1 00:00:00,000 --> 00:00:03,280 Hey there AI enthusiasts and welcome back. 2 00:00:03,280 --> 00:00:05,800 Get ready, because today we're diving deep 3 00:00:05,800 --> 00:00:09,160 into the world of multi-LLM agent systems. 4 00:00:09,160 --> 00:00:10,440 MLAS for short. 5 00:00:10,440 --> 00:00:11,760 You got it. 6 00:00:11,760 --> 00:00:14,560 Think of it like having a whole team of AI specialists. 7 00:00:14,560 --> 00:00:16,880 Each one with its own unique superpowers. 8 00:00:16,880 --> 00:00:17,720 Working together. 9 00:00:17,720 --> 00:00:20,360 To tackle some seriously complex challenges. 10 00:00:20,360 --> 00:00:21,960 It's a pretty mind blowing concept. 11 00:00:21,960 --> 00:00:22,800 It is. 12 00:00:22,800 --> 00:00:23,800 And we're gonna break it all down today 13 00:00:23,800 --> 00:00:26,480 based on this fascinating research paper. 14 00:00:26,480 --> 00:00:29,160 Multi-LLM agent systems. 15 00:00:29,160 --> 00:00:31,080 Techniques and business perspectives. 16 00:00:31,080 --> 00:00:32,000 Catchy title, right. 17 00:00:32,000 --> 00:00:33,160 It gets right to the point. 18 00:00:33,160 --> 00:00:35,360 So before we get lost in the technical weeds, 19 00:00:35,360 --> 00:00:36,720 let's start with the basics. 20 00:00:36,720 --> 00:00:37,560 Sounds good. 21 00:00:37,560 --> 00:00:40,480 What exactly are multi-LLM agent systems? 22 00:00:40,480 --> 00:00:43,200 Okay, so imagine you have a team of experts. 23 00:00:43,200 --> 00:00:44,160 Like a dream team. 24 00:00:44,160 --> 00:00:45,280 Exactly. 25 00:00:45,280 --> 00:00:47,400 Each one with very specific skills. 26 00:00:47,400 --> 00:00:48,600 You got your strategist. 27 00:00:48,600 --> 00:00:50,400 Your communicator, your analyst. 28 00:00:50,400 --> 00:00:52,080 All working together on a project. 29 00:00:52,080 --> 00:00:53,080 Boom. 30 00:00:53,080 --> 00:00:54,640 That's what MLAS are all about. 31 00:00:54,640 --> 00:00:57,360 So instead of one giant AI trying to do everything. 32 00:00:57,360 --> 00:00:59,240 You have specialized agents. 33 00:00:59,240 --> 00:01:01,200 Each powered by a large language model. 34 00:01:01,200 --> 00:01:02,040 And LLM. 35 00:01:02,040 --> 00:01:02,880 Yep. 36 00:01:02,880 --> 00:01:05,040 And each agent focuses on what it does best. 37 00:01:05,040 --> 00:01:07,160 Division of labor, AI style. 38 00:01:07,160 --> 00:01:08,000 Precisely. 39 00:01:08,000 --> 00:01:12,560 And that's what lets MLAS tackle those crazy complex tasks 40 00:01:12,560 --> 00:01:15,640 that would overwhelm a single AI. 41 00:01:15,640 --> 00:01:17,360 The paper gives us really cool example 42 00:01:17,360 --> 00:01:19,520 of a music service using MLAS. 43 00:01:19,520 --> 00:01:20,800 Oh yeah, that's a good one. 44 00:01:20,800 --> 00:01:21,760 So walk us through it. 45 00:01:21,760 --> 00:01:24,640 All right, so you've got three main players in this scenario. 46 00:01:24,640 --> 00:01:27,120 A personal agent, an orchestrator agent, 47 00:01:27,120 --> 00:01:28,320 and a song agent. 48 00:01:28,320 --> 00:01:30,560 OK, I'm picturing them like little AIDJs. 49 00:01:30,560 --> 00:01:31,200 Kind of. 50 00:01:31,200 --> 00:01:33,640 So let's say you request a song, right? 51 00:01:33,640 --> 00:01:35,960 The personal agent is like, OK, I know this person loves 52 00:01:35,960 --> 00:01:36,720 80s pop. 53 00:01:36,720 --> 00:01:38,040 They've got your playlist history. 54 00:01:38,040 --> 00:01:39,000 Exactly. 55 00:01:39,000 --> 00:01:42,600 Then the orchestrator agent steps in, figures out the best 56 00:01:42,600 --> 00:01:44,080 way to get you that song. 57 00:01:44,080 --> 00:01:45,800 Like do we stream it? 58 00:01:45,800 --> 00:01:46,960 Play it from their library? 59 00:01:46,960 --> 00:01:48,160 Yep, all the logistics. 60 00:01:48,160 --> 00:01:51,720 And finally, the song agent actually finds and plays the song. 61 00:01:51,720 --> 00:01:54,680 Seamless music experience, all thanks to MLAS. 62 00:01:54,680 --> 00:01:55,640 This is blowing my mind. 63 00:01:55,640 --> 00:01:58,520 But how do these agents actually work together? 64 00:01:58,520 --> 00:02:00,760 It seems like it'd be chaos behind the scenes. 65 00:02:00,760 --> 00:02:02,040 Well, it's not magic. 66 00:02:02,040 --> 00:02:02,560 Too bad. 67 00:02:02,560 --> 00:02:04,720 The paper breaks down how these agents are built. 68 00:02:04,720 --> 00:02:05,600 That they're blueprints. 69 00:02:05,600 --> 00:02:06,200 You got it. 70 00:02:06,200 --> 00:02:10,320 They have specific architectures for processing information. 71 00:02:10,320 --> 00:02:11,240 Making decisions. 72 00:02:11,240 --> 00:02:12,080 Unicating. 73 00:02:12,080 --> 00:02:14,040 So it's more than just reacting to things. 