1 00:00:00,000 --> 00:00:02,880 Welcome to our deep dive into AI. 2 00:00:02,880 --> 00:00:04,240 And today we're gonna be talking about something 3 00:00:04,240 --> 00:00:05,800 called AI distillation. 4 00:00:05,800 --> 00:00:06,920 It's super fascinating. 5 00:00:06,920 --> 00:00:07,980 Yeah, it really is. 6 00:00:07,980 --> 00:00:11,760 So imagine you could take like a massive super complex AI 7 00:00:11,760 --> 00:00:15,280 and kind of shrink it down into a more efficient model. 8 00:00:15,280 --> 00:00:16,800 Like how would that change the game? 9 00:00:16,800 --> 00:00:18,200 It really is a paradigm shift 10 00:00:18,200 --> 00:00:20,560 and we're seeing it play out in a bunch of different ways. 11 00:00:20,560 --> 00:00:22,560 Yeah, and one of the things that's really caught our eye 12 00:00:22,560 --> 00:00:24,360 is this company DeepSeek. 13 00:00:24,360 --> 00:00:27,520 They're using distillation to do some pretty incredible things 14 00:00:27,520 --> 00:00:30,600 and in a fraction of the time 15 00:00:30,600 --> 00:00:32,600 and costs that you would normally expect. 16 00:00:32,600 --> 00:00:34,600 Like they built a model that rivals 17 00:00:34,600 --> 00:00:36,080 some of the biggest names in AI. 18 00:00:36,080 --> 00:00:37,320 Yeah, in just two months. 19 00:00:37,320 --> 00:00:39,800 And for under $6 million for the final training. 20 00:00:39,800 --> 00:00:41,160 Yeah, for the final training phase. 21 00:00:41,160 --> 00:00:42,920 Which is crazy when you think about it. 22 00:00:42,920 --> 00:00:45,200 And the really cool thing is they're not just copying 23 00:00:45,200 --> 00:00:46,160 what others have done. 24 00:00:46,160 --> 00:00:48,400 They're bringing real innovation to the table. 25 00:00:48,400 --> 00:00:49,800 You know, building upon distillation 26 00:00:49,800 --> 00:00:51,640 to create something totally new. 27 00:00:51,640 --> 00:00:52,960 It's like they found a shortcut 28 00:00:52,960 --> 00:00:54,800 and now everyone's trying to catch up. 29 00:00:54,800 --> 00:00:55,620 But here's the thing, 30 00:00:55,620 --> 00:00:58,320 instead of keeping their secrets locked away, 31 00:00:58,320 --> 00:01:00,480 they decided to open source their model. 32 00:01:00,480 --> 00:01:01,320 It's huge. 33 00:01:01,320 --> 00:01:03,080 Yeah, they're basically giving away the blueprint. 34 00:01:03,080 --> 00:01:05,040 Which is a pretty big deal when you think about 35 00:01:05,040 --> 00:01:07,480 how companies like OpenAI have traditionally 36 00:01:07,480 --> 00:01:09,360 kept their models proprietary. 37 00:01:09,360 --> 00:01:11,040 Right, it's a total shift in thinking. 38 00:01:11,040 --> 00:01:14,080 And Arvin Jane, the CEO of Glean put it perfectly. 39 00:01:14,080 --> 00:01:17,600 He said, my joke is everybody's model is open source. 40 00:01:17,600 --> 00:01:18,680 They just don't know it yet. 41 00:01:18,680 --> 00:01:20,080 It's funny, because it's true. 42 00:01:20,080 --> 00:01:20,920 It really is. 43 00:01:20,920 --> 00:01:22,800 So it feels like we're at a turning point 44 00:01:22,800 --> 00:01:24,880 in how AI is developed and shared. 45 00:01:24,880 --> 00:01:25,560 For sure. 46 00:01:25,560 --> 00:01:26,520 And speaking of sharing, 47 00:01:26,520 --> 00:01:28,800 we've got all these stories of other researchers 48 00:01:28,800 --> 00:01:31,400 and developers doing some crazy things with distillation. 