1 00:00:00,000 --> 00:00:01,480 Welcome back everyone. 2 00:00:01,480 --> 00:00:03,260 Ready for another deep dive? 3 00:00:03,260 --> 00:00:05,400 This time we're looking at a paper titled, 4 00:00:05,400 --> 00:00:09,520 Neurosymbolic Graph Enrichment for Grounded World Models. 5 00:00:09,520 --> 00:00:11,520 Oh yeah, this one's really interesting. 6 00:00:11,520 --> 00:00:12,360 It is, right? 7 00:00:12,360 --> 00:00:16,200 It's tackling this big question of how to build AI 8 00:00:16,200 --> 00:00:18,960 that actually understands the world the way we do. 9 00:00:18,960 --> 00:00:20,720 You're not just processing information 10 00:00:20,720 --> 00:00:22,880 but getting all the nuance and the complexity. 11 00:00:22,880 --> 00:00:24,160 Yeah, it's really ambitious. 12 00:00:24,160 --> 00:00:25,000 Totally. 13 00:00:25,000 --> 00:00:26,960 And what I found really cool about this paper 14 00:00:26,960 --> 00:00:30,760 is that it tries to bring together these two huge approaches 15 00:00:30,760 --> 00:00:33,320 to AI that we usually think of as separate. 16 00:00:33,320 --> 00:00:36,200 Right, like you've got generative AI on the one hand. 17 00:00:36,200 --> 00:00:37,120 Like JET-TPT. 18 00:00:37,120 --> 00:00:37,840 Exactly. 19 00:00:37,840 --> 00:00:38,400 Exactly. 20 00:00:38,400 --> 00:00:40,680 Which is amazing at producing creative text. 21 00:00:40,680 --> 00:00:42,880 And then on the other hand, you have this whole world 22 00:00:42,880 --> 00:00:46,400 of knowledge-based AI which relies on structured data 23 00:00:46,400 --> 00:00:47,360 and logic. 24 00:00:47,360 --> 00:00:48,920 And each one has its own limitations. 25 00:00:48,920 --> 00:00:49,720 Really? 26 00:00:49,720 --> 00:00:52,080 Like neither one on its own seems quite enough 27 00:00:52,080 --> 00:00:55,400 to build those grounded world models that can truly understand 28 00:00:55,400 --> 00:00:57,040 the world through human experience. 29 00:00:57,040 --> 00:01:01,200 Like trying to bake a cake with only flour or only eggs. 30 00:01:01,200 --> 00:01:02,000 Perfect analogy. 31 00:01:02,000 --> 00:01:02,800 You need both. 32 00:01:02,800 --> 00:01:04,080 Exactly. 33 00:01:04,080 --> 00:01:06,640 So how do they try to bridge that gap in this paper? 34 00:01:06,640 --> 00:01:10,000 Well, this is where the idea of neuro-symbolic AI comes in. 35 00:01:10,000 --> 00:01:11,200 Neurosymbolic AI. 36 00:01:11,200 --> 00:01:15,120 Yeah, it's all about combining the strengths of LLMs, 37 00:01:15,120 --> 00:01:18,920 like your JET-TPT, with the power of knowledge graphs, 38 00:01:18,920 --> 00:01:21,760 which are essentially these structured databases 39 00:01:21,760 --> 00:01:22,440 of information. 40 00:01:22,440 --> 00:01:24,120 OK, so instead of just spitting out text, 41 00:01:24,120 --> 00:01:26,720 they're using LLMs to build up a knowledge base that's 42 00:01:26,720 --> 00:01:27,840 organized and logical. 43 00:01:27,840 --> 00:01:28,880 Exactly. 44 00:01:28,880 --> 00:01:30,560 But it goes even deeper than that. 45 00:01:30,560 --> 00:01:34,640 They're using LLMs to extract what's called implicit knowledge. 46 00:01:34,640 --> 00:01:35,680 Implicit knowledge. 47 00:01:35,680 --> 00:01:36,560 I'm not sure I follow. 48 00:01:36,560 --> 00:01:39,040 It's the kind of understanding that we humans just 49 00:01:39,040 --> 00:01:42,640 have naturally unspoken roles and stuff. 50 00:01:42,640 --> 00:01:44,880 Think about how you can tell someone's mood 51 00:01:44,880 --> 00:01:45,800 by their tone of voice. 52 00:01:45,800 --> 00:01:46,320 Oh, right. 53 00:01:46,320 --> 00:01:47,800 Or how you know not to interrupt someone 54 00:01:47,800 --> 00:01:48,760 when they're telling a story. 55 00:01:48,760 --> 00:01:49,440 Exactly. 56 00:01:49,440 --> 00:01:52,400 It's all that unspoken stuff that we pick up on as humans. 57 00:01:52,400 --> 00:01:53,000 OK, I see. 58 00:01:53,000 --> 00:01:54,440 So they're trying to teach machines 59 00:01:54,440 --> 00:01:56,120 to read between the lines, basically. 60 00:01:56,120 --> 00:01:56,600 Exactly. 61 00:01:56,600 --> 00:01:59,720 And to do that, they develop 11 heuristics. 62 00:01:59,720 --> 00:02:00,280 Heuristics. 63 00:02:00,280 --> 00:02:02,320 You can think of them as rules of thumb 64 00:02:02,320 --> 00:02:05,720 that help guide the LLM in extracting implicit knowledge 65 00:02:05,720 --> 00:02:07,480 from both text and images. 66 00:02:07,480 --> 00:02:10,160 So these heuristics represent different facets 67 00:02:10,160 --> 00:02:11,160 of human understanding. 68 00:02:11,160 --> 00:02:12,080 Precisely. 69 00:02:12,080 --> 00:02:13,560 And one that really stood out to me 70 00:02:13,560 --> 00:02:15,880 was the idea of image schemas. 71 00:02:15,880 --> 00:02:16,680 Image schemas? 72 00:02:16,680 --> 00:02:17,440 What are those? 73 00:02:17,440 --> 00:02:19,840 Well, they're these basic cognitive structures 74 00:02:19,840 --> 00:02:23,080 that we derive from our physical experiences, 75 00:02:23,080 --> 00:02:24,680 like container or path. 76 00:02:24,680 --> 00:02:26,520 It's like in or out or through. 77 00:02:26,520 --> 00:02:27,200 Exactly. 78 00:02:27,200 --> 00:02:28,760 We use these schemas to understand 79 00:02:28,760 --> 00:02:30,600 all sorts of abstract concepts. 80 00:02:30,600 --> 00:02:34,400 Even though I'm not literally in trouble the way I'm in a box, 81 00:02:34,400 --> 00:02:38,040 my brain uses that same spatial schema to make sense of it. 82 00:02:38,040 --> 00:02:38,640 Wow. 83 00:02:38,640 --> 00:02:39,840 I never thought of it that way. 84 00:02:39,840 --> 00:02:41,480 So it's like these physical metaphors 85 00:02:41,480 --> 00:02:43,840 shape our understanding of more abstract ideas. 86 00:02:43,840 --> 00:02:44,600 Precisely. 87 00:02:44,600 --> 00:02:47,440 And that's just one of the 11 heuristics they use. 88 00:02:47,440 --> 00:02:50,000 Some of the others tackle even more complex aspects 89 00:02:50,000 --> 00:02:51,280 of human cognition. 90 00:02:51,280 --> 00:02:51,800 Like what? 91 00:02:51,800 --> 00:02:55,080 Well, there's one called moral value-driven coercions, 92 00:02:55,080 --> 00:02:58,840 which recognizes how our own values and social norms influence 93 00:02:58,840 --> 00:03:00,040 how we interpret things. 94 00:03:00,040 --> 00:03:01,440 So like if someone cuts in line, 95 00:03:01,440 --> 00:03:03,800 we might judge them not just on the action itself, 96 00:03:03,800 --> 00:03:06,240 but also on our own sense of fairness. 97 00:03:06,240 --> 00:03:07,040 Exactly. 98 00:03:07,040 --> 00:03:11,080 Or how we might view someone breaking a promise as not 99 00:03:11,080 --> 00:03:13,960 just factually wrong, but also as a sign 100 00:03:13,960 --> 00:03:15,400 that they're untrustworthy. 101 00:03:15,400 --> 00:03:15,960 I see. 102 00:03:15,960 --> 00:03:18,560 So they're building a system that tries to understand 103 00:03:18,560 --> 00:03:21,760 not just what happened, but also the moral implications. 104 00:03:21,760 --> 00:03:22,760 Exactly. 105 00:03:22,760 --> 00:03:24,280 And they represent all this information 106 00:03:24,280 --> 00:03:25,800 using knowledge graphs. 107 00:03:25,800 --> 00:03:26,520 Knowledge graphs. 