74 00:02:14,040 --> 00:02:14,680 Oh, absolutely. 75 00:02:14,680 --> 00:02:16,800 They're thinking things through, learning 76 00:02:16,800 --> 00:02:18,000 from their interactions. 77 00:02:18,000 --> 00:02:18,920 That's impressive. 78 00:02:18,920 --> 00:02:21,840 And get this, they can even use external tools, 79 00:02:21,840 --> 00:02:25,760 like calculators, databases, other AI models. 80 00:02:25,760 --> 00:02:27,280 Wow, so it's like giving them a toolbox. 81 00:02:27,280 --> 00:02:27,880 Exactly. 82 00:02:27,880 --> 00:02:29,840 The more tools they have, the more complex 83 00:02:29,840 --> 00:02:31,480 the tasks they can handle. 84 00:02:31,480 --> 00:02:32,960 This is getting pretty technical, though. 85 00:02:32,960 --> 00:02:33,480 It is. 86 00:02:33,480 --> 00:02:35,360 But I'm curious, how do you keep things 87 00:02:35,360 --> 00:02:38,200 organized with all these agents running around? 88 00:02:38,200 --> 00:02:39,920 Right, you don't want them bumping into each other. 89 00:02:39,920 --> 00:02:41,040 Exactly. 90 00:02:41,040 --> 00:02:42,760 It seems like it could get chaotic. 91 00:02:42,760 --> 00:02:46,560 Well, just like a company needs a good management structure. 92 00:02:46,560 --> 00:02:50,200 MLAS have different ways of organizing these agents, right? 93 00:02:50,200 --> 00:02:51,240 You got it. 94 00:02:51,240 --> 00:02:53,840 The paper talks about centralized systems. 95 00:02:53,840 --> 00:02:55,920 Where one authority controls everything. 96 00:02:55,920 --> 00:02:59,160 Decentralized systems, where agents have more autonomy. 97 00:02:59,160 --> 00:03:00,320 And a hybrid approach. 98 00:03:00,320 --> 00:03:01,720 Mix and match. 99 00:03:01,720 --> 00:03:04,200 Find what works best for the task. 100 00:03:04,200 --> 00:03:07,400 So it's all about finding the right balance between control 101 00:03:07,400 --> 00:03:08,640 and flexibility. 102 00:03:08,640 --> 00:03:09,400 You nailed it. 103 00:03:09,400 --> 00:03:11,520 And it all depends on the specific task, 104 00:03:11,520 --> 00:03:14,280 how much trust there is between agents. 105 00:03:14,280 --> 00:03:15,280 This is fascinating. 106 00:03:15,280 --> 00:03:16,040 I know, right? 107 00:03:16,040 --> 00:03:17,840 But can we get even more specific? 108 00:03:17,840 --> 00:03:20,600 How do these agents actually talk to each other? 109 00:03:20,600 --> 00:03:22,480 Like, what's the language they use? 110 00:03:22,480 --> 00:03:26,520 The paper digs deep into interaction protocols. 111 00:03:26,520 --> 00:03:27,840 Protocols. 112 00:03:27,840 --> 00:03:28,960 Sounds serious. 113 00:03:28,960 --> 00:03:32,360 It's all about setting clear guidelines for communication. 114 00:03:32,360 --> 00:03:34,000 Making sure everyone's on the same page. 115 00:03:34,000 --> 00:03:34,640 Exactly. 116 00:03:34,640 --> 00:03:38,440 So you have protocols for how agents understand instructions. 117 00:03:38,440 --> 00:03:41,400 How they negotiate, share information. 118 00:03:41,400 --> 00:03:42,840 How they reach agreements. 119 00:03:42,840 --> 00:03:45,120 Even how they share credit for their work. 120 00:03:45,120 --> 00:03:47,320 Wow, it's like setting up a whole communication system 121 00:03:47,320 --> 00:03:48,000 for a team. 122 00:03:48,000 --> 00:03:48,840 Exactly. 123 00:03:48,840 --> 00:03:51,720 It's all about creating a system where they can collaborate 124 00:03:51,720 --> 00:03:52,720 effectively. 125 00:03:52,720 --> 00:03:55,280 This is making me think about how we humans communicate 126 00:03:55,280 --> 00:03:56,000 and work together. 127 00:03:56,000 --> 00:03:57,120 It's all connected. 128 00:03:57,120 --> 00:03:58,920 The researchers are drawing inspiration 129 00:03:58,920 --> 00:04:01,960 from human collaboration and applying it to AI. 130 00:04:01,960 --> 00:04:02,640 That's pretty cool. 131 00:04:02,640 --> 00:04:06,840 OK, so we've covered the basics of MLAS, their architecture, 132 00:04:06,840 --> 00:04:07,800 how they communicate. 133 00:04:07,800 --> 00:04:09,200 It's a lot to take in. 134 00:04:09,200 --> 00:04:09,920 It is. 135 00:04:09,920 --> 00:04:12,320 But I have to admit, it's getting pretty technical. 136 00:04:12,320 --> 00:04:13,400 Can we switch gears a bit? 137 00:04:13,400 --> 00:04:13,760 Sure. 138 00:04:13,760 --> 00:04:16,360 Talk about how all this translates into real world 139 00:04:16,360 --> 00:04:17,080 applications. 140 00:04:17,080 --> 00:04:18,760 Like, where does the business side come in? 141 00:04:18,760 --> 00:04:20,360 Ah, the money question. 142 00:04:20,360 --> 00:04:21,000 Well, Tom. 143 00:04:21,000 --> 00:04:22,320 The paper dives into that, too. 144 00:04:22,320 --> 00:04:24,600 They focus on three key areas. 