49 00:01:31,400 --> 00:01:33,320 Like you have researchers at Berkeley 50 00:01:33,320 --> 00:01:35,400 building a near state of the art reasoning model 51 00:01:35,400 --> 00:01:37,920 for just $450 in 19 hours. 52 00:01:37,920 --> 00:01:38,760 That's insane. 53 00:01:38,760 --> 00:01:39,600 It is. 54 00:01:39,600 --> 00:01:41,680 And then you have Stanford and the University of Washington 55 00:01:41,680 --> 00:01:44,000 doing it in 26 minutes for under $50. 56 00:01:44,000 --> 00:01:44,840 It's mind blowing. 57 00:01:44,840 --> 00:01:47,360 And then there's the 24 hour challenge by Hugging Face 58 00:01:47,360 --> 00:01:49,200 where they built an entire research agent. 59 00:01:49,200 --> 00:01:51,280 It seems like everyone's getting in on the action. 60 00:01:51,280 --> 00:01:52,800 And that's what's so exciting about all this. 61 00:01:52,800 --> 00:01:55,280 It's like AI is finally becoming accessible 62 00:01:55,280 --> 00:01:58,360 to anyone, even if you don't have a billion dollar budget. 63 00:01:58,360 --> 00:01:59,840 Yeah, it's leveling the playing field, 64 00:01:59,840 --> 00:02:01,000 which begs the question, 65 00:02:01,000 --> 00:02:02,440 what does this mean for the big guys 66 00:02:02,440 --> 00:02:03,760 like Google and Microsoft? 67 00:02:03,760 --> 00:02:06,680 You know, they've poured billions into their AI research. 68 00:02:06,680 --> 00:02:08,840 Do they lose their edge if smaller teams 69 00:02:08,840 --> 00:02:11,360 can just catch up using distillation? 70 00:02:11,360 --> 00:02:12,360 That's a great question. 71 00:02:12,360 --> 00:02:14,120 And honestly, it's not that simple. 72 00:02:14,120 --> 00:02:16,800 While distillation is amazing for replication, 73 00:02:16,800 --> 00:02:18,560 it might not be the key to achieving 74 00:02:18,560 --> 00:02:19,960 what's considered the holy grail 75 00:02:19,960 --> 00:02:23,320 of AI artificial general intelligence or AGI. 76 00:02:23,320 --> 00:02:25,360 AGI being the concept of an AI 77 00:02:25,360 --> 00:02:27,560 that can basically do anything a human can. 78 00:02:27,560 --> 00:02:28,400 Exactly. 79 00:02:28,400 --> 00:02:29,480 It's been the dream for a long time. 80 00:02:29,480 --> 00:02:31,440 And companies like OpenAI are all in 81 00:02:31,440 --> 00:02:33,560 on being the first to get there, no matter the cost. 82 00:02:33,560 --> 00:02:36,080 They call it their AGI at all costs strategy. 83 00:02:36,080 --> 00:02:37,520 Which is kind of wild when you think about it. 84 00:02:37,520 --> 00:02:39,040 So they're still going full steam ahead 85 00:02:39,040 --> 00:02:41,280 with these massive models and closed development. 86 00:02:41,280 --> 00:02:42,960 But then you have deep seek over here. 87 00:02:42,960 --> 00:02:47,000 Like there are one model costs a measly 2.19 per million 88 00:02:47,000 --> 00:02:51,000 tokens to run compared to OpenAI $60 for the same thing. 89 00:02:51,000 --> 00:02:52,880 That's like almost 30 times cheaper. 90 00:02:52,880 --> 00:02:53,720 I know. 91 00:02:53,720 --> 00:02:54,540 It's insane. 92 00:02:54,540 --> 00:02:56,280 It completely changes the economics of AI. 93 00:02:56,280 --> 00:02:57,120 It does. 94 00:02:57,120 --> 00:02:58,080 And here's another twist. 95 00:02:58,080 --> 00:03:01,160 Some of our sources are saying that with distillation data 96 00:03:01,160 --> 00:03:02,960 might not be as important as it used to be. 97 00:03:02,960 --> 00:03:03,800 Yeah. 98 00:03:03,800 --> 00:03:05,040 If models can just learn from each other, 99 00:03:05,040 --> 00:03:07,000 effectively the size of your data set 100 00:03:07,000 --> 00:03:08,400 might not matter as much. 101 00:03:08,400 --> 00:03:09,360 It's a wild thought. 