108 00:03:26,520 --> 00:03:26,840 Yeah. 109 00:03:26,840 --> 00:03:28,640 They're like these structured databases 110 00:03:28,640 --> 00:03:31,480 that represent information as a network 111 00:03:31,480 --> 00:03:33,160 of interconnected concepts. 112 00:03:33,160 --> 00:03:35,640 So instead of just storing data as text, 113 00:03:35,640 --> 00:03:38,640 it's like they're creating a map of how different ideas 114 00:03:38,640 --> 00:03:39,440 relate to each other. 115 00:03:39,440 --> 00:03:40,480 A map of knowledge. 116 00:03:40,480 --> 00:03:41,720 That's a great way to put it. 117 00:03:41,720 --> 00:03:44,240 And by adding all these layers of implicit knowledge, 118 00:03:44,240 --> 00:03:47,040 they're making that map much richer, much more 119 00:03:47,040 --> 00:03:49,560 like how we humans see the world. 120 00:03:49,560 --> 00:03:52,920 So they're combining generative and knowledge-based AI 121 00:03:52,920 --> 00:03:55,880 using LLMs to extract this implicit knowledge 122 00:03:55,880 --> 00:03:59,120 guided by these heuristics and then representing it all 123 00:03:59,120 --> 00:04:00,600 in this structured knowledge graph. 124 00:04:00,600 --> 00:04:01,200 You got it. 125 00:04:01,200 --> 00:04:02,040 It's pretty complex. 126 00:04:02,040 --> 00:04:02,600 It is. 127 00:04:02,600 --> 00:04:03,920 To really see how it works, though, 128 00:04:03,920 --> 00:04:05,520 we need to look at an example from the paper. 129 00:04:05,520 --> 00:04:06,080 OK, perfect. 130 00:04:06,080 --> 00:04:06,920 Let's dive into that. 131 00:04:06,920 --> 00:04:07,160 All right. 132 00:04:07,160 --> 00:04:09,360 So they start with this picture of an Olympic athlete 133 00:04:09,360 --> 00:04:10,840 celebrating a victory. 134 00:04:10,840 --> 00:04:11,480 OK. 135 00:04:11,480 --> 00:04:13,560 And they feed that picture to GPT-4. 136 00:04:13,560 --> 00:04:15,680 And they ask it to describe what it sees. 137 00:04:15,680 --> 00:04:16,160 Interesting. 138 00:04:16,160 --> 00:04:19,440 So the LLM is creating the initial text description 139 00:04:19,440 --> 00:04:21,400 that they'll use to build the knowledge graph from. 140 00:04:21,400 --> 00:04:22,080 Exactly. 141 00:04:22,080 --> 00:04:25,360 And GPT-4 generates this really detailed description. 142 00:04:25,360 --> 00:04:27,320 You know, talking about the athlete's expression, 143 00:04:27,320 --> 00:04:30,160 their uniform, the cheering crowd, all those details. 144 00:04:30,160 --> 00:04:33,080 OK, so now they've got this rich text description. 145 00:04:33,080 --> 00:04:34,160 What do they do with it? 146 00:04:34,160 --> 00:04:39,120 They use a tool called Text2AMR2FRD 147 00:04:39,120 --> 00:04:41,000 to transform that natural language 148 00:04:41,000 --> 00:04:43,680 description into a structured knowledge graph. 149 00:04:43,680 --> 00:04:47,560 Well, Olan, Text2AMR2FRA, that sounds intense. 150 00:04:47,560 --> 00:04:48,880 What exactly does it do? 151 00:04:48,880 --> 00:04:51,400 Think of it like a super sophisticated translator. 152 00:04:51,400 --> 00:04:51,800 OK. 153 00:04:51,800 --> 00:04:53,480 It takes natural language like English 154 00:04:53,480 --> 00:04:56,200 and converts it into a format that machines can understand 155 00:04:56,200 --> 00:04:57,360 and use for reasoning. 156 00:04:57,360 --> 00:04:59,120 So it's like translating human language 157 00:04:59,120 --> 00:05:01,080 into machine language so the computer can 158 00:05:01,080 --> 00:05:02,200 build its knowledge graph. 159 00:05:02,200 --> 00:05:03,040 You got it. 160 00:05:03,040 --> 00:05:05,160 But that initial graph is still just based 161 00:05:05,160 --> 00:05:07,760 on the literal content of the image description. 162 00:05:07,760 --> 00:05:09,760 It needs to be fleshed out with all those layers 163 00:05:09,760 --> 00:05:10,680 of implicit knowledge. 164 00:05:10,680 --> 00:05:11,280 Right. 165 00:05:11,280 --> 00:05:13,280 This is where those 11 heuristics come in. 166 00:05:13,280 --> 00:05:14,120 Exactly. 167 00:05:14,120 --> 00:05:16,320 They use those heuristics to guide the LLM 168 00:05:16,320 --> 00:05:19,440 in adding those juicy details that make the knowledge graph 169 00:05:19,440 --> 00:05:22,040 truly grounded in human understanding. 170 00:05:22,040 --> 00:05:24,440 So they feed the base graph back to the LLM 171 00:05:24,440 --> 00:05:26,320 and use specific prompts to activate 172 00:05:26,320 --> 00:05:27,760 each of the 11 heuristics. 173 00:05:27,760 --> 00:05:28,440 Exactly. 174 00:05:28,440 --> 00:05:30,400 Like for the factual impact heuristic, 175 00:05:30,400 --> 00:05:32,400 they might prompt the LLM to consider 176 00:05:32,400 --> 00:05:34,880 what emotions the athlete might be experiencing. 177 00:05:34,880 --> 00:05:38,160 So the LLM would add things like athlete is feeling joy 178 00:05:38,160 --> 00:05:40,440 or athlete's heart rate is elevated. 179 00:05:40,440 --> 00:05:41,160 Exactly. 180 00:05:41,160 --> 00:05:43,240 And they do this for all 11 heuristics, 181 00:05:43,240 --> 00:05:45,360 creating separate graphs that each capture 182 00:05:45,360 --> 00:05:47,160 a different layer of implicit knowledge. 183 00:05:47,160 --> 00:05:47,400 Wow. 184 00:05:47,400 --> 00:05:50,040 So they end up with a whole stack of knowledge graphs. 185 00:05:50,040 --> 00:05:50,480 Right. 186 00:05:50,480 --> 00:05:52,200 And then they merge them all together 187 00:05:52,200 --> 00:05:55,360 to create this final multi-layered representation 188 00:05:55,360 --> 00:05:56,840 of the original image. 189 00:05:56,840 --> 00:05:58,600 It's like taking a flat map and then 190 00:05:58,600 --> 00:06:01,520 adding layers for terrain climate population density. 191 00:06:01,520 --> 00:06:03,240 You get this much richer picture. 192 00:06:03,240 --> 00:06:03,520 OK. 193 00:06:03,520 --> 00:06:04,600 That makes sense. 194 00:06:04,600 --> 00:06:08,040 But how do they know if this super detailed knowledge 195 00:06:08,040 --> 00:06:10,120 graph is actually any good? 196 00:06:10,120 --> 00:06:11,760 Are they just trusting the LLM to come up 197 00:06:11,760 --> 00:06:13,080 with plausible stuff? 198 00:06:13,080 --> 00:06:14,720 That's a great question. 199 00:06:14,720 --> 00:06:17,040 And to answer it, we need to talk about their evaluation 200 00:06:17,040 --> 00:06:19,640 process, which is actually pretty amazing. 201 00:06:19,640 --> 00:06:21,280 OK, let's hear it. 202 00:06:21,280 --> 00:06:24,160 How do they test whether this knowledge graph is truly 203 00:06:24,160 --> 00:06:27,080 reflecting a human-like understanding of the world? 204 00:06:27,080 --> 00:06:29,960 Well, they use a three-tiered evaluation process. 205 00:06:29,960 --> 00:06:31,720 And each tier is designed to assess 206 00:06:31,720 --> 00:06:34,360 a different aspect of the knowledge graph's quality. 207 00:06:34,360 --> 00:06:36,320 Three tiers. 208 00:06:36,320 --> 00:06:37,480 Sounds thorough. 209 00:06:37,480 --> 00:06:38,000 It is. 210 00:06:38,000 --> 00:06:40,680 The first tier focuses on something called logical 211 00:06:40,680 --> 00:06:41,960 validation. 212 00:06:41,960 --> 00:06:44,160 Basically, they're making sure that the knowledge graph 213 00:06:44,160 --> 00:06:45,560 makes sense internally. 