1 00:00:00,000 --> 00:00:04,760 Okay, so let's dive into AI and how it's changing software development. 2 00:00:04,760 --> 00:00:05,520 Sounds good. 3 00:00:05,520 --> 00:00:06,960 We're taking a look at this paper today. 4 00:00:06,960 --> 00:00:07,400 Okay. 5 00:00:07,400 --> 00:00:11,000 It's called SES-TUE Bench. 6 00:00:11,000 --> 00:00:15,440 Can language models resolve real world GitHub issues? 7 00:00:15,440 --> 00:00:16,120 All right. 8 00:00:16,120 --> 00:00:18,320 It's a pretty interesting concept they're exploring here. 9 00:00:18,320 --> 00:00:22,760 It's about seeing how AI can handle real coding challenges. 10 00:00:22,760 --> 00:00:27,840 Yeah, you know, moving beyond those like simplified coding puzzles we've seen in the past. 11 00:00:27,840 --> 00:00:28,480 Right, exactly. 12 00:00:28,480 --> 00:00:32,120 This is about testing AI in a more realistic setting. 13 00:00:32,120 --> 00:00:35,480 So like real world scenarios, the kind of stuff developers face every day. 14 00:00:35,480 --> 00:00:36,000 Exactly. 15 00:00:36,000 --> 00:00:38,840 They're putting these language models to the test. 16 00:00:38,840 --> 00:00:42,920 You know, the same kind of AI that powers tools like chat GPT or Claude. 17 00:00:42,920 --> 00:00:43,520 Oh, well. 18 00:00:43,520 --> 00:00:45,640 So we're not just talking about writing code from scratch. 19 00:00:45,640 --> 00:00:46,120 You know. 20 00:00:46,120 --> 00:00:49,440 We're talking about AI actually diving into existing projects. 21 00:00:49,440 --> 00:00:51,360 Yeah, like massive messy code bases. 22 00:00:51,360 --> 00:00:56,080 Finding and fixing bugs and even adding new features to really complex software. 23 00:00:56,080 --> 00:00:56,840 Exactly. 24 00:00:56,840 --> 00:01:00,120 And the researchers behind this paper are making a really interesting point. 25 00:01:00,120 --> 00:01:05,720 They're saying that the existing benchmarks we have for AI, they're becoming too easy. 26 00:01:05,720 --> 00:01:09,240 They're not really pushing the limits of what AI can do these days. 27 00:01:09,240 --> 00:01:10,240 Yeah, I can see that. 28 00:01:10,240 --> 00:01:12,320 I mean, think about how fast this field is moving. 29 00:01:12,320 --> 00:01:13,560 Exactly. 30 00:01:13,560 --> 00:01:15,680 And real world software development. 31 00:01:15,680 --> 00:01:17,320 That's a whole different ball game. 32 00:01:17,320 --> 00:01:18,520 Absolutely. 33 00:01:18,520 --> 00:01:20,280 It's not just about knowing the syntax. 34 00:01:20,280 --> 00:01:22,640 It's about understanding how everything sits together. 35 00:01:22,640 --> 00:01:23,400 Right. 36 00:01:23,400 --> 00:01:26,640 You've got these huge open source projects on GitHub. 37 00:01:26,640 --> 00:01:27,720 Thousands of files. 38 00:01:27,720 --> 00:01:29,640 Hundreds of thousands of lines of code. 39 00:01:29,640 --> 00:01:33,520 And all these intricate interactions between different parts of the system. 40 00:01:33,520 --> 00:01:35,880 It's mind bogglingly complex. 41 00:01:35,880 --> 00:01:37,360 Definitely not for the faint of heart. 42 00:01:37,360 --> 00:01:39,800 So that's where SWE Bench comes in. 43 00:01:39,800 --> 00:01:40,800 OK, tell me more. 44 00:01:40,800 --> 00:01:42,200 It's this new benchmark. 45 00:01:42,200 --> 00:01:42,640 OK. 46 00:01:42,640 --> 00:01:47,880 And it's a collection of over 2,000 real software engineering problems. 47 00:01:47,880 --> 00:01:48,440 Wow. 48 00:01:48,440 --> 00:01:49,000 2,000. 49 00:01:49,000 --> 00:01:50,800 They can straight from GitHub issues. 50 00:01:50,800 --> 00:01:51,720 Oh, wow. 51 00:01:51,720 --> 00:01:54,080 So real problems that developers have actually faced. 52 00:01:54,080 --> 00:01:55,080 Real problems. 53 00:01:55,080 --> 00:01:55,440 Yeah. 54 00:01:55,440 --> 00:01:59,600 And they've selected these problems from 12 popular Python repositories. 55 00:01:59,600 --> 00:02:02,720 So it's a good mix of different types of projects and challenges. 56 00:02:02,720 --> 00:02:03,800 Exactly. 57 00:02:03,800 --> 00:02:06,280 It's designed to be a really representative sample. 58 00:02:06,280 --> 00:02:09,800 I'm starting to see how this is different from those simpler coding challenges. 59 00:02:09,800 --> 00:02:10,200 Right. 60 00:02:10,200 --> 00:02:15,600 It's about putting the AI in a situation that's much closer to what a human developer would experience. 61 00:02:15,600 --> 00:02:17,040 So how does it actually work? 62 00:02:17,040 --> 00:02:20,360 Like how do they test the AI with these problems? 63 00:02:20,360 --> 00:02:24,000 So basically they give the AI a broken piece of software. 64 00:02:24,000 --> 00:02:24,320 OK. 65 00:02:24,320 --> 00:02:26,560 And they provide a description of the issue. 66 00:02:26,560 --> 00:02:27,440 Like a bug report. 67 00:02:27,440 --> 00:02:28,080 Exactly. 68 00:02:28,080 --> 00:02:28,840 Like a bug report. 69 00:02:28,840 --> 00:02:33,440 And then the AI has to actually edit the code to fix it, just like a human would. 70 00:02:33,440 --> 00:02:34,200 Wow. 71 00:02:34,200 --> 00:02:36,560 So it's not just generating code from scratch. 72 00:02:36,560 --> 00:02:39,640 It's actually going in and modifying existing code. 73 00:02:39,640 --> 00:02:40,400 Exactly. 74 00:02:40,400 --> 00:02:41,960 And here's the really clever part. 75 00:02:41,960 --> 00:02:42,280 OK. 76 00:02:42,280 --> 00:02:42,960 I'm intrigued. 77 00:02:42,960 --> 00:02:49,040 Once the AI has come up with its solution, they actually test it using the repository's own testing framework. 78 00:02:49,040 --> 00:02:49,960 Oh, that's smart. 79 00:02:49,960 --> 00:02:51,880 So it's not just about whether the code compiles. 80 00:02:51,880 --> 00:02:54,240 It's about whether it actually solves the problem. 