1 00:00:01,920 --> 00:00:05,320 Hey everybody, welcome back to the Elon Musk Podcast. 2 00:00:05,400 --> 00:00:08,680 This is a show where we discuss the critical crossroads, The 3 00:00:08,680 --> 00:00:13,560 Shape, SpaceX, Tesla X, The Boring Company, and Neurolink. 4 00:00:13,880 --> 00:00:18,560 I'm your host, Will Walden. What makes Elon Musk's Colossus 5 00:00:18,600 --> 00:00:22,120 the most powerful AI supercomputer in the world? 6 00:00:22,360 --> 00:00:25,480 And why was Memphis, TN chosen as its home base? 7 00:00:25,840 --> 00:00:29,640 How is this system changing the landscape of AI research? 8 00:00:29,880 --> 00:00:33,120 Let's break it all down and uncover the details behind this 9 00:00:33,120 --> 00:00:36,640 project. So in a nondescript industrial 10 00:00:36,640 --> 00:00:41,120 park southwest of Memphis, TN, on the banks of the Mississippi 11 00:00:41,120 --> 00:00:46,240 River, lies a facility that houses the largest AI training 12 00:00:46,240 --> 00:00:51,160 supercomputer on Earth. Dubbed Colossus by its creator 13 00:00:51,720 --> 00:00:55,200 Elon Musk, this massive computing powerhouse was 14 00:00:55,200 --> 00:00:59,600 constructed by XAI, Musk's artificial intelligence startup. 15 00:00:59,920 --> 00:01:03,680 Built within a repurposed Electrolux manufacturing 16 00:01:03,680 --> 00:01:07,760 facility, Colossus stands as a testament to Musk's vision 17 00:01:08,080 --> 00:01:10,280 pushing the boundaries of AI development. 18 00:01:11,280 --> 00:01:14,640 Now, why Memphis, would you ask? Well, choosing Memphis for this 19 00:01:14,640 --> 00:01:17,960 technological feat might seem unconventional, especially when 20 00:01:18,040 --> 00:01:22,800 Austin, TX has emerged as a hub of innovation for Elon Musk's 21 00:01:22,800 --> 00:01:25,400 companies. However, the decision boiled 22 00:01:25,400 --> 00:01:28,600 down to practicality. The location offered the right 23 00:01:28,600 --> 00:01:32,400 building with enough space and infrastructure to launch 24 00:01:32,400 --> 00:01:36,160 Colossus in record time. Musk's team completed 25 00:01:36,160 --> 00:01:40,840 construction in an astonishing 122 days. 26 00:01:41,360 --> 00:01:46,920 Now urgency and scale were at their peak during the 27 00:01:46,920 --> 00:01:49,440 construction. Now inside the industrial 28 00:01:49,440 --> 00:01:58,440 facility, Colossus hosts over 100,000 NVIDIA HGX H100 GPU's 29 00:01:58,840 --> 00:02:02,440 interconnected through an ultrafast fiber optic network. 30 00:02:02,880 --> 00:02:06,120 These GPU's, considered the current state-of-the-art for AI 31 00:02:06,120 --> 00:02:09,560 training, are supported by exabytes of data storage. 32 00:02:09,759 --> 00:02:13,560 This level of computational power allows for unparalleled 33 00:02:13,560 --> 00:02:18,520 capabilities, setting Colossus apart as the most advanced AI 34 00:02:18,520 --> 00:02:22,880 system in existence. Jensen Wang, CEO of NVIDIA, 35 00:02:23,160 --> 00:02:27,240 described Colossus as easily the fastest supercomputer on the 36 00:02:27,240 --> 00:02:29,480 planet. While other supercomputers may 37 00:02:29,480 --> 00:02:32,840 take years to assemble, Colossus's swift development 38 00:02:33,080 --> 00:02:36,920 ensures it remains at the cutting edge of AI research. 39 00:02:37,800 --> 00:02:40,400 Now they used A3 tiered approach for this. 40 00:02:40,840 --> 00:02:44,840 Colossus uses a raised floor data hall design that splits its 41 00:02:44,840 --> 00:02:47,480 infrastructure into three different levels. 42 00:02:47,960 --> 00:02:52,120 The power systems are housed above, the GPU clusters occupy 43 00:02:52,120 --> 00:02:55,760 the middle, and the cooling mechanisms are below. 