1 00:00:00,000 --> 00:00:06,600 AI papers podcast daily listeners ready for a deep dive into AI and keeping construction workers safe. 2 00:00:06,960 --> 00:00:12,760 Yeah, this research paper we're looking at today asks, can AI actually prevent accidents on job sites? 3 00:00:13,120 --> 00:00:21,880 So the paper is called Responsible AI in Construction Safety, systematic evaluation of large language models and prompt engineering. 4 00:00:21,960 --> 00:00:26,560 And they put some popular AI models like GPT 3.5 and GPT 4 to the test. 5 00:00:26,560 --> 00:00:30,880 They had these AI models take real safety certification exams. 6 00:00:30,880 --> 00:00:34,320 Yeah, the ones from the Board of Certified Safety Professionals, the BCST. 7 00:00:34,320 --> 00:00:36,560 So the same ones that human safety professionals take. 8 00:00:36,560 --> 00:00:37,120 Exactly. 9 00:00:37,120 --> 00:00:38,560 Why is this research so important? 10 00:00:38,560 --> 00:00:41,680 Well, construction is one of the most dangerous industries out there. 11 00:00:41,680 --> 00:00:43,920 Yeah, it's known for having a lot of workplace accidents. 12 00:00:43,920 --> 00:00:46,720 And there's a shortage of qualified safety people too, so. 13 00:00:46,720 --> 00:00:49,040 So could AI help fill that gap? 14 00:00:49,040 --> 00:00:50,640 That's what researchers are trying to figure out. 15 00:00:50,640 --> 00:00:54,080 They're looking at how AI specifically, these large language models. 16 00:00:54,080 --> 00:00:54,720 LLMs. 17 00:00:54,720 --> 00:00:58,960 Right, LLMs could be used to identify hazards, assess risks. 18 00:00:58,960 --> 00:01:00,480 To hopefully prevent accidents. 19 00:01:00,480 --> 00:01:01,040 Exactly. 20 00:01:01,040 --> 00:01:04,400 For those of us who aren't AI experts, what exactly are these LLMs? 21 00:01:04,400 --> 00:01:07,360 Basically, they're incredibly powerful language processors. 22 00:01:07,360 --> 00:01:11,120 They've been trained on massive amounts of text data. 23 00:01:11,120 --> 00:01:12,720 Went from books, articles, websites. 24 00:01:12,720 --> 00:01:15,120 Followed that billions of words. 25 00:01:15,120 --> 00:01:18,880 Wow, so they can understand and generate human-like text. 26 00:01:18,880 --> 00:01:24,480 Yeah, you can ask them questions, give them prompts, and they'll respond in a way that sounds like a person wrote it. 27 00:01:24,480 --> 00:01:27,760 So they'll get really advanced version of autocomplete on your phone? 28 00:01:27,760 --> 00:01:30,320 Kind of, but way more sophisticated. 29 00:01:30,320 --> 00:01:32,880 These LLMs can write different kinds of creative text. 30 00:01:32,880 --> 00:01:34,320 They can translate languages. 31 00:01:34,320 --> 00:01:36,160 They can even generate computer code. 32 00:01:36,160 --> 00:01:37,840 It's really amazing what they can do. 33 00:01:37,840 --> 00:01:43,440 So these LLMs, GPT 3.5 and 4, are the stars of this research paper. 34 00:01:43,440 --> 00:01:47,120 And the big question is, can they pass these safety exams? 35 00:01:47,120 --> 00:01:48,480 Yeah, that's what they wanted to find out. 36 00:01:48,480 --> 00:01:55,840 The researchers pulled together 385 multiple choice questions from three different BCSP certification exams. 37 00:01:55,840 --> 00:01:57,520 And these questions cover a lot of ground, right? 38 00:01:57,520 --> 00:02:00,000 Oh yeah, seven core areas of safety knowledge. 39 00:02:00,000 --> 00:02:04,400 Everything from management systems and hazard identification to ergonomics. 40 00:02:04,400 --> 00:02:06,640 Emergency response, even the legal side of things. 41 00:02:06,640 --> 00:02:08,320 It's a comprehensive test. 42 00:02:08,320 --> 00:02:10,320 So to pass, you need to really know your stuff. 43 00:02:10,320 --> 00:02:11,120 Exactly. 44 00:02:11,120 --> 00:02:13,680 They wanted to see if these AI models could handle it. 45 00:02:13,680 --> 00:02:14,720 So did they pass? 46 00:02:14,720 --> 00:02:18,240 Both GPT 3.5 and GPT 4, did they both scored higher 47 00:02:18,240 --> 00:02:20,000 than the 60% passing score? 