81 00:02:54,240 --> 00:02:54,920 Exactly. 82 00:02:54,920 --> 00:02:58,280 It has to pass all the tests just like a human developer's code would. 83 00:02:58,280 --> 00:02:59,320 That's a pretty high bar. 84 00:02:59,320 --> 00:03:00,000 It is. 85 00:03:00,000 --> 00:03:03,520 It really puts the AI in the shoes of a real software developer. 86 00:03:03,520 --> 00:03:05,920 So it's not just a theoretical exercise. 87 00:03:05,920 --> 00:03:10,400 It's about seeing how AI can actually perform in a real world setting. 88 00:03:10,400 --> 00:03:11,720 Exactly. 89 00:03:11,720 --> 00:03:14,080 And as you can imagine, it's not a walk in the park. 90 00:03:14,080 --> 00:03:16,920 I can imagine real world coding is full of challenges. 91 00:03:16,920 --> 00:03:18,120 It is. 92 00:03:18,120 --> 00:03:20,720 There are a lot of hurdles for these language models. 93 00:03:20,720 --> 00:03:23,560 The sheer size of these code bases is one thing. 94 00:03:23,560 --> 00:03:27,080 Just finding the right lines of code to change must be a nightmare. 95 00:03:27,080 --> 00:03:29,440 It's like finding a needle in a haystack. 96 00:03:29,440 --> 00:03:33,960 Sometimes you're talking about tens of thousands or even hundreds of thousands of lines of code. 97 00:03:33,960 --> 00:03:38,360 And then on top of that, you have all these dependencies and interactions between different parts of the code. 98 00:03:38,360 --> 00:03:38,680 Right. 99 00:03:38,680 --> 00:03:39,560 Exactly. 100 00:03:39,560 --> 00:03:42,040 It's what we call cross context code editing. 101 00:03:42,040 --> 00:03:42,960 Cross context. 102 00:03:42,960 --> 00:03:43,960 What does that mean? 103 00:03:43,960 --> 00:03:52,040 Well, in a real world project, fixing a bug or adding a feature often means making changes across multiple files and functions. 104 00:03:52,040 --> 00:03:52,640 Oh, I see. 105 00:03:52,640 --> 00:03:55,080 It's not just about fixing a single line of code. 106 00:03:55,080 --> 00:03:57,240 You have to understand how everything fits together. 107 00:03:57,240 --> 00:03:57,640 Right. 108 00:03:57,640 --> 00:04:02,040 The AI needs to grasp how all these pieces of code relate to each other. 109 00:04:02,040 --> 00:04:05,600 And how a change in one place might affect something else somewhere else. 110 00:04:05,600 --> 00:04:06,400 Exactly. 111 00:04:06,400 --> 00:04:08,800 It needs to have that big picture understanding. 112 00:04:08,800 --> 00:04:11,920 It sounds like that would be incredibly difficult for an AI to do. 113 00:04:11,920 --> 00:04:12,640 It is. 114 00:04:12,640 --> 00:04:15,040 It's one of the biggest challenges in this field. 115 00:04:15,040 --> 00:04:17,960 So on top of that, they have to understand the problem itself. 116 00:04:17,960 --> 00:04:18,320 Right. 117 00:04:18,320 --> 00:04:20,960 They have to read these long issue descriptions. 118 00:04:20,960 --> 00:04:22,160 And then sift through all that code. 119 00:04:22,160 --> 00:04:22,920 Exactly. 120 00:04:22,920 --> 00:04:24,960 It's a lot of information to process. 121 00:04:24,960 --> 00:04:28,920 So how are these language models actually doing against these challenges? 122 00:04:28,920 --> 00:04:34,040 Are they able to solve these complex real world problems? 123 00:04:34,040 --> 00:04:35,840 Well, the results are pretty interesting. 124 00:04:35,840 --> 00:04:38,800 They show just how difficult this really is. 125 00:04:38,800 --> 00:04:41,840 Even the most advanced language models, like Cod 2, 126 00:04:41,840 --> 00:04:45,760 they were only able to solve about 2% of the SESTA-BUE bench issues. 127 00:04:45,760 --> 00:04:46,360 2%. 128 00:04:46,360 --> 00:04:47,080 2%. 129 00:04:47,080 --> 00:04:48,080 Yeah. 130 00:04:48,080 --> 00:04:54,480 It's a pretty stark reminder of how far we still have to go before AI can truly replace human software developers. 131 00:04:54,480 --> 00:04:55,320 Wow. 132 00:04:55,320 --> 00:04:56,480 That's a humbling number. 133 00:04:56,480 --> 00:04:57,360 It is. 134 00:04:57,360 --> 00:04:59,680 But it also makes this research all the more important. 135 00:04:59,680 --> 00:05:00,560 I see what you mean. 136 00:05:00,560 --> 00:05:03,480 It shows us where the gaps are, what AI still struggles with. 137 00:05:03,480 --> 00:05:04,280 Exactly. 138 00:05:04,280 --> 00:05:08,280 And it highlights the areas where we need to focus our efforts if we want to make progress. 139 00:05:08,280 --> 00:05:11,160 So this isn't just about building cool AI toys. 140 00:05:11,160 --> 00:05:12,000 No. 141 00:05:12,000 --> 00:05:16,040 This is about understanding the fundamental challenges of AI and software development. 142 00:05:16,040 --> 00:05:16,640 Exactly. 143 00:05:16,640 --> 00:05:20,000 And this is where things get even more interesting. 144 00:05:20,000 --> 00:05:22,840 The researchers didn't just test existing AI models. 145 00:05:22,840 --> 00:05:25,600 They also created their own specialized model. 146 00:05:25,600 --> 00:05:26,120 Oh, really? 147 00:05:26,120 --> 00:05:26,560 Yeah. 148 00:05:26,560 --> 00:05:28,600 They called it SWE Llama. 149 00:05:28,600 --> 00:05:30,320 SWE Llama. 150 00:05:30,320 --> 00:05:30,800 Yeah. 151 00:05:30,800 --> 00:05:35,200 And they specifically fine-tuned it for these real world coding tasks. 152 00:05:35,200 --> 00:05:39,800 So they gave it some extra training, some specialized knowledge to help it tackle these challenges. 153 00:05:39,800 --> 00:05:40,800 Exactly. 154 00:05:40,800 --> 00:05:45,960 They wanted to see if they could improve AI's performance by focusing on this specific domain. 155 00:05:45,960 --> 00:05:46,840 That makes sense. 