214 00:06:45,560 --> 00:06:47,440 So no contradictions or nonsensical statements. 215 00:06:47,440 --> 00:06:48,320 Exactly. 216 00:06:48,320 --> 00:06:51,680 And making sure that any new information the LLM added 217 00:06:51,680 --> 00:06:54,360 is properly linked to existing concepts. 218 00:06:54,360 --> 00:06:56,560 Like imagine if it tried to tell you that an apple is 219 00:06:56,560 --> 00:06:57,360 a type of fish. 220 00:06:57,360 --> 00:06:58,200 It wouldn't make sense. 221 00:06:58,200 --> 00:06:58,600 Right. 222 00:06:58,600 --> 00:07:00,200 They're making sure the AI is building 223 00:07:00,200 --> 00:07:02,000 on a solid foundation of knowledge. 224 00:07:02,000 --> 00:07:02,800 Exactly. 225 00:07:02,800 --> 00:07:04,560 And the second tier of the evaluation 226 00:07:04,560 --> 00:07:07,480 dives into foundational ontology alignment. 227 00:07:07,480 --> 00:07:09,520 OK, that sounds a bit complicated. 228 00:07:09,520 --> 00:07:10,520 Break it down for me. 229 00:07:10,520 --> 00:07:13,800 Ontology basically refers to a standardized way 230 00:07:13,800 --> 00:07:16,760 of defining concepts and their relationships. 231 00:07:16,760 --> 00:07:19,040 Think of it like a dictionary that the AI uses 232 00:07:19,040 --> 00:07:21,800 to make sure it's speaking the same language as the existing 233 00:07:21,800 --> 00:07:23,400 body of knowledge. 234 00:07:23,400 --> 00:07:25,840 So it's like checking the AI's work 235 00:07:25,840 --> 00:07:27,680 against a reference guide to make sure everything 236 00:07:27,680 --> 00:07:29,840 is defined correctly and fits together neatly. 237 00:07:29,840 --> 00:07:30,920 Precisely. 238 00:07:30,920 --> 00:07:33,920 And then finally, we get to the third tier of the evaluation. 239 00:07:33,920 --> 00:07:35,840 And this is where it gets really interesting. 240 00:07:35,840 --> 00:07:38,080 Oh, tell me more. 241 00:07:38,080 --> 00:07:40,120 They bring in human experts to evaluate 242 00:07:40,120 --> 00:07:43,200 the plausibility of the knowledge that's been generated. 243 00:07:43,200 --> 00:07:46,200 Ah, so real people get to judge whether the AI is 244 00:07:46,200 --> 00:07:49,680 capturing those subtle nuances of human understanding. 245 00:07:49,680 --> 00:07:51,440 That's crucial, because that's ultimately 246 00:07:51,440 --> 00:07:52,480 what they're trying to replicate. 247 00:07:52,480 --> 00:07:53,120 Absolutely. 248 00:07:53,120 --> 00:07:55,480 And the results of this three tiered evaluation 249 00:07:55,480 --> 00:07:56,640 were really revealing. 250 00:07:56,640 --> 00:07:59,560 They shed light on both the AI's remarkable capabilities 251 00:07:59,560 --> 00:08:02,360 and the areas where it still faces challenges. 252 00:08:02,360 --> 00:08:04,640 OK, I'm dying to hear those results. 253 00:08:04,640 --> 00:08:07,880 Which areas did the AI excel in and where did it struggle? 254 00:08:07,880 --> 00:08:08,960 Give me the highlights. 255 00:08:08,960 --> 00:08:10,720 All right, well, let's start with the AI's strengths, 256 00:08:10,720 --> 00:08:13,320 because there were definitely some impressive achievements. 257 00:08:13,320 --> 00:08:15,880 OK, let's hear about the AI's shining moments. 258 00:08:15,880 --> 00:08:18,720 What impressed the human judges the most? 259 00:08:18,720 --> 00:08:21,000 One area where the AI really excelled 260 00:08:21,000 --> 00:08:23,760 was in capturing what we call factual impacts. 261 00:08:23,760 --> 00:08:24,640 Factual impacts. 262 00:08:24,640 --> 00:08:25,960 What does that mean, exactly? 263 00:08:25,960 --> 00:08:28,840 It refers to the likely consequences of an event, 264 00:08:28,840 --> 00:08:31,160 both physical and emotional. 265 00:08:31,160 --> 00:08:33,760 For example, in the case of the Olympic athlete, 266 00:08:33,760 --> 00:08:36,360 the AI was able to infer that the athlete was likely 267 00:08:36,360 --> 00:08:38,360 feeling a surge of joy and excitement 268 00:08:38,360 --> 00:08:39,680 after winning the race. 269 00:08:39,680 --> 00:08:41,320 OK, that makes sense. 270 00:08:41,320 --> 00:08:44,960 But did it go beyond just recognizing basic emotions? 271 00:08:44,960 --> 00:08:45,440 It did. 272 00:08:45,440 --> 00:08:47,680 It was able to infer more subtle details, 273 00:08:47,680 --> 00:08:49,440 like the athlete's elevated heart rate 274 00:08:49,440 --> 00:08:52,240 and even the possibility of receiving endorsements. 275 00:08:52,240 --> 00:08:54,600 It was like the AI was thinking through the ripple effects 276 00:08:54,600 --> 00:08:56,560 of the victory, almost like a human would. 277 00:08:56,560 --> 00:08:57,400 Wow. 278 00:08:57,400 --> 00:08:59,960 So it wasn't just recognizing the event itself. 279 00:08:59,960 --> 00:09:02,520 It was able to understand the broader context 280 00:09:02,520 --> 00:09:04,840 and anticipate potential consequences. 281 00:09:04,840 --> 00:09:05,840 That's pretty impressive. 282 00:09:05,840 --> 00:09:06,360 It is. 283 00:09:06,360 --> 00:09:07,960 And it suggests that the AI is starting 284 00:09:07,960 --> 00:09:10,480 to develop a real capacity for reasoning about cause 285 00:09:10,480 --> 00:09:13,200 and effect, which is a fundamental aspect of human 286 00:09:13,200 --> 00:09:14,240 intelligence. 287 00:09:14,240 --> 00:09:14,920 Absolutely. 288 00:09:14,920 --> 00:09:16,160 We don't just perceive the world 289 00:09:16,160 --> 00:09:18,360 as a series of isolated events. 290 00:09:18,360 --> 00:09:20,560 We understand how those events are interconnected, 291 00:09:20,560 --> 00:09:22,800 how one action can lead to another. 292 00:09:22,800 --> 00:09:25,000 And it sounds like this AI is starting 293 00:09:25,000 --> 00:09:28,080 to develop that same ability to see the bigger picture. 294 00:09:28,080 --> 00:09:29,280 Exactly. 295 00:09:29,280 --> 00:09:32,880 But the AI's strengths weren't limited to factual impacts. 296 00:09:32,880 --> 00:09:35,000 It also did surprisingly well in grasping 297 00:09:35,000 --> 00:09:37,480 some of the more subtle aspects of human communication, 298 00:09:37,480 --> 00:09:39,080 like implied meaning. 299 00:09:39,080 --> 00:09:41,520 You're talking about those times when we say one thing 300 00:09:41,520 --> 00:09:43,320 but mean something else, like when someone says, 301 00:09:43,320 --> 00:09:46,200 oh, fantastic, after spilling coffee all over themselves. 302 00:09:46,200 --> 00:09:46,880 Exactly. 303 00:09:46,880 --> 00:09:48,960 We call those conversational implicatures. 304 00:09:48,960 --> 00:09:51,040 And they're incredibly common in human language. 305 00:09:51,040 --> 00:09:52,640 And the AI was actually able to pick up 306 00:09:52,640 --> 00:09:55,280 on some of those implied meanings and incorporate 307 00:09:55,280 --> 00:09:57,440 that information into the knowledge graph. 308 00:09:57,440 --> 00:09:57,880 Wow. 309 00:09:57,880 --> 00:10:00,000 So it was able to read between the lines 310 00:10:00,000 --> 00:10:02,480 and understand that the literal words don't always 311 00:10:02,480 --> 00:10:03,640 tell the whole story. 312 00:10:03,640 --> 00:10:04,560 That's pretty remarkable. 313 00:10:04,560 --> 00:10:06,680 Can you give me a specific example of how it did that? 314 00:10:06,680 --> 00:10:07,640 Of course. 