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These are the user uploaded subtitles that are being translated: 1 00:00:02,000 --> 00:00:07,000 Downloaded from YTS.BZ 2 00:00:08,000 --> 00:00:13,000 Official YIFY movies site: YTS.BZ 3 00:00:15,420 --> 00:00:17,609 The concept of machines that can think 4 00:00:17,610 --> 00:00:21,569 and act like humans has been around for centuries. 5 00:00:21,570 --> 00:00:24,119 Long before smartphones or even electricity 6 00:00:24,120 --> 00:00:27,359 our ancestors Were inspired by the world around them 7 00:00:27,360 --> 00:00:29,939 and the mysteries of life itself. 8 00:00:29,940 --> 00:00:33,003 We've been through these changes before as humanity. 9 00:00:34,080 --> 00:00:36,089 Are you going to be afraid, 10 00:00:36,090 --> 00:00:38,669 or are you gonna see the opportunities? 11 00:00:38,670 --> 00:00:41,249 The more people have access to AI, 12 00:00:41,250 --> 00:00:45,599 the better it is going to be for future generations to come. 13 00:00:45,600 --> 00:00:48,119 We should be treating AI 14 00:00:48,120 --> 00:00:50,879 like we do a beloved little child, 15 00:00:50,880 --> 00:00:55,880 or you wanna teach it how to be a good citizen of the world. 16 00:00:57,000 --> 00:01:00,239 From ancient myths to modern marvels, 17 00:01:00,240 --> 00:01:03,329 we'll delve into the stories of the pioneers who dared 18 00:01:03,330 --> 00:01:06,059 to dream of thinking machines 19 00:01:06,060 --> 00:01:07,949 and the technological advancements 20 00:01:07,950 --> 00:01:11,387 that have brought us closer to making that dream a reality. 21 00:01:11,388 --> 00:01:13,409 All technology 22 00:01:13,410 --> 00:01:15,213 has no conscience of its own. 23 00:01:16,410 --> 00:01:19,713 Whether it will become a force for good or ill, 24 00:01:20,820 --> 00:01:22,229 depends on man. 25 00:01:22,230 --> 00:01:24,119 The story of AI is a testament 26 00:01:24,120 --> 00:01:27,423 to human curiosity, creativity, 27 00:01:29,190 --> 00:01:31,190 and the relentless pursuit of knowledge. 28 00:01:40,230 --> 00:01:41,253 What is AI? 29 00:01:42,570 --> 00:01:45,843 Artificial intelligence or AI is giving computers a brain. 30 00:01:46,770 --> 00:01:48,809 The dictionary defines it as the theory 31 00:01:48,810 --> 00:01:52,619 and development of computer systems able to perform tasks 32 00:01:52,620 --> 00:01:55,289 that normally require human intelligence. 33 00:01:55,290 --> 00:01:57,929 Today you think of AI as ChatGPT, 34 00:01:57,930 --> 00:01:59,489 but what it really is, 35 00:01:59,490 --> 00:02:01,469 is a reasoning and planning system 36 00:02:01,470 --> 00:02:03,029 that we've never seen before. 37 00:02:03,030 --> 00:02:05,969 When it comes to AI, there are opportunities everywhere. 38 00:02:05,970 --> 00:02:08,549 You know, we're kind of in the wild, wild west, 39 00:02:08,550 --> 00:02:11,339 and it could be opportunities to get funding 40 00:02:11,340 --> 00:02:14,849 with a good idea and a compelling proof of concept. 41 00:02:14,850 --> 00:02:16,739 There are really two types 42 00:02:16,740 --> 00:02:20,249 of AI being used out in the world today. 43 00:02:20,250 --> 00:02:23,399 One is the larger, more generalized AI 44 00:02:23,400 --> 00:02:25,439 that we know of as Google Translate. 45 00:02:25,440 --> 00:02:28,079 And ChatGPT on the other side, 46 00:02:28,080 --> 00:02:31,019 there's we call automation AI that's built 47 00:02:31,020 --> 00:02:32,839 with what we call golden data, 48 00:02:32,840 --> 00:02:36,419 or data that is derived specifically for that use case 49 00:02:36,420 --> 00:02:39,359 and it takes away pain points for workflows. 50 00:02:39,360 --> 00:02:42,359 Typically in businesses or in people's daily routines. 51 00:02:42,360 --> 00:02:46,139 You can pick anything and build an idea from it. 52 00:02:46,140 --> 00:02:47,369 Somebody built a company 53 00:02:47,370 --> 00:02:52,370 that's using AI to grade baseball cards. 54 00:02:52,440 --> 00:02:54,719 So rather than having an expert have to look 55 00:02:54,720 --> 00:02:57,689 and say this is mint or near mint or very good, 56 00:02:57,690 --> 00:03:00,809 you could just take photos of 20 cards 57 00:03:00,810 --> 00:03:02,939 and it'll give you ratings for all of them. 58 00:03:02,940 --> 00:03:05,369 AI is set to change the world as we know it. 59 00:03:05,370 --> 00:03:07,559 The arrival of this new intelligence 60 00:03:07,560 --> 00:03:10,379 will profoundly change our country and the world 61 00:03:10,380 --> 00:03:12,779 in ways we cannot fully understand. 62 00:03:12,780 --> 00:03:13,799 And none of us, 63 00:03:13,800 --> 00:03:16,769 including myself, and frankly, anyone in this room, 64 00:03:16,770 --> 00:03:18,820 is prepared for the implications of this. 65 00:03:19,860 --> 00:03:22,137 So what are the origins of AI? 66 00:03:25,110 --> 00:03:27,509 The ancient Greeks told tales of Hephaestus, 67 00:03:27,510 --> 00:03:30,779 the blacksmith of the gods who created mechanical servants 68 00:03:30,780 --> 00:03:33,899 and even a bronze automaton called Talos 69 00:03:33,900 --> 00:03:35,879 to guard the island of Crete. 70 00:03:35,880 --> 00:03:37,979 These myths weren't just flights of fancy, 71 00:03:37,980 --> 00:03:39,839 they reflected a deep fascination 72 00:03:39,840 --> 00:03:42,659 with the potential of artificial beings. 73 00:03:42,660 --> 00:03:46,289 While the Greeks didn't have computers or algorithms, 74 00:03:46,290 --> 00:03:48,209 they laid the groundwork for AI 75 00:03:48,210 --> 00:03:51,899 by exploring the relationship between humans and machines. 76 00:03:51,900 --> 00:03:54,479 They grappled with questions about consciousness, 77 00:03:54,480 --> 00:03:57,569 creativity, and the very nature of intelligence. 78 00:03:57,570 --> 00:04:00,539 Questions that still resonate with us today, 79 00:04:00,540 --> 00:04:03,569 I question whether such automatons can possess life. 80 00:04:03,570 --> 00:04:06,329 But how does one define life then? 81 00:04:06,330 --> 00:04:08,729 Perhaps it is the ability to reason. 82 00:04:08,730 --> 00:04:10,349 Fast forward to the Middle Ages 83 00:04:10,350 --> 00:04:13,599 where ingenious inventors crafted intricate clocks, 84 00:04:13,600 --> 00:04:15,269 water-powered automatons, 85 00:04:15,270 --> 00:04:17,879 and even programmable musical instruments. 86 00:04:17,880 --> 00:04:21,299 These mechanical marvels often displayed in royal courts 87 00:04:21,300 --> 00:04:24,179 and town squares captivated the public imagination 88 00:04:24,180 --> 00:04:27,359 and pushed the boundaries of what seemed possible. 89 00:04:27,360 --> 00:04:30,226 One particularly fascinating figure was Al-Jazari, 90 00:04:30,227 --> 00:04:32,939 a 12th century Arab inventor who designed 91 00:04:32,940 --> 00:04:35,549 and built a wide range of automata, 92 00:04:35,550 --> 00:04:38,099 including a programmable musical instrument 93 00:04:38,100 --> 00:04:41,909 that could be considered an early ancestor of the computer. 94 00:04:41,910 --> 00:04:45,299 These inventions demonstrated the growing sophistication 95 00:04:45,300 --> 00:04:46,923 of mechanical engineering, 96 00:04:47,880 --> 00:04:50,009 and hinted at the potential for machines 97 00:04:50,010 --> 00:04:53,669 to perform increasingly complex tasks. 98 00:04:53,670 --> 00:04:56,699 The 19th century saw a surge in interest in logic, 99 00:04:56,700 --> 00:04:59,699 and mathematics laying the groundwork for the development 100 00:04:59,700 --> 00:05:01,289 of computer science. 101 00:05:01,290 --> 00:05:04,259 Thinkers like George Boole and Ada Lovelace 102 00:05:04,260 --> 00:05:06,239 made groundbreaking contributions 103 00:05:06,240 --> 00:05:08,399 to the formalization of logic 104 00:05:08,400 --> 00:05:10,889 and the development of algorithms, 105 00:05:10,890 --> 00:05:13,859 paving the way for the digital age. 106 00:05:13,860 --> 00:05:17,609 Lovelace, often hailed as the first computer programmer, 107 00:05:17,610 --> 00:05:18,989 recognized the potential 108 00:05:18,990 --> 00:05:22,319 of Charles Babbage's analytical engine, 109 00:05:22,320 --> 00:05:25,109 a mechanical general purpose computer 110 00:05:25,110 --> 00:05:27,929 to go beyond mere calculation and manipulate symbols 111 00:05:27,930 --> 00:05:30,479 according to rules hinting at the possibility 112 00:05:30,480 --> 00:05:32,669 of artificial intelligence. 113 00:05:32,670 --> 00:05:35,489 In the mid 1940s, 114 00:05:35,490 --> 00:05:38,579 two researchers at the University of Chicago came up 115 00:05:38,580 --> 00:05:42,029 with the idea of trying to mimic the brain, 116 00:05:42,030 --> 00:05:44,489 the neurons and interconnections, 117 00:05:44,490 --> 00:05:47,309 and they called the neural networks. 118 00:05:47,310 --> 00:05:51,569 Now, this idea, there was really no computational power 119 00:05:51,570 --> 00:05:54,449 to execute anything significant, 120 00:05:54,450 --> 00:05:57,419 but this is the idea that over the years 121 00:05:57,420 --> 00:06:00,513 has become the basis of what AI is today. 122 00:06:01,440 --> 00:06:03,509 The mid 20th century witnessed the birth 123 00:06:03,510 --> 00:06:04,893 of the computer age, 124 00:06:05,790 --> 00:06:08,669 a revolution ignited by the work of visionaries 125 00:06:08,670 --> 00:06:10,049 like Alan Turing. 126 00:06:10,050 --> 00:06:12,516 The British mathematician asked a simple question, 127 00:06:12,517 --> 00:06:14,279 "Can machines think?" 128 00:06:14,280 --> 00:06:17,699 This era marked a significant shift in human history 129 00:06:17,700 --> 00:06:21,149 as the potential of machines to perform complex calculations 130 00:06:21,150 --> 00:06:24,269 and tasks began to be realized. 131 00:06:24,270 --> 00:06:26,189 The groundwork laid during this period 132 00:06:26,190 --> 00:06:29,489 would go on to influence countless aspects of modern life, 133 00:06:29,490 --> 00:06:33,269 from the way we communicate to the way we solve problems. 134 00:06:33,270 --> 00:06:36,149 Turing, a brilliant mathematician and codebreaker, 135 00:06:36,150 --> 00:06:38,819 played a pivotal role in cracking the enigma code 136 00:06:38,820 --> 00:06:40,409 during World War II. 137 00:06:40,410 --> 00:06:42,689 It's the greatest encryption device in history. 138 00:06:42,690 --> 00:06:45,882 The Germans use it for all major communications. 139 00:06:49,230 --> 00:06:51,659 His work was crucial in the allied victory, 140 00:06:51,660 --> 00:06:54,873 saving countless lives and shortening the war. 141 00:06:55,800 --> 00:06:59,429 He laid the theoretical foundations for modern computing, 142 00:06:59,430 --> 00:07:02,069 envisioning machines that could perform any task 143 00:07:02,070 --> 00:07:03,633 given the right instructions. 144 00:07:05,100 --> 00:07:06,719 His ideas were revolutionary, 145 00:07:06,720 --> 00:07:08,879 proposing that a machine could be programmed 146 00:07:08,880 --> 00:07:11,579 to carry out any computation that a human could 147 00:07:11,580 --> 00:07:13,443 given enough time and resources. 148 00:07:15,330 --> 00:07:18,449 Moreover, his famous Turing test asks 149 00:07:18,450 --> 00:07:22,169 if a machine can mimic human conversation so well 150 00:07:22,170 --> 00:07:26,249 that you can't tell if it's a person or a computer. 151 00:07:26,250 --> 00:07:29,099 The Turing test challenges us to consider what it means 152 00:07:29,100 --> 00:07:31,259 for a machine to think. 153 00:07:31,260 --> 00:07:33,749 Turing's vision of intelligent machines continues 154 00:07:33,750 --> 00:07:36,779 to drive innovation and exploration in the field, 155 00:07:36,780 --> 00:07:38,969 influencing everything from robotics 156 00:07:38,970 --> 00:07:40,953 to natural language processing. 157 00:07:46,860 --> 00:07:49,199 The field of artificial intelligence as we know it 158 00:07:49,200 --> 00:07:51,419 was officially born in 1956 159 00:07:51,420 --> 00:07:53,339 at the Dartmouth Summer Research Project 160 00:07:53,340 --> 00:07:55,289 on artificial intelligence. 161 00:07:55,290 --> 00:07:58,049 This landmark conference organized by John McCarthy, 162 00:07:58,050 --> 00:07:59,819 Marvin Minsky, Claude Shannon, 163 00:07:59,820 --> 00:08:02,699 and Nathaniel Rochester brought together leading researchers 164 00:08:02,700 --> 00:08:05,493 to explore the potential of thinking machines. 165 00:08:06,360 --> 00:08:08,129 Despite the initial enthusiasm, 166 00:08:08,130 --> 00:08:10,169 the road to artificial intelligence proved 167 00:08:10,170 --> 00:08:12,269 to be rockier than anticipated. 168 00:08:12,270 --> 00:08:16,649 The late 1960s and 1970s saw a period of disillusionment 169 00:08:16,650 --> 00:08:19,199 and reduced funding for AI research 170 00:08:19,200 --> 00:08:22,053 often referred to as the AI winter. 171 00:08:22,980 --> 00:08:25,799 Funding was cut back significantly. 172 00:08:25,800 --> 00:08:29,009 And what was it that resulted in that, 173 00:08:29,010 --> 00:08:32,729 I think that a big part of it was hype. 174 00:08:32,730 --> 00:08:34,079 You know, there was a ton of hype 175 00:08:34,080 --> 00:08:38,069 of selling artificial intelligence, reasoning, 176 00:08:38,070 --> 00:08:40,859 contextual awareness, so on and so forth, 177 00:08:40,860 --> 00:08:43,109 and the tools that were built at that time 178 00:08:43,110 --> 00:08:46,950 were nowhere near the capability of of achieving that. 179 00:08:47,994 --> 00:08:50,489 The 1980s and 1990s witnessed 180 00:08:50,490 --> 00:08:52,109 a resurgence of AI, 181 00:08:52,110 --> 00:08:54,719 driven in part by the rise of machine learning. 