156 00:05:46,840 --> 00:05:50,240 So instead of trying to create an AI that knows everything about everything, 157 00:05:50,240 --> 00:05:54,760 they focused on creating an AI that's really good at this one specific thing. 158 00:05:54,760 --> 00:05:55,520 Exactly. 159 00:05:55,520 --> 00:06:00,240 And SWE Llama is designed to handle even larger amounts of code than other models. 160 00:06:00,240 --> 00:06:02,360 So it can tackle those massive code bases. 161 00:06:02,360 --> 00:06:02,960 Yeah. 162 00:06:02,960 --> 00:06:08,240 And in some cases, it actually outperforms those more general purpose models, like Clawed 2. 163 00:06:08,240 --> 00:06:09,160 That's pretty impressive. 164 00:06:09,160 --> 00:06:10,080 It is. 165 00:06:10,080 --> 00:06:12,560 It's a promising sign that we're moving in the right direction. 166 00:06:12,560 --> 00:06:16,000 So while AI isn't ready to take over the coding world just yet. 167 00:06:16,000 --> 00:06:17,080 Not just yet. 168 00:06:17,080 --> 00:06:20,360 It seems like this research is pushing us closer to that goal. 169 00:06:20,360 --> 00:06:21,520 Definitely. 170 00:06:21,520 --> 00:06:26,480 And one of the most valuable things about this research is the insights it provides. 171 00:06:26,480 --> 00:06:27,320 Like what? 172 00:06:27,320 --> 00:06:30,640 Well, for example, they found that the difficulty of the problems varied a lot, 173 00:06:30,640 --> 00:06:34,720 depending on the specific coding style and structure of each repository. 174 00:06:34,720 --> 00:06:35,600 Oh, that's interesting. 175 00:06:35,600 --> 00:06:36,640 Yeah. 176 00:06:36,640 --> 00:06:41,920 It suggests that some ways of writing code might be easier for AI to understand than others. 177 00:06:41,920 --> 00:06:43,000 That makes sense. 178 00:06:43,000 --> 00:06:47,240 Even for humans, some code is more readable and easier to work with than others. 179 00:06:47,240 --> 00:06:47,920 Exactly. 180 00:06:47,920 --> 00:06:53,520 So maybe we need to start thinking about how to write code in a way that's more AI-friendly. 181 00:06:53,520 --> 00:06:55,080 That's a fascinating idea. 182 00:06:55,080 --> 00:06:55,160 Yeah. 183 00:06:55,160 --> 00:06:57,520 So it's not just about AI learning from humans. 184 00:06:57,520 --> 00:06:59,640 It's also about humans learning from AI. 185 00:06:59,640 --> 00:07:00,240 Exactly. 186 00:07:00,240 --> 00:07:03,600 It's this kind of back and forth, this collaborative learning process. 187 00:07:03,600 --> 00:07:04,400 That's really cool. 188 00:07:04,400 --> 00:07:06,040 So what other insights did they find? 189 00:07:06,040 --> 00:07:11,040 Well, another intriguing finding was that giving the AI even more code to analyze didn't always help. 190 00:07:11,040 --> 00:07:11,840 Really? 191 00:07:11,840 --> 00:07:13,880 I would have thought more information is always better. 192 00:07:13,880 --> 00:07:15,600 You'd think so right. 193 00:07:15,600 --> 00:07:20,160 But it turns out that sometimes too much information can actually hurt the AI's performance. 194 00:07:20,160 --> 00:07:20,880 Oh, interesting. 195 00:07:20,880 --> 00:07:24,120 It can make it harder for the AI to focus on the relevant parts. 196 00:07:24,120 --> 00:07:28,920 It's like trying to find a specific sentence in a giant textbook without any page numbers. 197 00:07:28,920 --> 00:07:30,840 That's a great analogy. 198 00:07:30,840 --> 00:07:32,400 Sometimes less is more. 199 00:07:32,400 --> 00:07:34,920 So it's not just about processing power. 200 00:07:34,920 --> 00:07:37,160 It's about processing information intelligently. 201 00:07:37,160 --> 00:07:38,000 Exactly. 202 00:07:38,000 --> 00:07:40,480 And that's something that AI is still learning to do. 203 00:07:40,480 --> 00:07:42,480 So what about the solutions themselves? 204 00:07:42,480 --> 00:07:47,960 Did they find any differences between the solutions generated by AI and those written by humans? 205 00:07:47,960 --> 00:07:49,280 Yeah, they did. 206 00:07:49,280 --> 00:07:57,600 Actually, one of the most consistent observations was that the AI solutions tended to be much simpler and shorter than the human solutions. 207 00:07:57,600 --> 00:08:00,000 So the AI was finding ways to fix the problems? 208 00:08:00,000 --> 00:08:00,560 Yeah. 209 00:08:00,560 --> 00:08:03,480 But maybe not in the most elegant or efficient way? 210 00:08:03,480 --> 00:08:04,240 Precisely. 211 00:08:04,240 --> 00:08:08,480 It seemed like the AI was more focused on finding the most direct solution. 212 00:08:08,480 --> 00:08:08,880 OK. 213 00:08:08,880 --> 00:08:12,880 Often overlooking opportunities to make the code more elegant, efficient, or maintainable. 214 00:08:12,880 --> 00:08:14,560 So it's kind of like patching a hole in a roof. 215 00:08:14,560 --> 00:08:17,680 Instead of redesigning it to be more weather resistant. 216 00:08:17,680 --> 00:08:19,160 That's a great way to put it. 217 00:08:19,160 --> 00:08:22,600 The AI was doing the bare minimum to fix the bug. 218 00:08:22,600 --> 00:08:22,960 OK. 219 00:08:22,960 --> 00:08:27,160 But not necessarily going the extra mile to make the code better overall. 220 00:08:27,160 --> 00:08:31,160 So it sounds like AI still lacks that finesse that comes with human experience. 221 00:08:31,160 --> 00:08:31,720 It does. 222 00:08:31,720 --> 00:08:36,400 It seems to be missing that kind of holistic understanding that human developers have. 223 00:08:36,400 --> 00:08:40,160 That ability to see the big picture and anticipate potential problems down the line. 224 00:08:40,160 --> 00:08:40,800 Exactly. 225 00:08:40,800 --> 00:08:44,160 It's about more than just writing code that works. 226 00:08:44,160 --> 00:08:47,320 It's about writing code that's well designed and maintainable. 227 00:08:47,320 --> 00:08:52,160 So this research is really highlighting the difference between just coding and true software craftsmanship. 