315 00:10:07,640 --> 00:10:09,520 Remember that caption we talked about earlier 316 00:10:09,520 --> 00:10:12,200 about the exhausted but ecstatic athlete collapsing 317 00:10:12,200 --> 00:10:13,240 onto the track? 318 00:10:13,240 --> 00:10:15,400 A human reading that would naturally infer 319 00:10:15,400 --> 00:10:17,400 that the athlete had just finished a race, 320 00:10:17,400 --> 00:10:19,320 even though it's not explicitly stated. 321 00:10:19,320 --> 00:10:19,720 Right. 322 00:10:19,720 --> 00:10:21,840 It's just something we understand based on the context. 323 00:10:21,840 --> 00:10:23,080 Exactly. 324 00:10:23,080 --> 00:10:26,280 And the AI, guided by the conversational implicatures 325 00:10:26,280 --> 00:10:29,880 heuristic, was able to pick up on that implied meaning 326 00:10:29,880 --> 00:10:32,120 and add it to the knowledge graph. 327 00:10:32,120 --> 00:10:34,520 It was like the AI was filling in the gaps 328 00:10:34,520 --> 00:10:37,840 using its understanding of how humans use language. 329 00:10:37,840 --> 00:10:38,560 That's really cool. 330 00:10:38,560 --> 00:10:41,120 It suggests that we're making real progress towards AI 331 00:10:41,120 --> 00:10:45,800 that can genuinely understand the nuances of human communication. 332 00:10:45,800 --> 00:10:48,880 But you mentioned earlier that the AI also faced some challenges 333 00:10:48,880 --> 00:10:50,440 during the evaluation. 334 00:10:50,440 --> 00:10:52,600 What were some of the areas where it struggled? 335 00:10:52,600 --> 00:10:55,320 Well, as you might expect, some aspects of human cognition 336 00:10:55,320 --> 00:10:58,200 are proving to be more difficult to replicate than others. 337 00:10:58,200 --> 00:11:01,080 And one area where the AI encountered a significant hurdle 338 00:11:01,080 --> 00:11:03,240 was in predicting future events. 339 00:11:03,240 --> 00:11:04,600 Predicting the future. 340 00:11:04,600 --> 00:11:06,720 That's a tough one even for us humans. 341 00:11:06,720 --> 00:11:09,400 But isn't that a key element of human intelligence 342 00:11:09,400 --> 00:11:12,360 being able to anticipate what might happen next, 343 00:11:12,360 --> 00:11:14,400 based on what we know about the world? 344 00:11:14,400 --> 00:11:15,120 It is. 345 00:11:15,120 --> 00:11:17,520 And while the AI could make some educated guesses 346 00:11:17,520 --> 00:11:19,720 about what might happen next, its predictions 347 00:11:19,720 --> 00:11:22,360 were often limited in their accuracy and nuance. 348 00:11:22,360 --> 00:11:25,080 It seems that capturing the full scope of our ability 349 00:11:25,080 --> 00:11:27,640 to anticipate and plan for the future 350 00:11:27,640 --> 00:11:29,800 is still a major challenge for AI. 351 00:11:29,800 --> 00:11:32,000 I can see why that would be tricky. 352 00:11:32,000 --> 00:11:35,160 There are just so many factors that can influence future events. 353 00:11:35,160 --> 00:11:38,240 And even we humans get it wrong all the time. 354 00:11:38,240 --> 00:11:40,920 So is this a sign that we're still a long way 355 00:11:40,920 --> 00:11:43,600 from achieving truly human-like AI? 356 00:11:43,600 --> 00:11:44,400 It's a great question. 357 00:11:44,400 --> 00:11:48,240 And it highlights just how complex human reasoning really is. 358 00:11:48,240 --> 00:11:50,480 Even with all the advancements we've made in AI, 359 00:11:50,480 --> 00:11:53,120 there are still some fundamental aspects of human intelligence 360 00:11:53,120 --> 00:11:54,360 that we haven't fully cracked. 361 00:11:54,360 --> 00:11:56,720 It's both exciting and humbling to realize that. 362 00:11:56,720 --> 00:11:57,360 Absolutely. 363 00:11:57,360 --> 00:11:59,440 And it just makes this whole field of research 364 00:11:59,440 --> 00:12:00,880 even more fascinating. 365 00:12:00,880 --> 00:12:01,640 I agree. 366 00:12:01,640 --> 00:12:03,680 So we've talked about the AI's strengths 367 00:12:03,680 --> 00:12:05,600 in capturing factual impacts and even 368 00:12:05,600 --> 00:12:07,960 those subtle conversational implicatures. 369 00:12:07,960 --> 00:12:10,520 But you mentioned that there are other areas where it struggled. 370 00:12:10,520 --> 00:12:12,360 What else did the evaluation reveal? 371 00:12:12,360 --> 00:12:14,400 Well, another area where the AI faced difficulties 372 00:12:14,400 --> 00:12:16,080 was in dealing with heuristics that 373 00:12:16,080 --> 00:12:18,600 involve moral judgments and social norms. 374 00:12:18,600 --> 00:12:19,680 That makes sense. 375 00:12:19,680 --> 00:12:22,760 Morality and social norms can be so complex and context 376 00:12:22,760 --> 00:12:25,320 dependent they can vary widely across cultures 377 00:12:25,320 --> 00:12:26,680 and even within a single culture, 378 00:12:26,680 --> 00:12:27,840 depending on the situation. 379 00:12:27,840 --> 00:12:28,960 Exactly. 380 00:12:28,960 --> 00:12:30,960 And while the AI had been trained 381 00:12:30,960 --> 00:12:33,600 on this massive data set of text, 382 00:12:33,600 --> 00:12:36,400 it hadn't yet developed that nuanced understanding 383 00:12:36,400 --> 00:12:38,360 of social and cultural dynamics that 384 00:12:38,360 --> 00:12:41,360 would allow it to consistently make accurate judgments 385 00:12:41,360 --> 00:12:44,440 about what's considered right or wrong or appropriate 386 00:12:44,440 --> 00:12:45,680 in different situations. 387 00:12:45,680 --> 00:12:47,520 So it's like the AI was still learning 388 00:12:47,520 --> 00:12:50,080 the ropes of human social interaction, 389 00:12:50,080 --> 00:12:52,520 trying to figure out the often unspoken rules 390 00:12:52,520 --> 00:12:53,480 that govern our behavior. 391 00:12:53,480 --> 00:12:54,320 Precisely. 392 00:12:54,320 --> 00:12:56,840 And that's an ongoing challenge for AI research, 393 00:12:56,840 --> 00:12:59,600 how to build systems that are not only intelligent, 394 00:12:59,600 --> 00:13:01,920 but also socially and culturally aware systems 395 00:13:01,920 --> 00:13:05,040 that can understand the subtle cues and unspoken rules that 396 00:13:05,040 --> 00:13:06,480 shape human interactions. 397 00:13:06,480 --> 00:13:09,160 It sounds like we're still in the early stages of teaching AI 398 00:13:09,160 --> 00:13:11,960 how to navigate the complexities of human society. 399 00:13:11,960 --> 00:13:13,200 And it's definitely a challenge that 400 00:13:13,200 --> 00:13:16,160 requires careful consideration of the ethical implications. 401 00:13:16,160 --> 00:13:16,800 Absolutely. 402 00:13:16,800 --> 00:13:19,120 But even with these challenges, the fact 403 00:13:19,120 --> 00:13:22,040 that we're starting to see AI systems that can grasp 404 00:13:22,040 --> 00:13:24,520 even some of these subtle aspects of human understanding 405 00:13:24,520 --> 00:13:26,280 is incredibly exciting. 406 00:13:26,280 --> 00:13:27,480 It really is. 407 00:13:27,480 --> 00:13:30,560 This deep dive is giving me a whole new appreciation 408 00:13:30,560 --> 00:13:33,840 for the incredible progress being made in AI 409 00:13:33,840 --> 00:13:36,080 and the potential it holds for the future. 410 00:13:36,080 --> 00:13:37,840 It's a field that's constantly evolving 411 00:13:37,840 --> 00:13:39,120 and it's full of surprises. 