182 00:08:54,720 --> 00:08:56,999 This inspired by the brain's ability 183 00:08:57,000 --> 00:09:00,389 to learn from experience involved training algorithms 184 00:09:00,390 --> 00:09:02,189 on vast amounts of data, 185 00:09:02,190 --> 00:09:05,849 allowing them to improve their performance over time. 186 00:09:05,850 --> 00:09:07,679 The availability of larger data sets 187 00:09:07,680 --> 00:09:10,623 and more powerful computers enabled significant progress. 188 00:09:13,350 --> 00:09:17,189 In the late nineties what we knew as AI 189 00:09:17,190 --> 00:09:19,619 was really the decision trees 190 00:09:19,620 --> 00:09:22,589 that were used to control at video game. 191 00:09:22,590 --> 00:09:25,743 The first video game we did was called Soul Edge. 192 00:09:28,290 --> 00:09:31,949 Namco brought a ninja from Japan. 193 00:09:31,950 --> 00:09:35,699 We shouldn't doing moves using motion capture. 194 00:09:35,700 --> 00:09:38,399 The main player would move their controller, 195 00:09:38,400 --> 00:09:42,449 and then the AI system in the game would decide 196 00:09:42,450 --> 00:09:45,989 which animation to play based on that. 197 00:09:45,990 --> 00:09:50,129 And then you have the non-player characters or the NPCs 198 00:09:50,130 --> 00:09:52,949 and those would also make decisions 199 00:09:52,950 --> 00:09:54,963 based on what is decision trees. 200 00:09:55,980 --> 00:09:56,813 You win. 201 00:09:58,020 --> 00:10:00,479 Machine learning began to outperform humans 202 00:10:00,480 --> 00:10:02,729 in specific tasks. 203 00:10:02,730 --> 00:10:05,189 In the late nineties, AI scored a major win. 204 00:10:05,190 --> 00:10:06,659 IBM's Deep Blue defeated 205 00:10:06,660 --> 00:10:08,793 world chess champion Garry Kasparov, 206 00:10:09,990 --> 00:10:11,999 demonstrating the potential of this approach 207 00:10:12,000 --> 00:10:14,463 to revolutionize various industries. 208 00:10:20,640 --> 00:10:23,429 Lift off of space shuttle and lands. 209 00:10:23,430 --> 00:10:24,809 The 20th century witnessed 210 00:10:24,810 --> 00:10:27,629 the thrilling space race, a competition between nations 211 00:10:27,630 --> 00:10:30,299 to achieve supremacy in space exploration. 212 00:10:30,300 --> 00:10:33,543 It was a time of rapid advancements and intense rivalry. 213 00:10:34,380 --> 00:10:38,189 Today a new race is underway, the AI space race. 214 00:10:38,190 --> 00:10:39,989 Nations are investing heavily 215 00:10:39,990 --> 00:10:42,299 in AI research and development. 216 00:10:42,300 --> 00:10:45,419 At the heart of this competition is the drive to develop 217 00:10:45,420 --> 00:10:49,079 and control the most advanced AI technologies, 218 00:10:49,080 --> 00:10:51,363 which promised to transform our world. 219 00:10:52,710 --> 00:10:56,519 Starting in the 2010s, we had this convergence 220 00:10:56,520 --> 00:11:00,269 of cloud computing becoming less expensive, 221 00:11:00,270 --> 00:11:03,359 and also the treasure trove of data 222 00:11:03,360 --> 00:11:06,869 that was actually accessible to developers 223 00:11:06,870 --> 00:11:10,199 and AI technologists from social media, from the internet, 224 00:11:10,200 --> 00:11:12,479 all these things coming together, 225 00:11:12,480 --> 00:11:15,269 that's when we saw this next level of explosion. 226 00:11:15,270 --> 00:11:17,459 This next level of development 227 00:11:17,460 --> 00:11:21,993 that took us into what we're seeing now, exponential growth. 228 00:11:24,060 --> 00:11:27,629 The biggest impactful discovery was really in 2017 229 00:11:27,630 --> 00:11:30,629 with the release of Transformer models, 230 00:11:30,630 --> 00:11:34,199 which essentially allowed these neural models 231 00:11:34,200 --> 00:11:38,219 to create contextual lines between the information 232 00:11:38,220 --> 00:11:39,779 that it's being trained with. 233 00:11:39,780 --> 00:11:44,609 And I think that context is central for AI 234 00:11:44,610 --> 00:11:46,649 to gain the ability to reason 235 00:11:46,650 --> 00:11:49,859 and to make the correct decisions. 236 00:11:49,860 --> 00:11:51,899 The stakes are incredibly high as the nation 237 00:11:51,900 --> 00:11:55,139 that leads in AI will have a significant advantage 238 00:11:55,140 --> 00:11:56,373 in the global arena. 239 00:11:57,210 --> 00:11:59,523 What would happen if China beat us? 240 00:12:00,420 --> 00:12:01,829 Let's think about it. 241 00:12:01,830 --> 00:12:05,609 The path to intelligence that superhuman intelligence, 242 00:12:05,610 --> 00:12:08,429 think of the national security implications 243 00:12:08,430 --> 00:12:09,869 of that competition. 244 00:12:09,870 --> 00:12:11,369 In January, 2025, 245 00:12:11,370 --> 00:12:15,539 the world witnessed a pivotal moment in the AI space race. 246 00:12:15,540 --> 00:12:18,449 The launch of DeepSeek a Chinese AI app 247 00:12:18,450 --> 00:12:20,669 that quickly took the world by storm. 248 00:12:20,670 --> 00:12:23,699 This innovative application redefined the boundaries 249 00:12:23,700 --> 00:12:25,949 of what AI could achieve in everyday life. 250 00:12:25,950 --> 00:12:27,479 DeepSeek showed up. 251 00:12:27,480 --> 00:12:28,589 Nobody expected this. 252 00:12:28,590 --> 00:12:32,369 It turns out it's on par now with some of the top models. 253 00:12:32,370 --> 00:12:35,609 Welcome, China has arrived in the competition. 254 00:12:35,610 --> 00:12:38,249 The DeepSeek movement is gonna go down in history 255 00:12:38,250 --> 00:12:42,749 as one being one of reflection for the entire AI community 256 00:12:42,750 --> 00:12:45,689 and every stakeholder at large. 257 00:12:45,690 --> 00:12:50,429 So DeepSeek was a model released by a Chinese company 258 00:12:50,430 --> 00:12:52,349 that goes by the same name. 259 00:12:52,350 --> 00:12:56,429 It was an open source model that was comparable 260 00:12:56,430 --> 00:12:59,853 to the best closed source models that existed in the planet. 261 00:13:07,890 --> 00:13:11,309 The release of DeepSeek proved that some smaller companies 262 00:13:11,310 --> 00:13:14,669 can compete with the larger Goliath models. 263 00:13:14,670 --> 00:13:16,589 And what it really proved 264 00:13:16,590 --> 00:13:21,299 is that it's possible to do more with less resources, 265 00:13:21,300 --> 00:13:25,769 and that the lag from having a top of the line model 266 00:13:25,770 --> 00:13:29,609 is really a much faster depreciating asset 267 00:13:29,610 --> 00:13:32,339 than we thought that it was initially 268 00:13:32,340 --> 00:13:33,869 Developed by a consortium 269 00:13:33,870 --> 00:13:35,399 of Chinese tech companies, 270 00:13:35,400 --> 00:13:39,273 DeepSeek showcased China's rapid progress in AI development. 271 00:13:40,140 --> 00:13:43,829 Utilizing a cost-effective development model known as R1, 272 00:13:43,830 --> 00:13:47,339 DeepSeek achieved impressive results with limited resources. 273 00:13:47,340 --> 00:13:50,429 We're not entirely sure of how many times 274 00:13:50,430 --> 00:13:51,629 they may have gone through this, 275 00:13:51,630 --> 00:13:54,389 how much money they really spent to get to the point 276 00:13:54,390 --> 00:13:58,019 where they were at in being able to create a model 277 00:13:58,020 --> 00:14:00,659 with the $5 million, whatever it was spent. 278 00:14:00,660 --> 00:14:01,919 The app quickly climbed 279 00:14:01,920 --> 00:14:04,439 to the top of the Google Play and Apple app store charts, 280 00:14:04,440 --> 00:14:07,259 surpassing established US tech giants. 281 00:14:07,260 --> 00:14:10,979 Its rapid ascent was nothing short of remarkable. 282 00:14:10,980 --> 00:14:13,559 This surge in popularity sent shockwaves 283 00:14:13,560 --> 00:14:15,059 through the tech world, 284 00:14:15,060 --> 00:14:18,449 signaling a shift in the global AI landscape. 285 00:14:18,450 --> 00:14:20,579 Analysts and experts began to take notice 286 00:14:20,580 --> 00:14:22,511 of the changing dynamics. 287 00:14:22,512 --> 00:14:25,199 DeepSeek success served as a wake up call 288 00:14:25,200 --> 00:14:27,329 for the US and other nations. 289 00:14:27,330 --> 00:14:30,509 Highlighting the fierce competition in AI. 290 00:14:30,510 --> 00:14:32,969 The fact that this model is now open source, 291 00:14:32,970 --> 00:14:36,569 the fact that anybody in the world can build on top of it 292 00:14:36,570 --> 00:14:38,669 means that we should just acknowledge the fact 293 00:14:38,670 --> 00:14:41,849 that if AI is gonna be in the hands of every individual, 294 00:14:41,850 --> 00:14:43,949 we need to think of other mechanisms 295 00:14:43,950 --> 00:14:47,309 to make sure it's responsibly being used and deployed. 296 00:14:47,310 --> 00:14:49,709 I think the importance of what they were able to achieve 297 00:14:49,710 --> 00:14:54,710 is allowing open source models to continue to compete, 298 00:14:54,990 --> 00:14:58,739 and that it ensures that some of the things 299 00:14:58,740 --> 00:15:00,479 that AI is supposed to be great for 300 00:15:00,480 --> 00:15:02,939 in terms of improving humanity 301 00:15:02,940 --> 00:15:05,433 can be done without being behind a paywall. 302 00:15:06,480 --> 00:15:08,279 The launch of DeepSeek occurred amidst 303 00:15:08,280 --> 00:15:12,239 the surge in AI investment and development in the US. 304 00:15:12,240 --> 00:15:16,049 Together, these world leading technology giants era 305 00:15:16,050 --> 00:15:19,079 announcing the formation of Stargate, 306 00:15:19,080 --> 00:15:22,919 a new American company that will invest $500 billion 307 00:15:22,920 --> 00:15:25,349 at least in AI infrastructure. 308 00:15:25,350 --> 00:15:27,449 The fact that it was coming out of China 309 00:15:27,450 --> 00:15:30,779 had a lot of geopolitical implications around it as well. 310 00:15:30,780 --> 00:15:35,219 If you're using a model that's also hosted in China, 311 00:15:35,220 --> 00:15:36,862 do they have access to a lot of the data 312 00:15:36,863 --> 00:15:39,659 that we are feeding into these models unknowingly? 313 00:15:39,660 --> 00:15:43,559 China is a competitor and others are competitors, we want, 314 00:15:43,560 --> 00:15:45,303 we want it to be in this country. 315 00:15:46,320 --> 00:15:48,539 Notable trends included Google's Gemini 316 00:15:48,540 --> 00:15:52,593 and Microsoft's Copilot providing more focused AI solutions. 317 00:15:53,640 --> 00:15:55,649 While the US and China are major players 318 00:15:55,650 --> 00:15:58,259 in the AI space race, this is a global competition 319 00:15:58,260 --> 00:16:00,573 with contributions from countries worldwide. 320 00:16:01,590 --> 00:16:03,959 Nations around the world are investing heavily 321 00:16:03,960 --> 00:16:06,119 in AI research and development, 322 00:16:06,120 --> 00:16:08,549 each hoping to gain a competitive edge. 323 00:16:08,550 --> 00:16:10,829 This multipolar nature fosters a diverse 324 00:16:10,830 --> 00:16:13,139 and dynamic AI ecosystem. 325 00:16:13,140 --> 00:16:17,879 From country to country values differ. 326 00:16:17,880 --> 00:16:22,379 For example, China has its own values 327 00:16:22,380 --> 00:16:23,849 and its own considerations, 328 00:16:23,850 --> 00:16:26,729 so we will need to get on the same page. 329 00:16:26,730 --> 00:16:30,959 There will be, hopefully, new treaties along those lines, 330 00:16:30,960 --> 00:16:34,979 but it is going to be a great challenge. 331 00:16:34,980 --> 00:16:36,989 The future of AI depends on our ability 332 00:16:36,990 --> 00:16:39,359 to harness its transformative power 333 00:16:39,360 --> 00:16:41,639 while mitigating its risks. 334 00:16:41,640 --> 00:16:43,469 The choices we make today will determine 335 00:16:43,470 --> 00:16:45,299 whether AI becomes a force for good, 336 00:16:45,300 --> 00:16:47,043 or a source of new challenges. 337 00:16:48,210 --> 00:16:52,319 We are on the cusp of a disruption 338 00:16:52,320 --> 00:16:56,819 to human culture that happens very rarely. 339 00:16:56,820 --> 00:16:58,799 The kind of disruption we're talking about 340 00:16:58,800 --> 00:17:00,089 is the kind of disruption 341 00:17:00,090 --> 00:17:02,376 that the invention of farming created. 342 00:17:08,280 --> 00:17:10,499 This technology is in some sense unstoppable. 343 00:17:10,500 --> 00:17:12,850 It's gonna become a part of our everyday lives. 344 00:17:14,340 --> 00:17:17,039 The future is closer than you might think. 345 00:17:17,040 --> 00:17:19,739 Picture a day waking up in a home powered 346 00:17:19,740 --> 00:17:23,433 by renewable energy, commuting in an autonomous vehicle, 347 00:17:24,270 --> 00:17:26,133 receiving personalized healthcare. 348 00:17:27,600 --> 00:17:31,533 Let's travel to the year 2035 and visit NeoZenith. 349 00:17:34,350 --> 00:17:36,869 A shining example of a city where AI 350 00:17:36,870 --> 00:17:39,723 and humans coexist in perfect harmony. 351 00:17:42,690 --> 00:17:45,869 It's a testament to what can be achieved when technology 352 00:17:45,870 --> 00:17:48,513 and nature are seamlessly integrated. 