228 00:08:52,160 --> 00:08:52,960 It is. 229 00:08:52,960 --> 00:09:01,000 And it's raising some really interesting questions about how we can teach AI to not only write correct code, but also to write code that's truly well designed. 230 00:09:01,000 --> 00:09:02,160 This is all fascinating. 231 00:09:02,160 --> 00:09:02,880 Yeah, that is. 232 00:09:02,880 --> 00:09:07,760 But I'm wondering why someone who isn't a software developer should care about this research. 233 00:09:07,760 --> 00:09:09,240 What's the bigger picture here? 234 00:09:09,240 --> 00:09:10,720 That's a great question. 235 00:09:10,720 --> 00:09:17,720 This research is really a glimpse into the future of AI and its potential to transform how software is built. 236 00:09:17,720 --> 00:09:20,880 Imagine a world where AI can automatically fix bugs. 237 00:09:20,880 --> 00:09:21,320 Wow. 238 00:09:21,320 --> 00:09:26,480 Freeing up human developers to focus on more creative and strategic tasks. 239 00:09:26,480 --> 00:09:29,120 That sounds like a dream come true for many developers. 240 00:09:29,120 --> 00:09:30,280 It could be. 241 00:09:30,280 --> 00:09:33,920 And it could lead to a huge increase in productivity and innovation. 242 00:09:33,920 --> 00:09:36,840 So it's not just about making developers' lives easier. 243 00:09:36,840 --> 00:09:37,480 No. 244 00:09:37,480 --> 00:09:41,920 It's about unlocking a whole new level of potential in software development. 245 00:09:41,920 --> 00:09:42,680 Exactly. 246 00:09:42,680 --> 00:09:45,800 And it could have a ripple effect across all sorts of industries. 247 00:09:45,800 --> 00:09:46,920 That's pretty exciting stuff. 248 00:09:46,920 --> 00:09:47,800 It is. 249 00:09:47,800 --> 00:09:50,320 But it also raises some important questions. 250 00:09:50,320 --> 00:09:50,880 Like what? 251 00:09:50,880 --> 00:09:57,200 Well, could AI one day become so advanced that it surpasses human developers? 252 00:09:57,200 --> 00:09:58,360 That's a big question. 253 00:09:58,360 --> 00:09:59,040 It is. 254 00:09:59,040 --> 00:10:02,600 What would that mean for the role of humans in software development? 255 00:10:02,600 --> 00:10:04,160 And for society as a whole. 256 00:10:04,160 --> 00:10:04,960 Exactly. 257 00:10:04,960 --> 00:10:07,160 These are questions that we need to start thinking about now. 258 00:10:07,160 --> 00:10:07,520 I agree. 259 00:10:07,520 --> 00:10:11,160 This is definitely something we'll be exploring further in the next part of our deep dive. 260 00:10:11,160 --> 00:10:12,080 Looking forward to it. 261 00:10:12,080 --> 00:10:12,680 Me too. 262 00:10:12,680 --> 00:10:17,080 It's a it really makes you think you know about what sets human intelligence apart. 263 00:10:17,080 --> 00:10:17,600 It does. 264 00:10:17,600 --> 00:10:20,360 But getting back to SWE Lama for a second. 265 00:10:20,360 --> 00:10:24,320 You mentioned it can work with, you know, larger chunks of code. 266 00:10:24,320 --> 00:10:24,560 Yeah. 267 00:10:24,560 --> 00:10:25,880 How does that actually work? 268 00:10:25,880 --> 00:10:28,720 Like what makes it different from those other models? 269 00:10:28,720 --> 00:10:34,480 One of the key things about SWE Lama is its ability to handle these massive code contexts. 270 00:10:34,480 --> 00:10:34,960 OK. 271 00:10:34,960 --> 00:10:36,280 Traditional language models. 272 00:10:36,280 --> 00:10:37,640 They often struggle with this. 273 00:10:37,640 --> 00:10:37,920 Yeah. 274 00:10:37,920 --> 00:10:40,480 It's like trying to read an entire encyclopedia. 275 00:10:40,480 --> 00:10:40,920 Right. 276 00:10:40,920 --> 00:10:42,680 Just to find the answer to a single question. 277 00:10:42,680 --> 00:10:43,240 Exactly. 278 00:10:43,240 --> 00:10:46,600 SWE Lama though it uses some specific techniques. 279 00:10:46,600 --> 00:10:46,880 OK. 280 00:10:46,880 --> 00:10:49,600 That help it zero in on the most relevant parts of the code. 281 00:10:49,600 --> 00:10:49,840 Yeah. 282 00:10:49,840 --> 00:10:52,920 So it's not just about brute force processing power. 283 00:10:52,920 --> 00:10:58,240 It's about being more selective, more intelligent about how it processes information. 284 00:10:58,240 --> 00:10:59,400 Exactly. 285 00:10:59,400 --> 00:11:02,080 And one of the techniques it uses is called attention. 286 00:11:02,080 --> 00:11:02,840 Attention. 287 00:11:02,840 --> 00:11:03,520 Yeah. 288 00:11:03,520 --> 00:11:08,240 It's basically a way for the AI to prioritize different parts of the code. 289 00:11:08,240 --> 00:11:08,840 OK. 290 00:11:08,840 --> 00:11:11,760 Based on how relevant they are to the problem at hand. 291 00:11:11,760 --> 00:11:13,440 So it's like having a built in highlighter. 292 00:11:13,440 --> 00:11:13,880 Yeah. 293 00:11:13,880 --> 00:11:15,840 That marks the most important passages. 294 00:11:15,840 --> 00:11:17,480 That's a great way to think about it. 295 00:11:17,480 --> 00:11:20,000 It helps the AI focus on what matters most. 296 00:11:20,000 --> 00:11:21,160 That makes a lot of sense. 297 00:11:21,160 --> 00:11:24,240 But even with these, you know, specialized models. 298 00:11:24,240 --> 00:11:24,840 Yeah. 299 00:11:24,840 --> 00:11:29,600 It sounds like AI still has a lot to learn from us from human developers. 300 00:11:29,600 --> 00:11:30,280 It does. 301 00:11:30,280 --> 00:11:34,120 You mentioned earlier that the AI solutions tend to be simpler and shorter. 302 00:11:34,120 --> 00:11:34,720 Yeah. 303 00:11:34,720 --> 00:11:36,360 What are the implications of that? 304 00:11:36,360 --> 00:11:38,440 It's a really interesting observation. 305 00:11:38,440 --> 00:11:44,440 It suggests that the AI is maybe a little too focused on finding the most direct solution. 