412 00:13:39,120 --> 00:13:41,040 It's amazing to see how researchers are pushing 413 00:13:41,040 --> 00:13:43,080 the boundaries of what's possible. 414 00:13:43,080 --> 00:13:44,640 And it's even more amazing to think 415 00:13:44,640 --> 00:13:47,880 about what the future holds for AI and its role in our lives. 416 00:13:47,880 --> 00:13:50,240 This has been a truly mind-expanding conversation. 417 00:13:50,240 --> 00:13:51,880 Thank you so much for sharing your insights 418 00:13:51,880 --> 00:13:54,160 and expertise on this cutting-edge research. 419 00:13:54,160 --> 00:13:55,040 It's been my pleasure. 420 00:13:55,040 --> 00:13:57,200 And thank you for joining us on this deep dive 421 00:13:57,200 --> 00:13:59,800 into the fascinating world of nurse symbolic AI 422 00:13:59,800 --> 00:14:01,200 and grounded world models. 423 00:14:01,200 --> 00:14:02,600 We hope you've enjoyed the journey. 424 00:14:02,600 --> 00:14:04,360 It's been a wild ride. 425 00:14:04,360 --> 00:14:05,800 And to our listeners out there, we 426 00:14:05,800 --> 00:14:08,600 encourage you to keep asking questions, keep exploring, 427 00:14:08,600 --> 00:14:12,080 and stay curious about the ever-evolving world of AI. 428 00:14:12,080 --> 00:14:14,600 So the first tier is all about making sure 429 00:14:14,600 --> 00:14:16,960 that the knowledge graph is logically sound. 430 00:14:16,960 --> 00:14:19,360 We're talking about checking for contradictions, 431 00:14:19,360 --> 00:14:21,520 making sure the information flows in a way that 432 00:14:21,520 --> 00:14:24,560 makes sense, and also ensuring that any new information 433 00:14:24,560 --> 00:14:27,640 that the LLM added is properly linked back 434 00:14:27,640 --> 00:14:30,120 to the original concepts in that base graph. 435 00:14:30,120 --> 00:14:32,280 So it's like making sure the AI isn't building 436 00:14:32,280 --> 00:14:35,720 a tower of knowledge on a foundation of sand. 437 00:14:35,720 --> 00:14:38,720 It has to be solid and consistent for the ground up. 438 00:14:38,720 --> 00:14:39,520 Exactly. 439 00:14:39,520 --> 00:14:41,920 And then the second tier dives into this idea 440 00:14:41,920 --> 00:14:45,120 of what they call foundational ontology alignment. 441 00:14:45,120 --> 00:14:46,880 OK, ontology. 442 00:14:46,880 --> 00:14:48,080 That sounds a little intimidating. 443 00:14:48,080 --> 00:14:49,320 What exactly are we talking about here? 444 00:14:49,320 --> 00:14:50,560 Yeah, it's one of those words that 445 00:14:50,560 --> 00:14:52,240 sounds really complicated, but it's actually 446 00:14:52,240 --> 00:14:53,640 a pretty simple idea. 447 00:14:53,640 --> 00:14:56,280 Ontology basically refers to a structured way 448 00:14:56,280 --> 00:14:58,560 of defining concepts in their relationships. 449 00:14:58,560 --> 00:15:01,000 OK, so it's like a blueprint for knowledge, 450 00:15:01,000 --> 00:15:03,680 a set of rules that make sure everything is categorized 451 00:15:03,680 --> 00:15:05,480 and defined in a consistent way. 452 00:15:05,480 --> 00:15:06,600 That's a great way to put it. 453 00:15:06,600 --> 00:15:08,840 So they're essentially checking that the AI is using 454 00:15:08,840 --> 00:15:12,040 the same blueprint as the existing body of knowledge, 455 00:15:12,040 --> 00:15:15,200 making sure it doesn't go off creating its own wacky definitions 456 00:15:15,200 --> 00:15:16,080 for things. 457 00:15:16,080 --> 00:15:16,680 Right. 458 00:15:16,680 --> 00:15:18,960 You wouldn't want the AI to decide that a cat is 459 00:15:18,960 --> 00:15:20,400 a type of food or something. 460 00:15:20,400 --> 00:15:22,240 It has to be grounded in the same reality 461 00:15:22,240 --> 00:15:23,640 that we humans operate in. 462 00:15:23,640 --> 00:15:24,280 Precisely. 463 00:15:24,280 --> 00:15:25,920 And then comes the most interesting part, 464 00:15:25,920 --> 00:15:28,240 the third tier of the evaluation process. 465 00:15:28,240 --> 00:15:30,840 This is where they bring in the human factor. 466 00:15:30,840 --> 00:15:32,080 The moment of truth. 467 00:15:32,080 --> 00:15:36,000 So real people get to judge whether the AI has truly 468 00:15:36,000 --> 00:15:39,520 captured those subtle unspoken layers of human understanding. 469 00:15:39,520 --> 00:15:40,560 That's exactly it. 470 00:15:40,560 --> 00:15:43,440 They have human experts in knowledge representation 471 00:15:43,440 --> 00:15:46,240 evaluate the plausibility of the information 472 00:15:46,240 --> 00:15:48,040 that the LLM has added to the graph. 473 00:15:48,040 --> 00:15:50,200 So it's like having a panel of judges 474 00:15:50,200 --> 00:15:53,040 assess whether the AI is truly getting it, 475 00:15:53,040 --> 00:15:55,920 whether it's able to grasp those deeper layers of meaning 476 00:15:55,920 --> 00:15:57,840 that we humans naturally pick up on. 477 00:15:57,840 --> 00:15:59,000 Precisely. 478 00:15:59,000 --> 00:16:01,840 And what's fascinating is that this human evaluation isn't just 479 00:16:01,840 --> 00:16:03,320 a thumbs up or thumbs down. 480 00:16:03,320 --> 00:16:07,480 It reveals this nuanced spectrum of successes and challenges. 481 00:16:07,480 --> 00:16:07,720 I see. 482 00:16:07,720 --> 00:16:10,600 So it gives us more realistic picture of where the AI is at 483 00:16:10,600 --> 00:16:13,240 in terms of its ability to understand the world like we do. 484 00:16:13,240 --> 00:16:13,680 Exactly. 485 00:16:13,680 --> 00:16:15,080 It's not perfect, but it's definitely 486 00:16:15,080 --> 00:16:16,640 showing some real progress. 487 00:16:16,640 --> 00:16:16,840 OK. 488 00:16:16,840 --> 00:16:18,600 I'm on the edge of my seat here. 489 00:16:18,600 --> 00:16:22,360 How did the AI actually fare in this human evaluation? 490 00:16:22,360 --> 00:16:25,600 Was it able to pass the test and convince these expert judges 491 00:16:25,600 --> 00:16:27,840 that it was truly understanding the world like we do? 492 00:16:27,840 --> 00:16:29,760 Well, the results were quite fascinating. 493 00:16:29,760 --> 00:16:33,000 The AI actually did remarkably well in some areas. 494 00:16:33,000 --> 00:16:34,800 But as you might expect, there were also 495 00:16:34,800 --> 00:16:36,320 areas where it struggled. 496 00:16:36,320 --> 00:16:38,920 So let's start by looking at the AI strengths, 497 00:16:38,920 --> 00:16:41,480 you know, the things that really impressed the human judges. 498 00:16:41,480 --> 00:16:41,760 OK. 499 00:16:41,760 --> 00:16:44,520 Let's hear about the AI's shining moments. 500 00:16:44,520 --> 00:16:46,960 What were some of its standout achievements? 501 00:16:46,960 --> 00:16:49,280 One area where the AI really excelled 502 00:16:49,280 --> 00:16:52,120 was in capturing what's called factual impacts. 503 00:16:52,120 --> 00:16:53,080 Factual impacts. 504 00:16:53,080 --> 00:16:54,040 We're not new at that means again. 505 00:16:54,040 --> 00:16:54,480 Yeah, sure. 506 00:16:54,480 --> 00:16:56,840 So factual impacts basically refer 507 00:16:56,840 --> 00:16:59,000 to the likely consequences of an event, 508 00:16:59,000 --> 00:17:01,120 both physical and emotional. 