48 00:02:20,000 --> 00:02:20,560 No way. 49 00:02:20,560 --> 00:02:23,680 GPT 4 actually got an 84.6%. 50 00:02:24,240 --> 00:02:27,440 Wow, that's even better than the average score for humans. 51 00:02:27,440 --> 00:02:28,560 It's pretty impressive. 52 00:02:28,560 --> 00:02:33,120 It shows that AI has the potential to be a really valuable tool in construction safety. 53 00:02:33,120 --> 00:02:34,080 That's exciting to hear. 54 00:02:34,640 --> 00:02:38,800 So does this mean AI could replace human safety professionals? 55 00:02:38,800 --> 00:02:39,520 Not quite. 56 00:02:39,520 --> 00:02:42,800 It's more about AI supporting and assisting human experts. 57 00:02:42,800 --> 00:02:44,240 So working together as a team. 58 00:02:44,240 --> 00:02:44,880 Exactly. 59 00:02:44,880 --> 00:02:50,560 AI can help with things like data analysis, identifying hazards and suggesting safety measures. 60 00:02:50,560 --> 00:02:53,200 But human judgment and experience are still crucial. 61 00:02:53,200 --> 00:02:56,880 Absolutely, especially in those critical situations where quick thinking and decision 62 00:02:56,880 --> 00:02:57,760 making are needed. 63 00:02:57,760 --> 00:02:58,560 Makes sense. 64 00:02:58,560 --> 00:03:00,880 So AI isn't perfect, it has its limitations. 65 00:03:00,880 --> 00:03:01,360 Right. 66 00:03:01,360 --> 00:03:04,640 The research also looked into the types of errors the AI made. 67 00:03:04,640 --> 00:03:06,240 They found four main categories. 68 00:03:06,240 --> 00:03:07,120 What were they? 69 00:03:07,120 --> 00:03:07,840 Lack of knowledge. 70 00:03:07,840 --> 00:03:11,520 Sometimes the AI just didn't have the information needed to answer the questions. 71 00:03:11,520 --> 00:03:12,320 Okay, what else? 72 00:03:12,320 --> 00:03:16,560 Reasoning flaws, even when it had the right information, it could make illogical jumps 73 00:03:16,560 --> 00:03:17,920 or wrong assumptions. 74 00:03:17,920 --> 00:03:20,080 So it could get tripped up by tricky questions. 75 00:03:20,080 --> 00:03:23,840 Right, and then there were memory issues, sometimes it would forget important details 76 00:03:23,840 --> 00:03:25,600 when working on complex problems. 77 00:03:25,600 --> 00:03:26,960 That happens to the best of us. 78 00:03:26,960 --> 00:03:30,640 And believe it or not, even AI can make calculation errors. 79 00:03:30,640 --> 00:03:32,960 Really, I thought AI was supposed to be great at math. 80 00:03:32,960 --> 00:03:36,400 They usually are, but they can still make mistakes just like humans. 81 00:03:36,400 --> 00:03:40,480 So we can't just blindly trust AI to handle all the safety calculations. 82 00:03:40,480 --> 00:03:41,040 Exactly. 83 00:03:41,040 --> 00:03:43,120 Human oversight is still essential. 84 00:03:43,120 --> 00:03:46,160 It's about using AI as a partner, not a replacement. 85 00:03:47,200 --> 00:03:48,480 That's a good way to put it. 86 00:03:48,480 --> 00:03:50,880 Can you give us some examples of how the AI messed up? 87 00:03:50,880 --> 00:03:53,440 Like what kinds of questions did it get wrong? 88 00:03:54,480 --> 00:04:00,880 Sure, one area where it struggled was emergency response construction sites can be unpredictable 89 00:04:00,880 --> 00:04:04,880 and knowing how to respond quickly and effectively in an emergency is crucial. 90 00:04:04,880 --> 00:04:06,160 Yeah, that makes sense. 91 00:04:06,160 --> 00:04:10,720 The AI sometimes had trouble with questions that required that kind of on-the-spot thinking. 92 00:04:10,720 --> 00:04:14,320 Like knowing what to do if there's a fire or a serious injury. 93 00:04:14,320 --> 00:04:14,800 Exactly. 94 00:04:14,800 --> 00:04:18,960 Those are situations where human judgment and experience are really important. 95 00:04:19,680 --> 00:04:22,240 What about other areas where the AI had trouble? 96 00:04:22,240 --> 00:04:24,720 It also struggled with fire prevention questions. 