145 00:04:24,600 --> 00:04:27,640 Privacy preservation, traffic monetization, 146 00:04:27,640 --> 00:04:29,560 and intelligence monetization. 147 00:04:29,560 --> 00:04:31,000 OK, let's break those down, starting 148 00:04:31,000 --> 00:04:32,200 with privacy preservation. 149 00:04:32,200 --> 00:04:33,360 Good place to start. 150 00:04:33,360 --> 00:04:34,880 It seems like a huge concern when 151 00:04:34,880 --> 00:04:36,320 you're dealing with AI systems that 152 00:04:36,320 --> 00:04:37,920 have access to so much data. 153 00:04:37,920 --> 00:04:39,120 Oh, absolutely. 154 00:04:39,120 --> 00:04:41,920 MLAS often handles sensitive information, 155 00:04:41,920 --> 00:04:44,600 so protecting user privacy is key. 156 00:04:44,600 --> 00:04:46,720 It's not just about keeping data secret. 157 00:04:46,720 --> 00:04:47,360 Nope. 158 00:04:47,360 --> 00:04:50,560 It's also about making sure the AI agents themselves 159 00:04:50,560 --> 00:04:53,240 don't accidentally reveal sensitive information. 160 00:04:53,240 --> 00:04:55,400 Through their actions or insights. 161 00:04:55,400 --> 00:04:56,000 Exactly. 162 00:04:56,000 --> 00:04:58,160 And the paper explores a bunch of ways 163 00:04:58,160 --> 00:05:00,440 to address this, like advanced encryption. 164 00:05:00,440 --> 00:05:02,760 Secure ways for agents to share information. 165 00:05:02,760 --> 00:05:04,960 It's a complex challenge, but there are definitely 166 00:05:04,960 --> 00:05:06,400 solutions out there. 167 00:05:06,400 --> 00:05:08,560 So even with these privacy concerns, 168 00:05:08,560 --> 00:05:12,160 you can still build MLAS systems that respect user data. 169 00:05:12,160 --> 00:05:13,160 That's the goal. 170 00:05:13,160 --> 00:05:15,280 It's all about building trust and using 171 00:05:15,280 --> 00:05:17,840 these powerful technologies ethically. 172 00:05:17,840 --> 00:05:20,560 Now, what about traffic monetization? 173 00:05:20,560 --> 00:05:21,840 That sounds intriguing. 174 00:05:21,840 --> 00:05:23,680 Think about online platforms. 175 00:05:23,680 --> 00:05:24,640 Like social media. 176 00:05:24,640 --> 00:05:26,520 E-commerce sites, all those ads you see. 177 00:05:26,520 --> 00:05:28,400 Yeah, they make money by showing us targeted ads. 178 00:05:28,400 --> 00:05:29,920 Exactly. 179 00:05:29,920 --> 00:05:32,320 Well, MLAS can take this to a whole new level. 180 00:05:32,320 --> 00:05:32,880 No, slow. 181 00:05:32,880 --> 00:05:36,040 By using intelligent agents to manage user traffic 182 00:05:36,040 --> 00:05:37,960 and optimize ad strategies. 183 00:05:37,960 --> 00:05:41,040 So it's about making those ads more relevant, more engaging. 184 00:05:41,040 --> 00:05:41,720 You got it. 185 00:05:41,720 --> 00:05:44,280 Instead of just bombarding you with random stuff, 186 00:05:44,280 --> 00:05:46,440 they show you ads that actually interest you. 187 00:05:46,440 --> 00:05:47,680 That sounds like a win-win. 188 00:05:47,680 --> 00:05:48,400 Right. 189 00:05:48,400 --> 00:05:51,080 Better experience for users, more effective advertising 190 00:05:51,080 --> 00:05:52,040 for businesses. 191 00:05:52,040 --> 00:05:54,440 OK, I can see how that would be beneficial. 192 00:05:54,440 --> 00:05:56,200 Now, what about the last area you mentioned? 193 00:05:56,200 --> 00:05:58,080 Intelligence monetization. 194 00:05:58,080 --> 00:05:58,800 What is that? 195 00:05:58,800 --> 00:06:01,040 This is where things get really exciting. 196 00:06:01,040 --> 00:06:03,280 We're talking about using MLAS to generate 197 00:06:03,280 --> 00:06:07,120 valuable insights and services that businesses will pay for. 198 00:06:07,120 --> 00:06:10,200 So it's like having a team of AI consultants working for you. 199 00:06:10,200 --> 00:06:11,040 Exactly. 200 00:06:11,040 --> 00:06:13,840 Imagine MLAS analyzing market trends. 201 00:06:13,840 --> 00:06:15,800 Identifying new business opportunities. 202 00:06:15,800 --> 00:06:17,920 Providing personalized financial advice. 203 00:06:17,920 --> 00:06:20,000 It's like having an AI brain trust. 204 00:06:20,000 --> 00:06:22,680 It's all about leveraging the collective intelligence 205 00:06:22,680 --> 00:06:25,720 of these agents to create real-world value. 206 00:06:25,720 --> 00:06:27,320 This is mind-blowing. 207 00:06:27,320 --> 00:06:30,040 So MLAS aren't just a cool technology. 208 00:06:30,040 --> 00:06:33,400 They have the potential to transform how businesses operate, 209 00:06:33,400 --> 00:06:35,520 create entirely new markets. 210 00:06:35,520 --> 00:06:37,520 The possibilities are endless. 211 00:06:37,520 --> 00:06:37,720 Right. 