1 00:00:00,000 --> 00:00:04,640 Welcome back everyone to the deep dive, ready to explore some more cutting edge AI research 2 00:00:04,640 --> 00:00:05,640 with me today. 3 00:00:05,640 --> 00:00:06,640 Always. 4 00:00:06,640 --> 00:00:07,640 Alright. 5 00:00:07,640 --> 00:00:12,240 So today we're diving into the world of AI and how it's transforming the way we write 6 00:00:12,240 --> 00:00:13,440 and debug code. 7 00:00:13,440 --> 00:00:14,440 Cool. 8 00:00:14,440 --> 00:00:18,360 We're looking at a research paper titled, A Comprehensive Survey of AI-Driven Advancements 9 00:00:18,360 --> 00:00:22,880 and Techniques in Automated Program Repair and Code Generation. 10 00:00:22,880 --> 00:00:23,880 Oh, interesting. 11 00:00:23,880 --> 00:00:25,560 Sounds like a pretty dense one. 12 00:00:25,560 --> 00:00:30,120 Yeah, this paper analyzes 27 other papers, gives us a really nice overview of what's 13 00:00:30,120 --> 00:00:31,120 happening in the field. 14 00:00:31,120 --> 00:00:35,400 So it's like a roadmap of all the exciting research going on in AI-powered coding. 15 00:00:35,400 --> 00:00:36,400 Exactly. 16 00:00:36,400 --> 00:00:40,600 We'll be exploring everything from AI that can fix buds in your code to AI that can 17 00:00:40,600 --> 00:00:42,840 write new code based on your instructions. 18 00:00:42,840 --> 00:00:46,480 Like, whoa, are we on the verge of a coding revolution or what? 19 00:00:46,480 --> 00:00:47,480 It really feels like it. 20 00:00:47,480 --> 00:00:51,840 Now the paper splits its focus into two main areas, automated program repair, which is 21 00:00:51,840 --> 00:00:55,280 also called APR, and code generation. 22 00:00:55,280 --> 00:00:59,720 Did you break those down for us, for those of us who aren't AI experts and all that? 23 00:00:59,720 --> 00:01:00,720 Sure. 24 00:01:00,720 --> 00:01:03,440 So APR, imagine you're coding and you run into a bug. 25 00:01:03,440 --> 00:01:04,440 Happens all the time. 26 00:01:04,440 --> 00:01:05,440 Right. 27 00:01:05,440 --> 00:01:11,680 So APR is having an AI partner that can swoop in, analyze your code, and then identify that 28 00:01:11,680 --> 00:01:16,000 bug and even suggest a fix or even implement the fix itself. 29 00:01:16,000 --> 00:01:17,000 That's amazing. 30 00:01:17,000 --> 00:01:18,240 Like having a super-powered debugger. 31 00:01:18,240 --> 00:01:19,480 Yeah, exactly. 32 00:01:19,480 --> 00:01:20,480 Awesome. 33 00:01:20,480 --> 00:01:21,720 So then what about code generation? 34 00:01:21,720 --> 00:01:25,040 How does AI actually write new code? 35 00:01:25,040 --> 00:01:26,760 So that's where things get really interesting. 36 00:01:26,760 --> 00:01:31,400 It uses these things called large language models or LLMs for short. 37 00:01:31,400 --> 00:01:32,560 LLMs, got it. 38 00:01:32,560 --> 00:01:37,960 And these LLMs are trained on these massive, massive data sets of code, learning all the 39 00:01:37,960 --> 00:01:41,200 patterns and structures of different programming languages. 40 00:01:41,200 --> 00:01:45,480 So then when you give an LLM a set of instructions, it can generate new code to fulfill those 41 00:01:45,480 --> 00:01:46,480 instructions. 42 00:01:46,480 --> 00:01:50,880 So instead of writing every single line of code ourselves, we can just tell the AI what 43 00:01:50,880 --> 00:01:53,280 we want and it'll just do it for us. 44 00:01:53,280 --> 00:01:54,280 That's the goal. 45 00:01:54,280 --> 00:01:57,080 It's still under development, of course, but the potential is huge. 46 00:01:57,080 --> 00:02:02,440 You think about things like creating basic web forms or handling file input or output. 47 00:02:02,440 --> 00:02:03,640 All that can be automated. 48 00:02:03,640 --> 00:02:04,640 Wow. 49 00:02:04,640 --> 00:02:09,120 That would free up so much time for developers to focus on more challenging things. 50 00:02:09,120 --> 00:02:10,120 Exactly. 51 00:02:10,120 --> 00:02:11,520 Yeah, more of the complex challenges. 52 00:02:11,520 --> 00:02:12,520 That's really cool. 53 00:02:12,520 --> 00:02:15,840 And this paper, it goes beyond just explaining these concepts, right? 54 00:02:15,840 --> 00:02:21,160 It dives into specifics like how AI is being used to tackle security vulnerabilities, which 55 00:02:21,160 --> 00:02:23,520 is a huge topic these days. 56 00:02:23,520 --> 00:02:24,520 Right. 57 00:02:24,520 --> 00:02:25,520 Security is paramount. 