44 00:02:56,160 --> 00:02:59,160 This three tiered structure enables efficient maintenance 45 00:02:59,280 --> 00:03:03,360 and operation, crucial for a machine of this scale. 46 00:03:03,800 --> 00:03:08,880 The facility contains four data halls, each equipped with 25,000 47 00:03:08,880 --> 00:03:11,560 GPU's. These are paired with storage 48 00:03:11,560 --> 00:03:15,520 units and a high speed network to facilitate data exchange, and 49 00:03:15,520 --> 00:03:19,080 every GPU cabinet has its own independent cooling and 50 00:03:19,080 --> 00:03:21,720 monitoring systems, ensuring minimal downtime. 51 00:03:22,160 --> 00:03:25,720 Technicians can service individual units without 52 00:03:25,720 --> 00:03:30,400 disrupting the entire cluster, a feature unique to Xai's design. 53 00:03:31,480 --> 00:03:35,440 Now, cooling such a large scale computing system is no small 54 00:03:35,440 --> 00:03:37,800 feat though. Colossus employs in Advanced 55 00:03:37,800 --> 00:03:40,520 Liquid Cooling system, using water to regulate the 56 00:03:40,520 --> 00:03:44,200 temperature of the GPU's. A network of pipes circulates 57 00:03:44,200 --> 00:03:47,280 water through the facility, drawing heat away from the 58 00:03:47,280 --> 00:03:51,000 hardware, and the water is then sent to chillers outside. 59 00:03:51,360 --> 00:03:54,480 Worst temperature is reduced before being recirculated. 60 00:03:54,840 --> 00:03:56,760 The system doesn't really add cold water. 61 00:03:56,760 --> 00:04:00,360 As long as the water is cooler than the GPU's, it effectively 62 00:04:00,360 --> 00:04:04,840 absorbs the heat, and each GPU rack is equipped with its own 63 00:04:04,840 --> 00:04:08,000 cooling system, complete with colored lights for monitoring. 64 00:04:08,480 --> 00:04:11,760 A blue light indicates normal operation, while red light 65 00:04:11,760 --> 00:04:15,240 signals a malfunction, allowing for quick and precise 66 00:04:15,240 --> 00:04:19,160 maintenance. Now, each rack in Colossus 67 00:04:19,279 --> 00:04:25,960 houses 8 NVIDIA H1 GPUs paired with 16 CPUs to manage data and 68 00:04:25,960 --> 00:04:29,200 run the operating system. The GPU's handle the heavy 69 00:04:29,200 --> 00:04:33,000 lifting of AI training while CPUs prepare the data in managed 70 00:04:33,000 --> 00:04:35,920 system operations. The setup ensures seamless 71 00:04:35,920 --> 00:04:39,520 processing of the exabytes of data stored within the facility. 72 00:04:40,440 --> 00:04:43,720 Now a system with this magnitude demands immense power though. 73 00:04:44,520 --> 00:04:48,800 To ensure stable energy delivery, XAI uses Tesla Mega 74 00:04:48,800 --> 00:04:51,480 Pack battery units. The batteries act as 75 00:04:51,480 --> 00:04:55,160 intermediaries, drawing power from the grid and discharging it 76 00:04:55,240 --> 00:04:58,040 into the supercomputer. This setup eliminates 77 00:04:58,040 --> 00:05:01,680 millisecond variations in grid power that could disrupt the 78 00:05:01,680 --> 00:05:06,760 training, providing a consistent energy that's needed for optimal 79 00:05:06,760 --> 00:05:10,080 performance. Now, Elon Musk has very 80 00:05:10,080 --> 00:05:13,480 ambitious plans for classes, aiming to double his GPU count 81 00:05:13,760 --> 00:05:18,960 to 200,000 in a few months. Such an expansion would solidify 82 00:05:18,960 --> 00:05:22,560 its position as the most powerful AI system on the 83 00:05:22,560 --> 00:05:27,080 planet, potentially outpacing competitors like Open AI and 84 00:05:27,080 --> 00:05:30,600 Google's Gemini. A report suggests that Open AI 85 00:05:30,600 --> 00:05:34,280 CEO Sam Altman has already expressed concerns about Musk's 86 00:05:34,360 --> 00:05:37,080 increased access to computational resources. 