102 00:03:09,360 --> 00:03:12,240 Like what if you didn't need access to all that data 103 00:03:12,240 --> 00:03:13,760 to build powerful AI? 104 00:03:13,760 --> 00:03:15,840 That's what companies like Glean or Xamaloring. 105 00:03:15,840 --> 00:03:16,680 Right. 106 00:03:16,680 --> 00:03:18,680 They're building platforms that make AI accessible 107 00:03:18,680 --> 00:03:21,000 and applicable to real world problems 108 00:03:21,000 --> 00:03:22,840 regardless of data limitations. 109 00:03:22,840 --> 00:03:24,880 And Glean's CEO, Arvin Jane, believes 110 00:03:24,880 --> 00:03:26,080 there's no plateau in sight. 111 00:03:26,080 --> 00:03:28,640 He says there's no plateauing on any front, by the way, 112 00:03:28,640 --> 00:03:29,920 when it comes to AI. 113 00:03:29,920 --> 00:03:31,720 So the landscape is shifting constantly. 114 00:03:31,720 --> 00:03:33,600 And it's exciting to watch it all unfold. 115 00:03:33,600 --> 00:03:34,880 But let's take a step back for a second 116 00:03:34,880 --> 00:03:38,280 and talk about how AI itself is evolving. 117 00:03:38,280 --> 00:03:40,800 Like we're seeing some crazy advancements 118 00:03:40,800 --> 00:03:44,640 in its ability to reason, analyze, and even understand 119 00:03:44,640 --> 00:03:45,800 complex processes. 120 00:03:45,800 --> 00:03:48,360 It's what some people are calling the reasoning revolution. 121 00:03:48,360 --> 00:03:48,860 Right. 122 00:03:48,860 --> 00:03:50,320 It's like AI is learning to think. 123 00:03:50,320 --> 00:03:51,160 For itself. 124 00:03:51,160 --> 00:03:51,660 Yeah. 125 00:03:51,660 --> 00:03:52,160 Exactly. 126 00:03:52,160 --> 00:03:53,040 It's changing everything. 127 00:03:53,040 --> 00:03:55,640 A great example is DeepSeek's R1 model. 128 00:03:55,640 --> 00:03:58,640 It has this feature that lets you see the AI's thought process 129 00:03:58,640 --> 00:03:59,840 as it works through problems. 130 00:03:59,840 --> 00:04:01,440 It's kind of like peeking into its mind. 131 00:04:01,440 --> 00:04:02,280 It's fascinating. 132 00:04:02,280 --> 00:04:03,320 It really is. 133 00:04:03,320 --> 00:04:06,000 And this is key for building trust and transparency, 134 00:04:06,000 --> 00:04:08,840 especially as AI becomes more integrated into our lives. 135 00:04:08,840 --> 00:04:09,480 Absolutely. 136 00:04:09,480 --> 00:04:12,120 We need to be able to understand how AI is making decisions, 137 00:04:12,120 --> 00:04:14,640 especially when it comes to things that impact our lives. 138 00:04:14,640 --> 00:04:15,140 Exactly. 139 00:04:15,140 --> 00:04:16,680 So this reasoning revolution is something 140 00:04:16,680 --> 00:04:18,560 we're going to explore in more detail later. 141 00:04:18,560 --> 00:04:20,040 But for now, let's shift gears and talk 142 00:04:20,040 --> 00:04:22,320 about the elephant in the room, open source. 143 00:04:22,320 --> 00:04:24,040 Open source has been a game changer. 144 00:04:24,040 --> 00:04:27,000 And with DeepSeek's decision to open source their model, 145 00:04:27,000 --> 00:04:29,400 it's created a ripple effect throughout the industry. 