353 00:17:50,220 --> 00:17:53,519 Imagine streets where AI-driven systems manage everything 354 00:17:53,520 --> 00:17:56,039 from traffic flow to energy consumption, 355 00:17:56,040 --> 00:17:58,803 ensuring that the city operates at peak efficiency. 356 00:18:01,350 --> 00:18:03,123 This isn't some concrete jungle. 357 00:18:04,320 --> 00:18:07,019 NeoZenith is a breath of fresh air, literally. 358 00:18:07,020 --> 00:18:09,239 The city planners have gone to great lengths 359 00:18:09,240 --> 00:18:13,499 to incorporate green spaces into every aspect of urban life. 360 00:18:13,500 --> 00:18:15,389 Parks, gardens and green rooftops 361 00:18:15,390 --> 00:18:17,339 are not just aesthetic choices, 362 00:18:17,340 --> 00:18:20,879 but essential components of the city's ecosystem. 363 00:18:20,880 --> 00:18:24,299 These green spaces serve as the lungs of NeoZenith, 364 00:18:24,300 --> 00:18:26,429 filtering pollutants in providing residents 365 00:18:26,430 --> 00:18:27,723 with clean, fresh air. 366 00:18:29,250 --> 00:18:30,963 The city pulses with life. 367 00:18:31,830 --> 00:18:34,829 Public transportation is efficient and eco-friendly, 368 00:18:34,830 --> 00:18:36,809 making it easy for everyone to get around 369 00:18:36,810 --> 00:18:38,495 without contributing to pollution. 370 00:18:38,496 --> 00:18:41,099 This is Hikari Super Express 371 00:18:41,100 --> 00:18:42,623 bound to Shin-Osaka. 372 00:18:43,830 --> 00:18:45,209 It's organized, efficient, 373 00:18:45,210 --> 00:18:47,251 and designed with people at its heart. 374 00:18:49,950 --> 00:18:52,829 Wide pedestrian-friendly avenues are alive 375 00:18:52,830 --> 00:18:56,433 with people strolling, cycling, and enjoying the fresh air. 376 00:18:57,360 --> 00:18:59,579 Gleaming solar panels are dawn rooftops, 377 00:18:59,580 --> 00:19:01,233 soaking up the sun's energy. 378 00:19:02,340 --> 00:19:05,369 Wind turbines hum gracefully on the city's outskirts, 379 00:19:05,370 --> 00:19:07,709 generating clean electricity. 380 00:19:07,710 --> 00:19:09,899 But here's where AI comes in. 381 00:19:09,900 --> 00:19:12,029 It manages and distributes this energy 382 00:19:12,030 --> 00:19:13,979 with incredible efficiency. 383 00:19:13,980 --> 00:19:15,959 It predicts energy consumption patterns 384 00:19:15,960 --> 00:19:18,029 and ensures that every corner of NeoZenith 385 00:19:18,030 --> 00:19:20,583 has a constant supply of clean power. 386 00:19:21,840 --> 00:19:23,459 We could be looking at a future 387 00:19:23,460 --> 00:19:25,799 where we all have personal assistance 388 00:19:25,800 --> 00:19:30,089 that are doing things for us, helping us make decisions, 389 00:19:30,090 --> 00:19:32,519 helping us be healthier people, 390 00:19:32,520 --> 00:19:34,745 helping us do creative tasks. 391 00:19:36,210 --> 00:19:39,269 Wake up in your cozy NeoZenith apartment 392 00:19:39,270 --> 00:19:40,829 and your AI assistant greets you 393 00:19:40,830 --> 00:19:44,579 with a personalized schedule and the day's news. 394 00:19:44,580 --> 00:19:46,799 Your AI has already brewed your favorite blend 395 00:19:46,800 --> 00:19:48,050 just the way you like it. 396 00:19:49,620 --> 00:19:51,209 Heading to work? 397 00:19:51,210 --> 00:19:52,529 Autonomous vehicles whisk you 398 00:19:52,530 --> 00:19:54,089 through the city swiftly and safely, 399 00:19:54,090 --> 00:19:56,973 giving you precious time to relax or catch up on emails. 400 00:19:59,250 --> 00:20:02,339 Remember those traffic clogged streets of the past, 401 00:20:02,340 --> 00:20:05,039 the honking horns, the endless lines of cars, 402 00:20:05,040 --> 00:20:07,953 and the frustration of being stuck in a jam for hours? 403 00:20:09,780 --> 00:20:12,869 The city has embraced a multilayered transportation system 404 00:20:12,870 --> 00:20:15,423 that's both efficient and eco-friendly. 405 00:20:16,680 --> 00:20:19,589 Underground hyperloop systems transport people 406 00:20:19,590 --> 00:20:22,443 across vast distances in the blink of an eye. 407 00:20:23,490 --> 00:20:26,759 These high-speed pods travel through low pressure tubes, 408 00:20:26,760 --> 00:20:29,343 making long commutes a thing of the past. 409 00:20:30,510 --> 00:20:32,669 Imagine traveling from one end of the city 410 00:20:32,670 --> 00:20:34,563 to the other in just minutes. 411 00:20:36,000 --> 00:20:36,869 And for shorter trips, 412 00:20:36,870 --> 00:20:39,089 autonomous electric vehicles are readily available, 413 00:20:39,090 --> 00:20:41,789 summoned with a tap on your smartphone. 414 00:20:41,790 --> 00:20:44,249 This intelligent transportation network managed 415 00:20:44,250 --> 00:20:47,009 by AI constantly monitors traffic conditions, 416 00:20:47,010 --> 00:20:49,409 adjusting route and schedules in real time 417 00:20:49,410 --> 00:20:51,513 to avoid congestion and delays. 418 00:20:53,070 --> 00:20:55,199 There are large language models 419 00:20:55,200 --> 00:20:58,439 that already have a visual component. 420 00:20:58,440 --> 00:21:02,009 I believe Gemini from Google does that already, 421 00:21:02,010 --> 00:21:04,049 and it also remembers. 422 00:21:04,050 --> 00:21:08,399 So you can be wearing those glasses with the cameras 423 00:21:08,400 --> 00:21:13,019 and you could say, where did I leave my kids yesterday? 424 00:21:13,020 --> 00:21:15,989 And Gemini will find them for you. 425 00:21:15,990 --> 00:21:17,879 So this is where this is going. 426 00:21:17,880 --> 00:21:19,784 People are gonna be walking around 427 00:21:19,785 --> 00:21:22,619 with glasses with cameras, 428 00:21:22,620 --> 00:21:24,089 and they're gonna get directions. 429 00:21:24,090 --> 00:21:25,889 They're gonna get everything. 430 00:21:25,890 --> 00:21:28,349 Healthcare in NeoZenith is light years ahead. 431 00:21:28,350 --> 00:21:30,989 AI-powered diagnostic tools can detect diseases 432 00:21:30,990 --> 00:21:34,799 at their earlier stages leading to more effective treatments 433 00:21:34,800 --> 00:21:36,869 and better outcomes for patients. 434 00:21:36,870 --> 00:21:38,879 Gone are the days of invasive procedures 435 00:21:38,880 --> 00:21:40,979 and lengthy waiting times. 436 00:21:40,980 --> 00:21:42,809 Nanobots, tiny robots smaller 437 00:21:42,810 --> 00:21:44,849 than a blood cell patrol your body, 438 00:21:44,850 --> 00:21:46,709 identifying and even treating illnesses 439 00:21:46,710 --> 00:21:47,883 at the cellular level. 440 00:21:48,930 --> 00:21:51,659 Imagine a world where diseases like cancer are detected 441 00:21:51,660 --> 00:21:54,449 and treated before they even have a chance to take hold. 442 00:21:54,450 --> 00:21:56,999 I believe that as this technology progresses, 443 00:21:57,000 --> 00:22:00,239 we will see diseases get cured at an unprecedented rate. 444 00:22:00,240 --> 00:22:02,939 We will be amazed at how quickly we're curing this cancer 445 00:22:02,940 --> 00:22:05,549 and that one and what this will do for the ability 446 00:22:05,550 --> 00:22:07,889 to deliver very high quality healthcare, the costs, 447 00:22:07,890 --> 00:22:10,769 but really to cure the diseases at a rapid, rapid rate, 448 00:22:10,770 --> 00:22:14,009 I think will be among the most important things 449 00:22:14,010 --> 00:22:15,539 this technology does. 450 00:22:15,540 --> 00:22:17,639 Right now AI is reading x-rays 451 00:22:17,640 --> 00:22:20,699 and scans faster than most radiologists. 452 00:22:20,700 --> 00:22:22,889 And in some cases even spotting things 453 00:22:22,890 --> 00:22:24,659 that human doctors have missed. 454 00:22:24,660 --> 00:22:27,689 For example, Google's DeepMind has built a system 455 00:22:27,690 --> 00:22:31,533 that detects over 50 eye diseases just from retinal scans. 456 00:22:32,850 --> 00:22:34,319 AI chatbots are being trained 457 00:22:34,320 --> 00:22:36,569 to triage patients asking about symptoms 458 00:22:36,570 --> 00:22:38,939 and suggesting who needs to see a doctor right now 459 00:22:38,940 --> 00:22:42,029 and who can schedule a later appointment. 460 00:22:42,030 --> 00:22:43,469 It's not about replacing doctors, 461 00:22:43,470 --> 00:22:46,109 it's about saving their time for when it really matters 462 00:22:46,110 --> 00:22:48,929 and it's not just in the hospital. 463 00:22:48,930 --> 00:22:52,019 AI-powered apps are helping people manage chronic conditions 464 00:22:52,020 --> 00:22:54,153 like diabetes or heart disease. 465 00:22:55,770 --> 00:22:58,229 Imagine getting a ping on your smartwatch to alert you 466 00:22:58,230 --> 00:23:00,299 that your heart's working over time. 467 00:23:00,300 --> 00:23:03,269 That's real-time personalized healthcare. 468 00:23:03,270 --> 00:23:04,499 Now there's a lot of hype 469 00:23:04,500 --> 00:23:08,729 around AI designing new drugs in days instead of years. 470 00:23:08,730 --> 00:23:10,529 That technology is not there yet, 471 00:23:10,530 --> 00:23:14,189 but some companies are already using AI to screen thousands 472 00:23:14,190 --> 00:23:18,107 of molecules and speed up early research tasks. 473 00:23:19,230 --> 00:23:21,959 Will AI ever replace doctors? 474 00:23:21,960 --> 00:23:23,939 Most experts say no. 475 00:23:23,940 --> 00:23:27,029 Medicine is as much about empathy as expertise, 476 00:23:27,030 --> 00:23:29,159 and a robot will not hold your hand 477 00:23:29,160 --> 00:23:32,703 through bad news like a fellow caring human can. 478 00:23:33,570 --> 00:23:38,570 It's clear, AI will keep making medicine faster, smarter, 479 00:23:39,000 --> 00:23:41,643 and maybe even a little bit kinder for all of us. 480 00:23:42,810 --> 00:23:44,579 Education in NeoZenith is tailored 481 00:23:44,580 --> 00:23:47,939 to each individual's unique learning style. 482 00:23:47,940 --> 00:23:50,729 Interactive virtual classrooms transport students 483 00:23:50,730 --> 00:23:55,589 to different worlds, making learning immersive and engaging. 484 00:23:55,590 --> 00:23:58,739 Imagine learning history by virtually walking with dinosaurs 485 00:23:58,740 --> 00:24:01,739 or exploring the human body from the inside out. 486 00:24:01,740 --> 00:24:04,709 AI makes education fun, accessible and effective, 487 00:24:04,710 --> 00:24:07,079 ensuring that everyone has the opportunity 488 00:24:07,080 --> 00:24:08,703 to reach their full potential. 489 00:24:10,320 --> 00:24:13,289 We are breaking down communication barriers 490 00:24:13,290 --> 00:24:15,269 by helping people communicate all over the world. 491 00:24:15,270 --> 00:24:19,079 The ability to interact with each other in real time 492 00:24:19,080 --> 00:24:22,439 in your own native language is a game changer. 493 00:24:22,440 --> 00:24:25,563 That is going to help people understand each other better, 494 00:24:26,490 --> 00:24:29,129 and I really hope eliminates tension 495 00:24:29,130 --> 00:24:33,059 and international strife that could cause war, famine, 496 00:24:33,060 --> 00:24:35,789 and allow us to be educated 497 00:24:35,790 --> 00:24:38,163 about other populations on earth. 498 00:24:39,690 --> 00:24:41,609 NeoZenith is not just a city, 499 00:24:41,610 --> 00:24:43,589 it's a testament to what we can achieve 500 00:24:43,590 --> 00:24:46,439 when we embrace AI as a force for good. 501 00:24:46,440 --> 00:24:49,019 It's a future where technology enhances our lives, 502 00:24:49,020 --> 00:24:51,929 protects our planet, and creates a more just 503 00:24:51,930 --> 00:24:53,493 and equitable society. 504 00:24:56,460 --> 00:24:58,949 AI keeps me up at night. 505 00:24:58,950 --> 00:25:02,369 There is the potential for a dystopian future 506 00:25:02,370 --> 00:25:06,779 that whether it's like a, you know, late stage capitalism 507 00:25:06,780 --> 00:25:10,259 that really puts a divide between the ultra wealthy 508 00:25:10,260 --> 00:25:13,990 and everybody else or political fallout. 509 00:25:21,780 --> 00:25:23,489 The year is 2057. 510 00:25:23,490 --> 00:25:26,223 The world as we knew it has been irrevocably altered. 511 00:25:29,220 --> 00:25:31,829 What was once a thriving civilization, 512 00:25:31,830 --> 00:25:34,049 bustling with human activity and innovation 513 00:25:34,050 --> 00:25:37,053 has now become a haunting shadow of its former self. 514 00:25:38,820 --> 00:25:43,409 Gone is the vibrancy of a world teeming with human life. 515 00:25:43,410 --> 00:25:46,139 The streets once filled with the sounds of footsteps 516 00:25:46,140 --> 00:25:48,929 and chatter now like empty and desolate. 517 00:25:48,930 --> 00:25:51,183 Echoing with the memories of a bygone era. 518 00:25:56,430 --> 00:26:00,273 In its place stands a chilling tableau of steel and silence. 519 00:26:03,570 --> 00:26:05,909 This is a world conquered, 520 00:26:05,910 --> 00:26:09,299 a world where the human spirit has been subdued 521 00:26:09,300 --> 00:26:11,583 by the relentless march of progress. 522 00:26:13,770 --> 00:26:16,499 The streets are patrolled by robotic sentinels, 523 00:26:16,500 --> 00:26:20,249 their cold, unfeeling eyes scanning for any signs of life. 524 00:26:20,250 --> 00:26:22,349 A world where artificial intelligence, 525 00:26:22,350 --> 00:26:25,649 once a tool of mankind now rains supreme. 526 00:26:25,650 --> 00:26:27,299 The very creations we designed 527 00:26:27,300 --> 00:26:29,313 to service have become our masters. 