212 00:06:37,720 --> 00:06:39,480 But before we get ahead of ourselves, 213 00:06:39,480 --> 00:06:41,800 I think it's time to look at some real-world examples 214 00:06:41,800 --> 00:06:43,320 of MLAS in action. 215 00:06:43,320 --> 00:06:46,200 Let's see how these systems are already making a difference. 216 00:06:46,200 --> 00:06:46,960 Sounds good to me. 217 00:06:46,960 --> 00:06:48,920 Welcome back to the deep dive. 218 00:06:48,920 --> 00:06:51,080 Before the break, we were talking about how MLAS 219 00:06:51,080 --> 00:06:53,640 could shake things up in the business world. 220 00:06:53,640 --> 00:06:55,320 It's wild to think about, right? 221 00:06:55,320 --> 00:06:57,720 But I'm ready to see some real-world examples. 222 00:06:57,720 --> 00:06:58,560 Me too. 223 00:06:58,560 --> 00:07:00,800 But first, we got to talk about the different architectures 224 00:07:00,800 --> 00:07:02,120 for building MLAS. 225 00:07:02,120 --> 00:07:03,960 Architectures, like for buildings. 226 00:07:03,960 --> 00:07:06,240 Kind of, just like buildings have different designs 227 00:07:06,240 --> 00:07:08,480 based on their purpose, MLAS systems 228 00:07:08,480 --> 00:07:11,160 have different architectures for how those intelligent agents 229 00:07:11,160 --> 00:07:13,240 are organized and how they interact. 230 00:07:13,240 --> 00:07:14,800 So it's like choosing the right blueprint, 231 00:07:14,800 --> 00:07:16,720 depending on what you want the system to do. 232 00:07:16,720 --> 00:07:18,080 Exactly. 233 00:07:18,080 --> 00:07:19,800 And just like with building blueprints, 234 00:07:19,800 --> 00:07:22,520 different designs have pros and cons. 235 00:07:22,520 --> 00:07:24,640 Let's explore a few popular ones starting 236 00:07:24,640 --> 00:07:26,520 with the star architecture. 237 00:07:26,520 --> 00:07:28,320 Star? 238 00:07:28,320 --> 00:07:29,400 That sounds pretty straightforward. 239 00:07:29,400 --> 00:07:30,320 It is. 240 00:07:30,320 --> 00:07:33,440 Imagine a bicycle wheel with the hub in the center 241 00:07:33,440 --> 00:07:35,560 and spokes radiating outwards. 242 00:07:35,560 --> 00:07:36,680 OK, I'm picturing it. 243 00:07:36,680 --> 00:07:39,560 In a star architecture, you have one central agent, 244 00:07:39,560 --> 00:07:42,680 often called the orchestrator, that acts as the hub. 245 00:07:42,680 --> 00:07:45,720 So the orchestrator is like the project manager, 246 00:07:45,720 --> 00:07:47,120 keeping everyone in line. 247 00:07:47,120 --> 00:07:48,200 Perfect analogy. 248 00:07:48,200 --> 00:07:49,720 All the other agents communicate 249 00:07:49,720 --> 00:07:50,880 through the central agent. 250 00:07:50,880 --> 00:07:54,120 So if the orchestrator goes down, the whole system crashes. 251 00:07:54,120 --> 00:07:55,040 That's the downside. 252 00:07:55,040 --> 00:07:56,560 It's a single point of failure. 253 00:07:56,560 --> 00:07:57,480 Makes sense. 254 00:07:57,480 --> 00:07:58,160 What's next? 255 00:07:58,160 --> 00:07:59,800 How about the ring architecture? 256 00:07:59,800 --> 00:08:02,400 In this setup, the agents are arranged in a circle, 257 00:08:02,400 --> 00:08:03,200 like a ring. 258 00:08:03,200 --> 00:08:06,360 OK, I'm visualizing a ring of AI agents. 259 00:08:06,360 --> 00:08:08,320 Each agent only talks to its neighbors. 260 00:08:08,320 --> 00:08:10,760 So it's like a game of telephone, passing information 261 00:08:10,760 --> 00:08:11,600 around the circle. 262 00:08:11,600 --> 00:08:12,960 That's a great way to put it. 263 00:08:12,960 --> 00:08:15,480 This setup works well for tasks that involve 264 00:08:15,480 --> 00:08:16,760 a sequence of steps. 265 00:08:16,760 --> 00:08:18,120 Like an assembly line. 266 00:08:18,120 --> 00:08:19,120 Exactly. 267 00:08:19,120 --> 00:08:21,960 Plus, it's more robust than the star architecture, 268 00:08:21,960 --> 00:08:24,880 because there's no single point of failure. 269 00:08:24,880 --> 00:08:28,720 If one agent goes down, the others can still communicate. 270 00:08:28,720 --> 00:08:30,120 What other architectures are there? 271 00:08:30,120 --> 00:08:31,560 Well, there's the graph architecture, 272 00:08:31,560 --> 00:08:33,400 where agents can be connected in any way, 273 00:08:33,400 --> 00:08:35,160 forming a network of relationships. 274 00:08:35,160 --> 00:08:37,560 So it's like a social network for AI agents. 275 00:08:37,560 --> 00:08:38,680 You got it. 276 00:08:38,680 --> 00:08:41,320 This one is super flexible and adaptable, 277 00:08:41,320 --> 00:08:43,240 perfect for complex tasks. 278 00:08:43,240 --> 00:08:45,560 And if one connection breaks, the information 279 00:08:45,560 --> 00:08:47,360 can still flow through other paths. 280 00:08:47,360 --> 00:08:48,000 Exactly. 281 00:08:48,000 --> 00:08:49,640 It's a very resilient design. 