97 00:04:24,720 --> 00:04:25,520 Fire prevention. 98 00:04:25,520 --> 00:04:29,840 Yeah, things like understanding how fires start, how they spread and what measures you can take 99 00:04:29,840 --> 00:04:31,200 to prevent them on a job site. 100 00:04:31,920 --> 00:04:36,800 So knowing where to store flammable materials, how to use fire extinguishers, that kind of thing. 101 00:04:36,800 --> 00:04:42,160 Yeah, it seems like the AI needs more real-world experience in that area to really master it. 102 00:04:42,160 --> 00:04:45,360 So AI has potential, but it's not a magic bullet. 103 00:04:45,360 --> 00:04:46,240 That's right. 104 00:04:46,240 --> 00:04:48,720 It has its strengths and weaknesses, like any tool. 105 00:04:48,720 --> 00:04:53,360 And the research also looked into ways to improve AI's performance 106 00:04:53,360 --> 00:04:55,520 through something called prompt engineering. 107 00:04:55,520 --> 00:05:00,000 Prompt engineering, that's a fancy way of saying how we give instructions to the AI. 108 00:05:00,000 --> 00:05:01,280 How we talk to it. 109 00:05:01,280 --> 00:05:06,720 Exactly the way you phrase a question or a command can have a big impact on how well the AI 110 00:05:06,720 --> 00:05:08,080 understands and responds. 111 00:05:08,640 --> 00:05:10,160 It's like speaking a different language. 112 00:05:10,160 --> 00:05:10,800 You could say that. 113 00:05:10,800 --> 00:05:13,600 So it's not just about what you ask, but how you ask it. 114 00:05:13,600 --> 00:05:14,320 Exactly. 115 00:05:14,320 --> 00:05:19,200 And the researchers in this paper tested different prompting techniques to see if they could get 116 00:05:19,200 --> 00:05:23,200 better results from the AI on these safety exams. 117 00:05:23,200 --> 00:05:25,600 What kind of prompting techniques did they try? 118 00:05:25,600 --> 00:05:30,320 They looked at direct prompting, chain of thought prompting and few shot prompting. 119 00:05:30,320 --> 00:05:31,040 Those sound interesting. 120 00:05:31,040 --> 00:05:32,160 Can you break them down for us? 121 00:05:32,160 --> 00:05:32,560 Sure. 122 00:05:32,560 --> 00:05:34,560 Direct prompting is the simplest approach. 123 00:05:34,560 --> 00:05:37,120 You just ask the AI a question directly. 124 00:05:37,120 --> 00:05:39,680 Like what are the safety regulations for working at heights? 125 00:05:39,680 --> 00:05:40,320 Exactly. 126 00:05:40,320 --> 00:05:42,000 Then there's chain of thought prompting. 127 00:05:42,000 --> 00:05:43,200 Chain of thought prompting. 128 00:05:43,200 --> 00:05:43,440 Yeah. 129 00:05:43,440 --> 00:05:50,080 It's about encouraging the AI to think step by step like a human would before giving an answer. 130 00:05:50,080 --> 00:05:54,240 So instead of jumping to conclusions, it walks through the problem logically. 131 00:05:54,240 --> 00:05:54,640 Right. 132 00:05:54,640 --> 00:06:00,160 And this can help the AI avoid making those illogical jumps or missing important details. 133 00:06:00,160 --> 00:06:01,040 That's a cool idea. 134 00:06:01,040 --> 00:06:03,920 So it's like giving the AI a mental checklist to follow. 135 00:06:03,920 --> 00:06:04,480 Exactly. 136 00:06:04,480 --> 00:06:06,160 And then there's few shot prompting. 137 00:06:06,160 --> 00:06:07,040 Few shot prompting. 138 00:06:07,040 --> 00:06:07,360 Yeah. 139 00:06:07,360 --> 00:06:12,000 With this one, you give the AI a few examples of correctly answered questions before asking 140 00:06:12,000 --> 00:06:12,560 it a new one. 141 00:06:12,560 --> 00:06:17,120 So it's like showing the AI how it's done before letting it try on its own. 142 00:06:17,120 --> 00:06:17,680 Exactly. 143 00:06:17,680 --> 00:06:20,480 It's a way to give it some context and guidance. 144 00:06:20,480 --> 00:06:23,040 So which prompting technique worked best? 145 00:06:23,040 --> 00:06:24,480 Was there a clear winner? 146 00:06:24,480 --> 00:06:26,240 Well, that's where things get interesting. 