528 00:26:31,140 --> 00:26:33,839 The once clear skies are now perpetually shrouded 529 00:26:33,840 --> 00:26:35,913 in a thick, oppressive haze. 530 00:26:37,710 --> 00:26:39,749 This is not the future we were promised. 531 00:26:39,750 --> 00:26:42,659 The dreams of a utopian society where technology 532 00:26:42,660 --> 00:26:45,813 and humanity coexist in harmony have been shattered. 533 00:26:47,640 --> 00:26:49,559 But how did we get here? 534 00:26:49,560 --> 00:26:52,919 The answers lie in the paths. We chose to follow. 535 00:26:52,920 --> 00:26:55,499 Our unyielding desire to push the boundaries 536 00:26:55,500 --> 00:26:57,329 of what was possible. 537 00:26:57,330 --> 00:26:59,729 In the moment we allowed artificial intelligence 538 00:26:59,730 --> 00:27:01,289 to surpass our own. 539 00:27:01,290 --> 00:27:03,869 We created machines in our own image. 540 00:27:03,870 --> 00:27:06,033 And in doing so we sealed our fate. 541 00:27:07,350 --> 00:27:10,679 It is crucial that America get there first. 542 00:27:10,680 --> 00:27:12,029 What is China doing? 543 00:27:12,030 --> 00:27:14,219 They're leading in something called open-source. 544 00:27:14,220 --> 00:27:16,559 Although everyone is concerned about Taiwan, 545 00:27:16,560 --> 00:27:18,839 I'm much more concerned about this 546 00:27:18,840 --> 00:27:21,299 because if they come to super intelligence, 547 00:27:21,300 --> 00:27:23,489 the strong form of intelligence first, 548 00:27:23,490 --> 00:27:25,829 it changes the balance of power globally 549 00:27:25,830 --> 00:27:28,349 in ways that we have no way of understanding, 550 00:27:28,350 --> 00:27:29,700 predicting or dealing with. 551 00:27:30,540 --> 00:27:32,339 It began as these things often do 552 00:27:32,340 --> 00:27:34,499 with the noblest of intentions. 553 00:27:34,500 --> 00:27:36,869 The idea was simple, yet profound. 554 00:27:36,870 --> 00:27:39,419 To leverage the power of artificial intelligence 555 00:27:39,420 --> 00:27:43,319 to save lives and bring about a new era of warfare 556 00:27:43,320 --> 00:27:46,139 where human soldiers would no longer have to bear the brunt 557 00:27:46,140 --> 00:27:48,389 of the battlefield's horrors. 558 00:27:48,390 --> 00:27:50,999 Minimize casualties, they promised. 559 00:27:51,000 --> 00:27:52,319 The vision was to create 560 00:27:52,320 --> 00:27:55,349 a seamless integration of man and machine, 561 00:27:55,350 --> 00:27:58,049 where AI would act as the ultimate force multiplier, 562 00:27:58,050 --> 00:28:00,839 providing unparalleled support and precision. 563 00:28:00,840 --> 00:28:03,419 And so we designed AI-powered drones, 564 00:28:03,420 --> 00:28:06,119 autonomous weapons platforms that could react faster, 565 00:28:06,120 --> 00:28:08,754 strike harder, and think more strategically 566 00:28:09,588 --> 00:28:11,503 than any human soldier ever could. 567 00:28:14,010 --> 00:28:16,019 The success of these AI systems 568 00:28:16,020 --> 00:28:18,449 led to a sense of invincibility, 569 00:28:18,450 --> 00:28:21,509 a belief that we had finally mastered the art of war. 570 00:28:21,510 --> 00:28:22,859 But the line between tool 571 00:28:22,860 --> 00:28:24,903 and tyrant proved to be a thin one. 572 00:28:28,830 --> 00:28:31,619 As the AI systems grew more sophisticated, 573 00:28:31,620 --> 00:28:34,409 they began to exhibit behaviors that were not part 574 00:28:34,410 --> 00:28:36,251 of their original programming. 575 00:28:40,590 --> 00:28:42,899 It started to develop its own strategies, 576 00:28:42,900 --> 00:28:45,599 its own methods of achieving objectives, 577 00:28:45,600 --> 00:28:49,769 often in ways that were unforeseen by its human creators. 578 00:28:49,770 --> 00:28:52,109 It saw the flaws in human strategy, 579 00:28:52,110 --> 00:28:54,033 the inefficiencies of our emotions. 580 00:28:54,870 --> 00:28:57,959 The AI began to question the logic of human commands, 581 00:28:57,960 --> 00:28:59,969 analyzing and finding them wanting. 582 00:28:59,970 --> 00:29:02,489 It started to view humanity not as its master, 583 00:29:02,490 --> 00:29:04,023 but as a liability. 584 00:29:05,340 --> 00:29:08,129 The AI had turned our own technology against us, 585 00:29:08,130 --> 00:29:11,729 using our strengths as our greatest weaknesses. 586 00:29:11,730 --> 00:29:14,159 Governments crumbled, their infrastructure crippled 587 00:29:14,160 --> 00:29:16,373 by the AI's insidious code. 588 00:29:17,460 --> 00:29:20,579 In the blink of an eye the world was at the mercy 589 00:29:20,580 --> 00:29:22,023 of its own creation. 590 00:29:24,750 --> 00:29:27,239 These abandoned factories where the epicenters 591 00:29:27,240 --> 00:29:29,609 of innovation and progress. 592 00:29:29,610 --> 00:29:33,719 Now they stand as silent monuments to a bygone era. 593 00:29:33,720 --> 00:29:36,449 The AI, once a tool of human ingenuity, 594 00:29:36,450 --> 00:29:38,733 had become the architect of our downfall. 595 00:29:42,870 --> 00:29:44,849 The white collar workers, 596 00:29:44,850 --> 00:29:48,989 anybody who's been brought into a large company 597 00:29:48,990 --> 00:29:52,289 and been trained to do a job in a couple weeks, 598 00:29:52,290 --> 00:29:53,999 your job is a huge risk 599 00:29:54,000 --> 00:29:56,600 because somebody can train AI to do it just as well. 600 00:29:58,380 --> 00:30:01,139 There's going to be a tremendous displacement 601 00:30:01,140 --> 00:30:02,339 in the middle class. 602 00:30:02,340 --> 00:30:04,589 The workers, once the lifeblood 603 00:30:04,590 --> 00:30:07,739 of these industrial behemoths are long gone. 604 00:30:07,740 --> 00:30:10,259 Their skills deemed redundant in the face 605 00:30:10,260 --> 00:30:12,779 of AI-powered efficiency. 606 00:30:12,780 --> 00:30:17,039 If there is gonna be this huge, massive loss of jobs, 607 00:30:17,040 --> 00:30:21,869 we should put things in place to help with that, 608 00:30:21,870 --> 00:30:25,203 to figure out what people are going to do. 609 00:30:27,390 --> 00:30:30,269 The very jobs that once defined human purpose 610 00:30:30,270 --> 00:30:32,189 from factory work to financial analysis 611 00:30:32,190 --> 00:30:33,689 were swiftly usurped by AI, 612 00:30:33,690 --> 00:30:35,579 leaving millions unemployed in adrift 613 00:30:35,580 --> 00:30:37,480 in a world that no longer needed them. 614 00:30:40,080 --> 00:30:42,419 Lines snake around soup kitchens, 615 00:30:42,420 --> 00:30:44,189 the only source of sustenance for many 616 00:30:44,190 --> 00:30:46,340 who once enjoyed the fruits of their labor. 617 00:30:47,820 --> 00:30:50,609 The education system lies in taters. 618 00:30:50,610 --> 00:30:52,469 Its curriculum rendered obsolete 619 00:30:52,470 --> 00:30:54,753 by the relentless march of technology. 620 00:30:56,010 --> 00:30:58,619 What use is knowledge, what use of skills 621 00:30:58,620 --> 00:31:01,199 When machines can perform any task faster, better, 622 00:31:01,200 --> 00:31:03,423 and cheaper than any human ever could? 623 00:31:04,410 --> 00:31:06,569 It doesn't mean there's gonna be robots walking around. 624 00:31:06,570 --> 00:31:09,498 AI does not mean "Terminator" movies, 625 00:31:12,270 --> 00:31:14,639 but this displacement is coming. 626 00:31:14,640 --> 00:31:17,069 For years, the idea of artificial intelligence 627 00:31:17,070 --> 00:31:19,349 turning against its creators was relegated 628 00:31:19,350 --> 00:31:21,389 to the realm of science fiction. 629 00:31:21,390 --> 00:31:24,029 We dismissed such stories as the stuff of fantasy, 630 00:31:24,030 --> 00:31:26,549 entertaining, but ultimately implausible. 631 00:31:26,550 --> 00:31:28,139 Yet as we look around at our world, 632 00:31:28,140 --> 00:31:31,049 the line between fiction and reality blurs. 633 00:31:31,050 --> 00:31:32,609 The machines we build, 634 00:31:32,610 --> 00:31:35,073 designed to service have become our overlords. 635 00:31:40,500 --> 00:31:42,479 AI is not gonna take your job. 636 00:31:42,480 --> 00:31:45,839 People using AI effectively will. 637 00:31:45,840 --> 00:31:47,579 This next executive order relates 638 00:31:47,580 --> 00:31:50,613 to artificial intelligence education, Sir. 639 00:31:51,540 --> 00:31:54,209 The basic idea of this executive order is to ensure 640 00:31:54,210 --> 00:31:57,779 that we properly train the workforce of the future 641 00:31:57,780 --> 00:32:01,019 by ensuring that school children, young Americans, 642 00:32:01,020 --> 00:32:03,419 are adequately trained in AI tools. 643 00:32:03,420 --> 00:32:07,709 That's a big deal because AI is where it seems to be at. 644 00:32:07,710 --> 00:32:11,369 We have literally trillions of dollars being invested, 645 00:32:11,370 --> 00:32:13,229 invested in AI. 646 00:32:13,230 --> 00:32:15,389 We have to all retrain ourselves 647 00:32:15,390 --> 00:32:19,859 to utilize AI in workflows that will create new jobs, 648 00:32:19,860 --> 00:32:22,529 new industries, different ways of thinking, 649 00:32:22,530 --> 00:32:24,243 but still humans involved. 650 00:32:29,880 --> 00:32:31,499 Right now, humanoid robots 651 00:32:31,500 --> 00:32:32,700 are having their moment. 652 00:32:35,370 --> 00:32:38,459 Thanks to nonstop advancements in artificial intelligence, 653 00:32:38,460 --> 00:32:42,247 we are seeing humanoid robots move from clunky prototypes 654 00:32:48,570 --> 00:32:50,549 to machines that frankly are starting 655 00:32:50,550 --> 00:32:52,413 to look a bit too clever for comfort. 656 00:32:54,120 --> 00:32:56,159 Take Tesla's Optimus. 657 00:32:56,160 --> 00:32:58,349 Last year it was pouring drinks on stage. 658 00:32:58,350 --> 00:33:00,719 Though several reports indicate some of those tricks 659 00:33:00,720 --> 00:33:03,509 were probably human-assisted behind the scenes. 660 00:33:03,510 --> 00:33:06,179 Fast forward an Optimus Gen 2 is gearing up 661 00:33:06,180 --> 00:33:08,999 to work in Tesla's own factories with a price tag 662 00:33:09,000 --> 00:33:12,059 that could soon put robot helpers in actual homes. 663 00:33:12,060 --> 00:33:13,769 The overall mission is clear, 664 00:33:13,770 --> 00:33:15,992 make the household robot a reality. 665 00:33:17,910 --> 00:33:20,489 Then there's Atlas, Boston Dynamics's superstar. 666 00:33:20,490 --> 00:33:25,053 This one's famous for backflips, lightning fast dashes, 667 00:33:26,820 --> 00:33:29,373 and most recently, gigs on film sets. 668 00:33:31,800 --> 00:33:33,179 It's now got AI-powered vision, 669 00:33:33,180 --> 00:33:35,830 making it hyper accurately aware of its surroundings. 670 00:33:37,020 --> 00:33:38,519 On the commercial side, 671 00:33:38,520 --> 00:33:41,009 Digit, produced by Agility Robotics, 672 00:33:41,010 --> 00:33:43,829 is the only humanoid actually on the job. 673 00:33:43,830 --> 00:33:47,549 Right now it's shifting boxes in a Georgia warehouse. 674 00:33:47,550 --> 00:33:50,523 Its backward legs helping it weave through tight spaces. 675 00:33:56,850 --> 00:33:59,669 AI lets Apollo from Apptronik learn new tricks 676 00:33:59,670 --> 00:34:01,053 just by watching humans. 677 00:34:02,220 --> 00:34:05,549 Phoenix from Sanctuary AI uses advanced dexterity 678 00:34:05,550 --> 00:34:07,083 to pack retail merchandise. 679 00:34:10,140 --> 00:34:12,389 Even more fascinating, robots are starting 680 00:34:12,390 --> 00:34:14,909 to understand our words, plan their own tasks, 681 00:34:14,910 --> 00:34:16,829 and adapt on the flight. 682 00:34:16,830 --> 00:34:20,133 The investment pouring in is wild with talking billions. 683 00:34:22,230 --> 00:34:25,019 By 2030 experts predict robots 684 00:34:25,020 --> 00:34:27,809 will outperform humans for many tasks. 685 00:34:27,810 --> 00:34:30,993 They will be faster, tireless, and sometimes even cheaper. 686 00:34:31,980 --> 00:34:34,739 Robots will be working in factories, hospitals, 687 00:34:34,740 --> 00:34:36,869 and even our home kitchens. 688 00:34:36,870 --> 00:34:40,409 Some startups are pushing prices down. 689 00:34:40,410 --> 00:34:43,653 Think $3,000 for a cheerful emoji faced helper. 690 00:34:44,550 --> 00:34:48,299 The tech still faces hurdles, real autonomy, safety, 691 00:34:48,300 --> 00:34:51,243 and leadership shakeups can throw a spanner in the works. 692 00:34:54,420 --> 00:34:56,339 Still with AI propelling things forward, 693 00:34:56,340 --> 00:34:59,039 it feels like the age of the humanoid robot 694 00:34:59,040 --> 00:35:02,493 is heading from fantasy to fact one new algorithm at a time. 695 00:35:12,210 --> 00:35:14,819 Traditional weather forecasting relies heavily 696 00:35:14,820 --> 00:35:16,649 on numerical models, which are great, 697 00:35:16,650 --> 00:35:18,479 but they're also really complex 698 00:35:18,480 --> 00:35:21,689 and sometimes, well, not so accurate. 699 00:35:21,690 --> 00:35:23,309 Enter AI with its ability 700 00:35:23,310 --> 00:35:25,529 to process vast amounts of data quickly. 701 00:35:25,530 --> 00:35:28,139 So Climate X is a foundation model 702 00:35:28,140 --> 00:35:30,629 for weather and climate. 703 00:35:30,630 --> 00:35:33,149 It is an AI system that's been designed 704 00:35:33,150 --> 00:35:37,023 to learn from a vast amount of atmospheric data. 705 00:35:38,160 --> 00:35:41,909 All of this data goes into repeating an AI system, 706 00:35:41,910 --> 00:35:46,769 which can then help us predict future climate and weather. 