509 00:17:01,120 --> 00:17:03,920 So for example, in the case of the Olympic athlete, 510 00:17:03,920 --> 00:17:06,560 the AI was able to infer that the athlete was probably 511 00:17:06,560 --> 00:17:09,640 feeling a surge of joy and excitement after winning the race. 512 00:17:09,640 --> 00:17:10,000 OK. 513 00:17:10,000 --> 00:17:10,920 That makes sense. 514 00:17:10,920 --> 00:17:12,560 But did it go beyond just recognizing 515 00:17:12,560 --> 00:17:13,840 those basic emotions? 516 00:17:13,840 --> 00:17:14,600 It did. 517 00:17:14,600 --> 00:17:17,720 It was able to infer more subtle details like the athletes 518 00:17:17,720 --> 00:17:19,960 elevated a heart rate and even the possibility 519 00:17:19,960 --> 00:17:21,720 of receiving endorsements. 520 00:17:21,720 --> 00:17:23,840 You know, it's almost like the AI was thinking 521 00:17:23,840 --> 00:17:26,280 through the ripple effects of the victory in a way 522 00:17:26,280 --> 00:17:28,680 that's similar to how a human might. 523 00:17:28,680 --> 00:17:29,280 Wow. 524 00:17:29,280 --> 00:17:32,120 So it wasn't just recognizing the event itself. 525 00:17:32,120 --> 00:17:34,440 It was able to understand the broader context 526 00:17:34,440 --> 00:17:37,040 and even anticipate potential consequences. 527 00:17:37,040 --> 00:17:37,960 That's pretty amazing. 528 00:17:37,960 --> 00:17:38,720 It is. 529 00:17:38,720 --> 00:17:41,080 And it suggests that the AI is developing 530 00:17:41,080 --> 00:17:44,960 this real capacity for reasoning about cause and effect, 531 00:17:44,960 --> 00:17:47,320 understanding that actions have consequences. 532 00:17:47,320 --> 00:17:47,880 Absolutely. 533 00:17:47,880 --> 00:17:50,760 We don't just perceive the world as this random series 534 00:17:50,760 --> 00:17:51,400 of events. 535 00:17:51,400 --> 00:17:53,920 We understand how those events are interconnected, 536 00:17:53,920 --> 00:17:55,800 how one action can lead to another. 537 00:17:55,800 --> 00:17:57,440 And it sounds like this AI is starting 538 00:17:57,440 --> 00:18:00,040 to develop that same ability to see the bigger picture. 539 00:18:00,040 --> 00:18:01,320 Exactly. 540 00:18:01,320 --> 00:18:05,080 But the AI's strengths weren't limited to factual impacts. 541 00:18:05,080 --> 00:18:07,160 It also did surprisingly well in grasping 542 00:18:07,160 --> 00:18:10,320 some of the more subtle aspects of human communication. 543 00:18:10,320 --> 00:18:12,280 Things like implied meaning. 544 00:18:12,280 --> 00:18:14,600 Oh, you mean like those times when we say one thing, 545 00:18:14,600 --> 00:18:15,520 but mean something else? 546 00:18:15,520 --> 00:18:16,960 Like when someone says, oh, fantastic, 547 00:18:16,960 --> 00:18:19,560 after spilling coffee all over themselves? 548 00:18:19,560 --> 00:18:20,440 That kind of thing. 549 00:18:20,440 --> 00:18:21,440 Exactly. 550 00:18:21,440 --> 00:18:23,560 We call those conversational implicatures. 551 00:18:23,560 --> 00:18:26,080 And they're super common in human language. 552 00:18:26,080 --> 00:18:28,320 And the AI was actually able to pick up 553 00:18:28,320 --> 00:18:29,920 on some of those implied meanings 554 00:18:29,920 --> 00:18:32,760 and incorporate that information into the knowledge graph. 555 00:18:32,760 --> 00:18:33,240 Wow. 556 00:18:33,240 --> 00:18:35,760 So it was able to read between the lines 557 00:18:35,760 --> 00:18:38,400 and understand that the literal words don't always 558 00:18:38,400 --> 00:18:40,040 tell the whole story. 559 00:18:40,040 --> 00:18:41,520 That's pretty remarkable. 560 00:18:41,520 --> 00:18:43,680 Do you have a specific example of how it did that? 561 00:18:43,680 --> 00:18:44,800 Of course. 562 00:18:44,800 --> 00:18:46,640 Remember that caption we talked about earlier 563 00:18:46,640 --> 00:18:49,680 about the exhausted but ecstatic athlete collapsing 564 00:18:49,680 --> 00:18:50,240 on the track? 565 00:18:50,240 --> 00:18:51,000 Yeah, I remember. 566 00:18:51,000 --> 00:18:53,080 Well, human reading that would naturally 567 00:18:53,080 --> 00:18:55,320 infer that the athlete had just finished a race, 568 00:18:55,320 --> 00:18:57,120 even though it's not explicitly stated. 569 00:18:57,120 --> 00:18:57,480 Right. 570 00:18:57,480 --> 00:18:59,480 It's just something we understand based on the context. 571 00:18:59,480 --> 00:19:00,240 Exactly. 572 00:19:00,240 --> 00:19:03,920 And the AI, guided by the conversational implicatures 573 00:19:03,920 --> 00:19:06,800 heuristic, was able to pick up on that implied meaning 574 00:19:06,800 --> 00:19:08,080 and add it to the knowledge graph. 575 00:19:08,080 --> 00:19:09,880 It was like the AI was filling in the gaps, 576 00:19:09,880 --> 00:19:12,520 using its understanding of how humans use language. 577 00:19:12,520 --> 00:19:13,200 That's really cool. 578 00:19:13,200 --> 00:19:15,960 It suggests that we're making some real progress toward AI 579 00:19:15,960 --> 00:19:18,360 that can genuinely understand the nuances 580 00:19:18,360 --> 00:19:20,440 of human communication. 581 00:19:20,440 --> 00:19:23,120 But you mentioned earlier that the AI also faced some challenges 582 00:19:23,120 --> 00:19:24,800 during the evaluation. 583 00:19:24,800 --> 00:19:26,840 What were some of the areas where it struggled? 584 00:19:26,840 --> 00:19:29,880 Well, as you might expect, some aspects of human cognition 585 00:19:29,880 --> 00:19:33,160 are proving to be more difficult to replicate than others. 586 00:19:33,160 --> 00:19:35,800 And one area where the AI ran into a bit of a wall 587 00:19:35,800 --> 00:19:37,760 was in predicting future events. 588 00:19:37,760 --> 00:19:39,520 Predicting the future. 589 00:19:39,520 --> 00:19:42,200 Yeah, that's a tough one, even for us humans. 590 00:19:42,200 --> 00:19:44,680 But isn't that a key part of human intelligence, 591 00:19:44,680 --> 00:19:47,200 being able to anticipate what might happen next 592 00:19:47,200 --> 00:19:48,680 based on what we know about the world? 593 00:19:48,680 --> 00:19:49,360 It is. 594 00:19:49,360 --> 00:19:51,880 And while the AI could make some educated guesses 595 00:19:51,880 --> 00:19:53,600 about what might happen, its predictions 596 00:19:53,600 --> 00:19:56,760 were often limited in their accuracy and nuance. 597 00:19:56,760 --> 00:19:59,440 It seems that capturing the full scope of our ability 598 00:19:59,440 --> 00:20:01,680 to anticipate and plan for the future 599 00:20:01,680 --> 00:20:03,480 is still a big challenge for AI. 600 00:20:03,480 --> 00:20:05,160 I can see why that would be tricky. 601 00:20:05,160 --> 00:20:07,640 There are just so many factors that can influence future events. 602 00:20:07,640 --> 00:20:10,520 And even we humans get it wrong all the time. 603 00:20:10,520 --> 00:20:12,440 So is this a sign that we're still a long way 604 00:20:12,440 --> 00:20:14,960 from achieving truly human-like AI? 605 00:20:14,960 --> 00:20:16,120 It's a great question. 606 00:20:16,120 --> 00:20:20,400 And it really highlights just how complex human reasoning is. 607 00:20:20,400 --> 00:20:24,240 Even with all the incredible advancements we've made in AI, 608 00:20:24,240 --> 00:20:26,240 there are still some fundamental aspects 609 00:20:26,240 --> 00:20:29,000 of human intelligence that we haven't quite figured out 610 00:20:29,000 --> 00:20:30,240 how to replicate. 