146 00:04:29,400 --> 00:04:31,360 Researchers and platforms like Huggingface 147 00:04:31,360 --> 00:04:32,720 are following suit. 148 00:04:32,720 --> 00:04:35,080 And it's really making us question the closed source 149 00:04:35,080 --> 00:04:36,920 approach of companies like OpenAI. 150 00:04:36,920 --> 00:04:39,880 It's like a philosophical debate about the nature of knowledge. 151 00:04:39,880 --> 00:04:43,680 Should these powerful AI models be controlled or shared 152 00:04:43,680 --> 00:04:45,240 for the benefit of everyone? 153 00:04:45,240 --> 00:04:46,240 It's a tough question. 154 00:04:46,240 --> 00:04:47,960 And one will unpack more thoroughly in a bit, 155 00:04:47,960 --> 00:04:49,480 but for now, we'll leave you with that thought. 156 00:04:49,480 --> 00:04:50,480 I'm going to start with an aponder. 157 00:04:50,480 --> 00:04:50,880 Exactly. 158 00:04:50,880 --> 00:04:51,600 Stay with us. 159 00:04:51,600 --> 00:04:51,880 Yeah. 160 00:04:51,880 --> 00:04:52,920 It's a tough one, for sure. 161 00:04:52,920 --> 00:04:54,880 There are definitely strong arguments on both sides. 162 00:04:54,880 --> 00:04:57,480 On the one hand, you have the whole safety thing, 163 00:04:57,480 --> 00:05:00,160 making sure these powerful AI models aren't misused. 164 00:05:00,160 --> 00:05:02,360 Especially with all the concerns about AI being used 165 00:05:02,360 --> 00:05:05,680 to create fake content or manipulate information. 166 00:05:05,680 --> 00:05:07,120 It's definitely a valid worry. 167 00:05:07,120 --> 00:05:07,360 Yeah. 168 00:05:07,360 --> 00:05:09,000 And then on the other hand, open sourcing 169 00:05:09,000 --> 00:05:12,400 allows for crazy fast innovation and collaboration. 170 00:05:12,400 --> 00:05:16,360 And it makes these advancements accessible to way more people. 171 00:05:16,360 --> 00:05:17,520 It's a tough trade-off. 172 00:05:17,520 --> 00:05:18,120 It really is. 173 00:05:18,120 --> 00:05:20,440 And it seems like the pressure's on for companies 174 00:05:20,440 --> 00:05:23,400 like OpenAI to change their stance on open source. 175 00:05:23,400 --> 00:05:25,280 I mean, even Sam Altman, their CEO, 176 00:05:25,280 --> 00:05:27,480 recently admitted that they might have been wrong about it. 177 00:05:27,480 --> 00:05:28,920 That's a big deal coming from him, 178 00:05:28,920 --> 00:05:30,800 especially since he's been a huge advocate 179 00:05:30,800 --> 00:05:31,920 for closed development. 180 00:05:31,920 --> 00:05:32,200 Yeah. 181 00:05:32,200 --> 00:05:33,320 It feels like a damn breaking. 182 00:05:33,320 --> 00:05:35,840 Once the flow of information starts, it's hard to stop. 183 00:05:35,840 --> 00:05:37,440 And for developers and businesses, 184 00:05:37,440 --> 00:05:39,440 this open source wave is a game changer. 185 00:05:39,440 --> 00:05:41,480 They're not stuck with just one provider anymore. 186 00:05:41,480 --> 00:05:43,560 And that puts pressure on pricing, too. 187 00:05:43,560 --> 00:05:44,320 Exactly. 188 00:05:44,320 --> 00:05:46,720 Open source keeps those proprietary players in check. 189 00:05:46,720 --> 00:05:48,800 They have to stay competitive, both in terms of price 190 00:05:48,800 --> 00:05:49,640 and performance. 191 00:05:49,640 --> 00:05:53,040 It makes for a much more dynamic and innovative environment. 192 00:05:53,040 --> 00:05:56,960 And with models like DeepSeqs R1 being so much cheaper 193 00:05:56,960 --> 00:05:59,600 than the competition, it really lowers the barrier 194 00:05:59,600 --> 00:06:01,200 to entry for smaller players. 