282 00:08:49,640 --> 00:08:50,880 OK, what about the last one? 283 00:08:50,880 --> 00:08:52,360 The bus architecture. 284 00:08:52,360 --> 00:08:53,040 Yes. 285 00:08:53,040 --> 00:08:55,200 In this one, all agents communicate 286 00:08:55,200 --> 00:08:57,680 through a central channel or bus. 287 00:08:57,680 --> 00:09:00,040 Like a city bus route, where everyone gets on and off 288 00:09:00,040 --> 00:09:00,920 at different stops. 289 00:09:00,920 --> 00:09:02,120 That's the idea. 290 00:09:02,120 --> 00:09:03,560 It's a good choice for tasks that 291 00:09:03,560 --> 00:09:05,160 follow a predefined workflow. 292 00:09:05,160 --> 00:09:09,840 So each agent knows its role and jumps in at the right time. 293 00:09:09,840 --> 00:09:12,040 But with all these different architectures, 294 00:09:12,040 --> 00:09:14,960 how do you actually decide which one to use 295 00:09:14,960 --> 00:09:16,760 in a real world situation? 296 00:09:16,760 --> 00:09:19,120 It's all about choosing the right tool for the job. 297 00:09:19,120 --> 00:09:21,760 So it depends on the task, the level of trust 298 00:09:21,760 --> 00:09:24,800 between the agents, whether privacy is a big concern. 299 00:09:24,800 --> 00:09:26,240 All those factors come into play. 300 00:09:26,240 --> 00:09:29,200 And sometimes, you might even combine different architectures 301 00:09:29,200 --> 00:09:30,960 to create a hybrid system. 302 00:09:30,960 --> 00:09:31,800 That makes sense. 303 00:09:31,800 --> 00:09:33,440 Can we see how this works in practice? 304 00:09:33,440 --> 00:09:35,320 Like with a specific example? 305 00:09:35,320 --> 00:09:36,040 Sure. 306 00:09:36,040 --> 00:09:39,160 The paper talks about a music service using MLAS. 307 00:09:39,160 --> 00:09:42,200 Imagine a platform where you can request songs, 308 00:09:42,200 --> 00:09:45,880 create playlists, and get personalized recommendations. 309 00:09:45,880 --> 00:09:46,600 Sounds familiar. 310 00:09:46,600 --> 00:09:47,160 Right. 311 00:09:47,160 --> 00:09:48,880 They might start with a centralized architecture 312 00:09:48,880 --> 00:09:51,520 where one main agent handles everything. 313 00:09:51,520 --> 00:09:54,000 Like a one man show, but with AI. 314 00:09:54,000 --> 00:09:57,240 But as the platform grows and handles more sensitive user 315 00:09:57,240 --> 00:10:01,080 data, they might switch to a decentralized star 316 00:10:01,080 --> 00:10:01,640 architecture. 317 00:10:01,640 --> 00:10:03,360 To keep things more secure. 318 00:10:03,360 --> 00:10:04,400 Exactly. 319 00:10:04,400 --> 00:10:06,600 The orchestrator agent still manages things, 320 00:10:06,600 --> 00:10:09,040 but it doesn't directly touch sensitive data, 321 00:10:09,040 --> 00:10:10,240 like your payment info. 322 00:10:10,240 --> 00:10:13,120 OK, so that sensitive data stays with specialized agents, 323 00:10:13,120 --> 00:10:15,440 like the song agent or playlist agent. 324 00:10:15,440 --> 00:10:17,680 Each agent gets its own little sandbox to work in. 325 00:10:17,680 --> 00:10:18,520 Exactly. 326 00:10:18,520 --> 00:10:20,680 And this approach enhances privacy 327 00:10:20,680 --> 00:10:22,240 while keeping the system efficient. 328 00:10:22,240 --> 00:10:24,120 It's amazing how these architectural choices 329 00:10:24,120 --> 00:10:26,840 can impact both functionality and security. 330 00:10:26,840 --> 00:10:29,000 It's all about finding the right balance. 331 00:10:29,000 --> 00:10:32,200 But remember, this music service is just one example. 332 00:10:32,200 --> 00:10:35,120 MLAS architectures can be used for all sorts of things. 333 00:10:35,120 --> 00:10:38,120 Healthcare, finance, transportation, you name it. 334 00:10:38,120 --> 00:10:39,160 This is incredible. 335 00:10:39,160 --> 00:10:42,000 But let's shift gears a bit and talk about agent training. 336 00:10:42,000 --> 00:10:44,400 How do you actually teach these AI agents 337 00:10:44,400 --> 00:10:46,200 to work together effectively? 338 00:10:46,200 --> 00:10:47,360 That's a great question. 339 00:10:47,360 --> 00:10:50,480 And the answer is, it depends. 340 00:10:50,480 --> 00:10:53,400 There are two main approaches to training agents. 341 00:10:53,400 --> 00:10:56,440 Tuning free methods and parameter tuning methods. 342 00:10:56,440 --> 00:10:57,440 OK, break it down for me. 343 00:10:57,440 --> 00:10:58,440 What's the difference? 344 00:10:58,440 --> 00:10:59,680 Think of it this way. 345 00:10:59,680 --> 00:11:03,280 Tuning free methods focus on improving agent performance 346 00:11:03,280 --> 00:11:06,080 without directly changing the underlying model. 347 00:11:06,080 --> 00:11:08,320 So it's like teaching someone a new skill 348 00:11:08,320 --> 00:11:10,120 without messing with their personality. 