306 00:11:44,440 --> 00:11:44,760 OK. 307 00:11:44,760 --> 00:11:49,120 And it often misses opportunities to make the code more elegant or efficient. 308 00:11:49,120 --> 00:11:51,240 Or even just easier to maintain down the road. 309 00:11:51,240 --> 00:11:51,600 Right. 310 00:11:51,600 --> 00:11:52,320 Exactly. 311 00:11:52,320 --> 00:11:55,440 Human developers, we often take a more holistic approach. 312 00:11:55,440 --> 00:11:55,720 Yeah. 313 00:11:55,720 --> 00:11:56,960 We think about the big picture. 314 00:11:56,960 --> 00:12:02,520 We consider not just the immediate fix, but also, you know, the long term health of the code base. 315 00:12:02,520 --> 00:12:05,880 So it's kind of like the difference between patching a hole in a roof. 316 00:12:05,880 --> 00:12:06,440 OK. 317 00:12:06,440 --> 00:12:09,800 Versus actually redesigning the roof to be more weather resistant. 318 00:12:09,800 --> 00:12:11,000 That's a great analogy. 319 00:12:11,000 --> 00:12:12,080 One is a quick fix. 320 00:12:12,080 --> 00:12:15,560 The other is a more thoughtful and long term solution. 321 00:12:15,560 --> 00:12:18,480 And it sounds like AI is still stuck in that quick fix mode. 322 00:12:18,480 --> 00:12:18,920 Yeah. 323 00:12:18,920 --> 00:12:19,800 For now, at least. 324 00:12:19,800 --> 00:12:23,040 It hasn't quite grasped those higher level design principles. 325 00:12:23,040 --> 00:12:25,760 So it's doing the bare minimum to solve the problem. 326 00:12:25,760 --> 00:12:26,200 Right. 327 00:12:26,200 --> 00:12:29,920 But not necessarily going the extra mile to make the code truly better. 328 00:12:29,920 --> 00:12:32,360 And that's a key area for future research. 329 00:12:32,360 --> 00:12:32,880 Yeah. 330 00:12:32,880 --> 00:12:33,160 OK. 331 00:12:33,160 --> 00:12:39,600 How do we teach AI to not just fix bugs, but also to write code that's well designed. 332 00:12:39,600 --> 00:12:42,000 So that's easy to understand and modify in extent. 333 00:12:42,000 --> 00:12:42,720 Exactly. 334 00:12:42,720 --> 00:12:45,240 It's about going beyond the mechanics of coding. 335 00:12:45,240 --> 00:12:47,400 And into the realm of software crashments. 336 00:12:47,400 --> 00:12:48,200 Exactly. 337 00:12:48,200 --> 00:12:52,200 It's about teaching AI to think like a human developer. 338 00:12:52,200 --> 00:12:54,640 That brings us to another interesting point from the research. 339 00:12:54,640 --> 00:12:55,280 OK. 340 00:12:55,280 --> 00:12:59,640 The fact that some coding styles seem to be more AI friendly than others. 341 00:12:59,640 --> 00:13:00,160 Right. 342 00:13:00,160 --> 00:13:04,400 What are some of the things that make code easier for AI to work with? 343 00:13:04,400 --> 00:13:07,480 Well, one of the biggest factors seems to be consistency. 344 00:13:07,480 --> 00:13:08,360 Consistency. 345 00:13:08,360 --> 00:13:08,800 Yeah. 346 00:13:08,800 --> 00:13:13,560 Code that follows clear conventions that uses meaningful names for variables. 347 00:13:13,560 --> 00:13:14,080 OK. 348 00:13:14,080 --> 00:13:15,560 And is well organized. 349 00:13:15,560 --> 00:13:18,840 That's generally easier for AI to parse and understand. 350 00:13:18,840 --> 00:13:20,080 It's like having a well written book. 351 00:13:20,080 --> 00:13:20,520 Yeah. 352 00:13:20,520 --> 00:13:22,040 With clear chapters and headings. 353 00:13:22,040 --> 00:13:22,760 Exactly. 354 00:13:22,760 --> 00:13:25,800 It makes it easier for the AI to follow the flow of the code. 355 00:13:25,800 --> 00:13:29,680 So good coding practices that make code easier for humans to understand. 356 00:13:29,680 --> 00:13:30,200 Right. 357 00:13:30,200 --> 00:13:31,720 They also benefit AI. 358 00:13:31,720 --> 00:13:32,960 It seems that way. 359 00:13:32,960 --> 00:13:36,600 And that has implications not just for how we train AI models. 360 00:13:36,600 --> 00:13:36,840 OK. 361 00:13:36,840 --> 00:13:39,600 But also for how we write code in general. 362 00:13:39,600 --> 00:13:40,760 Interesting. 363 00:13:40,760 --> 00:13:44,840 So if we want AI to be a valuable partner in software development. 364 00:13:44,840 --> 00:13:45,480 Yeah. 365 00:13:45,480 --> 00:13:47,680 We need to write code that it can understand. 366 00:13:47,680 --> 00:13:48,520 Exactly. 367 00:13:48,520 --> 00:13:51,640 It's almost like we need to develop a shared language. 368 00:13:51,640 --> 00:13:55,760 A way of writing code that's both human readable and AI compatible. 369 00:13:55,760 --> 00:13:56,800 That's a really cool idea. 370 00:13:56,800 --> 00:14:00,120 It's like we're building a bridge between human intelligence 371 00:14:00,120 --> 00:14:01,520 and artificial intelligence. 372 00:14:01,520 --> 00:14:04,760 And as AI models become more sophisticated. 373 00:14:04,760 --> 00:14:05,120 Yeah. 374 00:14:05,120 --> 00:14:08,040 They might even be able to help us improve our own coding practices. 375 00:14:08,040 --> 00:14:08,840 Oh, so. 376 00:14:08,840 --> 00:14:11,760 Well, they could point out areas where our code could be more consistent 377 00:14:11,760 --> 00:14:13,000 or easier to understand. 378 00:14:13,000 --> 00:14:14,480 It's like having an AI code reviewer. 379 00:14:14,480 --> 00:14:15,520 Exactly. 380 00:14:15,520 --> 00:14:17,480 It's this really interesting feedback loop 381 00:14:17,480 --> 00:14:20,280 where AI and humans are learning from each other. 382 00:14:20,280 --> 00:14:21,520 That's awesome. 383 00:14:21,520 --> 00:14:25,480 So turning to the specific findings of the SWLAMA research. 384 00:14:25,480 --> 00:14:26,400 OK. 385 00:14:26,400 --> 00:14:28,560 What were some of the standout results or surprises 386 00:14:28,560 --> 00:14:30,080 that came out of those experiments? 