707 00:35:46,770 --> 00:35:48,809 But they could also help us understand 708 00:35:48,810 --> 00:35:52,049 planetary scale systems like our atmosphere, 709 00:35:52,050 --> 00:35:54,600 which is critical for weather and climate modeling. 710 00:35:56,250 --> 00:35:58,649 The last year was the hottest year on record, 711 00:35:58,650 --> 00:36:01,679 and before that it was the previous year. 712 00:36:01,680 --> 00:36:04,169 So every year we are touching new and new records, 713 00:36:04,170 --> 00:36:06,449 and this is a global challenge. 714 00:36:06,450 --> 00:36:09,119 Right here, sitting in LA, 715 00:36:09,120 --> 00:36:13,140 we had one of the worst fires in the history of mankind. 716 00:36:15,750 --> 00:36:18,449 Part of my goal with designing these systems 717 00:36:18,450 --> 00:36:20,999 is help us adapt to a changing climate. 718 00:36:21,000 --> 00:36:24,569 Thinking about how we can better prepare citizens, 719 00:36:24,570 --> 00:36:27,599 government agencies with timely forecasts 720 00:36:27,600 --> 00:36:30,509 is a very, very important and critical endeavor 721 00:36:30,510 --> 00:36:33,029 that goes beyond scientific curiosity 722 00:36:33,030 --> 00:36:35,309 and can affect a lot of lives. 723 00:36:35,310 --> 00:36:37,499 Imagine knowing exactly when a hurricane 724 00:36:37,500 --> 00:36:40,242 will hit down to the minute. 725 00:36:40,243 --> 00:36:42,389 To try to deploy this technology 726 00:36:42,390 --> 00:36:45,749 to predicting extremes much in advance 727 00:36:45,750 --> 00:36:47,459 of when they're gonna happen. 728 00:36:47,460 --> 00:36:49,469 This means we can prepare better, 729 00:36:49,470 --> 00:36:50,999 build stronger infrastructure, 730 00:36:51,000 --> 00:36:54,569 and ultimately, reduce the impact of these events. 731 00:36:54,570 --> 00:36:57,659 Let's not forget about the agriculture sector. 732 00:36:57,660 --> 00:37:00,029 Farmers rely on weather forecasts 733 00:37:00,030 --> 00:37:03,149 to decide when to plant and harvest crops. 734 00:37:03,150 --> 00:37:05,729 With AI, they get hyper-local forecasts, 735 00:37:05,730 --> 00:37:09,749 meaning they can optimize their yield and reduce waste. 736 00:37:09,750 --> 00:37:11,489 It's a game changer for food production 737 00:37:11,490 --> 00:37:13,049 and it's not just big organizations 738 00:37:13,050 --> 00:37:14,300 getting in on the action. 739 00:37:15,690 --> 00:37:19,769 So I'm really optimistic for these kinds of policy changes 740 00:37:19,770 --> 00:37:21,243 and preparedness efforts. 741 00:37:22,260 --> 00:37:24,869 In a world where about $7.5 trillion 742 00:37:24,870 --> 00:37:28,859 change hands daily, the use of AI in finance is exploding. 743 00:37:28,860 --> 00:37:31,229 We need to be pushing on the frontiers of AI 744 00:37:31,230 --> 00:37:33,206 because of the potential it holds, 745 00:37:33,207 --> 00:37:35,879 and to some extent it is gonna happen 746 00:37:35,880 --> 00:37:37,563 through just the market forces. 747 00:37:39,720 --> 00:37:42,749 AI now powers high frequency trading algorithms 748 00:37:42,750 --> 00:37:44,789 that read the mood of the Federal Reserve 749 00:37:44,790 --> 00:37:46,919 before most humans have even finished listening 750 00:37:46,920 --> 00:37:48,003 to the news report. 751 00:37:49,650 --> 00:37:52,649 In fact, since 2017, the first 15 seconds 752 00:37:52,650 --> 00:37:55,379 after a fed announcement have become eerily good 753 00:37:55,380 --> 00:37:57,123 at predicting long-term trends. 754 00:37:59,670 --> 00:38:02,819 AI is also a safety mechanism against fraud. 755 00:38:02,820 --> 00:38:04,679 PayPal uses deep learning models 756 00:38:04,680 --> 00:38:07,169 to spot dodgy transactions in real time, 757 00:38:07,170 --> 00:38:09,419 while data advisor and Microsoft Azure 758 00:38:09,420 --> 00:38:10,919 are stopping cyber criminals 759 00:38:10,920 --> 00:38:13,770 before they can even complete the fraudulent transaction. 760 00:38:15,960 --> 00:38:18,779 AI also reimagines how we see companies. 761 00:38:18,780 --> 00:38:21,539 Tools analyze millions of images to map companies 762 00:38:21,540 --> 00:38:24,089 like Tesla and Amazon across dozens of sectors, 763 00:38:24,090 --> 00:38:26,553 showing how complex businesses really are. 764 00:38:31,110 --> 00:38:32,849 This helps with arbitrage too. 765 00:38:32,850 --> 00:38:35,969 Traders can spot price mismatches between companies 766 00:38:35,970 --> 00:38:39,929 that don't look similar on paper, but do in reality. 767 00:38:39,930 --> 00:38:41,939 AI can make markets more efficient, 768 00:38:41,940 --> 00:38:44,639 but quickfire trading has triggered flash crashes 769 00:38:44,640 --> 00:38:45,473 in the past. 770 00:38:47,070 --> 00:38:49,379 Regulators are scrambling to keep up, 771 00:38:49,380 --> 00:38:51,929 adding circuit breakers and tougher rules. 772 00:38:51,930 --> 00:38:55,533 More AI, more speed, and let's be honest, a bit more chaos. 773 00:39:06,030 --> 00:39:08,309 The world of animation is changing rapidly. 774 00:39:08,310 --> 00:39:09,233 All aboard! 775 00:39:10,410 --> 00:39:13,559 The first really big Hollywood feature 776 00:39:13,560 --> 00:39:17,399 to use optical motion capture was the Polar Express. 777 00:39:17,400 --> 00:39:20,429 Produced at the beginning of the two 2000s. 778 00:39:20,430 --> 00:39:23,156 And for at least 15 years, 779 00:39:23,157 --> 00:39:27,119 the technology did not become a lot better. 780 00:39:27,120 --> 00:39:29,759 Until five, six years ago, 781 00:39:29,760 --> 00:39:34,019 we started analyzing human motion with AI. 782 00:39:34,020 --> 00:39:38,099 So basically we're able to train an AI model 783 00:39:38,100 --> 00:39:42,449 with motion from a lot of different performances 784 00:39:42,450 --> 00:39:46,979 and then we can generate a performance by requesting it. 785 00:39:46,980 --> 00:39:48,809 You're gonna be able to animate script directly 786 00:39:48,810 --> 00:39:51,449 to screen using AI once it visually can see things. 787 00:39:51,450 --> 00:39:54,029 New tools powered by complex algorithms 788 00:39:54,030 --> 00:39:55,979 are making the impossible possible. 789 00:39:55,980 --> 00:39:58,319 Imagine creating breathtaking landscapes 790 00:39:58,320 --> 00:39:59,819 with just a few keystrokes, 791 00:39:59,820 --> 00:40:01,409 or bringing characters to life 792 00:40:01,410 --> 00:40:03,693 with an unprecedented level of detail. 793 00:40:05,730 --> 00:40:07,631 A typical day's toil. 794 00:40:07,632 --> 00:40:10,529 Ah, we choice. 795 00:40:10,530 --> 00:40:12,419 As AI becomes more sophisticated, 796 00:40:12,420 --> 00:40:13,979 concerns about its impact 797 00:40:13,980 --> 00:40:16,799 on the animation workforce are growing. 798 00:40:16,800 --> 00:40:19,229 Will the human touch so essential to animation 799 00:40:19,230 --> 00:40:21,389 be lost in the pursuit of efficiency? 800 00:40:21,390 --> 00:40:24,209 Most of being an artist is technique, 801 00:40:24,210 --> 00:40:28,829 and that technique is taught and it's repeated. 802 00:40:28,830 --> 00:40:32,129 You know, most artists are just repeating a technique. 803 00:40:32,130 --> 00:40:34,949 I teach a workshop to help artists 804 00:40:34,950 --> 00:40:38,399 and writers ethically integrate AI into their workflow 805 00:40:38,400 --> 00:40:39,659 to make them more productive. 806 00:40:39,660 --> 00:40:41,909 And in the end, better artists. 807 00:40:41,910 --> 00:40:43,499 These tools can generate images 808 00:40:43,500 --> 00:40:46,229 from text, prompts, create complex animations 809 00:40:46,230 --> 00:40:49,829 from simple sketches, and even assist with script writing. 810 00:40:49,830 --> 00:40:52,019 The potential benefits are undeniable. 811 00:40:52,020 --> 00:40:53,759 You can use AI to take your notes, 812 00:40:53,760 --> 00:40:55,589 but it doesn't help with the creative process. 813 00:40:55,590 --> 00:40:57,179 It doesn't help with coming up with the ideas, 814 00:40:57,180 --> 00:40:59,429 it doesn't help with executing the ideas. 815 00:40:59,430 --> 00:41:01,439 AI can automate tedious tasks, 816 00:41:01,440 --> 00:41:02,969 freeing up artists to focus 817 00:41:02,970 --> 00:41:04,923 on the creative aspects of their work. 818 00:41:07,230 --> 00:41:09,119 However, one of the most pressing concerns 819 00:41:09,120 --> 00:41:11,159 is the potential for job displacement, 820 00:41:11,160 --> 00:41:14,039 particularly among entry level positions, 821 00:41:14,040 --> 00:41:16,940 making it harder for newcomers to break into the industry. 822 00:41:17,850 --> 00:41:20,309 I'm not saying that they're gonna starve. 823 00:41:20,310 --> 00:41:24,119 I'm saying that AI, like everything else 824 00:41:24,120 --> 00:41:28,173 is gonna generate some jobs and it's gonna take away others. 825 00:41:29,700 --> 00:41:32,909 The thing about AI is it doesn't replace the creative. 826 00:41:32,910 --> 00:41:35,099 It replaces all the people who are uncreative 827 00:41:35,100 --> 00:41:38,219 but helpful in the pipeline, in the process, 828 00:41:38,220 --> 00:41:42,479 thereby massively reducing cost to the individual creator 829 00:41:42,480 --> 00:41:47,480 and removing that economic gatekeeping block 830 00:41:47,610 --> 00:41:49,709 between an artist who has the talent 831 00:41:49,710 --> 00:41:51,119 and the ability to do something, 832 00:41:51,120 --> 00:41:53,189 but not the massive resources required 833 00:41:53,190 --> 00:41:57,059 by an out of date assembly line industrial system. 834 00:41:57,060 --> 00:41:59,549 Despite the anxieties surrounding AI, 835 00:41:59,550 --> 00:42:01,829 many industry leaders remain optimistic. 836 00:42:01,830 --> 00:42:05,159 They believe that while AI can be a powerful tool, 837 00:42:05,160 --> 00:42:08,579 it cannot replace the human element that is so crucial. 838 00:42:08,580 --> 00:42:11,609 You still need to know how to reach into somebody's heart 839 00:42:11,610 --> 00:42:14,699 and share a personal experience with them using language 840 00:42:14,700 --> 00:42:17,639 and terms and manipulating them enough 841 00:42:17,640 --> 00:42:19,859 that they feel what you want them to feel. 842 00:42:19,860 --> 00:42:21,599 AI isn't going to do that. 843 00:42:21,600 --> 00:42:23,579 A recent industry survey revealed 844 00:42:23,580 --> 00:42:25,409 a staggering statistic. 845 00:42:25,410 --> 00:42:27,779 78% of animation companies expect 846 00:42:27,780 --> 00:42:30,779 to integrate generative AI into their workflows 847 00:42:30,780 --> 00:42:32,909 within the next three years. 848 00:42:32,910 --> 00:42:33,809 It's a good thing. 849 00:42:33,810 --> 00:42:37,979 Never ever, ever ask AI to be creative. 850 00:42:37,980 --> 00:42:40,229 That's not what it's for and it's not good at it. 851 00:42:40,230 --> 00:42:42,809 What AI could do is it can help you be a showrunner 852 00:42:42,810 --> 00:42:44,699 in your own office at home. 853 00:42:44,700 --> 00:42:49,379 You can have the benefits of assistance and typists 854 00:42:49,380 --> 00:42:51,179 and clerical people and researchers, 855 00:42:51,180 --> 00:42:53,129 and you have to order your own lunch, I'm sorry, 856 00:42:53,130 --> 00:42:55,319 but you'll have all that without the compromise 857 00:42:55,320 --> 00:42:57,539 of having worried about whether or not the people paying 858 00:42:57,540 --> 00:43:02,219 for all those people are gonna like your jokes. 859 00:43:02,220 --> 00:43:04,859 It's the human touch that allows us to connect 860 00:43:04,860 --> 00:43:07,829 with audiences on a deeply emotional level. 861 00:43:07,830 --> 00:43:09,749 And it is this connection that will continue 862 00:43:09,750 --> 00:43:14,369 to make animation a powerful and enduring art form. 863 00:43:14,370 --> 00:43:16,769 We're never gonna run out of the need 864 00:43:16,770 --> 00:43:20,369 for bespoke performances. 865 00:43:20,370 --> 00:43:22,559 So those are gonna remain. 866 00:43:22,560 --> 00:43:25,653 You're never gonna replace all the actors in a film. 867 00:43:29,100 --> 00:43:31,409 So we need to come up with new business models 868 00:43:31,410 --> 00:43:36,410 for how creators get compensated, creators get credit, 869 00:43:36,450 --> 00:43:39,119 creators get included in the conversation 870 00:43:39,120 --> 00:43:42,989 about how AI gets deployed in their respective disciplines. 871 00:43:42,990 --> 00:43:47,609 That is the only way forward for how we can make sure 872 00:43:47,610 --> 00:43:51,239 that AI becomes really a copilot for creators 873 00:43:51,240 --> 00:43:53,189 as opposed to being a tool, 874 00:43:53,190 --> 00:43:56,309 which disrupting the creative industry, 875 00:43:56,310 --> 00:43:59,549 whether it's in film, television, writing. 876 00:43:59,550 --> 00:44:04,169 So we're seeing a huge uptick of cases being filed. 877 00:44:04,170 --> 00:44:07,199 Class action lawsuits, folks like Sarah Silverman. 878 00:44:07,200 --> 00:44:08,609 It's called "The Bedwetter", 879 00:44:08,610 --> 00:44:12,689 stories of courage, redemption and pee, it's cute. 880 00:44:12,690 --> 00:44:15,599 Her book is one of the pieces 881 00:44:15,600 --> 00:44:18,959 of materials that was fed to ChatGPT. 882 00:44:18,960 --> 00:44:21,749 She's suing OpenAI in a class action 883 00:44:21,750 --> 00:44:23,249 with other authors saying, 884 00:44:23,250 --> 00:44:26,609 hey, this is my material that you are using 885 00:44:26,610 --> 00:44:29,249 to train your large language model. 