1 00:00:00,000 --> 00:00:03,400 Welcome back everyone, ready for another AI deep dive. 2 00:00:03,400 --> 00:00:04,460 Always. 3 00:00:04,460 --> 00:00:07,460 Today, we're gonna get into how two fields 4 00:00:07,460 --> 00:00:09,720 that you might not think go together 5 00:00:09,720 --> 00:00:11,400 are actually starting to intertwine. 6 00:00:11,400 --> 00:00:12,400 Oh, this is interesting. 7 00:00:12,400 --> 00:00:14,760 Yeah, we're talking about argumentation 8 00:00:14,760 --> 00:00:17,000 and machine learning. 9 00:00:17,000 --> 00:00:17,840 Interesting. 10 00:00:17,840 --> 00:00:20,760 So think like, formal logic meets AI algorithms. 11 00:00:20,760 --> 00:00:21,600 Okay. 12 00:00:21,600 --> 00:00:22,640 Kind of a cool combo, right? 13 00:00:22,640 --> 00:00:23,480 Yeah. 14 00:00:25,480 --> 00:00:27,300 Our source material today is a chapter called 15 00:00:27,300 --> 00:00:29,000 Argumentation and Machine Learning. 16 00:00:29,000 --> 00:00:29,840 Okay. 17 00:00:29,840 --> 00:00:32,400 Antonio Rago and his team. 18 00:00:32,400 --> 00:00:33,960 Sounds dense. 19 00:00:33,960 --> 00:00:34,800 It is. 20 00:00:34,800 --> 00:00:36,480 It's a little bit of a dense read. 21 00:00:36,480 --> 00:00:37,680 That's why we're here. 22 00:00:37,680 --> 00:00:38,680 We're gonna give you the highlights 23 00:00:38,680 --> 00:00:39,960 and explain what it all means. 24 00:00:39,960 --> 00:00:42,680 Yeah, it's a really comprehensive review 25 00:00:42,680 --> 00:00:44,360 of the research going on. 26 00:00:44,360 --> 00:00:47,080 And what's cool is that it's not just one field 27 00:00:47,080 --> 00:00:49,000 influencing the other, it's actually both. 28 00:00:49,000 --> 00:00:51,240 Okay, so it's not just AI learning to argue, 29 00:00:51,240 --> 00:00:54,440 but also using argumentation to make AI better. 30 00:00:54,440 --> 00:00:55,520 Exactly. 31 00:00:55,520 --> 00:00:58,360 The chapter breaks it down into two main themes. 32 00:00:58,360 --> 00:01:00,120 Argumentation for machine learning 33 00:01:00,120 --> 00:01:02,920 and machine learning for argumentation. 34 00:01:02,920 --> 00:01:05,920 So on one side, we're using logic and reasoning 35 00:01:05,920 --> 00:01:10,280 to enhance how algorithms learn and make decisions. 36 00:01:10,280 --> 00:01:14,080 And then on the other side, AI is being used to analyze. 37 00:01:14,080 --> 00:01:16,160 And potentially even generate arguments. 38 00:01:16,160 --> 00:01:17,000 You got it. 39 00:01:17,000 --> 00:01:18,240 And they explore different ways 40 00:01:18,240 --> 00:01:20,840 these fields are being integrated. 41 00:01:20,840 --> 00:01:22,560 And they actually categorize them 42 00:01:22,560 --> 00:01:24,640 into three types of interaction. 43 00:01:24,640 --> 00:01:25,480 Three types. 44 00:01:25,480 --> 00:01:26,300 Let's get into it. 45 00:01:26,300 --> 00:01:27,140 What's the first one? 46 00:01:27,140 --> 00:01:28,640 They call synergistic approaches. 47 00:01:28,640 --> 00:01:29,680 Okay. 48 00:01:29,680 --> 00:01:33,280 So imagine combining the strengths of both fields 49 00:01:33,280 --> 00:01:36,120 into like a powerful hybrid system. 50 00:01:36,120 --> 00:01:37,080 So they're intertwined, 51 00:01:37,080 --> 00:01:39,160 almost like creating something totally new. 52 00:01:39,160 --> 00:01:41,880 Okay, so true fusion of logic and learning. 53 00:01:41,880 --> 00:01:43,560 What about the second type? 54 00:01:43,560 --> 00:01:45,840 The second is segmented approaches. 55 00:01:45,840 --> 00:01:47,720 So it's more like a relay race. 56 00:01:47,720 --> 00:01:49,320 One field does its part, 57 00:01:49,320 --> 00:01:51,880 then passes the baton to the other to carry it forward. 58 00:01:51,880 --> 00:01:53,800 So they work in sequence, 59 00:01:53,800 --> 00:01:55,720 each contributing to the overall goal. 60 00:01:55,720 --> 00:01:57,040 And then what's the third one? 61 00:01:57,040 --> 00:01:59,480 The third type is approximated approaches. 62 00:01:59,480 --> 00:02:00,320 Okay. 63 00:02:00,320 --> 00:02:03,640 So here one field tries to mimic or simplify the other. 64 00:02:03,640 --> 00:02:06,660 You can think about using argumentation 65 00:02:06,660 --> 00:02:10,200 to explain a complex machine learning models decision. 66 00:02:10,200 --> 00:02:12,200 Like creating a simpler representation 67 00:02:12,200 --> 00:02:13,400 of what's happening under the hood. 68 00:02:13,400 --> 00:02:15,360 So argumentation acts as a translator. 69 00:02:15,360 --> 00:02:16,200 Exactly. 70 00:02:16,200 --> 00:02:18,200 Making the black box of AI a little more transparent. 71 00:02:18,200 --> 00:02:19,040 Right. 72 00:02:19,040 --> 00:02:19,880 Okay, cool. 73 00:02:19,880 --> 00:02:21,420 This is already making a lot more sense to me 74 00:02:21,420 --> 00:02:22,960 in terms of how these two fields 75 00:02:22,960 --> 00:02:24,480 are kind of working together. 76 00:02:24,480 --> 00:02:26,320 But now let's get specific. 77 00:02:26,320 --> 00:02:31,320 How can argumentation actually improve machine learning? 78 00:02:32,000 --> 00:02:35,400 What are some of the key findings from the chapter? 79 00:02:35,400 --> 00:02:36,800 So one area they highlight 80 00:02:36,800 --> 00:02:40,660 is how structured argumentation or essay 81 00:02:40,660 --> 00:02:43,400 can actually improve machine learning classification. 82 00:02:43,400 --> 00:02:44,240 Okay. 83 00:02:44,240 --> 00:02:45,640 So imagine you're training an AI 84 00:02:45,640 --> 00:02:47,840 to identify different types of flowers. 85 00:02:47,840 --> 00:02:48,680 Okay. 86 00:02:48,680 --> 00:02:49,520 Right. 87 00:02:49,520 --> 00:02:50,920 Instead of just feeding it a ton of pictures, 88 00:02:50,920 --> 00:02:54,440 you can use essay to incorporate rules 89 00:02:54,440 --> 00:02:56,280 about flower characteristics. 90 00:02:56,280 --> 00:02:57,120 Okay. 91 00:02:57,120 --> 00:02:59,880 Like petal shape or color, directly into the model. 92 00:02:59,880 --> 00:03:00,720 Oh, interesting. 