87 00:05:38,080 --> 00:05:40,000 A building colossus isn't cheap, though. 88 00:05:40,640 --> 00:05:45,440 XAI recently raised $6 billion in venture capital, bringing its 89 00:05:45,440 --> 00:05:50,640 valuation to $24 billion. Being that's only one year old 90 00:05:51,040 --> 00:05:54,120 and Musk is now reportedly seeking additional funding to 91 00:05:54,160 --> 00:05:58,800 elevate XA is valuation to 40 billion, positioning it as a 92 00:05:58,800 --> 00:06:03,320 major player in AI. Now, at the heart of XA is 93 00:06:03,320 --> 00:06:07,720 endeavors is Croc. Croc is an AI model designed to 94 00:06:07,720 --> 00:06:12,600 evolve far beyond a chatbot. Recently, Croc was updated to 95 00:06:12,920 --> 00:06:16,000 include vision capabilities, allowing it to analyze images 96 00:06:16,000 --> 00:06:20,480 alongside text features now integrated into X, giving 97 00:06:20,480 --> 00:06:23,840 premium users the ability to query images for detailed 98 00:06:23,840 --> 00:06:29,000 analysis and context. Now, Musk envisions Grok is a 99 00:06:29,000 --> 00:06:33,720 stepping stone toward artificial general intelligence, which is a 100 00:06:33,720 --> 00:06:37,280 concept that involves creating AI systems capable of performing 101 00:06:37,560 --> 00:06:40,760 any intelligence task that humans can do. 102 00:06:41,360 --> 00:06:44,400 Colossus is designed to train and refine such a system, 103 00:06:44,760 --> 00:06:48,160 utilizing its immense computational power to advance 104 00:06:48,240 --> 00:06:53,400 AI capabilities, and Musk has described AGI as a tool to 105 00:06:53,440 --> 00:06:57,040 unlock the mysteries of the universe and the very nature of 106 00:06:57,040 --> 00:07:00,280 our own existence. However, he has also 107 00:07:00,280 --> 00:07:03,240 acknowledged the potential risks, emphasizing the need for 108 00:07:03,240 --> 00:07:06,440 safeguards to prevent AI from going rogue. 109 00:07:06,640 --> 00:07:10,120 Artificial General Intelligence represents a long held ambition 110 00:07:10,400 --> 00:07:13,840 in the field of artificial intelligence, creating machines 111 00:07:13,840 --> 00:07:17,040 that match or exceed human cognitive abilities. 112 00:07:17,440 --> 00:07:21,000 Now, unlike current AI systems which are designed to do 113 00:07:21,000 --> 00:07:25,000 specific tasks like language processing or image recognition, 114 00:07:25,360 --> 00:07:29,000 AGI aims to replicate the versatility and adaptability of 115 00:07:29,000 --> 00:07:33,120 the human mind. For Elon Musk and XAI, Colossus 116 00:07:33,120 --> 00:07:37,440 is not just a tool to train AI models, it's a foundational step 117 00:07:37,440 --> 00:07:41,920 toward achieving AGI. And the potential of AGI lies in 118 00:07:41,920 --> 00:07:45,320 its ability to perform any intellectual task with the same 119 00:07:45,320 --> 00:07:49,200 competence as a human or even surpass human capabilities. 120 00:07:49,600 --> 00:07:53,640 This includes creative pursuits like writing music or inventing 121 00:07:53,640 --> 00:07:57,560 new technologies, analytical tasks such as solving complex 122 00:07:57,560 --> 00:08:01,640 equations, and problem solving abilities across diverse fields. 123 00:08:02,160 --> 00:08:06,440 Elon envisions AGI as a transformative force capable of 124 00:08:06,440 --> 00:08:09,840 unraveling fundamental questions about the universe in advancing 125 00:08:09,840 --> 00:08:14,400 humanity's understanding of our own existence, and Colossus lays 126 00:08:14,400 --> 00:08:17,200 a central role in that journey. It's unparalleled computational 127 00:08:17,200 --> 00:08:21,200 power allows for the training of advanced models like Grok, which 128 00:08:21,440 --> 00:08:23,640 XAI hopes to evolve into AGI overtime. 