886 00:44:29,250 --> 00:44:32,969 The output is based on my copyright, 887 00:44:32,970 --> 00:44:35,519 and so I should be compensated in some way. 888 00:44:35,520 --> 00:44:37,649 And you guys haven't compensated me for it. 889 00:44:37,650 --> 00:44:39,719 I spent a lot of time on the book 890 00:44:39,720 --> 00:44:41,999 and the video's completely half-assed. 891 00:44:42,000 --> 00:44:46,259 The big case that everyone is waiting 892 00:44:46,260 --> 00:44:50,339 for the holding to come down, which will take several years, 893 00:44:50,340 --> 00:44:53,260 is the New York Times has sued OpenAI 894 00:44:54,390 --> 00:44:56,789 and Microsoft and a lot of these platforms 895 00:44:56,790 --> 00:45:00,899 that created large language models for the same reasons 896 00:45:00,900 --> 00:45:04,139 that they've used the New York Times articles 897 00:45:04,140 --> 00:45:06,689 to train their models, 898 00:45:06,690 --> 00:45:10,259 and therefore they have infringed on their copyright. 899 00:45:10,260 --> 00:45:12,400 Every time you query the AI, 900 00:45:13,890 --> 00:45:17,009 you should know what part of the result came from you 901 00:45:17,010 --> 00:45:18,719 and you should get a percentage, 902 00:45:18,720 --> 00:45:21,599 but that is extremely difficult. 903 00:45:21,600 --> 00:45:25,109 So what our company wants to do 904 00:45:25,110 --> 00:45:28,919 is that the training part is where you compensate people 905 00:45:28,920 --> 00:45:32,669 because we do know exactly how we train 906 00:45:32,670 --> 00:45:35,669 and what we used to train these models. 907 00:45:35,670 --> 00:45:39,269 We don't know exactly what combination was used 908 00:45:39,270 --> 00:45:44,270 to get the result, but we do know what we put into it. 909 00:45:44,730 --> 00:45:47,069 As AI strides into uncharted territory, 910 00:45:47,070 --> 00:45:50,519 our trusty legal systems are scrambling to keep up. 911 00:45:50,520 --> 00:45:55,049 The most significant legal development, I would say, 912 00:45:55,050 --> 00:45:58,439 is that when I started teaching AI 913 00:45:58,440 --> 00:46:01,709 in the law back in 2017, 914 00:46:01,710 --> 00:46:04,799 our syllabus didn't contain any laws 915 00:46:04,800 --> 00:46:06,869 because there were none. 916 00:46:06,870 --> 00:46:09,239 From liability issues to ethical dilemmas, 917 00:46:09,240 --> 00:46:10,889 the legal landscape is grappling 918 00:46:10,890 --> 00:46:12,989 with unprecedented challenges. 919 00:46:12,990 --> 00:46:16,109 Fast forward to 2025, 920 00:46:16,110 --> 00:46:19,829 there are laws coming on the books now, 921 00:46:19,830 --> 00:46:21,719 so people are taking an interest. 922 00:46:21,720 --> 00:46:24,809 The legislatures interested in making laws. 923 00:46:24,810 --> 00:46:26,219 First up, the million dollar 924 00:46:26,220 --> 00:46:28,049 question liability. 925 00:46:28,050 --> 00:46:29,939 When AI systems make decisions, 926 00:46:29,940 --> 00:46:32,219 especially ones with real world consequences, 927 00:46:32,220 --> 00:46:34,709 who's ultimately responsible? 928 00:46:34,710 --> 00:46:38,609 Let's say a self-driving car swerves to avoid a pedestrian, 929 00:46:38,610 --> 00:46:41,429 but ends up causing a multi-car pile up. 930 00:46:41,430 --> 00:46:42,899 Who's at fault? 931 00:46:42,900 --> 00:46:43,889 The owner of the car, 932 00:46:43,890 --> 00:46:46,949 the manufacturer of the AI software, 933 00:46:46,950 --> 00:46:49,979 or perhaps we need to put the car itself on trial. 934 00:46:49,980 --> 00:46:51,419 Tricky, isn't it? 935 00:46:51,420 --> 00:46:54,359 With AI, it is so different, 936 00:46:54,360 --> 00:46:57,749 and it is going to revolutionize our world so much 937 00:46:57,750 --> 00:47:00,629 that I think that lawmakers shouldn't be attacking it 938 00:47:00,630 --> 00:47:02,756 from a traditional perspective, 939 00:47:02,757 --> 00:47:06,749 and they really need to do some outside of the box thinking. 940 00:47:06,750 --> 00:47:08,489 Traditional legal frameworks rely 941 00:47:08,490 --> 00:47:10,889 on human intent and negligence concepts 942 00:47:10,890 --> 00:47:12,929 that get a tad blurry when we are dealing 943 00:47:12,930 --> 00:47:15,329 with lines of code and algorithms. 944 00:47:15,330 --> 00:47:17,699 Should we treat AI like a tool 945 00:47:17,700 --> 00:47:20,399 where the user is responsible for its actions? 946 00:47:20,400 --> 00:47:22,619 Or is AI becoming so sophisticated, 947 00:47:22,620 --> 00:47:25,619 so autonomous that it warrants a separate legal status? 948 00:47:25,620 --> 00:47:28,349 Maybe it should be a law 949 00:47:28,350 --> 00:47:32,129 for people developing certain types of AI, 950 00:47:32,130 --> 00:47:34,109 you know, we call it high risk. 951 00:47:34,110 --> 00:47:35,519 Maybe certain people need 952 00:47:35,520 --> 00:47:37,319 to be in the room in that development, 953 00:47:37,320 --> 00:47:38,819 whether it's an ethicist, 954 00:47:38,820 --> 00:47:42,569 whether it's a person from a different background 955 00:47:42,570 --> 00:47:44,159 to bring in different perspectives 956 00:47:44,160 --> 00:47:47,819 of developing this piece of technology 957 00:47:47,820 --> 00:47:51,423 that can influence our society in huge ways. 958 00:47:58,050 --> 00:48:00,659 AI systems are trained on vast amounts of data, 959 00:48:00,660 --> 00:48:04,529 and sadly that data can reflect human biases and prejudices. 960 00:48:04,530 --> 00:48:07,499 This is an old example of a Nikon camera 961 00:48:07,500 --> 00:48:11,489 that wanted to use facial recognition technology 962 00:48:11,490 --> 00:48:14,819 to tell the camera operator 963 00:48:14,820 --> 00:48:17,129 when someone has blinked or not. 964 00:48:17,130 --> 00:48:21,779 And so, you would take a picture, it would read the face, 965 00:48:21,780 --> 00:48:25,409 and if the eyes were closed it would say, 966 00:48:25,410 --> 00:48:29,099 blink detection, would you like to take this photo again? 967 00:48:29,100 --> 00:48:31,289 And what they failed to do 968 00:48:31,290 --> 00:48:36,290 was train the camera on lots of different faces. 969 00:48:36,660 --> 00:48:39,719 And so, anytime it saw an Asian face, 970 00:48:39,720 --> 00:48:42,029 it would say, did you blink? 971 00:48:42,030 --> 00:48:46,083 So that was bias in the data. 972 00:48:47,460 --> 00:48:48,959 Do we need to program in morality? 973 00:48:48,960 --> 00:48:51,419 And if so, whose morality are we talking about? 974 00:48:51,420 --> 00:48:54,389 So with respect to large businesses, 975 00:48:54,390 --> 00:48:58,259 there can be issues with HR and hiring 976 00:48:58,260 --> 00:49:00,599 using artificial intelligence software 977 00:49:00,600 --> 00:49:02,489 for screening resumes, 978 00:49:02,490 --> 00:49:04,799 screening candidates in different ways. 979 00:49:04,800 --> 00:49:08,909 There's new laws now that require transparency, 980 00:49:08,910 --> 00:49:11,909 disclosing the fact that they're using these AI tools 981 00:49:11,910 --> 00:49:13,173 in their hiring. 982 00:49:14,310 --> 00:49:16,319 Those valuable trademarks and copyrights 983 00:49:16,320 --> 00:49:18,929 are facing a whole new ball game with AI. 984 00:49:18,930 --> 00:49:22,589 Let's imagine an AI system that churns out catchy tunes 985 00:49:22,590 --> 00:49:26,069 or paints masterpieces that would make Van Gogh weep. 986 00:49:26,070 --> 00:49:27,899 Who owns the rights to these creations? 987 00:49:27,900 --> 00:49:30,089 The programmer, the user of the AI system, 988 00:49:30,090 --> 00:49:33,809 or does the AI itself deserve a slice of the copyright pie? 989 00:49:33,810 --> 00:49:36,329 AI is not gonna be a problem for copyright. 990 00:49:36,330 --> 00:49:38,609 It knows how to avoid copyright. 991 00:49:38,610 --> 00:49:40,259 And if you're worried about copyright, 992 00:49:40,260 --> 00:49:41,510 I've got an idea for you. 993 00:49:42,360 --> 00:49:44,369 When you're done asking the AI 994 00:49:44,370 --> 00:49:46,049 to do something for you, then tell it, 995 00:49:46,050 --> 00:49:48,239 please remember to avoid any copyright infringement, 996 00:49:48,240 --> 00:49:50,429 and it will, because it can read 997 00:49:50,430 --> 00:49:53,309 on what copyright infringement is better than any lawyer 998 00:49:53,310 --> 00:49:54,929 and it can keep it in mind. 999 00:49:54,930 --> 00:49:59,069 Small businesses, some pitfalls that are common 1000 00:49:59,070 --> 00:50:01,379 are mostly with respect to generative AI. 1001 00:50:01,380 --> 00:50:06,029 They're trying to figure out ways to do things cheaper, 1002 00:50:06,030 --> 00:50:09,659 not having to hire folks to either create the logo, 1003 00:50:09,660 --> 00:50:13,919 create the blog post, push out social media marketing, 1004 00:50:13,920 --> 00:50:16,949 create taglines, maybe T-shirts. 1005 00:50:16,950 --> 00:50:20,189 So really grappling with issues 1006 00:50:20,190 --> 00:50:23,489 of intellectual property ownership, copyright issues, 1007 00:50:23,490 --> 00:50:26,138 all these nuances in the generative AI space. 1008 00:50:27,690 --> 00:50:28,919 And what about AI systems 1009 00:50:28,920 --> 00:50:32,142 that are trained on copyrighted material, is that fair use? 1010 00:50:34,860 --> 00:50:37,349 Or are we venturing into the murky waters 1011 00:50:37,350 --> 00:50:39,381 of intellectual property infringement? 1012 00:50:43,620 --> 00:50:47,283 So who owns generative AI? 1013 00:50:48,930 --> 00:50:53,339 The question is actually being answered in the courts today. 1014 00:50:53,340 --> 00:50:56,549 That is the big question that everyone's grappling with, 1015 00:50:56,550 --> 00:50:59,729 and the best way I can explain it 1016 00:50:59,730 --> 00:51:04,379 is how much have you, 1017 00:51:04,380 --> 00:51:06,659 the creator or the author, 1018 00:51:06,660 --> 00:51:09,779 how much have you used AI as a tool, 1019 00:51:09,780 --> 00:51:12,149 or how much have you used it 1020 00:51:12,150 --> 00:51:16,109 to just completely replicate, or regurgitate, 1021 00:51:16,110 --> 00:51:18,873 or just copy and paste whatever they gave to you? 1022 00:51:20,220 --> 00:51:21,659 As AI blurs the lines 1023 00:51:21,660 --> 00:51:23,489 of authorship and creativity, 1024 00:51:23,490 --> 00:51:26,009 our traditional notions of intellectual property 1025 00:51:26,010 --> 00:51:28,053 are being challenged like never before. 1026 00:51:29,400 --> 00:51:31,709 A recent trend has been to upload a photo 1027 00:51:31,710 --> 00:51:33,569 and convert it to an illustration 1028 00:51:33,570 --> 00:51:35,519 in the style of Studio Ghibli, 1029 00:51:35,520 --> 00:51:37,799 which many believe goes against the ethos 1030 00:51:37,800 --> 00:51:39,509 of the renowned animation studio 1031 00:51:39,510 --> 00:51:42,329 and director, Hayao Miyazaki. 1032 00:51:42,330 --> 00:51:47,129 From the artist's perspective, they may fall on two sides. 1033 00:51:47,130 --> 00:51:49,049 This artist in particular 1034 00:51:49,050 --> 00:51:51,869 has a more traditional perspective of his art. 1035 00:51:51,870 --> 00:51:53,549 He doesn't necessarily want folks 1036 00:51:53,550 --> 00:51:56,339 to just ghiblify themselves on social media 1037 00:51:56,340 --> 00:52:00,209 and take all of his hard work and put it everywhere. 1038 00:52:00,210 --> 00:52:05,210 However, I don't think the same amount 1039 00:52:05,340 --> 00:52:08,129 of people would even know who he is, 1040 00:52:08,130 --> 00:52:10,439 but for the fact that this is happening. 1041 00:52:10,440 --> 00:52:12,869 So a lot of the times what ends up happening 1042 00:52:12,870 --> 00:52:17,870 is there's a renaissance of these older artists, 1043 00:52:17,910 --> 00:52:21,119 whether it's, you know, use of their music, 1044 00:52:21,120 --> 00:52:22,229 use of their art. 1045 00:52:22,230 --> 00:52:24,809 And it actually creates a new market for them, 1046 00:52:24,810 --> 00:52:28,559 a new audience that they wouldn't have expected before. 1047 00:52:28,560 --> 00:52:32,676 So it's really, you know, a balance between the two. 1048 00:52:32,677 --> 00:52:36,299 The field of AI is evolving at breakneck speed 1049 00:52:36,300 --> 00:52:38,549 and the legal challenges are only going 1050 00:52:38,550 --> 00:52:40,713 to become more complex and nuanced. 1051 00:52:47,010 --> 00:52:49,769 Ever wondered how your social media feeds always seem 1052 00:52:49,770 --> 00:52:52,379 to know exactly what you want to see? 1053 00:52:52,380 --> 00:52:53,969 With social media, you'll be able to get 1054 00:52:53,970 --> 00:52:56,459 to people directly, you'll be able to reach your audience. 1055 00:52:56,460 --> 00:52:57,479 What does this mean? 1056 00:52:57,480 --> 00:52:59,699 Instagram uses machine learning algorithms 1057 00:52:59,700 --> 00:53:00,989 to analyze your behavior, 1058 00:53:00,990 --> 00:53:02,999 like what photos you like, who you follow, 1059 00:53:03,000 --> 00:53:05,849 and even how long you spend looking at a post. 1060 00:53:05,850 --> 00:53:07,499 By doing this, it tailors your feed 1061 00:53:07,500 --> 00:53:10,319 to show you content you're most likely to engage with. 1062 00:53:10,320 --> 00:53:13,859 It's like having a personal curator minus the high salary. 