93 00:03:00,720 --> 00:03:04,160 So this gives the AI a more structured way to learn 94 00:03:04,160 --> 00:03:06,200 and often leads to better accuracy. 95 00:03:06,200 --> 00:03:08,320 It's like giving it a botanical cheat sheet. 96 00:03:08,320 --> 00:03:11,080 So it's not just about throwing data at the problem. 97 00:03:11,080 --> 00:03:13,480 It's also about providing that logical framework. 98 00:03:13,480 --> 00:03:14,320 Right. 99 00:03:14,320 --> 00:03:15,160 To make sense of it. 100 00:03:15,160 --> 00:03:16,000 Precisely. 101 00:03:16,000 --> 00:03:16,840 Oh, that's really cool. 102 00:03:16,840 --> 00:03:17,680 Right. 103 00:03:17,680 --> 00:03:18,880 And so is this just limited to classification 104 00:03:18,880 --> 00:03:19,720 or are there? 105 00:03:19,720 --> 00:03:20,560 No, not at all. 106 00:03:20,560 --> 00:03:22,800 Other ways that this can work. 107 00:03:22,800 --> 00:03:24,840 So the chapter also discusses 108 00:03:24,840 --> 00:03:28,160 how argumentation can enhance reinforcement learning, 109 00:03:28,160 --> 00:03:30,560 which is a type of learning where AI agents 110 00:03:30,560 --> 00:03:32,760 learn through trial and error. 111 00:03:32,760 --> 00:03:33,600 Okay. 112 00:03:33,600 --> 00:03:35,520 So for example, in a game of chess, right? 113 00:03:35,520 --> 00:03:36,360 Yeah. 114 00:03:36,360 --> 00:03:38,240 An AI agent can use argumentation 115 00:03:38,240 --> 00:03:41,120 to weigh the pros and cons of different moves 116 00:03:41,120 --> 00:03:43,640 leading to more strategic gameplay. 117 00:03:43,640 --> 00:03:47,280 So it's like it's thinking several steps ahead, 118 00:03:47,280 --> 00:03:49,680 debating with itself about the best course of action. 119 00:03:49,680 --> 00:03:50,520 Exactly. 120 00:03:50,520 --> 00:03:51,360 That's so cool. 121 00:03:51,360 --> 00:03:53,680 And another huge benefit they highlight 122 00:03:53,680 --> 00:03:57,200 is the ability to make AI more explainable. 123 00:03:57,200 --> 00:03:58,040 Oh, interesting. 124 00:03:58,040 --> 00:03:58,880 Okay. 125 00:03:58,880 --> 00:04:00,240 So traditionally, machine learning models 126 00:04:00,240 --> 00:04:02,080 have been these black boxes. 127 00:04:02,080 --> 00:04:04,400 You feed in data, you get an output. 128 00:04:04,400 --> 00:04:05,240 Right. 129 00:04:05,240 --> 00:04:07,240 But you don't really know why the AI 130 00:04:07,240 --> 00:04:08,640 made a particular decision. 131 00:04:08,640 --> 00:04:09,480 Yeah, yeah. 132 00:04:09,480 --> 00:04:12,880 So by incorporating argumentation, 133 00:04:12,880 --> 00:04:15,320 we can provide a line of reasoning, 134 00:04:15,320 --> 00:04:18,560 like a trail of breadcrumbs explaining how the AI 135 00:04:18,560 --> 00:04:19,960 arrived at its conclusion. 136 00:04:19,960 --> 00:04:21,280 That makes a lot of sense. 137 00:04:21,280 --> 00:04:23,320 Especially as AI becomes more prevalent 138 00:04:23,320 --> 00:04:26,200 in areas like healthcare or finance. 139 00:04:26,200 --> 00:04:27,040 Right. 140 00:04:27,040 --> 00:04:28,720 Being able to understand its reasoning 141 00:04:28,720 --> 00:04:31,160 is crucial for building trust 142 00:04:31,160 --> 00:04:33,240 and making sure that it's being used responsibly. 143 00:04:33,240 --> 00:04:34,080 Absolutely. 144 00:04:34,080 --> 00:04:36,320 The authors even discuss how this transparency 145 00:04:36,320 --> 00:04:38,840 can lead to more robust AI systems 146 00:04:38,840 --> 00:04:42,120 as argumentation can help identify potential biases 147 00:04:42,120 --> 00:04:44,600 or flaws in the model's logic. 148 00:04:44,600 --> 00:04:45,800 That's really fascinating. 149 00:04:45,800 --> 00:04:46,880 But I have to ask, 150 00:04:46,880 --> 00:04:48,800 what kind of argumentation are we talking about here? 151 00:04:48,800 --> 00:04:51,520 Is it like those formal debates that you see, 152 00:04:51,520 --> 00:04:52,920 you know, in schools? 153 00:04:52,920 --> 00:04:54,920 Or is it something else entirely? 154 00:04:54,920 --> 00:04:57,360 The chapter actually covers a range 155 00:04:57,360 --> 00:04:58,920 of argumentation frameworks, 156 00:04:58,920 --> 00:05:02,000 from the very abstract to the highly structured. 157 00:05:02,000 --> 00:05:02,840 Okay. 158 00:05:02,840 --> 00:05:06,520 So some frameworks like abstract argumentation frameworks, 159 00:05:06,520 --> 00:05:08,960 focus on the relationships between arguments 160 00:05:08,960 --> 00:05:10,320 without getting into the specifics 161 00:05:10,320 --> 00:05:11,520 of the arguments themselves. 162 00:05:11,520 --> 00:05:14,720 Others like value-based argumentation frameworks 163 00:05:14,720 --> 00:05:17,800 incorporate notions of preferences and goals. 164 00:05:17,800 --> 00:05:19,720 So depending on the application 165 00:05:19,720 --> 00:05:22,160 and the type of problem you're trying to solve, 166 00:05:22,160 --> 00:05:23,960 you can choose the framework that best fits. 167 00:05:23,960 --> 00:05:24,800 Exactly. 168 00:05:24,800 --> 00:05:25,640 Okay. 169 00:05:25,640 --> 00:05:28,040 And one framework that seems particularly promising 170 00:05:28,040 --> 00:05:31,240 for integrating with neural networks, 171 00:05:31,240 --> 00:05:34,640 is something called a quantitative bipolar argumentation 172 00:05:34,640 --> 00:05:35,480 framework. 173 00:05:35,480 --> 00:05:36,320 Oh, a mouthful. 174 00:05:36,320 --> 00:05:38,200 Or QBAF for short. 175 00:05:38,200 --> 00:05:39,240 QBAF, okay. 176 00:05:39,240 --> 00:05:40,080 Yeah. 177 00:05:40,080 --> 00:05:40,920 What makes that one so special? 178 00:05:40,920 --> 00:05:42,440 Well, QBAFs are interesting 179 00:05:42,440 --> 00:05:44,760 because they introduce numerical values 180 00:05:44,760 --> 00:05:46,480 into the argumentation process. 181 00:05:46,480 --> 00:05:47,320 Okay. 182 00:05:47,320 --> 00:05:48,960 So instead of just saying whether an argument 183 00:05:48,960 --> 00:05:50,280 is accepted or not, 184 00:05:50,280 --> 00:05:52,720 you can assign a strength or weight to it. 185 00:05:52,720 --> 00:05:53,560 Oh, interesting. 