58 00:02:25,520 --> 00:02:32,000 And AI is proving to be a really valuable tool in the fight against these cyber attacks 59 00:02:32,000 --> 00:02:33,480 and data breaches. 60 00:02:33,480 --> 00:02:38,640 The paper explores a bunch of techniques AI uses to fight security bugs. 61 00:02:38,640 --> 00:02:41,480 So can we peek under the hood a bit? 62 00:02:41,480 --> 00:02:44,800 How does AI actually tackle those issues? 63 00:02:44,800 --> 00:02:47,960 One approach that I think is really fascinating is template-based patching. 64 00:02:47,960 --> 00:02:49,560 Okay, template-based patching. 65 00:02:49,560 --> 00:02:50,560 Yeah. 66 00:02:50,560 --> 00:02:55,720 I think having a set of blueprints for fixing common security flaws, like a SQL injection, 67 00:02:55,720 --> 00:03:00,480 where hackers sneak malicious code into your database, AI can use these blueprints to patch 68 00:03:00,480 --> 00:03:03,480 these known vulnerabilities really quickly and accurately. 69 00:03:03,480 --> 00:03:07,120 Like having a security guard that can always spot those weaknesses, those common ones at 70 00:03:07,120 --> 00:03:08,120 least. 71 00:03:08,120 --> 00:03:09,120 Yeah, exactly. 72 00:03:09,120 --> 00:03:10,120 Cool. 73 00:03:10,120 --> 00:03:13,800 But what about those vulnerabilities that are more hidden, the ones that aren't so easily 74 00:03:13,800 --> 00:03:14,800 categorized? 75 00:03:14,800 --> 00:03:15,800 Right. 76 00:03:15,800 --> 00:03:18,160 So for that, there's something called dynamic analysis. 77 00:03:18,160 --> 00:03:19,800 It's a bit more proactive. 78 00:03:19,800 --> 00:03:25,000 The AI basically simulates attacks on your code, trying to find those hidden vulnerabilities. 79 00:03:25,000 --> 00:03:29,720 So it's like having an AI penetration tester on your team, always looking for those weaknesses. 80 00:03:29,720 --> 00:03:30,880 Yeah, pretty much. 81 00:03:30,880 --> 00:03:34,520 It's like playing both offense and defense when it comes to security. 82 00:03:34,520 --> 00:03:35,520 That's super cool. 83 00:03:35,520 --> 00:03:37,800 But how does it actually simulate and adapt? 84 00:03:37,800 --> 00:03:40,360 I mean, code can be really complex. 85 00:03:40,360 --> 00:03:44,240 Yeah, it involves running the code in a really controlled environment and feeding it all 86 00:03:44,240 --> 00:03:46,000 sorts of different inputs. 87 00:03:46,000 --> 00:03:51,880 Some of those inputs are designed to mimic how a hacker might behave, and the AI is watching 88 00:03:51,880 --> 00:03:57,480 how the code responds to those inputs, looking for anything that's unexpected or dangerous. 89 00:03:57,480 --> 00:04:03,560 Just like AI is thinking like a hacker, but for good to help us stay ahead. 90 00:04:03,560 --> 00:04:04,560 Exactly. 91 00:04:04,560 --> 00:04:05,560 That's really interesting. 92 00:04:05,560 --> 00:04:07,080 But I mean, there have to be limitations to that, right? 93 00:04:07,080 --> 00:04:09,040 I mean, code can be so complex. 94 00:04:09,040 --> 00:04:11,280 What are some of the challenges that AI faces here? 95 00:04:11,280 --> 00:04:12,680 Yeah, that's a great point. 96 00:04:12,680 --> 00:04:17,160 One of the biggest challenges is that AI can misinterpret code sometimes or like miss 97 00:04:17,160 --> 00:04:19,000 subtle nuances. 98 00:04:19,000 --> 00:04:21,400 And that's why we still need human oversight. 99 00:04:21,400 --> 00:04:25,600 It's a powerful tool, but it's not a replacement for human expertise. 100 00:04:25,600 --> 00:04:26,600 You know what I mean? 101 00:04:26,600 --> 00:04:27,600 Yeah, totally. 102 00:04:27,600 --> 00:04:32,640 It's like that partnership, human and machine intelligence working together. 103 00:04:32,640 --> 00:04:38,040 Now what about the resources needed to actually use these AI tools? 104 00:04:38,040 --> 00:04:41,720 Can any developer use them or are there some barriers to entry? 105 00:04:41,720 --> 00:04:42,840 That's a great question. 106 00:04:42,840 --> 00:04:49,200 A lot of these AI tools, especially those for dynamic analysis can be pretty demanding, 107 00:04:49,200 --> 00:04:50,200 computationally speaking. 108 00:04:50,200 --> 00:04:56,560 We need a lot of processing power and memory, and that can be a challenge for developers 109 00:04:56,560 --> 00:04:58,240 who might not have those resources. 110 00:04:58,240 --> 00:05:01,160 So it's not necessarily accessible to everyone at this point. 