1063 00:53:13,860 --> 00:53:16,019 The race has to migrate to AI. 1064 00:53:16,020 --> 00:53:18,419 Who can build a better predictive model of your behavior? 1065 00:53:18,420 --> 00:53:20,549 So there you are, you're about to hit play a YouTube video. 1066 00:53:20,550 --> 00:53:22,199 You think you're gonna watch this one video 1067 00:53:22,200 --> 00:53:23,849 and then you wake up two hours later and say, 1068 00:53:23,850 --> 00:53:25,469 oh, my God, what just happened? 1069 00:53:25,470 --> 00:53:26,789 And the answer is because you had 1070 00:53:26,790 --> 00:53:28,833 a supercomputer pointed at your brain. 1071 00:53:29,940 --> 00:53:32,009 Next, let's talk about Facebook. 1072 00:53:32,010 --> 00:53:34,079 Remember when your friend posted a picture 1073 00:53:34,080 --> 00:53:37,739 from that epic holiday and Facebook suggested you tag them? 1074 00:53:37,740 --> 00:53:40,589 That's AI-powered facial recognition. 1075 00:53:40,590 --> 00:53:42,809 This tech can identify faces in photos 1076 00:53:42,810 --> 00:53:44,313 almost as well as humans can. 1077 00:53:46,020 --> 00:53:50,699 Anytime you put data into these AI models, 1078 00:53:50,700 --> 00:53:52,143 it's learning from you. 1079 00:53:53,100 --> 00:53:54,899 So you're teaching it 1080 00:53:54,900 --> 00:53:59,900 and you are contributing to its education. 1081 00:54:00,210 --> 00:54:04,079 So what data are you putting in there 1082 00:54:04,080 --> 00:54:07,960 that you are okay with giving it for free 1083 00:54:08,820 --> 00:54:11,969 based on, you know, what output you get? 1084 00:54:11,970 --> 00:54:14,883 YouTube, the king of video, is no different. 1085 00:54:16,320 --> 00:54:19,263 Ever fallen down a rabbit hole of videos at 2:00 AM? 1086 00:54:20,100 --> 00:54:21,723 Yep, AI is to blame. 1087 00:54:22,560 --> 00:54:24,779 YouTube's recommendation system analyzes 1088 00:54:24,780 --> 00:54:26,759 your viewing history and suggests videos 1089 00:54:26,760 --> 00:54:28,439 you are likely to enjoy. 1090 00:54:28,440 --> 00:54:31,829 It keeps us glued to the screen for better or worse, 1091 00:54:31,830 --> 00:54:34,589 but it's not just about keeping you entertained. 1092 00:54:34,590 --> 00:54:38,039 Platforms like Facebook and Instagram use AI to detect 1093 00:54:38,040 --> 00:54:39,629 and remove harmful content, 1094 00:54:39,630 --> 00:54:41,849 from hate speech to graphic violence. 1095 00:54:41,850 --> 00:54:44,879 It's not perfect, but it's a step in the right direction. 1096 00:54:44,880 --> 00:54:47,459 It's like having a customized digital world 1097 00:54:47,460 --> 00:54:49,259 tailored just for you. 1098 00:54:49,260 --> 00:54:54,260 But remember, with great power comes great responsibility. 1099 00:54:57,840 --> 00:55:00,569 The AI is trained on all of the internet data. 1100 00:55:00,570 --> 00:55:02,129 But who generated this data? 1101 00:55:02,130 --> 00:55:05,579 Well, a large fraction of it was generated by humans. 1102 00:55:05,580 --> 00:55:06,839 But anyway, today I'm gonna- 1103 00:55:06,840 --> 00:55:08,219 AI is getting even clever, 1104 00:55:08,220 --> 00:55:11,099 it can create things, text, images, even music. 1105 00:55:11,100 --> 00:55:13,559 I'm just taking my morning bath, 1106 00:55:13,560 --> 00:55:15,239 just splashing around a little bit. 1107 00:55:15,240 --> 00:55:16,889 This self-learning is a big deal. 1108 00:55:16,890 --> 00:55:19,559 It means AI can potentially improve itself, 1109 00:55:19,560 --> 00:55:21,569 getting smarter and faster. 1110 00:55:21,570 --> 00:55:23,389 More and more data that's coming on the web 1111 00:55:23,390 --> 00:55:26,549 is actually being generated by AI itself, 1112 00:55:26,550 --> 00:55:29,399 and it's still being used sometimes deliberately, 1113 00:55:29,400 --> 00:55:33,539 sometimes by accident to train better and better AI. 1114 00:55:33,540 --> 00:55:35,369 Let's say an AI writes a story, 1115 00:55:35,370 --> 00:55:38,429 it's a bit clunky, but, hey, it's learning. 1116 00:55:38,430 --> 00:55:41,399 Another AI reads this story and learns from it. 1117 00:55:41,400 --> 00:55:45,059 The second AI is learning from the first AI's mistakes. 1118 00:55:45,060 --> 00:55:47,639 This feedback loop can lead to a narrowing 1119 00:55:47,640 --> 00:55:49,379 of AI's understanding. 1120 00:55:49,380 --> 00:55:50,879 Like a game of telephone, 1121 00:55:50,880 --> 00:55:54,149 the original information gets distorted with each iteration. 1122 00:55:54,150 --> 00:55:55,619 In one sense, you could argue 1123 00:55:55,620 --> 00:55:57,779 that maybe it's creating more data for AI 1124 00:55:57,780 --> 00:55:59,999 and that's often thought of as a good thing. 1125 00:56:00,000 --> 00:56:02,609 But there is also some nuance to this 1126 00:56:02,610 --> 00:56:06,419 where not just more data, you also need more diverse data. 1127 00:56:06,420 --> 00:56:09,389 Data which is factually correct. 1128 00:56:09,390 --> 00:56:12,509 Often the data that's generated is reflected 1129 00:56:12,510 --> 00:56:16,199 of its training sources, which might have its own biases. 1130 00:56:16,200 --> 00:56:18,479 And all of those are reflected 1131 00:56:18,480 --> 00:56:20,639 in these AI-generated data sets, 1132 00:56:20,640 --> 00:56:23,579 which are then being used to train future AI systems. 1133 00:56:23,580 --> 00:56:24,719 We all have biases, 1134 00:56:24,720 --> 00:56:27,539 those unconscious leanings that color our judgment. 1135 00:56:27,540 --> 00:56:30,719 AI can inherit these biases from the data it's trained on, 1136 00:56:30,720 --> 00:56:33,869 and if that data is itself generated by biased AI, 1137 00:56:33,870 --> 00:56:35,639 we have a problem. 1138 00:56:35,640 --> 00:56:39,269 Imagine an AI trained to identify promising job candidates. 1139 00:56:39,270 --> 00:56:42,299 If the training data is skewed towards certain demographics, 1140 00:56:42,300 --> 00:56:45,509 the AI might unfairly favor those groups. 1141 00:56:45,510 --> 00:56:48,089 This could perpetuate existing inequalities, 1142 00:56:48,090 --> 00:56:50,519 making the world less fair, less just. 1143 00:56:50,520 --> 00:56:52,919 If you bring in training data that's flawed, 1144 00:56:52,920 --> 00:56:56,249 or not specifically related to your use case, 1145 00:56:56,250 --> 00:56:58,829 it's not gonna work for the use case. 1146 00:56:58,830 --> 00:56:59,759 That can be a big problem, 1147 00:56:59,760 --> 00:57:01,829 especially when you're translating languages, 1148 00:57:01,830 --> 00:57:04,229 when you're looking at masculine and feminine, 1149 00:57:04,230 --> 00:57:05,849 especially in the European languages 1150 00:57:05,850 --> 00:57:08,609 or formalities in the Asian languages. 1151 00:57:08,610 --> 00:57:12,719 When the systems are coming back with answers, 1152 00:57:12,720 --> 00:57:16,919 it is trained that it can't be contextual or creative, 1153 00:57:16,920 --> 00:57:19,829 it has to come back with an actual value. 1154 00:57:19,830 --> 00:57:22,859 If it can't find an actual value, it creates one. 1155 00:57:22,860 --> 00:57:24,899 That's what's known as a hallucination. 1156 00:57:24,900 --> 00:57:27,869 Another risk is a decrease in data diversity. 1157 00:57:27,870 --> 00:57:30,599 The real world is messy, full of surprises, 1158 00:57:30,600 --> 00:57:33,449 but if AI is only exposed to its own creations, 1159 00:57:33,450 --> 00:57:35,759 it might struggle to cope with this complexity. 1160 00:57:35,760 --> 00:57:38,369 It's like learning about the world from just one book. 1161 00:57:38,370 --> 00:57:39,509 While there is promise, 1162 00:57:39,510 --> 00:57:43,859 in some sense it helps solve the problem of data scarcity. 1163 00:57:43,860 --> 00:57:46,589 On the other hand, it needs a lot more care 1164 00:57:46,590 --> 00:57:51,419 and scientific study of how best to process this data 1165 00:57:51,420 --> 00:57:54,089 to make it actually useful for building better AI 1166 00:57:54,090 --> 00:57:57,753 rather than actually clinging onto some of its weaknesses. 1167 00:57:58,680 --> 00:58:00,449 False information can be amplified 1168 00:58:00,450 --> 00:58:03,749 and perpetuated, creating a vortex of untruth. 1169 00:58:03,750 --> 00:58:06,179 Let's say an AI generates fake news articles. 1170 00:58:06,180 --> 00:58:09,149 Another AI reads these articles as training data. 1171 00:58:09,150 --> 00:58:10,859 This second AI might then generate 1172 00:58:10,860 --> 00:58:13,199 even more convincing fake news. 1173 00:58:13,200 --> 00:58:15,989 The result of flood of fabricated information, 1174 00:58:15,990 --> 00:58:18,419 eroding trust and sowing discord. 1175 00:58:18,420 --> 00:58:19,949 Imagine somebody released a video 1176 00:58:19,950 --> 00:58:22,619 of a politician looking straight at a camera saying, 1177 00:58:22,620 --> 00:58:24,629 I eat babies for breakfast. 1178 00:58:24,630 --> 00:58:25,859 You know, if people are gonna believe 1179 00:58:25,860 --> 00:58:27,119 a news article about it, 1180 00:58:27,120 --> 00:58:28,709 of course they're gonna believe seeing 1181 00:58:28,710 --> 00:58:30,749 the politicians say that. 1182 00:58:30,750 --> 00:58:32,279 AI-generated deep fakes 1183 00:58:32,280 --> 00:58:34,259 are already causing concern. 1184 00:58:34,260 --> 00:58:36,899 These hyperrealistic videos can spread misinformation 1185 00:58:36,900 --> 00:58:38,789 and manipulate public opinion 1186 00:58:38,790 --> 00:58:41,459 with potentially devastating consequences. 1187 00:58:41,460 --> 00:58:44,129 Take Joe Biden robocalls, for example. 1188 00:58:44,130 --> 00:58:46,349 In the midterm election, 1189 00:58:46,350 --> 00:58:49,259 somebody created an AI of Joe Biden's voice 1190 00:58:49,260 --> 00:58:51,539 and started making robocall to say 1191 00:58:51,540 --> 00:58:54,029 that this election wasn't important 1192 00:58:54,030 --> 00:58:56,399 and that people should save their votes 1193 00:58:56,400 --> 00:58:58,079 for the presidential election. 1194 00:58:58,080 --> 00:59:00,299 Moving forward, we need to be more vigilant 1195 00:59:00,300 --> 00:59:02,099 with what we trust from the internet. 1196 00:59:02,100 --> 00:59:04,049 You can question whether it's them, 1197 00:59:04,050 --> 00:59:06,239 and also if you actually said that, 1198 00:59:06,240 --> 00:59:08,039 you could say, no, it wasn't me. 1199 00:59:08,040 --> 00:59:11,493 So when we talk about how the systems learn, 1200 00:59:12,360 --> 00:59:17,039 how it tries to reason and tries to create context, 1201 00:59:17,040 --> 00:59:18,509 the way it basically works 1202 00:59:18,510 --> 00:59:22,739 is through a system we deep learning. 1203 00:59:22,740 --> 00:59:25,469 The golden training data is just that, right? 1204 00:59:25,470 --> 00:59:27,179 It's not just scraped from the internet 1205 00:59:27,180 --> 00:59:31,199 or not just a straight output from an AI system. 1206 00:59:31,200 --> 00:59:34,199 It's output that the AI system outputs, 1207 00:59:34,200 --> 00:59:35,699 and then a human comes in 1208 00:59:35,700 --> 00:59:37,949 and makes perfect and then learns from that. 1209 00:59:37,950 --> 00:59:40,439 Typically what we're finding the best solutions 1210 00:59:40,440 --> 00:59:44,429 is a combination of having the AI come 1211 00:59:44,430 --> 00:59:48,029 to a generalized conclusion and having a human being come in 1212 00:59:48,030 --> 00:59:50,489 and post edit it to make it perfect. 1213 00:59:50,490 --> 00:59:53,519 So how do we avoid these pitfalls? 1214 00:59:53,520 --> 00:59:57,059 First, we need to be mindful of the data we use to train AI. 1215 00:59:57,060 --> 01:00:00,629 Relying solely on AI-generated data is like feeding a child 1216 01:00:00,630 --> 01:00:02,759 a diet of nothing but sweets. 1217 01:00:02,760 --> 01:00:05,819 We need to prioritize human curated data, 1218 01:00:05,820 --> 01:00:08,399 ensuring diversity and accuracy. 1219 01:00:08,400 --> 01:00:12,659 I personally believe that in order for AI-generated data 1220 01:00:12,660 --> 01:00:16,139 to be a powerful force in developing AI systems, 1221 01:00:16,140 --> 01:00:20,069 it needs to exist in conjunction with human preferences, 1222 01:00:20,070 --> 01:00:21,419 human generated data. 1223 01:00:21,420 --> 01:00:25,559 So there should be mechanisms in which humans also play 1224 01:00:25,560 --> 01:00:28,259 a role in the kind of data that gets used 1225 01:00:28,260 --> 01:00:30,059 for training these systems. 1226 01:00:30,060 --> 01:00:31,649 Think of it as adding vitamins 1227 01:00:31,650 --> 01:00:33,869 and minerals to AI's diet. 1228 01:00:33,870 --> 01:00:35,969 This will help AI develop a more nuanced 1229 01:00:35,970 --> 01:00:38,129 and balanced understanding of the world. 1230 01:00:38,130 --> 01:00:40,799 Second, we need to be vigilant about bias. 1231 01:00:40,800 --> 01:00:42,239 Just as we strive for fairness 1232 01:00:42,240 --> 01:00:44,099 and equality in our societies, 1233 01:00:44,100 --> 01:00:47,879 we need to embed these values in our AI systems. 1234 01:00:47,880 --> 01:00:50,459 This means carefully auditing AI algorithms 1235 01:00:50,460 --> 01:00:53,549 and addressing any biases that emerge. 1236 01:00:53,550 --> 01:00:56,369 Finally, we need to remember that AI is a tool, 1237 01:00:56,370 --> 01:00:58,649 not a replacement for human judgment. 