talked about TAI reshaping civilization 451 00:13:58,240 --> 00:13:59,960 and all these challenges in the way. 452 00:13:59,960 --> 00:14:02,760 But now let's talk about the good stuff. 453 00:14:02,760 --> 00:14:03,240 Right. 454 00:14:03,240 --> 00:14:04,720 The reasons to be optimistic. 455 00:14:04,720 --> 00:14:05,520 Absolutely. 456 00:14:05,520 --> 00:14:07,680 Remember those green shoots we talked about? 457 00:14:07,680 --> 00:14:09,280 The promising areas of AI. 458 00:14:09,280 --> 00:14:10,440 Right. 459 00:14:10,440 --> 00:14:13,800 For one, the pace of progress is insane. 460 00:14:13,800 --> 00:14:14,440 It really is. 461 00:14:14,440 --> 00:14:16,080 Breakthroughs all the time. 462 00:14:16,080 --> 00:14:19,120 The paper also highlights AI ethics, which is good. 463 00:14:19,120 --> 00:14:22,240 So more people are thinking about the moral implications 464 00:14:22,240 --> 00:14:22,760 of all this. 465 00:14:22,760 --> 00:14:23,480 Exactly. 466 00:14:23,480 --> 00:14:26,200 We need to make sure AI is developed responsibly. 467 00:14:26,200 --> 00:14:27,200 Right. 468 00:14:27,200 --> 00:14:28,040 And that's happening. 469 00:14:28,040 --> 00:14:29,680 People are taking that seriously. 470 00:14:29,680 --> 00:14:31,000 Yeah, more and more. 471 00:14:31,000 --> 00:14:34,960 The paper even argues that the very questions TAI raises, 472 00:14:34,960 --> 00:14:36,720 those are a sign of progress. 473 00:14:36,720 --> 00:14:37,400 How so? 474 00:14:37,400 --> 00:14:39,760 It means we're thinking big picture, 475 00:14:39,760 --> 00:14:41,640 not just the technical details. 476 00:14:41,640 --> 00:14:43,480 We're grappling with the consequences. 477 00:14:43,480 --> 00:14:44,840 So it's a good sign that we're even 478 00:14:44,840 --> 00:14:46,080 having these conversations. 479 00:14:46,080 --> 00:14:46,640 Exactly. 480 00:14:46,640 --> 00:14:49,160 It shows we're not just blindly rushing ahead. 481 00:14:49,160 --> 00:14:50,400 OK, that makes sense. 482 00:14:50,400 --> 00:14:51,920 Any other reasons for optimism? 483 00:14:51,920 --> 00:14:54,200 Well, the paper points to human ingenuity. 484 00:14:54,200 --> 00:14:54,720 Right. 485 00:14:54,720 --> 00:14:56,600 We've solved tough problems before. 486 00:14:56,600 --> 00:14:57,040 Right. 487 00:14:57,040 --> 00:14:58,960 We can do it again with AI. 488 00:14:58,960 --> 00:15:01,520 They really emphasize collaboration too. 489 00:15:01,520 --> 00:15:03,200 Bringing in experts from different fields. 490 00:15:03,200 --> 00:15:03,840 Exactly. 491 00:15:03,840 --> 00:15:07,200 Computer scientists, ethicists, philosophers, you name it. 492 00:15:07,200 --> 00:15:09,320 Working together to figure this all out. 493 00:15:09,320 --> 00:15:11,000 That's what they say is key. 494 00:15:11,000 --> 00:15:15,760 So pace of progress, focus on ethics, human ingenuity, 495 00:15:15,760 --> 00:15:16,880 and working together. 496 00:15:16,880 --> 00:15:17,440 Yeah. 497 00:15:17,440 --> 00:15:20,280 Sounds like there's reason to be cautiously optimistic. 498 00:15:20,280 --> 00:15:21,200 I'd say so. 499 00:15:21,200 --> 00:15:23,680 The challenges are huge, but the potential benefits, 500 00:15:23,680 --> 00:15:24,680 those are huge too. 501 00:15:24,680 --> 00:15:25,120 Right. 502 00:15:25,120 --> 00:15:28,200 The paper's point is we can't ignore those benefits. 503 00:15:28,200 --> 00:15:33,080 We have a responsibility to explore TAI, but responsibly. 504 00:15:33,080 --> 00:15:34,080 I agree. 505 00:15:34,080 --> 00:15:37,040 It's not about being scared or ignoring the problems. 506 00:15:37,040 --> 00:15:39,640 It's about facing them head on, working together. 507 00:15:39,640 --> 00:15:40,600 Exactly. 508 00:15:40,600 --> 00:15:43,120 So AI benefits everyone, not just a few. 509 00:15:43,120 --> 00:15:45,880 That's the goal. 510 00:15:45,880 --> 00:15:47,400 Well, this has been quite a journey. 511 00:15:47,400 --> 00:15:48,040 It has. 512 00:15:48,040 --> 00:15:51,600 We've explored TAI, the potential, the challenges, 513 00:15:51,600 --> 00:15:53,040 the reasons for hope. 514 00:15:53,040 --> 00:15:54,400 A lot to cover. 515 00:15:54,400 --> 00:16:00,040 I'm walking away feeling excited, but also, I don't know, 516 00:16:00,040 --> 00:16:01,480 a sense of responsibility. 517 00:16:01,480 --> 00:16:02,560 Yeah, get that. 518 00:16:02,560 --> 00:16:04,320 The future of AI is still being written. 519 00:16:04,320 --> 00:16:04,800 You know? 520 00:16:04,800 --> 00:16:05,320 It is. 521 00:16:05,320 --> 00:16:06,600 And we all have a part to play in that. 522 00:16:06,600 --> 00:16:07,280 Absolutely. 523 00:16:07,280 --> 00:16:09,040 It's not just up to the experts. 524 00:16:09,040 --> 00:16:09,520 Right. 525 00:16:09,520 --> 00:16:12,720 Huge thanks to our expert for their insights. 526 00:16:12,720 --> 00:16:13,280 No problem. 527 00:16:13,280 --> 00:16:14,160 Happy to be here. 528 00:16:14,160 --> 00:16:16,280 And to all of you listening, what are your thoughts 529 00:16:16,280 --> 00:16:17,080 on all this? 530 00:16:17,080 --> 00:16:17,760 What excites you? 531 00:16:17,760 --> 00:16:18,720 What worries you? 532 00:16:18,720 --> 00:16:19,480 Let us know. 533 00:16:19,480 --> 00:16:20,480 Yeah, chime in. 534 00:16:20,480 --> 00:16:22,160 And if you want to dive even deeper, 535 00:16:22,160 --> 00:16:23,160 definitely recommend it. 536 00:16:23,160 --> 00:16:24,240 Check out the full paper. 537 00:16:24,240 --> 00:16:24,720 Yeah. 538 00:16:24,720 --> 00:16:28,920 Can transformative AI shave a new age for our civilization? 539 00:16:28,920 --> 00:16:30,520 That's catchy, title. 540 00:16:30,520 --> 00:16:32,640 It'll get you thinking, for sure. 541 00:16:32,640 --> 00:16:35,600 Until next time, keep exploring, keep learning, 542 00:16:35,600 --> 00:16:37,320 and keep those questions coming. 543 00:16:37,320 --> 00:16:38,640 Always more to learn. 544 00:16:38,640 --> 00:16:49,000 See you on our next Deep Dive.