186 00:05:53,560 --> 00:05:56,200 This makes it compatible with neural networks, 187 00:05:56,200 --> 00:05:58,240 which also deal with numerical data. 188 00:05:58,240 --> 00:06:00,200 So it's like bridging the gap 189 00:06:00,200 --> 00:06:02,200 between the symbolic world of logic. 190 00:06:02,200 --> 00:06:03,040 Right. 191 00:06:03,040 --> 00:06:04,680 And the numerical world of machine learning. 192 00:06:04,680 --> 00:06:05,680 Precisely. 193 00:06:05,680 --> 00:06:08,600 And this opens up some really exciting possibilities. 194 00:06:08,600 --> 00:06:09,440 Okay. 195 00:06:09,440 --> 00:06:11,120 The authors even suggest that QBAFs 196 00:06:11,120 --> 00:06:14,000 could provide a new architecture for neural networks. 197 00:06:14,000 --> 00:06:14,840 Wow. 198 00:06:14,840 --> 00:06:19,520 Potentially leading to AI that is more explainable 199 00:06:19,520 --> 00:06:21,320 and capable of reasoning in a way 200 00:06:21,320 --> 00:06:23,600 that's closer to how humans think. 201 00:06:23,600 --> 00:06:25,480 That's pretty mind blowing. 202 00:06:25,480 --> 00:06:26,320 Yeah. 203 00:06:26,320 --> 00:06:27,320 So we've talked about how argumentation 204 00:06:27,320 --> 00:06:29,000 can boost machine learning. 205 00:06:29,000 --> 00:06:30,560 But let's kind of flip the script here. 206 00:06:30,560 --> 00:06:31,400 Yeah. 207 00:06:31,400 --> 00:06:32,840 How is machine learning being used 208 00:06:32,840 --> 00:06:35,800 to enhance and analyze argumentation itself? 209 00:06:35,800 --> 00:06:37,600 So one area that's really taking off 210 00:06:37,600 --> 00:06:40,040 is using machine learning to build 211 00:06:40,040 --> 00:06:42,440 and analyze argumentation frameworks. 212 00:06:42,440 --> 00:06:43,280 Oh. 213 00:06:43,280 --> 00:06:44,880 So instead of relying on humans to like 214 00:06:44,880 --> 00:06:46,920 painstakingly define all the arguments 215 00:06:46,920 --> 00:06:48,680 and relationships in a complex debate. 216 00:06:48,680 --> 00:06:49,500 Right. 217 00:06:49,500 --> 00:06:52,160 So we now train AI to do some of the heavy lifting. 218 00:06:52,160 --> 00:06:54,240 So it's like having an AI research assistant. 219 00:06:54,240 --> 00:06:55,080 Exactly. 220 00:06:55,080 --> 00:06:56,400 That can help you understand the structure 221 00:06:56,400 --> 00:06:58,960 of an argument, identify key points, 222 00:06:58,960 --> 00:07:01,840 and maybe even spot inconsistencies or fallacies. 223 00:07:01,840 --> 00:07:02,920 You got it. 224 00:07:02,920 --> 00:07:06,840 And this is where those graph neural networks, or GNNs, 225 00:07:06,840 --> 00:07:07,800 come in handy again. 226 00:07:07,800 --> 00:07:08,640 Okay. 227 00:07:08,640 --> 00:07:10,440 Remember how we talked about them analyzing relationships? 228 00:07:10,440 --> 00:07:11,280 Yeah. 229 00:07:11,280 --> 00:07:12,680 Well GNNs are perfect for working 230 00:07:12,680 --> 00:07:14,320 with argumentation frameworks, 231 00:07:14,320 --> 00:07:15,720 which are essentially graphs, 232 00:07:15,720 --> 00:07:18,200 representing how arguments are connected. 233 00:07:18,200 --> 00:07:22,720 So we're using AI to analyze the very fabric of arguments. 234 00:07:22,720 --> 00:07:23,840 That's pretty meta. 235 00:07:23,840 --> 00:07:24,680 It is. 236 00:07:24,680 --> 00:07:26,600 And it's not just about analysis. 237 00:07:26,600 --> 00:07:28,920 Some researchers are even using machine learning 238 00:07:28,920 --> 00:07:31,040 to generate new arguments. 239 00:07:31,040 --> 00:07:34,760 Essentially training AI to become persuasive debaters. 240 00:07:34,760 --> 00:07:35,600 Wait. 241 00:07:35,600 --> 00:07:39,040 So AI could potentially write a winning argument 242 00:07:39,040 --> 00:07:41,120 for like a debate competition. 243 00:07:41,120 --> 00:07:41,960 Potentially. 244 00:07:41,960 --> 00:07:42,780 Yeah. 245 00:07:42,780 --> 00:07:45,760 I'm both impressed and slightly terrified. 246 00:07:45,760 --> 00:07:46,600 I understand that. 247 00:07:46,600 --> 00:07:48,600 This is all incredibly exciting. 248 00:07:48,600 --> 00:07:51,080 But I'm curious, are there any limitations 249 00:07:51,080 --> 00:07:53,800 or challenges to this whole argumentation 250 00:07:53,800 --> 00:07:55,240 and machine learning merger? 251 00:07:55,240 --> 00:07:56,080 Of course. 252 00:07:56,080 --> 00:07:57,840 Every relationship has its ups and downs. 253 00:07:57,840 --> 00:07:58,800 Right. 254 00:07:58,800 --> 00:08:01,440 One key challenge they highlight is scalability. 255 00:08:01,440 --> 00:08:02,280 Okay. 256 00:08:02,280 --> 00:08:04,280 Can these techniques handle the massive data sets 257 00:08:04,280 --> 00:08:06,560 and complex problems we face in the real world? 258 00:08:06,560 --> 00:08:07,400 Right. 259 00:08:07,400 --> 00:08:09,160 That's something researchers are still working on. 260 00:08:09,160 --> 00:08:10,520 So it's one thing to have a system 261 00:08:10,520 --> 00:08:13,040 that works well in a controlled environment. 262 00:08:13,040 --> 00:08:13,880 Right. 263 00:08:13,880 --> 00:08:15,760 But quite another to apply it to something 264 00:08:15,760 --> 00:08:19,880 like analyzing political discourse on social media. 265 00:08:19,880 --> 00:08:20,760 Exactly. 266 00:08:20,760 --> 00:08:22,920 Another challenge is that argumentation itself 267 00:08:22,920 --> 00:08:25,280 can be messy and subjective. 268 00:08:25,280 --> 00:08:27,000 Human arguments are often nuanced, 269 00:08:27,000 --> 00:08:30,280 full of implicit assumptions and contextual information 270 00:08:30,280 --> 00:08:32,760 that can be difficult for machines to grasp. 271 00:08:32,760 --> 00:08:35,280 So it's not just about teaching AI the rules 272 00:08:35,280 --> 00:08:38,120 of formal logic, but also about helping it grasp 273 00:08:38,120 --> 00:08:41,040 the nuances of how humans actually argue. 274 00:08:41,040 --> 00:08:41,880 Right. 275 00:08:41,880 --> 00:08:42,720 In real world situations. 276 00:08:42,720 --> 00:08:43,880 Exactly. 