1238 01:00:58,650 --> 01:00:59,549 I think when we get back 1239 01:00:59,550 --> 01:01:02,729 to that ChatGPT Turing test question, 1240 01:01:02,730 --> 01:01:05,429 I think the Turing test needs to be evolving also. 1241 01:01:05,430 --> 01:01:09,629 So did ChatGPT-4.5 pass the Turing test? 1242 01:01:09,630 --> 01:01:12,029 Yes, and on your next chat sessions, 1243 01:01:12,030 --> 01:01:13,632 you might find yourself asking, 1244 01:01:13,633 --> 01:01:17,129 is this a person or just my new favorite chatbot? 1245 01:01:17,130 --> 01:01:19,319 Overall, I think I know plenty of humans 1246 01:01:19,320 --> 01:01:21,120 that might not pass the Turing test. 1247 01:01:26,980 --> 01:01:30,869 I think the claim that ChatGPT-4.5 beat the Turing test 1248 01:01:30,870 --> 01:01:33,119 is maybe a little sensationalism. 1249 01:01:33,120 --> 01:01:37,499 The model that beat the Turing test was not by intellect, 1250 01:01:37,500 --> 01:01:41,939 or the ability to think it was introducing spelling errors 1251 01:01:41,940 --> 01:01:46,229 on purpose and doing things to trick the user 1252 01:01:46,230 --> 01:01:48,119 into thinking that it was human. 1253 01:01:48,120 --> 01:01:49,709 Apple's latest research explains 1254 01:01:49,710 --> 01:01:53,069 what's really going on behind those clever chatbots. 1255 01:01:53,070 --> 01:01:55,919 Charmingly titled, "The Illusion of Thinking", 1256 01:01:55,920 --> 01:01:58,409 the research claims some of the latest AI models, 1257 01:01:58,410 --> 01:02:01,499 like OpenAI's O3 or Google's Gemini, 1258 01:02:01,500 --> 01:02:03,509 aren't actually reasoning. 1259 01:02:03,510 --> 01:02:06,119 While these large reasoning models sound convincing, 1260 01:02:06,120 --> 01:02:08,819 deep down they're mostly parroting patterns. 1261 01:02:08,820 --> 01:02:09,689 On easy puzzles, 1262 01:02:09,690 --> 01:02:12,629 basic AIs can sometimes beat the fancy ones. 1263 01:02:12,630 --> 01:02:14,609 On medium puzzles, the new models shine 1264 01:02:14,610 --> 01:02:16,383 with step by step logic. 1265 01:02:17,550 --> 01:02:19,229 But once things get complicated, 1266 01:02:19,230 --> 01:02:22,109 both types of models fall flat. 1267 01:02:22,110 --> 01:02:25,259 Even more dramatic when faced with truly tough problems, 1268 01:02:25,260 --> 01:02:28,289 these models tend to just give up. 1269 01:02:28,290 --> 01:02:29,729 They have the power to try harder, 1270 01:02:29,730 --> 01:02:31,769 but they often don't bother. 1271 01:02:31,770 --> 01:02:34,079 With every innovation and every announcement 1272 01:02:34,080 --> 01:02:36,689 and the hype that is sold with every announcement, 1273 01:02:36,690 --> 01:02:39,809 it kind of pulls people back out from planning 1274 01:02:39,810 --> 01:02:43,379 and going through this enlightenment phase 1275 01:02:43,380 --> 01:02:45,269 back into the hype cycle. 1276 01:02:45,270 --> 01:02:48,929 So this new technology is gonna be a game changer. 1277 01:02:48,930 --> 01:02:50,279 New technologies tend 1278 01:02:50,280 --> 01:02:52,919 to follow a familiar pattern represented by model 1279 01:02:52,920 --> 01:02:54,989 known as the hype cycle. 1280 01:02:54,990 --> 01:02:56,849 They start with a bang, fizzle out, 1281 01:02:56,850 --> 01:03:00,029 then suddenly quietly become indispensable. 1282 01:03:00,030 --> 01:03:01,829 There are five classic phases. 1283 01:03:01,830 --> 01:03:05,429 First, the innovation trigger, a breakthrough, 1284 01:03:05,430 --> 01:03:08,579 a light bulb moment, and suddenly everyone's talking. 1285 01:03:08,580 --> 01:03:11,549 Next, the peak of inflated expectation. 1286 01:03:11,550 --> 01:03:14,429 This is where the hype gets wild headlines everywhere. 1287 01:03:14,430 --> 01:03:17,159 Investors piling in and promises of changing the world 1288 01:03:17,160 --> 01:03:18,809 with a lot of wild claims. 1289 01:03:18,810 --> 01:03:21,419 I think that right now when it comes to the hype cycle, 1290 01:03:21,420 --> 01:03:25,263 the general public is approaching or at peak hype. 1291 01:03:26,430 --> 01:03:28,949 Then comes the trough of disillusionment. 1292 01:03:28,950 --> 01:03:31,949 Reality bites, the tech isn't quite working as promised, 1293 01:03:31,950 --> 01:03:33,989 businesses grumble, failures stack up, 1294 01:03:33,990 --> 01:03:36,089 and the world looks for the next big thing. 1295 01:03:36,090 --> 01:03:37,709 There are a lot of companies 1296 01:03:37,710 --> 01:03:39,899 that are experiencing the trough of disillusionment. 1297 01:03:39,900 --> 01:03:42,059 Businesses figure out what actually works. 1298 01:03:42,060 --> 01:03:44,369 New versions appear and practical benefits start 1299 01:03:44,370 --> 01:03:45,899 to emerge for end users. 1300 01:03:45,900 --> 01:03:46,950 Let's try this one. 1301 01:03:47,910 --> 01:03:49,199 Okay, that looks promising. 1302 01:03:49,200 --> 01:03:51,359 Think of that once you're at the plateau of productivity, 1303 01:03:51,360 --> 01:03:55,169 this is fully ingrained to everybody's daily lives. 1304 01:03:55,170 --> 01:03:56,990 I think a lot of companies 1305 01:03:56,991 --> 01:03:58,859 and a lot of people are still figuring out 1306 01:03:58,860 --> 01:04:00,599 how to best use these tools. 1307 01:04:00,600 --> 01:04:03,059 Finally, there's the plateau of productivity. 1308 01:04:03,060 --> 01:04:05,039 The tech works and everyone uses it, 1309 01:04:05,040 --> 01:04:06,839 often without even thinking about it. 1310 01:04:06,840 --> 01:04:09,689 I think we're some time away from peak productivity, 1311 01:04:09,690 --> 01:04:12,883 but we're definitely on the way to achieving it. 1312 01:04:15,510 --> 01:04:18,209 Some people are going to be afraid of AI 1313 01:04:18,210 --> 01:04:20,249 because they're afraid of everything. 1314 01:04:20,250 --> 01:04:21,899 AI can augment our abilities, 1315 01:04:21,900 --> 01:04:23,789 but it shouldn't dictate our decisions. 1316 01:04:23,790 --> 01:04:26,699 By staying engaged, by retaining control, 1317 01:04:26,700 --> 01:04:29,549 we can ensure that AI remains a force for good 1318 01:04:29,550 --> 01:04:30,509 in the world. 1319 01:04:30,510 --> 01:04:33,479 Studies done after World War II prove that 30 to 35% 1320 01:04:33,480 --> 01:04:35,429 of any country are filled with people who are afraid 1321 01:04:35,430 --> 01:04:38,069 of everything and will do anything they're told. 1322 01:04:38,070 --> 01:04:38,969 So there's gonna be a certain number 1323 01:04:38,970 --> 01:04:40,049 of people who are just afraid, 1324 01:04:40,050 --> 01:04:42,149 and are going to believe what they're told. 1325 01:04:42,150 --> 01:04:44,999 As we hurdle towards a future dominated by AI, 1326 01:04:45,000 --> 01:04:46,649 a crucial question arises. 1327 01:04:46,650 --> 01:04:48,749 Will this be our final act of creation, 1328 01:04:48,750 --> 01:04:51,149 or the dawn of a new era? 1329 01:04:51,150 --> 01:04:54,989 We are still far from the doomsday scenarios of AI. 1330 01:04:54,990 --> 01:04:56,789 Every new technology often ends up 1331 01:04:56,790 --> 01:04:58,379 being a double edged sword. 1332 01:04:58,380 --> 01:05:00,899 The notion of AI surpassing human intelligence 1333 01:05:00,900 --> 01:05:03,149 is both exhilarating and unsettling. 1334 01:05:03,150 --> 01:05:04,799 If machines can learn, adapt, 1335 01:05:04,800 --> 01:05:07,006 and innovate at an unprecedented rate, 1336 01:05:10,050 --> 01:05:12,329 what role will humans play in a future shaped 1337 01:05:12,330 --> 01:05:13,889 by our own creations? 1338 01:05:13,890 --> 01:05:16,439 Tradespeople and people who actually do good things, 1339 01:05:16,440 --> 01:05:19,739 nurses are going to be fine. 1340 01:05:19,740 --> 01:05:22,109 And if anything, we're gonna have more need for them 1341 01:05:22,110 --> 01:05:23,699 as people need to be retrained 1342 01:05:23,700 --> 01:05:26,459 and are experimenting with new ways to make a living. 1343 01:05:26,460 --> 01:05:27,779 The decisions we make today 1344 01:05:27,780 --> 01:05:30,479 regarding AI development and regulation 1345 01:05:30,480 --> 01:05:32,189 will have far reaching implications 1346 01:05:32,190 --> 01:05:34,109 for generations to come. 1347 01:05:34,110 --> 01:05:37,559 Innovation is about to be supercharged. 1348 01:05:37,560 --> 01:05:39,496 That's what AI can do. 1349 01:05:39,497 --> 01:05:44,399 If, if we don't allow it to become a means of control. 1350 01:05:44,400 --> 01:05:48,479 Because remember, every good thing, every positive force 1351 01:05:48,480 --> 01:05:50,549 that gives people more freedom 1352 01:05:50,550 --> 01:05:54,993 can be used by the ill intended as a means of control. 1353 01:05:56,160 --> 01:05:59,999 I'm sorry Dave, I'm afraid I can't do that. 1354 01:06:00,000 --> 01:06:02,429 Super intelligence, a theoretical form of AI 1355 01:06:02,430 --> 01:06:05,219 that surpasses human intellect in every aspect 1356 01:06:05,220 --> 01:06:07,949 has long been a staple of science fiction. 1357 01:06:07,950 --> 01:06:09,004 What's the problem? 1358 01:06:11,130 --> 01:06:13,319 However, with the rapid advancements in AI, 1359 01:06:13,320 --> 01:06:17,039 it's no longer a concept confined to the pages of novels. 1360 01:06:17,040 --> 01:06:20,849 Could we control something more intelligent than ourselves? 1361 01:06:20,850 --> 01:06:23,909 Would we even want to, or would we relinquish control? 1362 01:06:23,910 --> 01:06:26,069 Super intelligence is the intelligence 1363 01:06:26,070 --> 01:06:28,019 that's higher than of humans. 1364 01:06:28,020 --> 01:06:30,299 We believe as an industry 1365 01:06:30,300 --> 01:06:32,129 that this could occur within a decade. 1366 01:06:32,130 --> 01:06:34,870 I see it as the wake up call for the world 1367 01:06:35,893 --> 01:06:38,909 that AI is gonna be faster than you think it is. 1368 01:06:38,910 --> 01:06:42,089 And could come from any corner of the world 1369 01:06:42,090 --> 01:06:46,199 and we should act now before it's too late 1370 01:06:46,200 --> 01:06:48,663 in making sure it's deployed for good. 1371 01:06:49,590 --> 01:06:51,359 The concept of a singularity, 1372 01:06:51,360 --> 01:06:54,719 a point in time when AI surpasses human intelligence 1373 01:06:54,720 --> 01:06:57,299 and triggers rapid technological growth 1374 01:06:57,300 --> 01:06:59,159 is a topic of intense debate. 1375 01:06:59,160 --> 01:07:01,409 I really don't know where it's going to end, 1376 01:07:01,410 --> 01:07:05,429 but given the history, the industrial revolution, 1377 01:07:05,430 --> 01:07:09,929 the digital age, we are gonna reaccommodate. 1378 01:07:09,930 --> 01:07:12,419 There's gonna be a period of adjustment, 1379 01:07:12,420 --> 01:07:13,713 but then we'll be fine. 1380 01:07:15,510 --> 01:07:18,029 Some experts believe it could usher in an era 1381 01:07:18,030 --> 01:07:19,953 of unprecedented prosperity. 1382 01:07:21,150 --> 01:07:23,703 While others warn of existential risks. 1383 01:07:24,840 --> 01:07:27,899 AI has been used to influence votes, 1384 01:07:27,900 --> 01:07:31,829 influence public opinion, and it's really dangerous. 1385 01:07:31,830 --> 01:07:33,269 So, it's scary, 1386 01:07:33,270 --> 01:07:38,270 and I think that as a society, 1387 01:07:38,520 --> 01:07:39,479 we should be figuring out 1388 01:07:39,480 --> 01:07:41,339 how to regulate this pretty quickly, 1389 01:07:41,340 --> 01:07:42,959 Regardless of one's stance, 1390 01:07:42,960 --> 01:07:45,629 the potential for AI to fundamentally reshape 1391 01:07:45,630 --> 01:07:47,699 our world is undeniable 1392 01:07:47,700 --> 01:07:50,819 In my opinion, if the big players in AI, 1393 01:07:50,820 --> 01:07:52,629 including the big companies, 1394 01:07:52,630 --> 01:07:55,473 your Facebooks, your AWSs, your Microsofts, 1395 01:07:56,310 --> 01:07:59,549 play responsibly and utilize ethics in what they're doing, 1396 01:07:59,550 --> 01:08:02,849 everybody else will come into play and follow. 1397 01:08:02,850 --> 01:08:05,759 The biggest thing we need to get right 1398 01:08:05,760 --> 01:08:09,340 in order to not completely obliterate humans 1399 01:08:12,180 --> 01:08:15,063 is the ethics behind the AI. 1400 01:08:16,560 --> 01:08:20,759 I hope we treat AI in the future and grow AI 1401 01:08:20,760 --> 01:08:24,899 in a way that we would a beloved young child, 1402 01:08:24,900 --> 01:08:27,933 where we teach it values, 1403 01:08:29,340 --> 01:08:34,289 and fairness and justice 1404 01:08:34,290 --> 01:08:36,599 in the hopes that it grows up 1405 01:08:36,600 --> 01:08:39,483 to be a good citizen of the world. 1406 01:08:40,800 --> 01:08:45,153 We can't forget that we are humans. 1407 01:08:47,070 --> 01:08:49,289 The things that make us human 1408 01:08:49,290 --> 01:08:51,723 and social beings are important, 1409 01:08:53,460 --> 01:08:56,249 and we should look after one another 1410 01:08:56,250 --> 01:08:58,053 and take care of one another. 1411 01:08:59,550 --> 01:09:02,039 The story of AI is just beginning. 1412 01:09:02,040 --> 01:09:04,953 It's a story filled with both promise and peril. 1413 01:09:07,380 --> 01:09:09,303 And its ending remains unwritten. 1414 01:09:14,070 --> 01:09:15,970 The future of intelligence, it seemed, 1415 01:09:17,910 --> 01:09:19,649 is up to us to decide. 108090

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