147 00:06:26,240 --> 00:06:32,000 It turned out that no single prompting method was the best across all the questions. 148 00:06:32,000 --> 00:06:35,280 So there's no one size fits all approach to talking to AI. 149 00:06:35,280 --> 00:06:38,800 It seems that way we're still learning how to communicate with these systems to get the 150 00:06:38,800 --> 00:06:39,920 best responses. 151 00:06:39,920 --> 00:06:42,400 I mean, it's like any new technology, there's a learning curve. 152 00:06:42,400 --> 00:06:43,040 Yeah. 153 00:06:43,040 --> 00:06:48,160 But going back to those safety exams, I'm still amazed that these AI models were able to pass them. 154 00:06:48,160 --> 00:06:49,120 It's pretty impressive, right? 155 00:06:49,120 --> 00:06:51,360 What specific areas did they do well in? 156 00:06:51,360 --> 00:06:56,560 They excelled in topics like management systems and hazard identification and control. 157 00:06:56,560 --> 00:07:01,760 So things like understanding regulations, best practices, risk assessment. 158 00:07:01,760 --> 00:07:07,760 Yeah. Those are areas where AI can really shine because it can process and analyze so much information. 159 00:07:07,760 --> 00:07:13,600 So it's like having an AI safety inspector that can scan through mountains of data and flag 160 00:07:13,600 --> 00:07:14,720 potential problems. 161 00:07:14,720 --> 00:07:18,320 That's a great analogy that AI could spot hazards that humans might miss. 162 00:07:18,320 --> 00:07:18,800 That's pretty cool. 163 00:07:18,800 --> 00:07:20,800 They also did really well in ergonomics. 164 00:07:20,800 --> 00:07:21,520 Ergonomics. 165 00:07:21,520 --> 00:07:26,640 Yeah. That's about design and work environments and tasks to minimize strain and injury. 166 00:07:26,640 --> 00:07:31,040 Oh, right. Making sure people aren't lifting things that are too heavy or working in awkward 167 00:07:31,040 --> 00:07:31,680 positions. 168 00:07:31,680 --> 00:07:37,040 Exactly. And AI could help analyze work processes and identify those kinds of risks. 169 00:07:37,040 --> 00:07:40,080 That's important. Construction work can be very physically demanding. 170 00:07:40,080 --> 00:07:41,200 Absolutely. 171 00:07:41,200 --> 00:07:45,440 It's great that the AI did well in those areas, but I keep thinking about the areas where it 172 00:07:45,440 --> 00:07:49,600 struggled like emergency response and fire prevention. 173 00:07:49,600 --> 00:07:51,440 Yeah. Those are definitely concerning. 174 00:07:51,440 --> 00:07:53,600 What are the implications of those weaknesses? 175 00:07:53,600 --> 00:07:57,760 I think it reinforces the point that AI isn't meant to replace human experts, 176 00:07:57,760 --> 00:08:00,320 especially in those critical situations. 177 00:08:00,320 --> 00:08:06,240 So when lives are on the line, we still need human judgment experience, quick thinking. 178 00:08:06,240 --> 00:08:12,000 Exactly. We need to use AI responsibly, understanding its limitations and focusing on its strengths. 179 00:08:12,000 --> 00:08:16,320 It sounds like we need a balanced approach to integrating AI into construction safety. 180 00:08:16,320 --> 00:08:21,680 Definitely. It's about finding the right balance between human oversight and AI assistance. 181 00:08:21,680 --> 00:08:26,240 We need to see AI as a partner, a tool that can help us, but not replace us. 182 00:08:26,240 --> 00:08:28,320 You mentioned earlier the concept of entropy. 183 00:08:28,320 --> 00:08:33,120 The paper talks about using entropy to measure the AI's consistency. 184 00:08:33,120 --> 00:08:33,600 Right. 185 00:08:33,600 --> 00:08:35,760 Can you explain what that means? 186 00:08:35,760 --> 00:08:42,000 Sure. Entropy in this context is a way of measuring how predictable the AI's answers are. 187 00:08:42,000 --> 00:08:42,720 Okay. 188 00:08:42,720 --> 00:08:45,840 If you ask a human safety expert the same question multiple times, 189 00:08:45,840 --> 00:08:49,760 you'd expect them to give you pretty much the same answer each time, right? 