277 00:08:43,880 --> 00:08:46,040 Another challenge is developing new ways 278 00:08:46,040 --> 00:08:49,520 to evaluate argumentation-based AI systems. 279 00:08:49,520 --> 00:08:50,360 Okay. 280 00:08:50,360 --> 00:08:51,880 Traditional metrics like accuracy 281 00:08:51,880 --> 00:08:54,040 don't always capture the full picture. 282 00:08:54,040 --> 00:08:54,880 Right. 283 00:08:54,880 --> 00:08:57,960 How do you measure the quality of an argument? 284 00:08:57,960 --> 00:09:00,080 How do you assess whether an AI agent 285 00:09:00,080 --> 00:09:04,080 is truly persuasive or just good at manipulating language? 286 00:09:04,080 --> 00:09:05,880 It seems like we need a whole new set of tools 287 00:09:05,880 --> 00:09:09,680 to assess AI's ability to argue effectively and ethically. 288 00:09:09,680 --> 00:09:10,520 Yeah. 289 00:09:10,520 --> 00:09:11,600 Are there any other limitations mentioned 290 00:09:11,600 --> 00:09:14,240 in the chapter that we should be aware of? 291 00:09:14,240 --> 00:09:17,160 They highlight the lack of user studies in this area. 292 00:09:17,160 --> 00:09:18,000 All right. 293 00:09:18,000 --> 00:09:19,720 While there's a lot of theoretical work being done, 294 00:09:19,720 --> 00:09:22,440 we need more research on how people actually interact 295 00:09:22,440 --> 00:09:25,080 with these argumentation-based AI systems. 296 00:09:25,080 --> 00:09:25,920 Right. 297 00:09:25,920 --> 00:09:26,840 Do people find them helpful? 298 00:09:26,840 --> 00:09:28,800 Do they trust their recommendations? 299 00:09:28,800 --> 00:09:32,160 Do they understand the AI's reasoning process? 300 00:09:32,160 --> 00:09:34,000 Yeah, these are crucial questions. 301 00:09:34,000 --> 00:09:37,920 It's one thing to have an AI that can argue like a lawyer. 302 00:09:37,920 --> 00:09:38,760 Yeah. 303 00:09:38,760 --> 00:09:40,640 But it's another thing entirely to ensure 304 00:09:40,640 --> 00:09:43,520 that humans can understand and trust its arguments. 305 00:09:43,520 --> 00:09:44,600 Absolutely. 306 00:09:44,600 --> 00:09:47,960 And this is where interdisciplinary collaboration 307 00:09:47,960 --> 00:09:49,640 becomes even more important. 308 00:09:49,640 --> 00:09:49,980 There. 309 00:09:49,980 --> 00:09:53,040 We need computer scientists working alongside psychologists, 310 00:09:53,040 --> 00:09:56,640 linguists, and philosophers to ensure that these systems are 311 00:09:56,640 --> 00:10:00,480 not only technically sound, but also aligned with human values 312 00:10:00,480 --> 00:10:01,840 and communication norms. 313 00:10:01,840 --> 00:10:04,320 It seems like this research is not only pushing 314 00:10:04,320 --> 00:10:07,280 the boundaries of AI, but also blurring the lines 315 00:10:07,280 --> 00:10:09,000 between different fields of study. 316 00:10:09,000 --> 00:10:11,200 Yeah, and that's what makes it so exciting. 317 00:10:11,200 --> 00:10:13,640 The intersection of argumentation and machine learning 318 00:10:13,640 --> 00:10:16,080 is a melting pot of ideas and approaches. 319 00:10:16,080 --> 00:10:16,680 Right. 320 00:10:16,680 --> 00:10:19,360 And it's leading to some truly groundbreaking research 321 00:10:19,360 --> 00:10:22,920 with the potential to transform how we interact with AI. 322 00:10:22,920 --> 00:10:24,560 Speaking of groundbreaking research, 323 00:10:24,560 --> 00:10:27,440 the chapter mentions a specific technique that's 324 00:10:27,440 --> 00:10:30,920 being used to train AI agents to argue more effectively. 325 00:10:30,920 --> 00:10:31,960 Yeah. 326 00:10:31,960 --> 00:10:34,640 They discuss using reinforcement learning 327 00:10:34,640 --> 00:10:38,000 to train AI agents to become better arguers. 328 00:10:38,000 --> 00:10:40,880 So reinforcement learning is a powerful technique 329 00:10:40,880 --> 00:10:43,880 where agents learn through trial and error, 330 00:10:43,880 --> 00:10:46,160 receiving rewards for good actions, 331 00:10:46,160 --> 00:10:48,080 and penalties for bad ones. 332 00:10:48,080 --> 00:10:48,800 OK. 333 00:10:48,800 --> 00:10:50,720 So in the context of argumentation, 334 00:10:50,720 --> 00:10:54,280 this means training AI agents to generate arguments that 335 00:10:54,280 --> 00:10:58,520 are more likely to be persuasive and to refine their strategies 336 00:10:58,520 --> 00:11:00,080 based on feedback. 337 00:11:00,080 --> 00:11:01,960 So it's like having an AI debate coach. 338 00:11:01,960 --> 00:11:02,960 Exactly. 339 00:11:02,960 --> 00:11:06,040 That helps the agent learn from its mistakes 340 00:11:06,040 --> 00:11:07,680 and become a more formidable opponent. 341 00:11:07,680 --> 00:11:08,720 Yes. 342 00:11:08,720 --> 00:11:11,440 And this approach has a lot of potential in areas 343 00:11:11,440 --> 00:11:14,280 like negotiation, where AI agents could 344 00:11:14,280 --> 00:11:16,800 be used to resolve conflicts and reach agreements 345 00:11:16,800 --> 00:11:18,320 in complex situations. 346 00:11:18,320 --> 00:11:21,360 I could imagine AI negotiators being used in everything 347 00:11:21,360 --> 00:11:24,840 from business deals to international diplomacy. 348 00:11:24,840 --> 00:11:25,400 Right. 349 00:11:25,400 --> 00:11:27,880 But doesn't that raise some ethical concerns? 350 00:11:27,880 --> 00:11:28,320 Right. 351 00:11:28,320 --> 00:11:30,000 What's to prevent these AI agents 352 00:11:30,000 --> 00:11:31,880 from becoming master manipulators, 353 00:11:31,880 --> 00:11:34,960 using their persuasive powers for unethical ends? 354 00:11:34,960 --> 00:11:37,080 That's a valid concern. 355 00:11:37,080 --> 00:11:38,880 And it highlights the importance of building 356 00:11:38,880 --> 00:11:42,120 ethical considerations into these systems from the ground up. 357 00:11:42,120 --> 00:11:42,640 Right. 358 00:11:42,640 --> 00:11:44,880 So researchers are already exploring ways 359 00:11:44,880 --> 00:11:47,600 to ensure that AI agents adhere to certain principles 360 00:11:47,600 --> 00:11:50,720 of fairness, transparency, and accountability 361 00:11:50,720 --> 00:11:52,560 in their argumentation strategies. 