129 00:08:24,520 --> 00:08:28,000 Current EI systems, including Croc, rely on large scale data 130 00:08:28,000 --> 00:08:31,280 sets of text, images and video to learn patterns, generate 131 00:08:31,280 --> 00:08:33,520 outputs. The sheer scale of Colossus 132 00:08:33,520 --> 00:08:37,520 enables these systems to process and synthesize vast amounts of 133 00:08:37,520 --> 00:08:41,880 data, a crucial step towards creating machines with 134 00:08:41,919 --> 00:08:43,880 generalized reasoning capabilities. 135 00:08:44,240 --> 00:08:46,760 And the recent upgrade to Croc, which introduced the vision 136 00:08:46,760 --> 00:08:50,360 capabilities, is a huge milestone in the path to AGI. 137 00:08:50,840 --> 00:08:53,560 By integrating the image analysis with text processing, 138 00:08:54,160 --> 00:08:57,520 it's developing systems that can handle multimodal inputs, an 139 00:08:57,720 --> 00:09:00,200 important feature of generalized intelligence. 140 00:09:00,440 --> 00:09:04,240 For instance, a human can seemingly combined visual and 141 00:09:04,240 --> 00:09:07,800 textual information to draw conclusions, and Grok is now 142 00:09:07,800 --> 00:09:10,400 moving closer to mimicking this ability. 143 00:09:10,920 --> 00:09:14,520 And despite his promise, though, the development of AGI is 144 00:09:14,520 --> 00:09:16,920 fraught with challenges and ethical considerations. 145 00:09:17,320 --> 00:09:20,160 Musk has been vocal about the potential dangers of advanced 146 00:09:20,160 --> 00:09:23,880 AI, warning that unchecked systems could act against human 147 00:09:23,880 --> 00:09:27,280 interests. To mitigate these risks, XAI is 148 00:09:27,280 --> 00:09:30,280 reportedly exploring safeguards to maintain control over the 149 00:09:30,280 --> 00:09:33,600 technology as it evolves. For AGI to become a reality, 150 00:09:33,920 --> 00:09:37,600 systems like Colossus must continue to scale, both in terms 151 00:09:37,600 --> 00:09:41,040 of hardware and sophistication of the AI models they train. 152 00:09:41,440 --> 00:09:43,840 The next steps involve increasing the models capability 153 00:09:43,840 --> 00:09:48,760 to adapt, learn independently in generalized knowledge across 154 00:09:48,760 --> 00:09:51,560 different domains, which is a goal that requires an 155 00:09:51,600 --> 00:09:55,160 unprecedented level of innovation and collaboration. 156 00:09:55,800 --> 00:10:00,600 Ultimately though, achieving AGI would mark a paradigm shift in 157 00:10:00,600 --> 00:10:04,560 human machine interactions, transforming industries, 158 00:10:04,800 --> 00:10:08,120 scientific research, and the way society functions. 159 00:10:08,520 --> 00:10:12,960 However, as Musk points out, the journey toward AGI is much about 160 00:10:13,280 --> 00:10:16,560 ensuring humanity safety and benefit it as it is about 161 00:10:16,760 --> 00:10:20,400 technological progress. Artificial general intelligence 162 00:10:20,400 --> 00:10:23,720 holds the potential to revolutionize scientific 163 00:10:23,920 --> 00:10:27,920 research by significantly accelerating discovery, solving 164 00:10:27,920 --> 00:10:31,040 long standing mysteries, and enabling breakthroughs across 165 00:10:31,040 --> 00:10:33,760 disciplines. Now, unlike narrow AI, which 166 00:10:33,760 --> 00:10:39,040 excels at specific tasks but lacks versatility, AGI could 167 00:10:39,040 --> 00:10:41,880 apply its cognitive abilities to a wide array of scientific 168 00:10:41,880 --> 00:10:46,400 challenges, adapting dynamically to new programs and problems and 169 00:10:46,400 --> 00:10:49,920 generating insights that might elude even the most skilled 170 00:10:49,920 --> 00:10:53,600 human researchers. Modern scientific research often 171 00:10:53,600 --> 00:10:57,360 involves analyzing massive data sets, which can be time 172 00:10:57,360 --> 00:11:00,040 consuming and error prone for human researchers. 