387 00:14:30,080 --> 00:14:31,560 One of the most interesting findings 388 00:14:31,560 --> 00:14:34,480 was that even with relatively limited fine tuning. 389 00:14:34,480 --> 00:14:35,080 OK. 390 00:14:35,080 --> 00:14:39,920 SWLAMA was able to outperform those much larger, more general purpose 391 00:14:39,920 --> 00:14:41,400 models in certain scenarios. 392 00:14:41,400 --> 00:14:42,440 Oh, wow. 393 00:14:42,440 --> 00:14:44,320 So bigger isn't always better. 394 00:14:44,320 --> 00:14:45,400 Not necessarily. 395 00:14:45,400 --> 00:14:47,240 It seems like specialization is key. 396 00:14:47,240 --> 00:14:47,760 OK. 397 00:14:47,760 --> 00:14:52,320 If you train AI models for specific types of coding tasks, 398 00:14:52,320 --> 00:14:54,760 you can get significant improvements in performance. 399 00:14:54,760 --> 00:14:57,600 So instead of trying to create like a one size fits all AI 400 00:14:57,600 --> 00:15:01,560 coder, it might be better to have specialized AI experts. 401 00:15:01,560 --> 00:15:02,120 Exactly. 402 00:15:02,120 --> 00:15:03,600 Think of it like an AI team. 403 00:15:03,600 --> 00:15:04,000 OK. 404 00:15:04,000 --> 00:15:08,640 With a front end specialist, a back end guru, a database whiz. 405 00:15:08,640 --> 00:15:09,600 I like that analogy. 406 00:15:09,600 --> 00:15:11,560 It's like assembling the ultimate coding dream team. 407 00:15:11,560 --> 00:15:12,280 Exactly. 408 00:15:12,280 --> 00:15:15,000 And each member of that team is highly skilled in their particular 409 00:15:15,000 --> 00:15:15,520 area. 410 00:15:15,520 --> 00:15:16,400 Makes sense. 411 00:15:16,400 --> 00:15:19,000 Were there any other insights from the SWLAMA research 412 00:15:19,000 --> 00:15:20,080 that stood out to you? 413 00:15:20,080 --> 00:15:20,600 Yeah. 414 00:15:20,600 --> 00:15:23,120 Another really important finding was the importance of data 415 00:15:23,120 --> 00:15:27,080 quality and relevance when fine tuning these AI models. 416 00:15:27,080 --> 00:15:30,400 So garbage in, garbage out still applies in the world of AI? 417 00:15:30,400 --> 00:15:31,160 Absolutely. 418 00:15:31,160 --> 00:15:33,320 You need to train the AI on data that's 419 00:15:33,320 --> 00:15:36,160 relevant to the tasks you want it to perform. 420 00:15:36,160 --> 00:15:39,360 So if you want an AI that's good at writing Python code, 421 00:15:39,360 --> 00:15:41,040 you need to train it on a lot of Python code. 422 00:15:41,040 --> 00:15:41,760 Exactly. 423 00:15:41,760 --> 00:15:43,600 And not just any Python code. 424 00:15:43,600 --> 00:15:45,880 It needs to be a high quality code that 425 00:15:45,880 --> 00:15:49,080 represents the best practices in that domain. 426 00:15:49,080 --> 00:15:51,360 It's like feeding an athlete the right kind of fuel. 427 00:15:51,360 --> 00:15:52,160 Exactly. 428 00:15:52,160 --> 00:15:54,040 If you want peak performance, you need 429 00:15:54,040 --> 00:15:56,040 to provide the right kind of input. 430 00:15:56,040 --> 00:15:58,400 What were some of the challenges the researchers faced 431 00:15:58,400 --> 00:16:01,480 in developing and testing SWLAMA? 432 00:16:01,480 --> 00:16:03,640 One of the key takeaways was that even 433 00:16:03,640 --> 00:16:06,320 with this specialized training, AI models 434 00:16:06,320 --> 00:16:09,680 still struggle with certain aspects of real world coding. 435 00:16:09,680 --> 00:16:10,160 OK. 436 00:16:10,160 --> 00:16:12,760 For example, SWLAMA, it tended to generate 437 00:16:12,760 --> 00:16:15,640 these simpler, shorter patches than human developers. 438 00:16:15,640 --> 00:16:16,120 OK. 439 00:16:16,120 --> 00:16:19,040 It often missed opportunities to refactor code 440 00:16:19,040 --> 00:16:21,200 or improve the overall structure. 441 00:16:21,200 --> 00:16:23,800 So it was fixing the bug, but not necessarily making 442 00:16:23,800 --> 00:16:25,920 the code better overall. 443 00:16:25,920 --> 00:16:26,440 Exactly. 444 00:16:26,440 --> 00:16:28,320 It was kind of like applying a Band-Aid instead 445 00:16:28,320 --> 00:16:30,440 of addressing the root cause of the problem. 446 00:16:30,440 --> 00:16:33,200 Like a quick fix instead of a long term solution. 447 00:16:33,200 --> 00:16:34,320 Exactly. 448 00:16:34,320 --> 00:16:38,120 And this highlights the need for AI systems that can not only 449 00:16:38,120 --> 00:16:39,360 write correct code. 450 00:16:39,360 --> 00:16:39,720 Right. 451 00:16:39,720 --> 00:16:41,080 But also understand the principles 452 00:16:41,080 --> 00:16:42,240 of good software design. 453 00:16:42,240 --> 00:16:44,720 Like readability, maintainability, and efficiency. 454 00:16:44,720 --> 00:16:45,400 Exactly. 455 00:16:45,400 --> 00:16:48,120 It's about more than just getting the code to work. 456 00:16:48,120 --> 00:16:50,840 It's about writing code that's elegant and robust 457 00:16:50,840 --> 00:16:51,840 and easy to understand. 458 00:16:51,840 --> 00:16:52,840 Exactly. 459 00:16:52,840 --> 00:16:54,560 And that's a big challenge for AI. 460 00:16:54,560 --> 00:16:57,120 It seems like we're still in the early stages of this journey. 461 00:16:57,120 --> 00:16:58,080 We are. 462 00:16:58,080 --> 00:17:01,200 There's a lot of potential for AI to transform software 463 00:17:01,200 --> 00:17:01,800 development. 464 00:17:01,800 --> 00:17:02,400 Yeah. 465 00:17:02,400 --> 00:17:04,560 But we still have a long way to go. 466 00:17:04,560 --> 00:17:05,720 Now, before we wrap up. 467 00:17:05,720 --> 00:17:06,640 OK. 468 00:17:06,640 --> 00:17:09,200 I'd like to get your thoughts on the broader implications 469 00:17:09,200 --> 00:17:10,120 of this research. 470 00:17:10,120 --> 00:17:10,800 Sure. 471 00:17:10,800 --> 00:17:13,160 What does it tell us about the future of AI and software 472 00:17:13,160 --> 00:17:14,240 development? 