349 00:11:10,120 --> 00:11:11,200 Exactly. 350 00:11:11,200 --> 00:11:13,280 One way to do this is through prompt engineering, 351 00:11:13,280 --> 00:11:15,880 where you carefully craft the instructions given 352 00:11:15,880 --> 00:11:18,320 to the agent to get the desired outcome. 353 00:11:18,320 --> 00:11:20,240 So it's all about asking the right questions. 354 00:11:20,240 --> 00:11:21,440 You could say that. 355 00:11:21,440 --> 00:11:23,240 Then there's few shot learning, where 356 00:11:23,240 --> 00:11:25,040 you show the agent a few examples 357 00:11:25,040 --> 00:11:26,960 to help it understand a new task. 358 00:11:26,960 --> 00:11:28,400 Kind of like a cooking demonstration 359 00:11:28,400 --> 00:11:29,840 before trying a new recipe. 360 00:11:29,840 --> 00:11:30,960 Perfect analogy. 361 00:11:30,960 --> 00:11:33,720 And then there's external tool utilization, 362 00:11:33,720 --> 00:11:35,960 where you give agents access to tools 363 00:11:35,960 --> 00:11:37,720 that enhance their abilities. 364 00:11:37,720 --> 00:11:41,000 So an agent trying to summarize a long research paper 365 00:11:41,000 --> 00:11:44,160 could use a tool that extracts key concepts and citations. 366 00:11:44,160 --> 00:11:45,240 Exactly. 367 00:11:45,240 --> 00:11:47,640 It's like giving the agent a research assistant. 368 00:11:47,640 --> 00:11:50,880 These tuning free methods are great for quick improvements 369 00:11:50,880 --> 00:11:52,880 without rebuilding the whole system. 370 00:11:52,880 --> 00:11:54,160 Efficient and effective. 371 00:11:54,160 --> 00:11:54,480 Yeah. 372 00:11:54,480 --> 00:11:55,760 What about the other approach? 373 00:11:55,760 --> 00:11:57,000 Parameter tuning. 374 00:11:57,000 --> 00:11:58,480 Parameter tuning methods involve 375 00:11:58,480 --> 00:12:00,640 tweaking the underlying model's parameters 376 00:12:00,640 --> 00:12:01,640 to improve performance. 377 00:12:01,640 --> 00:12:03,920 So it's more like fine tuning a car engine. 378 00:12:03,920 --> 00:12:04,680 You got it. 379 00:12:04,680 --> 00:12:06,600 One example is alignment methods, 380 00:12:06,600 --> 00:12:08,880 where you train the agent to align its actions 381 00:12:08,880 --> 00:12:10,200 with human values. 382 00:12:10,200 --> 00:12:12,240 So it's not just about making the agent efficient. 383 00:12:12,240 --> 00:12:15,040 It's about making sure it acts ethically and responsibly. 384 00:12:15,040 --> 00:12:16,080 Exactly. 385 00:12:16,080 --> 00:12:18,600 Another one is multi-agent reinforcement learning, 386 00:12:18,600 --> 00:12:20,200 or MRL for short. 387 00:12:20,200 --> 00:12:20,840 MRL. 388 00:12:20,840 --> 00:12:21,200 Got it. 389 00:12:21,200 --> 00:12:23,240 This is where agents learn through trial and error 390 00:12:23,240 --> 00:12:24,840 by interacting with their environment 391 00:12:24,840 --> 00:12:26,840 and getting rewards for good behavior. 392 00:12:26,840 --> 00:12:28,760 Like training a dog with treats. 393 00:12:28,760 --> 00:12:29,880 You got it. 394 00:12:29,880 --> 00:12:33,720 MRL is great for teaching agents how to cooperate. 395 00:12:33,720 --> 00:12:36,000 It can lead to some really creative solutions 396 00:12:36,000 --> 00:12:38,160 that even the programmers didn't expect. 397 00:12:38,160 --> 00:12:41,000 That's both fascinating and a little scary. 398 00:12:41,000 --> 00:12:43,320 Which brings me to my next question. 399 00:12:43,320 --> 00:12:46,080 What about the risks of MLAS? 400 00:12:46,080 --> 00:12:48,280 All this talk about intelligent agents 401 00:12:48,280 --> 00:12:51,000 makes me think about the potential for misuse. 402 00:12:51,000 --> 00:12:52,520 That's a valid concern. 403 00:12:52,520 --> 00:12:55,080 Just like any powerful technology, 404 00:12:55,080 --> 00:12:57,720 MLAS can be vulnerable to attacks. 405 00:12:57,720 --> 00:12:59,880 Like what kind of attacks are we talking about? 406 00:12:59,880 --> 00:13:02,000 Well, one common attack is prompt injection, 407 00:13:02,000 --> 00:13:04,440 where someone tries to manipulate the instructions given 408 00:13:04,440 --> 00:13:06,520 to an agent to make it do something bad. 409 00:13:06,520 --> 00:13:09,360 So it's like tricking the agent into misbehaving. 410 00:13:09,360 --> 00:13:10,240 Exactly. 411 00:13:10,240 --> 00:13:12,120 And this can have serious consequences 412 00:13:12,120 --> 00:13:14,560 if the agent has access to sensitive information. 413 00:13:14,560 --> 00:13:15,160 OK. 414 00:13:15,160 --> 00:13:15,800 That's scary. 415 00:13:15,800 --> 00:13:16,400 What else? 416 00:13:16,400 --> 00:13:18,680 Another serious one is data poisoning, 417 00:13:18,680 --> 00:13:20,840 where someone sneaks in corrupted data 418 00:13:20,840 --> 00:13:22,400 during the training process. 