195 00:06:01,200 --> 00:06:01,720 Yeah. 196 00:06:01,720 --> 00:06:03,600 It's like the cost of entry just went way down. 197 00:06:03,600 --> 00:06:06,800 So now we could see an explosion of new AI-powered applications 198 00:06:06,800 --> 00:06:07,440 and services. 199 00:06:07,440 --> 00:06:09,840 As more and more people can actually afford to experiment 200 00:06:09,840 --> 00:06:12,320 and build with this technology, it's pretty exciting. 201 00:06:12,320 --> 00:06:12,880 It really is. 202 00:06:12,880 --> 00:06:15,600 And it makes you wonder, what kind of world-changing ideas 203 00:06:15,600 --> 00:06:16,640 are going to come out of this? 204 00:06:16,640 --> 00:06:19,320 It's like we're on the verge of something truly revolutionary. 205 00:06:19,320 --> 00:06:21,400 And open source is a big part of that. 206 00:06:21,400 --> 00:06:23,600 It promotes transparency and collaboration, 207 00:06:23,600 --> 00:06:27,200 which are super important for a responsible AI development. 208 00:06:27,200 --> 00:06:27,400 Right. 209 00:06:27,400 --> 00:06:29,480 By working together and sharing knowledge, 210 00:06:29,480 --> 00:06:32,640 we can create better, more reliable AI systems that 211 00:06:32,640 --> 00:06:33,600 benefit everyone. 212 00:06:33,600 --> 00:06:34,080 Exactly. 213 00:06:34,080 --> 00:06:35,000 It's a win-win. 214 00:06:35,000 --> 00:06:36,240 But that being said, this doesn't 215 00:06:36,240 --> 00:06:38,480 mean the big tech companies are going away anytime soon. 216 00:06:38,480 --> 00:06:41,560 They still have tons of resources and expertise. 217 00:06:41,560 --> 00:06:41,800 Yeah. 218 00:06:41,800 --> 00:06:44,240 Google, Microsoft, Amazon, they're all investing heavily 219 00:06:44,240 --> 00:06:46,120 in AI research and development. 220 00:06:46,120 --> 00:06:47,960 And they're going to continue to shape the industry. 221 00:06:47,960 --> 00:06:49,880 What's changing is the nature of the game. 222 00:06:49,880 --> 00:06:52,280 It's not just about having the biggest model or the most 223 00:06:52,280 --> 00:06:53,120 data anymore. 224 00:06:53,120 --> 00:06:55,680 It's about building the best ecosystems, platforms, 225 00:06:55,680 --> 00:06:59,680 and tools that utilize AI in innovative and responsible ways. 226 00:06:59,680 --> 00:07:01,960 And that creates a more collaborative and competitive 227 00:07:01,960 --> 00:07:04,240 environment, which ultimately benefits everyone. 228 00:07:04,240 --> 00:07:06,760 And speaking of innovation, we can't forget about the incredible 229 00:07:06,760 --> 00:07:09,000 advancements we're seeing in AI reasoning. 230 00:07:09,000 --> 00:07:11,680 We touched on it earlier, but it's worth diving a little deeper 231 00:07:11,680 --> 00:07:14,360 into what this reasoning revolution really 232 00:07:14,360 --> 00:07:15,640 means for the future of AI. 233 00:07:15,640 --> 00:07:16,160 I agree. 234 00:07:16,160 --> 00:07:18,840 The progress we're seeing is mind-blowing. 235 00:07:18,840 --> 00:07:21,440 These models are going beyond simple instructions 236 00:07:21,440 --> 00:07:23,960 and actually starting to understand context logic 237 00:07:23,960 --> 00:07:24,960 and problem solving. 238 00:07:24,960 --> 00:07:26,800 It's like they're learning to think more like us. 239 00:07:26,800 --> 00:07:29,480 Analyzing situations, weighing different perspectives, 240 00:07:29,480 --> 00:07:31,200 coming up with logical solutions. 