473 00:17:14,240 --> 00:17:16,560 Well, I think this research is a really exciting glimpse 474 00:17:16,560 --> 00:17:20,760 into a future where AI is not just a tool, 475 00:17:20,760 --> 00:17:23,360 but a collaborator in software development. 476 00:17:23,360 --> 00:17:24,200 The collaborator. 477 00:17:24,200 --> 00:17:24,520 Yeah. 478 00:17:24,520 --> 00:17:27,400 Imagine AI systems that can automatically generate code, 479 00:17:27,400 --> 00:17:30,240 identify bugs, even suggest improvements. 480 00:17:30,240 --> 00:17:30,760 OK. 481 00:17:30,760 --> 00:17:35,040 And all while working seamlessly alongside human developers. 482 00:17:35,040 --> 00:17:37,240 So it's not about replacing human developers. 483 00:17:37,240 --> 00:17:39,560 It's about augmenting their abilities. 484 00:17:39,560 --> 00:17:40,600 Exactly. 485 00:17:40,600 --> 00:17:43,640 It's about creating a partnership where AI and humans 486 00:17:43,640 --> 00:17:46,600 can work together to create amazing things. 487 00:17:46,600 --> 00:17:47,760 I like that. 488 00:17:47,760 --> 00:17:50,000 That's a much more optimistic vision of the future. 489 00:17:50,000 --> 00:17:51,920 I think it's a more realistic vision, too. 490 00:17:51,920 --> 00:17:55,400 And as these AI models continue to evolve, 491 00:17:55,400 --> 00:17:58,600 we can expect to see even more profound changes in how 492 00:17:58,600 --> 00:17:59,480 software is developed. 493 00:17:59,480 --> 00:18:00,560 Absolutely. 494 00:18:00,560 --> 00:18:03,720 We might even see AI systems that can design entire software 495 00:18:03,720 --> 00:18:04,800 architectures. 496 00:18:04,800 --> 00:18:07,760 Or generate code in multiple programming languages. 497 00:18:07,760 --> 00:18:08,680 That would be incredible. 498 00:18:08,680 --> 00:18:11,720 Or even adapt to changing requirements on the fly. 499 00:18:11,720 --> 00:18:13,720 It's mind blowing to think about the possibility. 500 00:18:13,720 --> 00:18:14,440 It is. 501 00:18:14,440 --> 00:18:16,560 But it also raises some important questions. 502 00:18:16,560 --> 00:18:17,200 Like what? 503 00:18:17,200 --> 00:18:19,320 Well, what will the role of human developers 504 00:18:19,320 --> 00:18:20,520 be in this future? 505 00:18:20,520 --> 00:18:20,920 Right. 506 00:18:20,920 --> 00:18:23,320 How will we adapt to these changes? 507 00:18:23,320 --> 00:18:26,720 And what ethical considerations will we need to address? 508 00:18:26,720 --> 00:18:30,480 These are questions that we'll need to grapple with as AI 509 00:18:30,480 --> 00:18:33,040 becomes more integrated into software development. 510 00:18:33,040 --> 00:18:33,520 I agree. 511 00:18:33,520 --> 00:18:35,280 It's not just a technological challenge. 512 00:18:35,280 --> 00:18:35,800 No. 513 00:18:35,800 --> 00:18:36,880 It's a societal one. 514 00:18:36,880 --> 00:18:37,480 Exactly. 515 00:18:37,480 --> 00:18:39,960 And it's one that we need to start thinking about now. 516 00:18:39,960 --> 00:18:42,200 Well, this has been an incredibly insightful discussion. 517 00:18:42,200 --> 00:18:42,600 Yeah. 518 00:18:42,600 --> 00:18:44,720 Thank you for sharing your expertise with us today. 519 00:18:44,720 --> 00:18:45,720 It's been my pleasure. 520 00:18:45,720 --> 00:18:48,280 It's definitely a lot to think about. 521 00:18:48,280 --> 00:18:50,840 It's exciting, but also a little bit daunting. 522 00:18:50,840 --> 00:18:51,400 Yeah. 523 00:18:51,400 --> 00:18:52,760 I think that's a fair way to put it. 524 00:18:52,760 --> 00:18:55,040 We've talked about the impressive capabilities 525 00:18:55,040 --> 00:18:56,320 of these AI models. 526 00:18:56,320 --> 00:18:57,120 Right. 527 00:18:57,120 --> 00:18:58,880 But also their limitations. 528 00:18:58,880 --> 00:18:59,400 Yeah. 529 00:18:59,400 --> 00:19:03,080 The areas where they still fall short of human expertise. 530 00:19:03,080 --> 00:19:06,560 And it's those limitations, those gaps in understanding 531 00:19:06,560 --> 00:19:08,000 that really point the way forward. 532 00:19:08,000 --> 00:19:09,240 Exactly. 533 00:19:09,240 --> 00:19:11,560 They show us where the most interesting research questions 534 00:19:11,560 --> 00:19:12,040 are. 535 00:19:12,040 --> 00:19:13,680 So where do we go from here? 536 00:19:13,680 --> 00:19:15,760 What are the big unanswered questions 537 00:19:15,760 --> 00:19:17,560 that researchers are grappling with? 538 00:19:17,560 --> 00:19:19,320 Well, one of the biggest challenges 539 00:19:19,320 --> 00:19:24,400 is teaching AI to understand code, not just as text, 540 00:19:24,400 --> 00:19:26,440 but as part of a larger system. 541 00:19:26,440 --> 00:19:28,560 So not just the syntax, but the semantics. 542 00:19:28,560 --> 00:19:29,080 Right. 543 00:19:29,080 --> 00:19:34,040 We need models that can reason about the intent behind the code, 544 00:19:34,040 --> 00:19:36,080 the design decisions that shaped it, 545 00:19:36,080 --> 00:19:38,920 the potential consequences of making changes. 546 00:19:38,920 --> 00:19:40,520 It's like moving from understanding 547 00:19:40,520 --> 00:19:44,080 the grammar of a language to actually grasping 548 00:19:44,080 --> 00:19:47,520 its nuances, its idioms, its cultural context. 549 00:19:47,520 --> 00:19:48,440 Exactly. 550 00:19:48,440 --> 00:19:51,360 We need AI that can read between the lines of code, 551 00:19:51,360 --> 00:19:52,160 so to speak. 552 00:19:52,160 --> 00:19:54,360 Inferring the unspoken goals and constraints 553 00:19:54,360 --> 00:19:55,920 that guide human developers. 554 00:19:55,920 --> 00:19:56,560 Right. 555 00:19:56,560 --> 00:19:57,840 It's a really tough problem. 