611 00:20:30,240 --> 00:20:32,280 It's both exciting and a little bit humbling 612 00:20:32,280 --> 00:20:34,720 to realize that we've come so far, 613 00:20:34,720 --> 00:20:36,720 but there's still so much more to discover. 614 00:20:36,720 --> 00:20:37,560 Exactly. 615 00:20:37,560 --> 00:20:39,400 And it makes this whole field of research 616 00:20:39,400 --> 00:20:40,800 even more fascinating. 617 00:20:40,800 --> 00:20:41,640 I agree. 618 00:20:41,640 --> 00:20:43,400 So we talked about the AI strengths 619 00:20:43,400 --> 00:20:46,280 in capturing factual impacts and even those subtle 620 00:20:46,280 --> 00:20:48,240 conversational implicatures. 621 00:20:48,240 --> 00:20:50,120 But you mentioned that there were other areas 622 00:20:50,120 --> 00:20:51,160 where it struggled. 623 00:20:51,160 --> 00:20:52,880 What else did the evaluation reveal? 624 00:20:52,880 --> 00:20:55,400 Well, another area where the AI faced some difficulties 625 00:20:55,400 --> 00:20:56,920 was in dealing with those heuristics 626 00:20:56,920 --> 00:21:00,040 that involved moral judgments and social norms. 627 00:21:00,040 --> 00:21:01,400 Oh, that makes sense. 628 00:21:01,400 --> 00:21:03,920 Morality and social norms can be so complex 629 00:21:03,920 --> 00:21:05,080 and context-dependent. 630 00:21:05,080 --> 00:21:07,480 They can vary so much between cultures 631 00:21:07,480 --> 00:21:09,000 and even within the single culture, 632 00:21:09,000 --> 00:21:10,360 depending on the situation. 633 00:21:10,360 --> 00:21:10,960 Exactly. 634 00:21:10,960 --> 00:21:13,600 And while the AI had been trained on this massive data 635 00:21:13,600 --> 00:21:15,720 set of text, it hadn't yet developed 636 00:21:15,720 --> 00:21:18,480 the kind of nuanced understanding of social and cultural 637 00:21:18,480 --> 00:21:21,440 dynamics that would allow it to consistently make 638 00:21:21,440 --> 00:21:25,120 accurate judgments about what's considered right or wrong 639 00:21:25,120 --> 00:21:27,320 or what's appropriate behavior in different contexts. 640 00:21:27,320 --> 00:21:30,840 So it's like the AI was still learning the ropes 641 00:21:30,840 --> 00:21:33,240 of human social interaction, trying 642 00:21:33,240 --> 00:21:35,560 to figure out those often unspoken rules that 643 00:21:35,560 --> 00:21:36,560 govern our behavior. 644 00:21:36,560 --> 00:21:37,600 Precisely. 645 00:21:37,600 --> 00:21:39,720 And that's an ongoing challenge for AI research, 646 00:21:39,720 --> 00:21:42,320 figuring out how to build systems that are not only intelligent 647 00:21:42,320 --> 00:21:45,120 but also socially and culturally aware, 648 00:21:45,120 --> 00:21:48,600 systems that can understand the subtle cues and unspoken rules 649 00:21:48,600 --> 00:21:50,360 that shape human interactions. 650 00:21:50,360 --> 00:21:52,920 It sounds like we're still in the early stages of teaching AI 651 00:21:52,920 --> 00:21:56,080 how to navigate the complexities of human society. 652 00:21:56,080 --> 00:21:57,640 And it's definitely a challenge that 653 00:21:57,640 --> 00:22:00,520 requires us to think carefully about the ethical implications. 654 00:22:00,520 --> 00:22:01,360 Absolutely. 655 00:22:01,360 --> 00:22:02,960 But even with these challenges, the fact 656 00:22:02,960 --> 00:22:04,840 that we're starting to see AI systems that 657 00:22:04,840 --> 00:22:07,920 can grasp even some of these subtle aspects of human 658 00:22:07,920 --> 00:22:10,160 understanding is incredibly exciting. 659 00:22:10,160 --> 00:22:11,280 It really is. 660 00:22:11,280 --> 00:22:14,600 This deep dive has given me a whole new appreciation 661 00:22:14,600 --> 00:22:16,320 for the incredible progress that's 662 00:22:16,320 --> 00:22:20,040 being made in AI and the potential it holds for the future. 663 00:22:20,040 --> 00:22:22,000 It's a field that's constantly evolving 664 00:22:22,000 --> 00:22:23,960 and it's full of surprises. 665 00:22:23,960 --> 00:22:26,120 It's amazing to see how researchers are pushing 666 00:22:26,120 --> 00:22:28,120 the boundaries of what's possible. 667 00:22:28,120 --> 00:22:29,680 And it's even more amazing to think 668 00:22:29,680 --> 00:22:31,960 about what the future holds for AI 669 00:22:31,960 --> 00:22:34,040 and its role in our lives. 670 00:22:34,040 --> 00:22:36,200 This has been a truly mind-expanding conversation. 671 00:22:36,200 --> 00:22:39,360 Thank you so much for sharing your insights and expertise 672 00:22:39,360 --> 00:22:41,800 on this cutting-edge research. 673 00:22:41,800 --> 00:22:42,880 It's been my pleasure. 674 00:22:42,880 --> 00:22:44,640 And thank you for joining us on this deep dive 675 00:22:44,640 --> 00:22:47,280 into the fascinating world of neuro-symbolic AI 676 00:22:47,280 --> 00:22:49,120 and grounded world models. 677 00:22:49,120 --> 00:22:50,400 We hope you've enjoyed the journey. 678 00:22:50,400 --> 00:22:52,080 It's been a wild ride. 679 00:22:52,080 --> 00:22:53,800 And to our listeners out there, we encourage you 680 00:22:53,800 --> 00:22:56,640 to keep asking questions, keep exploring, 681 00:22:56,640 --> 00:23:00,040 and stay curious about the ever-evolving world of AI. 682 00:23:00,040 --> 00:23:02,680 So we talked about the AI's strengths, 683 00:23:02,680 --> 00:23:04,720 being able to capture those factual impacts 684 00:23:04,720 --> 00:23:07,720 and even those subtle conversational implicatures. 685 00:23:07,720 --> 00:23:09,080 But you mentioned there were also some areas 686 00:23:09,080 --> 00:23:09,920 where it struggled. 687 00:23:09,920 --> 00:23:11,440 Let's dive into that a bit. 688 00:23:11,440 --> 00:23:13,360 What were some of the things that tripped the AI up 689 00:23:13,360 --> 00:23:15,000 during the evaluation? 690 00:23:15,000 --> 00:23:17,360 Yeah, well, one of the biggest hurdles for the AI 691 00:23:17,360 --> 00:23:19,280 was predicting future events. 692 00:23:20,280 --> 00:23:22,760 You know, it seems like replicating that human ability 693 00:23:22,760 --> 00:23:25,240 to anticipate what might happen next 694 00:23:25,240 --> 00:23:27,600 based on our understanding of the world. 695 00:23:27,600 --> 00:23:30,360 That's still a really tough nut to crack for AI. 696 00:23:30,360 --> 00:23:32,080 I can see why that would be so challenging. 697 00:23:32,080 --> 00:23:34,280 Even for us, humans predicting the future 698 00:23:34,280 --> 00:23:36,080 is notoriously difficult. 699 00:23:36,080 --> 00:23:37,640 We can make educated guesses 700 00:23:37,640 --> 00:23:40,080 and try to plan for different possibilities, 701 00:23:40,080 --> 00:23:42,200 but there are always those unexpected curve balls 702 00:23:42,200 --> 00:23:44,360 that come along and throw us off course. 703 00:23:44,360 --> 00:23:45,240 Exactly. 704 00:23:45,240 --> 00:23:47,760 And it seems the AI ran into similar difficulties. 705 00:23:47,760 --> 00:23:49,880 You know, it could make some reasonable predictions 706 00:23:49,880 --> 00:23:51,720 about what might happen next, 707 00:23:51,720 --> 00:23:54,840 but its accuracy and its nuance were often limited. 708 00:23:54,840 --> 00:23:57,520 So it's like the AI was still learning to connect the dots 709 00:23:57,520 --> 00:23:59,480 between cause and effect. 