241 00:07:31,200 --> 00:07:31,880 Exactly. 242 00:07:31,880 --> 00:07:33,720 And this has huge implications for how 243 00:07:33,720 --> 00:07:37,600 we can use AI to solve complex problems and automate tasks 244 00:07:37,600 --> 00:07:39,560 that used to require human intelligence. 245 00:07:39,560 --> 00:07:42,200 Like, think about the thought process feature 246 00:07:42,200 --> 00:07:44,280 in DeepSeq's R1 model. 247 00:07:44,280 --> 00:07:47,000 Being able to see how the AI reaches its conclusions 248 00:07:47,000 --> 00:07:49,800 is so important for building trust and transparency. 249 00:07:49,800 --> 00:07:51,800 Especially when AI is making important decisions. 250 00:07:51,800 --> 00:07:52,400 Exactly. 251 00:07:52,400 --> 00:07:54,520 We need to understand how it's working and make sure it's 252 00:07:54,520 --> 00:07:56,480 being used ethically and responsibly. 253 00:07:56,480 --> 00:07:58,800 And as AI reasoning continues to improve, 254 00:07:58,800 --> 00:08:01,320 we'll probably see a lot more sophisticated AI agents 255 00:08:01,320 --> 00:08:02,080 being developed. 256 00:08:02,080 --> 00:08:04,760 Capable of automating even more complex tasks, 257 00:08:04,760 --> 00:08:07,560 from managing workflows to analyzing data, 258 00:08:07,560 --> 00:08:09,960 to maybe even helping with creative endeavors. 259 00:08:09,960 --> 00:08:12,160 It's pretty wild to think about the potential impact 260 00:08:12,160 --> 00:08:14,480 on different industries and the workforce as a whole. 261 00:08:14,480 --> 00:08:16,760 Definitely something worth exploring more in the future. 262 00:08:16,760 --> 00:08:17,360 For sure. 263 00:08:17,360 --> 00:08:19,920 But shifting gears a bit, let's talk about data for a minute. 264 00:08:19,920 --> 00:08:23,320 Traditionally, it's been seen as the fuel that powers AI. 265 00:08:23,320 --> 00:08:24,400 Well, more data, the better. 266 00:08:24,400 --> 00:08:24,720 Right. 267 00:08:24,720 --> 00:08:26,920 But with distillation, that might be changing. 268 00:08:26,920 --> 00:08:27,920 What are your thoughts on that? 269 00:08:27,920 --> 00:08:31,120 Well, if models can learn effectively from each other, 270 00:08:31,120 --> 00:08:34,120 the size of your data set might not be as crucial anymore. 271 00:08:34,120 --> 00:08:36,080 It's like saying you don't need the biggest library 272 00:08:36,080 --> 00:08:37,880 in the world to be incredibly knowledgeable. 273 00:08:37,880 --> 00:08:40,040 You can learn just as much from someone who's already 274 00:08:40,040 --> 00:08:41,160 studied all those books. 275 00:08:41,160 --> 00:08:41,960 Exactly. 276 00:08:41,960 --> 00:08:43,600 And this is huge for businesses that 277 00:08:43,600 --> 00:08:45,760 don't have access to those massive data sets. 278 00:08:45,760 --> 00:08:48,160 They can still leverage the knowledge that's out there. 279 00:08:48,160 --> 00:08:50,720 And that's where companies like Blean are doing amazing work. 280 00:08:50,720 --> 00:08:53,400 They're building platforms that make it easier for anyone 281 00:08:53,400 --> 00:08:57,160 to access and use AI regardless of how much data they have. 282 00:08:57,160 --> 00:08:58,840 They're making it a more level playing field. 283 00:08:58,840 --> 00:09:00,520 It's like creating an educational system 284 00:09:00,520 --> 00:09:03,520 where everyone has access to the knowledge and tools they 285 00:09:03,520 --> 00:09:04,840 need to learn and grow. 286 00:09:04,840 --> 00:09:05,920 I love that analogy. 