362 00:11:52,560 --> 00:11:55,640 So it's about making sure these AI agents argue, 363 00:11:55,640 --> 00:11:58,800 not just effectively, but also ethically. 364 00:11:58,800 --> 00:11:59,960 Precisely. 365 00:11:59,960 --> 00:12:02,120 And that requires a deep understanding 366 00:12:02,120 --> 00:12:04,520 of both the technical aspects of AI 367 00:12:04,520 --> 00:12:07,160 and the philosophical underpinnings of argumentation 368 00:12:07,160 --> 00:12:08,480 and ethical reasoning. 369 00:12:08,480 --> 00:12:10,600 This is all incredibly fascinating. 370 00:12:10,600 --> 00:12:12,200 And I feel like we've just scratched 371 00:12:12,200 --> 00:12:16,280 the surface of this complex and rapidly evolving field. 372 00:12:16,280 --> 00:12:16,560 Yeah. 373 00:12:16,560 --> 00:12:17,760 We've talked about the different ways 374 00:12:17,760 --> 00:12:19,640 that argumentation and machine learning 375 00:12:19,640 --> 00:12:20,760 are being integrated. 376 00:12:20,760 --> 00:12:21,080 Right. 377 00:12:21,080 --> 00:12:23,240 The potential benefits and challenges, 378 00:12:23,240 --> 00:12:27,600 the need for user studies, and ethical considerations. 379 00:12:27,600 --> 00:12:29,120 Let's pause here for a moment and recap 380 00:12:29,120 --> 00:12:32,600 what we've learned so far before we dive into some final thoughts. 381 00:12:32,600 --> 00:12:35,120 So we've been unpacking this idea of how argumentation 382 00:12:35,120 --> 00:12:36,880 and machine learning can work together. 383 00:12:36,880 --> 00:12:39,800 And it's in some pretty surprising ways. 384 00:12:39,800 --> 00:12:43,920 Like we've talked about making AI more transparent, 385 00:12:43,920 --> 00:12:47,720 potentially giving it this more human-like reasoning process. 386 00:12:47,720 --> 00:12:48,400 Right. 387 00:12:48,400 --> 00:12:50,480 It's really impressive how researchers 388 00:12:50,480 --> 00:12:52,800 are kind of blending these two worlds. 389 00:12:52,800 --> 00:12:54,280 What's particularly interesting is 390 00:12:54,280 --> 00:12:58,600 how these techniques are being applied to real world scenarios. 391 00:12:58,600 --> 00:13:03,400 The chapter touches on examples from medical diagnosis, 392 00:13:03,400 --> 00:13:07,000 where argumentation helps make AI's decisions more 393 00:13:07,000 --> 00:13:11,960 understandable to doctors, to robotics, 394 00:13:11,960 --> 00:13:14,680 where robots can debate the best course of action 395 00:13:14,680 --> 00:13:16,520 in complex environments. 396 00:13:16,520 --> 00:13:19,680 So it's like giving these AI systems the ability 397 00:13:19,680 --> 00:13:24,480 to kind of explain their thought process, which is crucial 398 00:13:24,480 --> 00:13:27,520 in areas where trust and accountability are paramount. 399 00:13:27,520 --> 00:13:28,320 Absolutely. 400 00:13:28,320 --> 00:13:30,280 But I want to kind of circle back to this idea 401 00:13:30,280 --> 00:13:33,200 of using machine learning for our argumentation. 402 00:13:33,200 --> 00:13:36,560 You mentioned AI helping us build and analyze arguments. 403 00:13:36,560 --> 00:13:38,640 Can you give us some more concrete examples 404 00:13:38,640 --> 00:13:39,880 of how that's playing out? 405 00:13:39,880 --> 00:13:41,240 Absolutely. 406 00:13:41,240 --> 00:13:43,640 One area that's gaining a lot of traction 407 00:13:43,640 --> 00:13:46,160 is using machine learning to actually predict 408 00:13:46,160 --> 00:13:49,920 the outcome of debates or arguments. 409 00:13:49,920 --> 00:13:51,000 Oh, interesting. 410 00:13:51,000 --> 00:13:53,480 So imagine you have two sides presenting their arguments 411 00:13:53,480 --> 00:13:56,040 on a complex issue, like climate change. 412 00:13:56,040 --> 00:13:56,720 OK. 413 00:13:56,720 --> 00:13:57,320 Right. 414 00:13:57,320 --> 00:14:01,440 A machine learning model trained on vast amounts of data 415 00:14:01,440 --> 00:14:05,760 on how arguments are structured and how people react to them 416 00:14:05,760 --> 00:14:08,320 could potentially analyze these arguments 417 00:14:08,320 --> 00:14:11,800 and predict which side is more likely to be persuasive. 418 00:14:11,800 --> 00:14:13,600 So it's like having an AI debate judge. 419 00:14:13,600 --> 00:14:14,120 Right. 420 00:14:14,120 --> 00:14:16,360 That can assess the strength of arguments 421 00:14:16,360 --> 00:14:18,760 and kind of predict who's going to win the crowd. 422 00:14:18,760 --> 00:14:19,840 That's a great analogy. 423 00:14:19,840 --> 00:14:23,600 And this isn't just about predicting winners and debates. 424 00:14:23,600 --> 00:14:27,880 This technology could be applied to things like legal cases. 425 00:14:27,880 --> 00:14:28,400 Oh, wow. 426 00:14:28,400 --> 00:14:31,320 Where an AI could analyze the arguments presented 427 00:14:31,320 --> 00:14:34,600 by both sides and predict the likely outcome of the trial. 428 00:14:34,600 --> 00:14:36,600 That sounds like it could have huge implications 429 00:14:36,600 --> 00:14:38,600 for the legal field. 430 00:14:38,600 --> 00:14:42,160 What about other areas where this could be useful? 431 00:14:42,160 --> 00:14:43,600 Think about policymaking. 432 00:14:43,600 --> 00:14:44,440 OK. 433 00:14:44,440 --> 00:14:51,200 Imagine a government using AI to analyze public arguments 434 00:14:51,200 --> 00:14:53,280 for and against a new law. 435 00:14:53,280 --> 00:14:53,800 OK. 436 00:14:53,800 --> 00:14:56,680 This could help them understand the different perspectives 437 00:14:56,680 --> 00:14:58,840 and potentially craft better policies. 438 00:14:58,840 --> 00:14:59,400 Oh, interesting. 439 00:14:59,400 --> 00:15:02,000 That address the concerns of different groups. 440 00:15:02,000 --> 00:15:05,600 So it's like using AI to facilitate a more informed 441 00:15:05,600 --> 00:15:07,600 and nuanced public debate. 442 00:15:07,600 --> 00:15:08,160 Exactly. 