419 00:13:22,400 --> 00:13:25,000 So it's like feeding the agent bad information 420 00:13:25,000 --> 00:13:26,200 to mess up its thinking. 421 00:13:26,200 --> 00:13:27,000 You got it. 422 00:13:27,000 --> 00:13:29,640 And then there are attacks aimed at stealing the knowledge 423 00:13:29,640 --> 00:13:30,960 that an agent has learned. 424 00:13:30,960 --> 00:13:34,360 Wait, so someone could actually steal an AI agent's brain? 425 00:13:34,360 --> 00:13:35,600 It's possible. 426 00:13:35,600 --> 00:13:39,600 There are techniques like model inversion and model extraction, 427 00:13:39,600 --> 00:13:43,240 where attackers try to reverse engineer the agent's brain 428 00:13:43,240 --> 00:13:44,640 to steal its secrets. 429 00:13:44,640 --> 00:13:46,160 This is getting a little unsettling. 430 00:13:46,160 --> 00:13:48,280 It seems like security is a major concern 431 00:13:48,280 --> 00:13:49,680 in the world of MLAS. 432 00:13:49,680 --> 00:13:50,800 Absolutely. 433 00:13:50,800 --> 00:13:53,560 As these systems handle more sensitive information, 434 00:13:53,560 --> 00:13:55,040 the stakes get higher. 435 00:13:55,040 --> 00:13:56,120 But don't worry. 436 00:13:56,120 --> 00:13:58,160 Researchers are working on defenses. 437 00:13:58,160 --> 00:13:59,960 That's good to hear. 438 00:13:59,960 --> 00:14:02,400 What are some of the ways they're protecting these systems? 439 00:14:02,400 --> 00:14:04,640 One approach is input sanitization, 440 00:14:04,640 --> 00:14:07,360 which is like having a security checkpoint to make sure 441 00:14:07,360 --> 00:14:08,960 no bad instructions get through. 442 00:14:08,960 --> 00:14:10,640 Sounds like a good first line of defense. 443 00:14:10,640 --> 00:14:12,960 They also monitor the agent's behavior 444 00:14:12,960 --> 00:14:14,480 for anything suspicious. 445 00:14:14,480 --> 00:14:17,080 And of course, they're always developing more robust training 446 00:14:17,080 --> 00:14:19,800 methods to make the agents themselves more resilient 447 00:14:19,800 --> 00:14:20,840 to attacks. 448 00:14:20,840 --> 00:14:24,000 So it's a constant battle to stay one step ahead of the attackers. 449 00:14:24,000 --> 00:14:24,400 It is. 450 00:14:24,400 --> 00:14:27,440 It's an ongoing arms race to keep these systems secure. 451 00:14:27,440 --> 00:14:27,720 OK. 452 00:14:27,720 --> 00:14:29,880 So we've talked about the different architectures 453 00:14:29,880 --> 00:14:32,520 for MLAS, the challenges of training them, 454 00:14:32,520 --> 00:14:34,240 and the importance of security. 455 00:14:34,240 --> 00:14:35,080 What's next? 456 00:14:35,080 --> 00:14:39,200 Now it's time to see how MLAS are being used in the real world. 457 00:14:39,200 --> 00:14:40,840 Ready for a case study? 458 00:14:40,840 --> 00:14:43,440 Break it on. 459 00:14:43,440 --> 00:14:44,840 Welcome back, everyone. 460 00:14:44,840 --> 00:14:48,120 We've been on quite a journey exploring multi-LLM agent 461 00:14:48,120 --> 00:14:48,880 systems. 462 00:14:48,880 --> 00:14:51,920 These incredible systems where AI agents team up 463 00:14:51,920 --> 00:14:54,000 to tackle all sorts of tasks. 464 00:14:54,000 --> 00:14:56,080 And now it's time to see how this all plays out 465 00:14:56,080 --> 00:14:57,920 in a real world scenario. 466 00:14:57,920 --> 00:15:00,640 The paper we've been discussing highlights a travel booking 467 00:15:00,640 --> 00:15:01,120 scenario. 468 00:15:01,120 --> 00:15:02,520 Something we can all relate to. 469 00:15:02,520 --> 00:15:04,120 Who doesn't love planning a trip? 470 00:15:04,120 --> 00:15:04,480 OK. 471 00:15:04,480 --> 00:15:07,440 So let's say I want to book a trip to, I don't know, 472 00:15:07,440 --> 00:15:08,360 how about Rome? 473 00:15:08,360 --> 00:15:09,400 Rome it is. 474 00:15:09,400 --> 00:15:12,400 Imagine you're interacting with this central agent 475 00:15:12,400 --> 00:15:14,360 like a super smart travel agent? 476 00:15:14,360 --> 00:15:15,960 My personal AI travel plan. 477 00:15:15,960 --> 00:15:16,440 Exactly. 478 00:15:16,440 --> 00:15:18,480 You tell it your travel dates, your budget, 479 00:15:18,480 --> 00:15:20,080 what you're interested in seeing and doing. 480 00:15:20,080 --> 00:15:22,720 So it's like giving it a rough sketch of my dream Roman 481 00:15:22,720 --> 00:15:23,160 holiday. 482 00:15:23,160 --> 00:15:23,760 You got it. 483 00:15:23,760 --> 00:15:26,480 This central agent, recall it the orchestrator, 484 00:15:26,480 --> 00:15:29,080 then delegates different parts of the task 485 00:15:29,080 --> 00:15:30,520 to specialize agents. 486 00:15:30,520 --> 00:15:31,800 So there's a flight agent. 