556 00:19:57,840 --> 00:19:58,960 It sounds like it. 557 00:19:58,960 --> 00:20:01,040 What kind of research approaches are being explored 558 00:20:01,040 --> 00:20:02,320 to tackle this challenge? 559 00:20:02,320 --> 00:20:06,040 Well, one promising avenue is to incorporate more domain 560 00:20:06,040 --> 00:20:08,000 specific knowledge into these models. 561 00:20:08,000 --> 00:20:08,720 OK. 562 00:20:08,720 --> 00:20:10,880 So instead of just training them on code, 563 00:20:10,880 --> 00:20:12,640 we can also feed them information 564 00:20:12,640 --> 00:20:16,680 about software design patterns, best practices, even 565 00:20:16,680 --> 00:20:20,040 the history of how certain software systems evolved over time. 566 00:20:20,040 --> 00:20:23,720 So it's about giving the AI a deeper understanding of the why 567 00:20:23,720 --> 00:20:24,440 behind the code. 568 00:20:24,440 --> 00:20:25,760 Exactly, not just the what. 569 00:20:25,760 --> 00:20:26,840 I see. 570 00:20:26,840 --> 00:20:29,960 Another area of active research is developing AI models 571 00:20:29,960 --> 00:20:31,920 that can learn from fewer examples. 572 00:20:31,920 --> 00:20:32,420 Right. 573 00:20:32,420 --> 00:20:34,560 Right now, these models require massive data sets 574 00:20:34,560 --> 00:20:35,200 for training. 575 00:20:35,200 --> 00:20:35,560 Yeah. 576 00:20:35,560 --> 00:20:37,840 Thousands or even millions of examples. 577 00:20:37,840 --> 00:20:38,480 Exactly. 578 00:20:38,480 --> 00:20:40,680 But imagine an AI that could learn to code 579 00:20:40,680 --> 00:20:42,680 by observing a human developer. 580 00:20:42,680 --> 00:20:43,480 Oh, wow. 581 00:20:43,480 --> 00:20:46,040 Just like an apprentice learning from a master craftsman. 582 00:20:46,040 --> 00:20:47,160 That would be incredible. 583 00:20:47,160 --> 00:20:50,160 It would make AI more accessible and adaptable 584 00:20:50,160 --> 00:20:52,440 to different coding styles and domains. 585 00:20:52,440 --> 00:20:54,320 And it would open up all sorts of possibilities 586 00:20:54,320 --> 00:20:55,360 for personalization. 587 00:20:55,360 --> 00:20:56,400 Exactly. 588 00:20:56,400 --> 00:20:58,680 Imagine an AI coding assistant that 589 00:20:58,680 --> 00:21:01,240 learns your specific coding preferences. 590 00:21:01,240 --> 00:21:01,720 OK. 591 00:21:01,720 --> 00:21:04,600 And adapts its suggestions accordingly. 592 00:21:04,600 --> 00:21:06,720 So it's like having an AI pair programmer that 593 00:21:06,720 --> 00:21:08,400 knows your strengths and weaknesses. 594 00:21:08,400 --> 00:21:08,880 Yeah. 595 00:21:08,880 --> 00:21:10,520 And can anticipate your next move. 596 00:21:10,520 --> 00:21:11,240 Exactly. 597 00:21:11,240 --> 00:21:13,160 It's a really exciting vision of the future. 598 00:21:13,160 --> 00:21:14,440 It is. 599 00:21:14,440 --> 00:21:17,640 As we look ahead, what are some of the broader implications 600 00:21:17,640 --> 00:21:20,920 of this research for the field of AI as a whole? 601 00:21:20,920 --> 00:21:22,520 Well, I think this work is really pushing 602 00:21:22,520 --> 00:21:24,320 the boundaries of what AI can do. 603 00:21:24,320 --> 00:21:24,680 OK. 604 00:21:24,680 --> 00:21:26,960 It's demonstrating its potential to tackle 605 00:21:26,960 --> 00:21:29,320 these complex real world problem. 606 00:21:29,320 --> 00:21:31,760 Problems that were once thought to be beyond the reach 607 00:21:31,760 --> 00:21:32,440 of machines. 608 00:21:32,440 --> 00:21:32,960 Exactly. 609 00:21:32,960 --> 00:21:34,880 And it's highlighting the importance of moving 610 00:21:34,880 --> 00:21:36,480 beyond those simple benchmarks. 611 00:21:36,480 --> 00:21:37,000 OK. 612 00:21:37,000 --> 00:21:39,280 We need to start evaluating AI systems 613 00:21:39,280 --> 00:21:41,440 in more realistic and challenging environments. 614 00:21:41,440 --> 00:21:44,360 Environments that better reflect the messy and unpredictable 615 00:21:44,360 --> 00:21:46,280 nature of the real world. 616 00:21:46,280 --> 00:21:48,000 Exactly. 617 00:21:48,000 --> 00:21:50,560 The true test of AI's intelligence 618 00:21:50,560 --> 00:21:54,920 lies not in its ability to solve puzzles in isolation, 619 00:21:54,920 --> 00:21:58,040 but in its ability to navigate the complexities of human 620 00:21:58,040 --> 00:21:58,960 endeavor. 621 00:21:58,960 --> 00:22:00,400 That's a profound thought. 622 00:22:00,400 --> 00:22:00,840 Yeah. 623 00:22:00,840 --> 00:22:02,680 And that's where things get really interesting, right? 624 00:22:02,680 --> 00:22:03,080 Yeah. 625 00:22:03,080 --> 00:22:05,760 As these AI systems become more capable, 626 00:22:05,760 --> 00:22:07,000 they're going to force us to confront 627 00:22:07,000 --> 00:22:08,480 some fundamental questions. 628 00:22:08,480 --> 00:22:11,240 About the nature of intelligence, creativity, 629 00:22:11,240 --> 00:22:12,080 even consciousness. 630 00:22:12,080 --> 00:22:12,840 Exactly. 631 00:22:12,840 --> 00:22:15,440 It's a journey that's sure to be filled with surprises, 632 00:22:15,440 --> 00:22:17,120 challenges, and profound insights. 633 00:22:17,120 --> 00:22:17,880 I agree. 634 00:22:17,880 --> 00:22:20,480 Well, this has been a truly fascinating exploration. 635 00:22:20,480 --> 00:22:22,680 Thank you so much for joining me on this deep dive. 636 00:22:22,680 --> 00:22:23,720 It's been my pleasure. 637 00:22:23,720 --> 00:22:26,200 And to our listeners, thank you for tuning in. 638 00:22:26,200 --> 00:22:28,040 The world of AI and software development 639 00:22:28,040 --> 00:22:29,480 is constantly evolving. 640 00:22:29,480 --> 00:22:32,760 So stay curious, keep exploring, and who knows? 641 00:22:32,760 --> 00:22:34,880 Maybe you'll be the one to write the next chapter 642 00:22:34,880 --> 00:22:51,440 in this incredible story.