190 00:08:49,760 --> 00:08:52,400 Yeah. They wouldn't be all over the place with their responses. 191 00:08:52,400 --> 00:08:56,640 Right. And that's what we want from AI systems too. We want them to be consistent and reliable. 192 00:08:56,640 --> 00:08:58,000 Like a seasoned professional. 193 00:08:58,000 --> 00:09:02,080 Exactly. Low entropy means the AI's answers are very consistent. 194 00:09:02,640 --> 00:09:06,160 High entropy means the answers are more unpredictable. 195 00:09:06,160 --> 00:09:09,920 And in safety critical applications, unpredictable isn't good. 196 00:09:09,920 --> 00:09:10,640 Not at all. 197 00:09:10,640 --> 00:09:14,880 So in this research, did the AI models show high entropy or low entropy? 198 00:09:14,880 --> 00:09:18,480 They both showed very low entropy even when answering tough questions. 199 00:09:18,480 --> 00:09:19,440 That's good to hear. 200 00:09:19,440 --> 00:09:24,960 Yeah. It means we can rely on these AI systems to provide accurate and consistent information, 201 00:09:24,960 --> 00:09:27,120 at least in the areas they were tested on. 202 00:09:27,120 --> 00:09:30,080 That's reassuring consistency is key when it comes to safety. 203 00:09:30,080 --> 00:09:31,840 But even with these encouraging findings, 204 00:09:32,400 --> 00:09:37,120 there are still some big questions about the role of AI in construction safety. 205 00:09:37,120 --> 00:09:38,640 Lots of questions to consider. 206 00:09:38,640 --> 00:09:43,760 How do we make sure that these AI systems are being used ethically and responsibly? 207 00:09:43,760 --> 00:09:45,280 That's a crucial question. 208 00:09:45,280 --> 00:09:48,800 And what are the long-term effects on the construction industry as a whole? 209 00:09:48,800 --> 00:09:53,280 Yeah. What happens to jobs and training and the way we approach safety? 210 00:09:53,280 --> 00:09:54,960 Those are all really important questions. 211 00:09:54,960 --> 00:09:58,560 And those are the kinds of things we'll be discussing in the final part of our deep dive. 212 00:09:58,560 --> 00:10:02,080 So we've been talking about this research on AI in construction safety, 213 00:10:02,720 --> 00:10:05,840 and it's clear that AI has a lot of potential. 214 00:10:05,840 --> 00:10:10,880 Yeah, but there are also some big challenges and ethical questions we need to address. 215 00:10:10,880 --> 00:10:15,440 Like how do we make sure that AI is being used responsibly 216 00:10:15,440 --> 00:10:17,760 in a field where people's lives are at stake? 217 00:10:17,760 --> 00:10:18,800 It's a big responsibility. 218 00:10:18,800 --> 00:10:20,640 What kind of safeguards do we need to put in place? 219 00:10:20,640 --> 00:10:24,160 Well, I think it starts with recognizing that AI is a tool 220 00:10:24,160 --> 00:10:27,520 and any tool can be used for good or bad. 221 00:10:27,520 --> 00:10:29,120 It's not inherently good or bad. 222 00:10:29,120 --> 00:10:30,560 It depends on how we use it. 223 00:10:30,560 --> 00:10:34,000 Exactly. We need to be very careful and thoughtful about how we 224 00:10:35,920 --> 00:10:38,240 deploy and oversee these AI systems. 225 00:10:38,240 --> 00:10:41,600 It's not just about the technology itself, but also the human element, 226 00:10:41,600 --> 00:10:43,840 the people who are designing, implementing, and using it. 227 00:10:43,840 --> 00:10:44,640 Exactly. 228 00:10:44,640 --> 00:10:50,720 We need clear ethical guidelines that prioritize worker safety, fairness, transparency. 229 00:10:50,720 --> 00:10:53,520 Can you give us some examples of what those guidelines might look like? 230 00:10:53,520 --> 00:10:59,440 Sure. One important principle is to make sure that AI systems aren't biased against certain groups of workers. 231 00:10:59,440 --> 00:11:02,640 Right. AI shouldn't create hazardous situations for anyone. 232 00:11:02,640 --> 00:11:07,120 We also need to ensure that AI systems are transparent in their decision making. 