443 00:15:08,160 --> 00:15:11,080 But with all these potential benefits, 444 00:15:11,080 --> 00:15:12,800 there must be some challenges as well. 445 00:15:12,800 --> 00:15:13,440 Of course. 446 00:15:13,440 --> 00:15:16,400 You mentioned scalability earlier. 447 00:15:16,400 --> 00:15:19,720 What are some other hurdles that researchers 448 00:15:19,720 --> 00:15:21,160 are facing in this field? 449 00:15:21,160 --> 00:15:23,600 One of the biggest challenges is capturing 450 00:15:23,600 --> 00:15:26,600 the complexity of human argumentation. 451 00:15:26,600 --> 00:15:27,240 OK. 452 00:15:27,240 --> 00:15:30,160 Arguments aren't always straightforward and logical. 453 00:15:30,160 --> 00:15:33,000 They can be filled with emotions, biases, 454 00:15:33,000 --> 00:15:35,080 and cultural context. 455 00:15:35,080 --> 00:15:37,680 That can be difficult for AI to understand. 456 00:15:37,680 --> 00:15:41,400 So it's not just about teaching AI the rules of formal logic. 457 00:15:41,400 --> 00:15:47,360 It's also about helping it grasp those nuances of how humans 458 00:15:47,360 --> 00:15:49,360 actually argue in real world situations. 459 00:15:49,360 --> 00:15:50,560 Exactly. 460 00:15:50,560 --> 00:15:52,720 Another challenge is developing new ways 461 00:15:52,720 --> 00:15:57,080 to evaluate argumentation-based AI systems. 462 00:15:57,080 --> 00:15:57,600 OK. 463 00:15:57,600 --> 00:16:00,120 So traditional metrics like accuracy 464 00:16:00,120 --> 00:16:01,960 don't always capture the full picture. 465 00:16:01,960 --> 00:16:02,280 Right. 466 00:16:02,280 --> 00:16:04,920 How do you measure the quality of an argument? 467 00:16:04,920 --> 00:16:05,240 Yeah. 468 00:16:05,240 --> 00:16:09,080 How do you assess whether an AI agent is truly persuasive 469 00:16:09,080 --> 00:16:11,240 or just good at manipulating language? 470 00:16:11,240 --> 00:16:13,440 Yeah, it seems like we need a whole new set of tools 471 00:16:13,440 --> 00:16:17,200 to assess AI's ability to argue effectively and ethically. 472 00:16:17,200 --> 00:16:18,160 Yeah. 473 00:16:18,160 --> 00:16:21,280 Are there any other limitations mentioned in the chapter 474 00:16:21,280 --> 00:16:22,640 that we should be aware of? 475 00:16:22,640 --> 00:16:26,680 They highlight the lack of user studies in this area. 476 00:16:26,680 --> 00:16:28,800 So while there's a lot of theoretical work being done, 477 00:16:28,800 --> 00:16:30,560 we need more research on how people actually 478 00:16:30,560 --> 00:16:33,320 interact with these argumentation-based AI systems. 479 00:16:33,320 --> 00:16:33,720 OK. 480 00:16:33,720 --> 00:16:35,920 So do people find them helpful? 481 00:16:35,920 --> 00:16:37,760 Do they trust their recommendations? 482 00:16:37,760 --> 00:16:40,400 Do they understand the AI's reasoning process? 483 00:16:40,400 --> 00:16:40,640 Right. 484 00:16:40,640 --> 00:16:42,680 Yeah, those are crucial questions. 485 00:16:42,680 --> 00:16:43,200 Yeah. 486 00:16:43,200 --> 00:16:46,680 It's one thing to have an AI that can argue like a lawyer. 487 00:16:46,680 --> 00:16:47,280 Right. 488 00:16:47,280 --> 00:16:49,840 But it's another thing entirely to make sure 489 00:16:49,840 --> 00:16:54,160 that humans can understand and trust its arguments. 490 00:16:54,160 --> 00:16:54,880 Absolutely. 491 00:16:54,880 --> 00:16:57,080 And this is where interdisciplinary collaboration 492 00:16:57,080 --> 00:16:58,320 becomes even more important. 493 00:16:58,320 --> 00:16:58,800 OK. 494 00:16:58,800 --> 00:17:00,920 We need computer scientists working 495 00:17:00,920 --> 00:17:04,280 alongside psychologists, linguists, and philosophers. 496 00:17:04,280 --> 00:17:04,960 Wow. 497 00:17:04,960 --> 00:17:06,040 That's a lot of brain power. 498 00:17:06,040 --> 00:17:07,320 Yeah, it is. 499 00:17:07,320 --> 00:17:10,240 To ensure that these systems are not only technically sound, 500 00:17:10,240 --> 00:17:14,760 but also aligned with human values and communication norms. 501 00:17:14,760 --> 00:17:16,840 It seems like this research is not only 502 00:17:16,840 --> 00:17:20,520 pushing the boundaries of AI, but also, again, 503 00:17:20,520 --> 00:17:23,480 blurring the lines between all these different fields of study. 504 00:17:23,480 --> 00:17:23,920 Right. 505 00:17:23,920 --> 00:17:25,480 And that's what makes it so exciting. 506 00:17:25,480 --> 00:17:25,960 Yeah. 507 00:17:25,960 --> 00:17:28,600 The intersection of argumentation and machine learning 508 00:17:28,600 --> 00:17:31,560 is a melting pot of ideas and approaches. 509 00:17:31,560 --> 00:17:32,000 Right. 510 00:17:32,000 --> 00:17:34,640 And it's leading to some truly groundbreaking research 511 00:17:34,640 --> 00:17:38,960 with the potential to transform how we interact with AI. 512 00:17:38,960 --> 00:17:41,000 Speaking of groundbreaking research, 513 00:17:41,000 --> 00:17:43,920 the chapter mentions a specific technique that's 514 00:17:43,920 --> 00:17:48,280 being used to train AI agents to argue more effectively. 515 00:17:48,280 --> 00:17:49,280 Yes. 516 00:17:49,280 --> 00:17:51,200 They discuss using reinforcement learning 517 00:17:51,200 --> 00:17:54,440 to train AI agents to become better arguers. 518 00:17:54,440 --> 00:17:55,320 OK. 519 00:17:55,320 --> 00:17:57,880 So reinforcement learning is this powerful technique 520 00:17:57,880 --> 00:18:00,920 where agents learn through trial and error, 521 00:18:00,920 --> 00:18:05,640 receiving rewards for good actions and penalties for bad ones. 522 00:18:05,640 --> 00:18:06,000 OK. 523 00:18:06,000 --> 00:18:08,440 In the context of argumentation, this 524 00:18:08,440 --> 00:18:11,720 means training AI agents to generate arguments that 525 00:18:11,720 --> 00:18:13,440 are more likely to be persuasive. 526 00:18:13,440 --> 00:18:14,160 OK. 527 00:18:14,160 --> 00:18:17,120 And to refine their strategies based on feedback. 528 00:18:17,120 --> 00:18:19,040 So it's like having an AI debate coach that's 529 00:18:19,040 --> 00:18:22,160 helping the agent learn from its mistakes 530 00:18:22,160 --> 00:18:22,640 Exactly. 