173 00:11:00,400 --> 00:11:04,440 AGI could process and interpret these data states at 174 00:11:04,440 --> 00:11:09,160 unparalleled speeds, identifying patterns, correlations, and 175 00:11:09,160 --> 00:11:11,880 anomalies with extraordinary precision. 176 00:11:12,200 --> 00:11:16,120 For instance, in genomics, AGI could analyze the vast 177 00:11:16,120 --> 00:11:19,840 complexity of DNA sequences to uncover genetic markers for 178 00:11:19,840 --> 00:11:23,720 diseases, identifying potential drug targets, and predict how 179 00:11:23,720 --> 00:11:28,200 generic variations affect human health, all in a fraction of the 180 00:11:28,200 --> 00:11:30,280 time it takes today. And one of the most 181 00:11:30,280 --> 00:11:33,840 transformative features of AGI in scientific research would be 182 00:11:33,840 --> 00:11:37,560 its ability to autonomously generate hypotheses, design 183 00:11:37,560 --> 00:11:41,800 experiments, and also test them. And by trying on its extensive 184 00:11:41,800 --> 00:11:45,920 knowledge base and reasoning capabilities, AGI could propose 185 00:11:45,920 --> 00:11:48,600 innovative approaches to unresolved questions. 186 00:11:48,920 --> 00:11:52,880 And, for example, in physics, AGI might identify new 187 00:11:52,880 --> 00:11:56,200 principles or interactions within quantum mechanics, 188 00:11:56,480 --> 00:12:00,680 offering pathways toward the elusive unifications of quantum 189 00:12:00,680 --> 00:12:05,040 theory in general relativity. Now, this ability to hypothesize 190 00:12:05,040 --> 00:12:08,280 an experiment could dramatically increase the pace of discovery, 191 00:12:08,280 --> 00:12:11,640 as AGI systems would operate continuously without the 192 00:12:11,640 --> 00:12:15,080 limitations of human fatigue or cognitive bias. 193 00:12:15,400 --> 00:12:18,520 They can also refine their hypotheses in real time, 194 00:12:18,520 --> 00:12:22,240 adapting their models based on experimental outcomes. 195 00:12:22,800 --> 00:12:26,760 An AG is ability to access and synthesize knowledge across 196 00:12:26,760 --> 00:12:29,880 disciplines would be particularly valuable for 197 00:12:29,880 --> 00:12:33,160 interdisciplinary research. Many of the world's most 198 00:12:33,160 --> 00:12:36,040 pressing scientific challenges, such as climate change, 199 00:12:36,040 --> 00:12:39,640 pandemics, and sustainable energy, require expertise. 200 00:12:39,640 --> 00:12:43,920 This spans multiple fields. An AGI system could integrate 201 00:12:43,920 --> 00:12:48,000 findings from biology, chemistry, physics, and 202 00:12:48,000 --> 00:12:50,800 environmental science to propose holistic solutions. 203 00:12:51,160 --> 00:12:54,120 For example, it could model complex climate systems to 204 00:12:54,120 --> 00:12:57,520 develop strategies for mitigating global warming while 205 00:12:57,520 --> 00:13:01,200 considering ecological, economic, and social impacts. 206 00:13:02,520 --> 00:13:06,400 The application of AGI to drug discovery in healthcare could 207 00:13:06,400 --> 00:13:10,480 redefine the Medical Sciences. AGI systems could simulate the 208 00:13:10,480 --> 00:13:13,520 behavior of molecules and predict their interactions with 209 00:13:13,520 --> 00:13:17,200 human biology, streamlining the identification of potential 210 00:13:17,200 --> 00:13:19,640 therapies. And in the case of diseases like 211 00:13:19,640 --> 00:13:24,000 cancer or Alzheimer's, AGI could explore treatment options by 212 00:13:24,120 --> 00:13:27,840 analyzing vast