710 00:23:59,480 --> 00:24:01,280 It could understand that certain actions 711 00:24:01,280 --> 00:24:03,400 might lead to certain outcomes, 712 00:24:03,400 --> 00:24:06,000 but it wasn't quite able to grasp the full complexity 713 00:24:06,000 --> 00:24:08,160 of how events unfold over time. 714 00:24:08,160 --> 00:24:09,280 That's a great way to put it. 715 00:24:09,280 --> 00:24:10,760 And it really highlights the fact 716 00:24:10,760 --> 00:24:13,720 that even though we've made some incredible strides in AI, 717 00:24:13,720 --> 00:24:15,440 there are still some fundamental aspects 718 00:24:15,440 --> 00:24:16,520 of human intelligence 719 00:24:16,520 --> 00:24:19,080 that we haven't quite figured out how to replicate. 720 00:24:19,080 --> 00:24:20,920 It's kind of humbling to realize, that isn't it? 721 00:24:20,920 --> 00:24:22,960 It is, you know, it makes you appreciate 722 00:24:22,960 --> 00:24:25,320 just how complex and sophisticated 723 00:24:25,320 --> 00:24:26,640 the human mind really is. 724 00:24:26,640 --> 00:24:27,920 Absolutely. 725 00:24:27,920 --> 00:24:29,200 Were there any other areas 726 00:24:29,200 --> 00:24:31,360 where the AI encountered difficulties? 727 00:24:31,360 --> 00:24:32,680 Yes. 728 00:24:32,680 --> 00:24:34,560 Another area where the AI struggled 729 00:24:34,560 --> 00:24:36,360 was in dealing with those heuristics 730 00:24:36,360 --> 00:24:39,480 that involved moral judgments and social norms. 731 00:24:39,480 --> 00:24:41,040 Ah, that makes sense. 732 00:24:41,040 --> 00:24:43,760 Morality and social norms can be so subjective 733 00:24:43,760 --> 00:24:45,480 and so context dependent. 734 00:24:45,480 --> 00:24:47,360 They can vary so much between cultures. 735 00:24:47,360 --> 00:24:48,760 And even within a single culture, 736 00:24:48,760 --> 00:24:50,720 things can change depending on the situation. 737 00:24:50,720 --> 00:24:51,560 Exactly. 738 00:24:51,560 --> 00:24:53,600 And while the AI had been trained 739 00:24:53,600 --> 00:24:55,840 on this massive data set of text, 740 00:24:55,840 --> 00:24:58,400 it hadn't yet developed that kind of nuanced understanding 741 00:24:58,400 --> 00:25:00,240 of social and cultural dynamics 742 00:25:00,240 --> 00:25:02,880 that would allow it to make those really accurate judgments 743 00:25:02,880 --> 00:25:04,280 about what's right or wrong 744 00:25:04,280 --> 00:25:06,440 or what's considered appropriate behavior 745 00:25:06,440 --> 00:25:07,600 in different contexts. 746 00:25:07,600 --> 00:25:09,400 So it's like the AI was still learning 747 00:25:09,400 --> 00:25:11,800 the unspoken rules of human interaction. 748 00:25:11,800 --> 00:25:14,440 You know, trying to decipher those subtle cues 749 00:25:14,440 --> 00:25:15,960 and unwritten codes of conduct 750 00:25:15,960 --> 00:25:17,280 that we often take for granted. 751 00:25:17,280 --> 00:25:18,400 That's a great analogy. 752 00:25:18,400 --> 00:25:21,320 It's like the AI was trying to learn this whole new language, 753 00:25:21,320 --> 00:25:23,760 but it wasn't just about the words themselves. 754 00:25:23,760 --> 00:25:25,960 It was about the subtle inflections, 755 00:25:25,960 --> 00:25:28,320 the unspoken rules of grammar. 756 00:25:28,320 --> 00:25:30,800 And, you know, that whole cultural context 757 00:25:30,800 --> 00:25:32,480 that gives those words meaning. 758 00:25:32,480 --> 00:25:33,840 It's fascinating to think about 759 00:25:33,840 --> 00:25:37,600 how we might even begin to teach a machine those things. 760 00:25:37,600 --> 00:25:39,720 You know, it's not just about feeding it data. 761 00:25:39,720 --> 00:25:42,160 It's about helping it develop a deeper understanding 762 00:25:42,160 --> 00:25:45,320 of human values and beliefs and motivations. 763 00:25:45,320 --> 00:25:46,160 Absolutely. 764 00:25:46,160 --> 00:25:48,680 And that's where things start to get really interesting 765 00:25:48,680 --> 00:25:49,960 because it forces us to ask 766 00:25:49,960 --> 00:25:52,520 some pretty fundamental questions about ourselves. 767 00:25:52,520 --> 00:25:55,320 What are the core values that shape our societies? 768 00:25:55,320 --> 00:25:57,600 How do we form our moral judgments? 769 00:25:57,600 --> 00:26:00,640 And how can we translate those often intangible concepts 770 00:26:00,640 --> 00:26:02,320 into something that a machine can grasp? 771 00:26:02,320 --> 00:26:05,400 It's like we're holding a mirror up to our own humanity in a way, 772 00:26:05,400 --> 00:26:07,760 trying to define those qualities that make us unique 773 00:26:07,760 --> 00:26:10,200 and then figuring out how to instill those same qualities 774 00:26:10,200 --> 00:26:11,160 in our machines. 775 00:26:11,160 --> 00:26:12,880 That's a beautiful way to put it. 776 00:26:12,880 --> 00:26:15,000 And I think that's one of the most exciting aspects 777 00:26:15,000 --> 00:26:15,800 of this research. 778 00:26:15,800 --> 00:26:18,400 It's not just about building smarter machines. 779 00:26:18,400 --> 00:26:20,880 It's also about deepening our own understanding 780 00:26:20,880 --> 00:26:22,480 of what it means to be human. 781 00:26:22,480 --> 00:26:25,400 This deep dive has been a real eye-opener for me. 782 00:26:25,400 --> 00:26:28,960 It's incredible to see just how far AI has come. 783 00:26:28,960 --> 00:26:32,600 And it's really exciting to think about how close we're getting 784 00:26:32,600 --> 00:26:35,840 to creating systems that can truly understand the world 785 00:26:35,840 --> 00:26:37,240 through a human lens. 786 00:26:37,240 --> 00:26:39,280 It's an amazing field to be working in. 787 00:26:39,280 --> 00:26:42,280 And the pace of innovation is just mind-blowing. 788 00:26:42,280 --> 00:26:44,360 It's hard to even imagine what breakthroughs 789 00:26:44,360 --> 00:26:45,920 might be just around the corner. 790 00:26:45,920 --> 00:26:47,520 It's both thrilling and a little bit daunting 791 00:26:47,520 --> 00:26:48,360 to think about that, isn't it? 792 00:26:48,360 --> 00:26:49,120 It is. 793 00:26:49,120 --> 00:26:50,520 But one thing's for sure. 794 00:26:50,520 --> 00:26:53,880 This journey of understanding AI is just beginning. 795 00:26:53,880 --> 00:26:55,960 And it's a journey that's going to continue to challenge 796 00:26:55,960 --> 00:26:57,760 and inspire us for years to come. 797 00:26:57,760 --> 00:26:58,880 Well said. 798 00:26:58,880 --> 00:27:01,480 And on that note, we've reached the end of our deep dive 799 00:27:01,480 --> 00:27:05,480 into the world of neurosymbolic AI and grounded world models. 800 00:27:05,480 --> 00:27:07,960 We hope you've enjoyed exploring these concepts with us. 801 00:27:07,960 --> 00:27:08,800 Thanks for joining us. 802 00:27:08,800 --> 00:27:10,160 It's been a great conversation. 803 00:27:10,160 --> 00:27:11,240 It has. 804 00:27:11,240 --> 00:27:13,440 And to all our listeners out there, remember, 805 00:27:13,440 --> 00:27:15,040 keep asking those questions. 806 00:27:15,040 --> 00:27:16,520 Keep exploring. 807 00:27:16,520 --> 00:27:19,040 And most importantly, stay curious about the ever-evolving 808 00:27:19,040 --> 00:27:24,040 world of AI.