531 00:18:22,640 --> 00:18:24,520 to become a more formidable opponent. 532 00:18:24,520 --> 00:18:24,920 Yeah. 533 00:18:24,920 --> 00:18:27,040 And this approach has a lot of potential in areas 534 00:18:27,040 --> 00:18:28,280 like negotiation. 535 00:18:28,280 --> 00:18:28,880 Oh, OK. 536 00:18:28,880 --> 00:18:32,160 Where AI agents could be used to resolve conflicts 537 00:18:32,160 --> 00:18:35,400 and reach agreements in complex situations. 538 00:18:35,400 --> 00:18:37,080 I can imagine like AI negotiators 539 00:18:37,080 --> 00:18:39,680 being used in everything from business deals 540 00:18:39,680 --> 00:18:41,760 to international diplomacy. 541 00:18:41,760 --> 00:18:42,200 Right. 542 00:18:42,200 --> 00:18:45,080 But doesn't that raise some ethical concerns? 543 00:18:45,080 --> 00:18:45,440 Right. 544 00:18:45,440 --> 00:18:47,320 I mean, what's to prevent these AI agents 545 00:18:47,320 --> 00:18:49,920 from becoming master manipulators, you know? 546 00:18:49,920 --> 00:18:50,240 Right. 547 00:18:50,240 --> 00:18:54,000 Using their persuasive powers for unethical ends. 548 00:18:54,000 --> 00:18:55,200 That's a valid concern. 549 00:18:55,200 --> 00:18:56,680 And it highlights the importance 550 00:18:56,680 --> 00:18:58,320 of building ethical considerations 551 00:18:58,320 --> 00:19:00,000 into these systems from the ground up. 552 00:19:00,000 --> 00:19:00,400 Right. 553 00:19:00,400 --> 00:19:02,720 So researchers are already exploring ways 554 00:19:02,720 --> 00:19:05,760 to ensure that AI agents adhere to certain principles 555 00:19:05,760 --> 00:19:09,280 of fairness, transparency, and accountability 556 00:19:09,280 --> 00:19:11,240 in their argumentation strategies. 557 00:19:11,240 --> 00:19:14,200 So it's about making sure these AI agents argue, 558 00:19:14,200 --> 00:19:17,320 not just effectively, but also ethically. 559 00:19:17,320 --> 00:19:18,400 Precisely. 560 00:19:18,400 --> 00:19:20,560 And that requires a deep understanding 561 00:19:20,560 --> 00:19:22,680 of both the technical aspects of AI 562 00:19:22,680 --> 00:19:24,440 and the philosophical underpinnings 563 00:19:24,440 --> 00:19:26,840 of argumentation and ethical reasoning. 564 00:19:26,840 --> 00:19:28,640 This is all incredibly fascinating. 565 00:19:28,640 --> 00:19:29,000 Yeah. 566 00:19:29,000 --> 00:19:31,640 And I feel like we've just scratched the surface of this 567 00:19:31,640 --> 00:19:34,600 complex and rapidly evolving field. 568 00:19:34,600 --> 00:19:36,720 We've talked about the different ways 569 00:19:36,720 --> 00:19:39,320 argumentation, machine learning, or being integrated 570 00:19:39,320 --> 00:19:42,120 into potential benefits and challenges, 571 00:19:42,120 --> 00:19:46,200 the need for user studies and ethical considerations. 572 00:19:46,200 --> 00:19:48,240 Let's pause here for a moment and just kind of recap 573 00:19:48,240 --> 00:19:51,200 what we've learned so far before we dive 574 00:19:51,200 --> 00:19:52,400 into some final thoughts. 575 00:19:52,400 --> 00:19:52,880 Yeah. 576 00:19:52,880 --> 00:19:53,080 All right. 577 00:19:53,080 --> 00:19:54,720 So we've learned some pretty amazing stuff 578 00:19:54,720 --> 00:19:57,560 about how we can combine argumentation and machine 579 00:19:57,560 --> 00:19:58,040 learning. 580 00:19:58,040 --> 00:19:58,680 We have. 581 00:19:58,680 --> 00:20:02,120 Creating AI that is not just smart. 582 00:20:02,120 --> 00:20:03,040 Right. 583 00:20:03,040 --> 00:20:08,600 But also explainable, adaptable, maybe even 584 00:20:08,600 --> 00:20:10,800 a bit more human-like in its reasoning. 585 00:20:10,800 --> 00:20:11,080 Yeah. 586 00:20:11,080 --> 00:20:13,600 It's clear that this research could be a real game 587 00:20:13,600 --> 00:20:15,240 changer in a lot of different areas. 588 00:20:15,240 --> 00:20:15,880 Yeah. 589 00:20:15,880 --> 00:20:19,280 What I find fascinating is that it's not just about making 590 00:20:19,280 --> 00:20:21,240 machines better, right? 591 00:20:21,240 --> 00:20:23,840 This research is forcing us to examine our own reasoning 592 00:20:23,840 --> 00:20:24,680 processes. 593 00:20:24,680 --> 00:20:25,320 Oh, interesting. 594 00:20:25,320 --> 00:20:27,160 You know how we construct arguments. 595 00:20:27,160 --> 00:20:27,400 Yeah. 596 00:20:27,400 --> 00:20:29,280 And what makes an argument convincing. 597 00:20:29,280 --> 00:20:29,680 Right. 598 00:20:29,680 --> 00:20:29,880 Yeah. 599 00:20:29,880 --> 00:20:32,320 It's like holding a mirror up to our own minds, in a way. 600 00:20:32,320 --> 00:20:32,880 Exactly. 601 00:20:32,880 --> 00:20:36,360 And using AI to understand something fundamental. 602 00:20:36,360 --> 00:20:37,000 Right. 603 00:20:37,000 --> 00:20:38,840 About being human, the art of persuasion. 604 00:20:38,840 --> 00:20:40,200 Yeah, exactly. 605 00:20:40,200 --> 00:20:44,880 And as we develop more sophisticated AI systems that 606 00:20:44,880 --> 00:20:50,440 can argue and reason, we need to be mindful of the potential 607 00:20:50,440 --> 00:20:52,160 impact this could have on society. 608 00:20:52,160 --> 00:20:52,600 Right. 609 00:20:52,600 --> 00:20:57,000 Imagine a world where AI is used to influence public opinion, 610 00:20:57,000 --> 00:21:01,160 sway legal decisions, or even immediate international 611 00:21:01,160 --> 00:21:01,880 conflicts. 612 00:21:01,880 --> 00:21:02,360 Right. 613 00:21:02,360 --> 00:21:04,200 The ethical implications are huge. 614 00:21:04,200 --> 00:21:04,680 They are. 615 00:21:04,680 --> 00:21:08,240 It's like we're on the verge of this whole new era of 616 00:21:08,240 --> 00:21:12,080 communication and decision making, where AI is playing a 617 00:21:12,080 --> 00:21:13,200 much more active role. 618 00:21:13,200 --> 00:21:13,880 Absolutely. 619 00:21:13,880 --> 00:21:15,880 And that obviously raises a lot of really important 620 00:21:15,880 --> 00:21:16,320 questions. 