amounts of biomedical data, identifying 213 00:13:27,840 --> 00:13:31,360 previously overlooked mechanisms, and suggesting novel 214 00:13:31,360 --> 00:13:35,240 therapeutic approaches. Moreover, AGI could personalize 215 00:13:35,240 --> 00:13:40,400 medicine by analyzing individual patients genetic, environmental, 216 00:13:40,480 --> 00:13:44,280 and lifestyle data to tailor treatments with unprecedented 217 00:13:44,280 --> 00:13:46,680 precision. This could lead to earlier 218 00:13:46,680 --> 00:13:50,240 diagnosis, more effective therapies, and better health 219 00:13:50,240 --> 00:13:54,120 outcomes. And also, AGI could tackle 220 00:13:54,120 --> 00:13:56,920 fundamental questions that remain unanswered in science. 221 00:13:57,440 --> 00:14:01,640 For example, in astronomy, you get analyzed vast amounts of 222 00:14:01,640 --> 00:14:05,600 data from telescopes to detect patterns in cosmic phenomena, 223 00:14:06,040 --> 00:14:09,240 aiding the search for extraterrestrial life or 224 00:14:09,240 --> 00:14:12,120 unraveling the mysteries of dark matter and dark energy. 225 00:14:12,640 --> 00:14:16,160 In mathematics, AGI might solve problems like a Rhyman 226 00:14:16,160 --> 00:14:19,640 hypothesis, which has implications across many areas 227 00:14:19,640 --> 00:14:23,120 of science and engineering. But while AG is potential in 228 00:14:23,120 --> 00:14:26,800 science, research is immense. It also raises ethical 229 00:14:26,800 --> 00:14:29,800 considerations. Decisions about how AGI 230 00:14:29,800 --> 00:14:33,920 prioritizes research objectives, shares discoveries, and 231 00:14:33,920 --> 00:14:36,720 interacts with human researchers will shape its impact. 232 00:14:37,280 --> 00:14:41,320 Collaboration between AGI and human scientists is likely to 233 00:14:41,320 --> 00:14:45,080 define the early stages of its integration into research, with 234 00:14:45,160 --> 00:14:49,080 AGI serving as an advanced tool rather than an independent 235 00:14:49,360 --> 00:14:53,000 agent. Now AG is contributions to 236 00:14:53,000 --> 00:14:58,320 science and research could lead to a new area of exploration and 237 00:14:58,320 --> 00:15:00,960 understanding. And by accelerating data 238 00:15:00,960 --> 00:15:05,360 analysis, automating hypothesis testing, fostering 239 00:15:05,360 --> 00:15:09,120 interdisciplinary collaboration, and expanding the limits of 240 00:15:09,120 --> 00:15:13,280 human knowledge, AGI promises to transform how science is 241 00:15:13,280 --> 00:15:16,360 conducted. And as it evolves, AGI may 242 00:15:16,360 --> 00:15:19,520 become not just a tool for discovery, but a partner in 243 00:15:19,520 --> 00:15:22,480 humanity's quest to unravel the mysteries of the universe. 244 00:15:22,920 --> 00:15:25,600 And Grok is at the forefront of this. 245 00:15:31,120 --> 00:15:32,880 Hey, thank you so much for listening today. 246 00:15:32,880 --> 00:15:34,920 I really do appreciate your support. 247 00:15:34,920 --> 00:15:37,720 If you could take a second and hit this subscribe or the follow 248 00:15:37,720 --> 00:15:39,920 button on whatever podcast platform that you're listening 249 00:15:39,920 --> 00:15:42,600 on right now, I greatly appreciate it. 250 00:15:42,600 --> 00:15:45,280 It helps out the show tremendously and you'll never 251 00:15:45,280 --> 00:15:48,200 miss an episode. And each episode is about 10 252 00:15:48,200 --> 00:15:50,800 minutes or less to get you caught up quickly. 253 00:15:51,240 --> 00:15:54,000 And please, if you want to support the show even more, go 254 00:15:54,000 --> 00:15:59,680 to patreon.com/stagezero and please take care of yourselves 255 00:15:59,680 --> 00:16:01,600 and each other and I'll see you tomorrow.