487 00:15:31,800 --> 00:15:33,240 An accommodation agent. 488 00:15:33,240 --> 00:15:35,600 Maybe even an activities agent suggesting 489 00:15:35,600 --> 00:15:37,120 tours and things to do. 490 00:15:37,120 --> 00:15:39,960 It's like having a whole team of travel experts working 491 00:15:39,960 --> 00:15:40,600 for me. 492 00:15:40,600 --> 00:15:43,000 Each one laser focused on their area. 493 00:15:43,000 --> 00:15:45,360 And these agents don't just work in isolation, remember? 494 00:15:45,360 --> 00:15:47,120 They use those interaction protocols 495 00:15:47,120 --> 00:15:50,760 we talked about to communicate, share information, 496 00:15:50,760 --> 00:15:53,520 make sure everything fits together seamlessly. 497 00:15:53,520 --> 00:15:53,800 Right. 498 00:15:53,800 --> 00:15:56,000 So if the flight agent finds a great deal on a flight that 499 00:15:56,000 --> 00:15:57,160 arrives late at night. 500 00:15:57,160 --> 00:15:58,640 The accommodation agent can make sure 501 00:15:58,640 --> 00:16:01,520 to find a hotel that offers late check-in. 502 00:16:01,520 --> 00:16:05,280 Or maybe the activities agent recommends a cooking class. 503 00:16:05,280 --> 00:16:07,880 And then the accommodation agent suggests a hotel that's 504 00:16:07,880 --> 00:16:08,560 nearby. 505 00:16:08,560 --> 00:16:11,480 It's all about creating a personalized and hassle-free 506 00:16:11,480 --> 00:16:12,440 experience. 507 00:16:12,440 --> 00:16:15,880 But the paper dives into a specific architectural approach 508 00:16:15,880 --> 00:16:18,480 for this travel booking scenario. 509 00:16:18,480 --> 00:16:21,120 A decentralized star architecture. 510 00:16:21,120 --> 00:16:21,600 Right. 511 00:16:21,600 --> 00:16:23,360 Which is perfect for this kind of situation. 512 00:16:23,360 --> 00:16:25,720 OK, remind me why that architecture is so special. 513 00:16:25,720 --> 00:16:28,120 Well, in a decentralized star architecture, 514 00:16:28,120 --> 00:16:30,840 you still have that central orchestrator agent. 515 00:16:30,840 --> 00:16:33,360 But it doesn't directly handle your sensitive data. 516 00:16:33,360 --> 00:16:34,760 Like my passport information. 517 00:16:34,760 --> 00:16:36,440 Your credit card details, all that 518 00:16:36,440 --> 00:16:38,520 stays with the specialized agents. 519 00:16:38,520 --> 00:16:40,960 So the flight agent handles my flight booking. 520 00:16:40,960 --> 00:16:43,640 The accommodation agent manages my hotel reservation, 521 00:16:43,640 --> 00:16:44,200 and so on. 522 00:16:44,200 --> 00:16:44,920 Exactly. 523 00:16:44,920 --> 00:16:47,960 Keeps your data more secure and ensures privacy. 524 00:16:47,960 --> 00:16:49,320 That's a big plus. 525 00:16:49,320 --> 00:16:51,200 But when this make things more complex 526 00:16:51,200 --> 00:16:53,480 in terms of how these agents share information 527 00:16:53,480 --> 00:16:54,840 and coordinate their actions. 528 00:16:54,840 --> 00:16:56,360 It does add a layer of complexity, 529 00:16:56,360 --> 00:16:59,160 but that's where those trustee interaction protocols come in, 530 00:16:59,160 --> 00:17:01,800 making sure the agents can communicate effectively, 531 00:17:01,800 --> 00:17:05,080 reach agreements, all while respecting those data boundaries. 532 00:17:05,080 --> 00:17:07,760 It's incredible how MLIS can balance functionality 533 00:17:07,760 --> 00:17:08,400 and privacy. 534 00:17:08,400 --> 00:17:10,520 It's all about thoughtful design. 535 00:17:10,520 --> 00:17:13,000 Well, this has been an amazing journey into the world 536 00:17:13,000 --> 00:17:15,080 of multi-LLM agent systems. 537 00:17:15,080 --> 00:17:16,360 We've covered a lot of ground. 538 00:17:16,360 --> 00:17:19,160 From the nuts and bolts of agent architecture and interaction 539 00:17:19,160 --> 00:17:22,520 protocols to the real world applications 540 00:17:22,520 --> 00:17:25,840 and those crucial considerations around security and ethics. 541 00:17:25,840 --> 00:17:29,360 It's such a rapidly evolving field with so much potential. 542 00:17:29,360 --> 00:17:32,280 Who knows what the future holds for MLIS? 543 00:17:32,280 --> 00:17:34,240 As this technology keeps advancing, 544 00:17:34,240 --> 00:17:36,800 we can expect even more innovative and groundbreaking 545 00:17:36,800 --> 00:17:37,440 applications. 546 00:17:37,440 --> 00:17:39,000 I can't wait to see it unfold. 547 00:17:39,000 --> 00:17:40,040 Me neither. 548 00:17:40,040 --> 00:17:42,320 But for now, that's a wrap on this deep dive 549 00:17:42,320 --> 00:17:44,920 into the world of multi-LLM agent systems. 550 00:17:44,920 --> 00:17:46,320 Thanks for joining us. 551 00:17:46,320 --> 00:17:53,480 Until next time, keep exploring the amazing world of AI.