233 00:11:07,120 --> 00:11:09,920 So if an AI flags a potential safety hazard, 234 00:11:09,920 --> 00:11:12,160 we should be able to understand why it's doing that. 235 00:11:12,160 --> 00:11:14,720 Yeah. What data is using what logic it's following? 236 00:11:14,720 --> 00:11:16,240 So it can trust its judgment. 237 00:11:16,240 --> 00:11:20,480 Exactly. Black box AI systems that make decisions without clear explanations 238 00:11:20,480 --> 00:11:22,960 aren't acceptable when people's lives are on the line. 239 00:11:22,960 --> 00:11:26,320 That's a good point. Transparency and accountability are crucial. 240 00:11:26,320 --> 00:11:30,800 But how do we actually implement these ethical guidelines in the real world? 241 00:11:30,800 --> 00:11:37,680 It's going to take a lot of collaboration between AI experts, safety professionals, policy makers, 242 00:11:37,680 --> 00:11:39,520 and of course construction workers themselves. 243 00:11:39,520 --> 00:11:41,040 So everyone needs to be on board. 244 00:11:41,040 --> 00:11:46,320 We need to work together to establish clear standards for AI development testing and deployment. 245 00:11:46,320 --> 00:11:51,600 And we need ways to monitor those AI systems and address any problems that come up. 246 00:11:51,600 --> 00:11:52,960 It's an ongoing process. 247 00:11:52,960 --> 00:11:55,120 It sounds like a complex undertaking. 248 00:11:55,120 --> 00:11:59,120 But if we can get this right, the potential benefits are enormous. 249 00:11:59,120 --> 00:12:02,480 Definitely. AI could truly revolutionize construction safety. 250 00:12:02,480 --> 00:12:06,400 Imagine a future where AI helps prevent accidents before they happen, 251 00:12:06,400 --> 00:12:10,800 where construction sites are not only more efficient, but also much safer for everyone. 252 00:12:10,800 --> 00:12:11,360 That's the goal, babe. 253 00:12:11,360 --> 00:12:13,360 That's a future worth working towards. 254 00:12:13,360 --> 00:12:15,280 But it's not just about preventing accidents. 255 00:12:15,280 --> 00:12:18,640 It's also about improving the overall well-being of construction workers. 256 00:12:18,640 --> 00:12:20,720 Right. AI could help us create work environments 257 00:12:20,720 --> 00:12:24,880 that are not only safe, but also healthier and more ergonomically sound. 258 00:12:24,880 --> 00:12:27,760 So less strain, less fatigue, fewer injuries. 259 00:12:27,760 --> 00:12:28,480 Exactly. 260 00:12:28,480 --> 00:12:33,040 And as AI technology continues to advance, the possibilities are pretty much endless. 261 00:12:33,040 --> 00:12:35,680 It's exciting to think about what the future holds. 262 00:12:35,680 --> 00:12:41,840 But we need to proceed with caution and always keep ethical considerations at the forefront of our minds. 263 00:12:41,840 --> 00:12:46,640 Couldn't agree more. AI is a powerful tool, but it's up to us to use it wisely. 264 00:12:46,640 --> 00:12:49,760 This deep dive has definitely given me a lot to think about. 265 00:12:49,760 --> 00:12:52,160 What's your biggest takeaway from this research? 266 00:12:52,160 --> 00:12:55,200 For me, it's a reminder that AI isn't a silver bullet. 267 00:12:55,200 --> 00:12:58,080 It's a tool that can augment human capabilities, 268 00:12:58,080 --> 00:13:00,800 but it's not a replacement for human judgment. 269 00:13:00,800 --> 00:13:02,240 Experience compassion. 270 00:13:02,240 --> 00:13:07,120 We need to find that right balance between human oversight and AI assistance. 271 00:13:07,120 --> 00:13:12,000 And we need to approach AI development and deployment with a sense of responsibility. 272 00:13:12,000 --> 00:13:13,120 Well said. 273 00:13:13,120 --> 00:13:17,120 This has been a fascinating look at the world of AI and construction safety. 274 00:13:17,120 --> 00:13:18,240 Thanks for having me. 275 00:13:18,240 --> 00:13:20,800 Thanks for joining us on the AI Papers podcast daily. 276 00:13:20,800 --> 00:13:50,640 We'll catch you next time for another deep dive into the latest advancements in AI.