287 00:09:05,920 --> 00:09:08,520 And as these techniques continue to develop, 288 00:09:08,520 --> 00:09:11,600 I think we'll see even more innovative approaches to AI 289 00:09:11,600 --> 00:09:14,240 that challenge our current assumptions about data. 290 00:09:14,240 --> 00:09:15,480 It's definitely shaking things up. 291 00:09:15,480 --> 00:09:17,440 But before we wrap up, I want to circle back 292 00:09:17,440 --> 00:09:20,600 to something you mentioned earlier about OpenAI's AGI 293 00:09:20,600 --> 00:09:21,800 at all cost strategy. 294 00:09:21,800 --> 00:09:23,240 What exactly does that mean? 295 00:09:23,240 --> 00:09:25,880 Basically, it's their commitment to achieving 296 00:09:25,880 --> 00:09:28,520 artificial general intelligence as quickly as possible, 297 00:09:28,520 --> 00:09:30,680 even if it means using a ton of resources 298 00:09:30,680 --> 00:09:32,440 and keeping things closed sourced. 299 00:09:32,440 --> 00:09:34,720 So they're going all in on building those big complex 300 00:09:34,720 --> 00:09:36,400 models while others are finding ways 301 00:09:36,400 --> 00:09:38,960 to get similar results with smaller, more efficient, 302 00:09:38,960 --> 00:09:39,840 distilled models. 303 00:09:39,840 --> 00:09:40,640 Exactly. 304 00:09:40,640 --> 00:09:42,920 They believe that achieving AGI first 305 00:09:42,920 --> 00:09:45,200 is super important for shaping its development 306 00:09:45,200 --> 00:09:46,800 and making sure it benefits everyone. 307 00:09:46,800 --> 00:09:48,520 It's like two different philosophies clashing. 308 00:09:48,520 --> 00:09:51,080 Yeah, you have the pursuit of ultimate intelligence 309 00:09:51,080 --> 00:09:54,120 on one hand and the democratization of AI on the other. 310 00:09:54,120 --> 00:09:56,400 It's fascinating to see how this plays out. 311 00:09:56,400 --> 00:09:58,800 And it raises some big questions about the future 312 00:09:58,800 --> 00:10:00,560 of AI research and development. 313 00:10:00,560 --> 00:10:02,920 It's a debate that's going to continue for a while. 314 00:10:02,920 --> 00:10:04,640 And it's exciting to be a part of it. 315 00:10:04,640 --> 00:10:05,240 I agree. 316 00:10:05,240 --> 00:10:08,080 This whole deep dive has been an incredible journey so far. 317 00:10:08,080 --> 00:10:10,280 And I'm sure our listeners are feeling the same way. 318 00:10:10,280 --> 00:10:11,800 But before we get too carried away, 319 00:10:11,800 --> 00:10:13,840 let's bring things back down to earth for a moment. 320 00:10:13,840 --> 00:10:14,560 Good point. 321 00:10:14,560 --> 00:10:16,440 Amidst all this excitement about AI, 322 00:10:16,440 --> 00:10:19,240 we need to remember that these are powerful tools 323 00:10:19,240 --> 00:10:21,080 and we need to use them responsibly. 324 00:10:21,080 --> 00:10:22,680 Yeah, it's easy to get caught up in all the hype, 325 00:10:22,680 --> 00:10:26,640 but we've got to remember that AI has a real impact on society. 326 00:10:26,640 --> 00:10:28,760 As it gets more powerful and easier to access, 327 00:10:28,760 --> 00:10:31,240 we have to make sure it's used for good, not for bad stuff. 328 00:10:31,240 --> 00:10:31,880 Absolutely. 329 00:10:31,880 --> 00:10:33,840 We have to think about the ethical side of things, 330 00:10:33,840 --> 00:10:35,400 the potential for job displacement, 331 00:10:35,400 --> 00:10:38,360 and making sure AI aligns with human values. 