621 00:21:16,320 --> 00:21:21,840 Like who gets to decide what values these AI systems are 622 00:21:21,840 --> 00:21:22,880 even trained on? 623 00:21:22,880 --> 00:21:23,640 Exactly. 624 00:21:23,640 --> 00:21:28,360 How do we ensure fairness and prevent bias in these systems? 625 00:21:28,360 --> 00:21:29,960 Those are crucial questions. 626 00:21:29,960 --> 00:21:33,760 And I think they really highlight the need for a more 627 00:21:33,760 --> 00:21:36,320 interdisciplinary approach to AI research. 628 00:21:36,320 --> 00:21:36,760 OK. 629 00:21:36,760 --> 00:21:40,560 You know, we need ethicists, social scientists, legal 630 00:21:40,560 --> 00:21:43,960 experts, all working alongside computer scientists to 631 00:21:43,960 --> 00:21:46,640 ensure that these technologies are developed responsibly. 632 00:21:46,640 --> 00:21:48,640 So it's not just about building cool tech. 633 00:21:48,640 --> 00:21:52,040 It's also about shaping the future of AI in a way that 634 00:21:52,040 --> 00:21:55,080 benefits humanity. 635 00:21:55,080 --> 00:21:56,360 Couldn't have said it better myself. 636 00:21:56,360 --> 00:21:59,680 So if you had to pick one area where you think this 637 00:21:59,680 --> 00:22:03,280 research will have the biggest impact, what would it be? 638 00:22:03,280 --> 00:22:04,920 That's a tough one. 639 00:22:04,920 --> 00:22:08,240 But if I had to choose, I'd probably say personalized 640 00:22:08,240 --> 00:22:10,480 education has huge potential. 641 00:22:10,480 --> 00:22:11,120 Oh, interesting. 642 00:22:11,120 --> 00:22:11,440 OK. 643 00:22:11,440 --> 00:22:16,080 So imagine AI tutors that can adapt to a student's 644 00:22:16,080 --> 00:22:17,800 individual learning style. 645 00:22:17,800 --> 00:22:18,520 OK. 646 00:22:18,520 --> 00:22:21,640 Providing explanations and arguments tailored to their 647 00:22:21,640 --> 00:22:23,960 specific needs and understanding. 648 00:22:23,960 --> 00:22:28,200 Yeah, that would totally revolutionize how we learn. 649 00:22:28,200 --> 00:22:31,160 Instead of this one size fits all approach, we'd have these 650 00:22:31,160 --> 00:22:35,800 AI systems that could actually engage in a true dialogue 651 00:22:35,800 --> 00:22:40,920 with students, helping them understand complex concepts 652 00:22:40,920 --> 00:22:42,680 in a way that makes sense to them. 653 00:22:42,680 --> 00:22:43,440 Exactly. 654 00:22:43,440 --> 00:22:45,920 And it's not just about delivering information. 655 00:22:45,920 --> 00:22:49,760 It's about teaching critical thinking skills, helping 656 00:22:49,760 --> 00:22:52,440 students learn how to construct their own arguments, 657 00:22:52,440 --> 00:22:54,720 evaluate information critically. 658 00:22:54,720 --> 00:22:57,080 Which are, I mean, those are such essential skills. 659 00:22:57,080 --> 00:22:57,680 Yeah, they are. 660 00:22:57,680 --> 00:22:59,200 For navigating the world. 661 00:22:59,200 --> 00:23:03,320 Especially in this age of information overload and 662 00:23:03,320 --> 00:23:04,120 misinformation. 663 00:23:04,120 --> 00:23:04,760 Absolutely. 664 00:23:04,760 --> 00:23:07,040 And I think AI has the potential to play a really, 665 00:23:07,040 --> 00:23:09,880 really crucial role in empowering people. 666 00:23:09,880 --> 00:23:13,320 To become more discerning consumers of information. 667 00:23:13,320 --> 00:23:15,320 And more effective communicators. 668 00:23:15,320 --> 00:23:16,800 Yeah, I love that. 669 00:23:16,800 --> 00:23:20,400 So for our listeners who are eager to dive deeper into 670 00:23:20,400 --> 00:23:23,920 this world, what advice would you give them? 671 00:23:23,920 --> 00:23:25,480 Where should they start? 672 00:23:25,480 --> 00:23:30,520 I'd recommend starting with the basics of argumentation theory. 673 00:23:30,520 --> 00:23:35,160 There are some great introductory books and online resources 674 00:23:35,160 --> 00:23:37,080 out there that can help you understand those fundamental 675 00:23:37,080 --> 00:23:38,080 concepts. 676 00:23:38,080 --> 00:23:40,760 From there, you can start exploring how these concepts 677 00:23:40,760 --> 00:23:44,240 are being applied in the context of machine learning. 678 00:23:44,240 --> 00:23:44,840 Great advice. 679 00:23:44,840 --> 00:23:46,560 And of course, our listeners can always check out the 680 00:23:46,560 --> 00:23:47,520 original chapter. 681 00:23:47,520 --> 00:23:47,880 Yes. 682 00:23:47,880 --> 00:23:49,360 By Antonio Rago and his team. 683 00:23:49,360 --> 00:23:49,920 Absolutely. 684 00:23:49,920 --> 00:23:53,000 It's a dense read, but it's packed with insightful research 685 00:23:53,000 --> 00:23:54,560 and thought-provoking ideas. 686 00:23:54,560 --> 00:23:55,680 Definitely worth the effort. 687 00:23:55,680 --> 00:23:58,080 And keep an eye out for new research in this field. 688 00:23:58,080 --> 00:24:00,160 It's a rapidly evolving area. 689 00:24:00,160 --> 00:24:02,960 And there are sure to be many more exciting discoveries in 690 00:24:02,960 --> 00:24:03,960 the years to come. 691 00:24:03,960 --> 00:24:06,920 Well, on that note, we'll leave our listeners with a final 692 00:24:06,920 --> 00:24:07,920 thought. 693 00:24:07,920 --> 00:24:10,760 As AI systems become more and more sophisticated in their 694 00:24:10,760 --> 00:24:16,480 ability to argue and reason, what role will we, as humans, 695 00:24:16,480 --> 00:24:18,120 play in this new landscape? 696 00:24:18,120 --> 00:24:19,080 That's the question. 697 00:24:19,080 --> 00:24:19,360 Right. 698 00:24:19,360 --> 00:24:23,280 Will we be the debaters, the judges, or maybe even the 699 00:24:23,280 --> 00:24:25,520 coaches for these AI agents? 700 00:24:25,520 --> 00:24:26,040 Right. 701 00:24:26,040 --> 00:24:29,000 It's a question worth pondering as we step into this 702 00:24:29,000 --> 00:24:32,000 exciting new frontier of artificial intelligence. 703 00:24:32,000 --> 00:24:34,160 Thanks for joining us on this deep dive into the world of 704 00:24:34,160 --> 00:24:36,160 argumentation and machine learning. 705 00:24:36,160 --> 00:24:54,160 Until next time.