332 00:10:38,360 --> 00:10:40,320 It's not just about the technology itself. 333 00:10:40,320 --> 00:10:42,360 It's a big conversation, and one that everyone 334 00:10:42,360 --> 00:10:44,240 needs to be a part of researchers, developers, 335 00:10:44,240 --> 00:10:46,040 policymakers, everyone. 336 00:10:46,040 --> 00:10:48,080 But even with all the challenges, 337 00:10:48,080 --> 00:10:50,320 I'm still optimistic about the future of AI. 338 00:10:50,320 --> 00:10:51,120 Yeah, me too. 339 00:10:51,120 --> 00:10:53,600 I mean, AI has the potential to solve some of the world's 340 00:10:53,600 --> 00:10:56,520 biggest problems, like climate change, disease, poverty. 341 00:10:56,520 --> 00:10:58,680 It can help us make better decisions 342 00:10:58,680 --> 00:11:00,080 and create new opportunities. 343 00:11:00,080 --> 00:11:02,320 It feels like we're at the beginning of something huge. 344 00:11:02,320 --> 00:11:02,840 It does. 345 00:11:02,840 --> 00:11:06,040 And it's up to us to make sure this new era is a good one, 346 00:11:06,040 --> 00:11:07,480 one where everyone benefits. 347 00:11:07,480 --> 00:11:08,400 Well said. 348 00:11:08,400 --> 00:11:10,360 This deep dive into AI distillation 349 00:11:10,360 --> 00:11:11,640 has been pretty eye-opening. 350 00:11:11,640 --> 00:11:13,640 We've covered a lot of ground from the technical stuff 351 00:11:13,640 --> 00:11:16,440 to the impact on open source, the reasoning revolution, 352 00:11:16,440 --> 00:11:18,120 the changing role of data. 353 00:11:18,120 --> 00:11:19,400 Yeah, it's been a wild ride. 354 00:11:19,400 --> 00:11:21,200 And hopefully, our listeners are walking away 355 00:11:21,200 --> 00:11:22,640 with a better understanding of what's 356 00:11:22,640 --> 00:11:24,040 happening in the world of AI. 357 00:11:24,040 --> 00:11:27,360 We've seen how distillation is making AI more accessible, 358 00:11:27,360 --> 00:11:30,040 accelerating innovation, and pushing the boundaries 359 00:11:30,040 --> 00:11:30,880 of what's possible. 360 00:11:30,880 --> 00:11:33,240 And we've talked about the ethical considerations 361 00:11:33,240 --> 00:11:35,520 that we need to keep in mind as we move forward. 362 00:11:35,520 --> 00:11:37,080 So as we wrap up this deep dive, 363 00:11:37,080 --> 00:11:39,120 the question for you listening is this. 364 00:11:39,120 --> 00:11:41,600 How are you going to engage with this world of AI 365 00:11:41,600 --> 00:11:42,880 that's constantly changing? 366 00:11:42,880 --> 00:11:44,640 Are you going to sit back and watch? 367 00:11:44,640 --> 00:11:46,160 Or are you going to jump in and help 368 00:11:46,160 --> 00:11:48,960 shape the future of this incredible technology? 369 00:11:48,960 --> 00:11:49,680 It's up to you. 370 00:11:49,680 --> 00:11:52,640 And as we keep exploring this amazing world of AI, 371 00:11:52,640 --> 00:11:55,560 one thing's for sure, the future is full of possibilities. 372 00:11:55,560 --> 00:11:57,600 And the only limit is our imagination. 373 00:11:57,600 --> 00:11:58,840 That's a great note to end on. 374 00:11:58,840 --> 00:12:00,520 So thanks for joining us on this deep dive 375 00:12:00,520 --> 00:12:02,080 into the world of AI distillation. 376 00:12:02,080 --> 00:12:02,960 It's been a pleasure. 377 00:12:02,960 --> 00:12:04,960 And until next time, keep exploring and keep asking 378 00:12:04,960 --> 00:12:07,200 questions.