111 00:05:01,160 --> 00:05:02,160 Yeah, exactly. 112 00:05:02,160 --> 00:05:03,160 Okay. 113 00:05:03,160 --> 00:05:07,760 Well, despite those challenges, it's clear that this field is moving really fast, and 114 00:05:07,760 --> 00:05:11,760 this paper highlights some of the emerging trends, like pre-trained models, which we've 115 00:05:11,760 --> 00:05:13,120 talked about a bit before. 116 00:05:13,120 --> 00:05:14,120 Yeah. 117 00:05:14,120 --> 00:05:15,120 But can you tell us more about those? 118 00:05:15,120 --> 00:05:16,120 How are they changing things? 119 00:05:16,120 --> 00:05:20,680 Yeah, pre-trained models are basically like AI experts that have already been trained on 120 00:05:20,680 --> 00:05:23,080 just tons and tons of code. 121 00:05:23,080 --> 00:05:28,080 So like Codex and Code T5 are a couple of examples they've learned from like millions of other 122 00:05:28,080 --> 00:05:33,680 developers and can be fine tuned for specific tasks like bug fixing or code generation, 123 00:05:33,680 --> 00:05:35,560 and the accuracy is really impressive. 124 00:05:35,560 --> 00:05:40,000 So it's like having an AI mentor who has all this experience to draw on. 125 00:05:40,000 --> 00:05:41,000 Exactly. 126 00:05:41,000 --> 00:05:42,000 Yeah. 127 00:05:42,000 --> 00:05:44,480 That's amazing, especially for newer developers who don't have as much experience. 128 00:05:44,480 --> 00:05:45,480 Right. 129 00:05:45,480 --> 00:05:46,480 Cool. 130 00:05:46,480 --> 00:05:52,080 So we've talked about how AI fixes bugs, but let's dive deeper into the AI actually writing 131 00:05:52,080 --> 00:05:54,680 code, the code generation side of things. 132 00:05:54,680 --> 00:05:57,560 What are some of the mind-blowing things happening there? 133 00:05:57,560 --> 00:06:00,120 So this is where it gets really interesting. 134 00:06:00,120 --> 00:06:03,240 Imagine describing what you want your code to do in plain English. 135 00:06:03,240 --> 00:06:04,240 Right. 136 00:06:04,240 --> 00:06:07,200 So AI just boom, generates the actual code for you. 137 00:06:07,200 --> 00:06:08,680 That's what LLNs are doing. 138 00:06:08,680 --> 00:06:10,680 Trained on these huge data sets of code. 139 00:06:10,680 --> 00:06:11,680 It sounds like magic. 140 00:06:11,680 --> 00:06:13,160 Like how does that even work? 141 00:06:13,160 --> 00:06:14,160 Right. 142 00:06:14,160 --> 00:06:17,720 There's obviously a lot of complex technology going on behind the scenes, but yeah, it's 143 00:06:17,720 --> 00:06:19,120 pretty incredible. 144 00:06:19,120 --> 00:06:23,920 So there are a bunch of different code generation models, each with their own strengths and 145 00:06:23,920 --> 00:06:24,920 weaknesses. 146 00:06:24,920 --> 00:06:25,920 Okay. 147 00:06:25,920 --> 00:06:27,200 So let's hear about some of those. 148 00:06:27,200 --> 00:06:31,120 Like I know we've talked about Codex before, but remind me what that one is all about. 149 00:06:31,120 --> 00:06:32,120 Sure. 150 00:06:32,120 --> 00:06:34,000 It's probably one of the more well-known ones. 151 00:06:34,000 --> 00:06:36,960 It powers things like GitHub Co-Pilot. 152 00:06:36,960 --> 00:06:42,320 And it's really good at generating pretty fluent and often surprisingly accurate code, 153 00:06:42,320 --> 00:06:43,920 especially for common tasks. 154 00:06:43,920 --> 00:06:49,080 It's like having a coding assistant who's learned from the best practices of thousands 155 00:06:49,080 --> 00:06:50,080 of developers. 156 00:06:50,080 --> 00:06:51,080 Right. 157 00:06:51,080 --> 00:06:55,640 So if I need to write some basic code for a web form or something, Codex could be a good 158 00:06:55,640 --> 00:06:56,640 go to. 159 00:06:56,640 --> 00:06:57,640 Exactly. 160 00:06:57,640 --> 00:06:58,640 Yeah. 161 00:06:58,640 --> 00:07:00,760 But I mean, like all AI, it probably has its limitations. 162 00:07:00,760 --> 00:07:02,440 What are some of the things it struggles with? 163 00:07:02,440 --> 00:07:07,720 So it's great for common tasks, but can struggle with like really specialized or complex scenarios. 164 00:07:07,720 --> 00:07:12,280 It might miss little details that a human would catch, especially in domains it hasn't 165 00:07:12,280 --> 00:07:14,320 been trained on specifically. 166 00:07:14,320 --> 00:07:19,680 So it's not quite a one size fits all solution, more like a specialized tool for certain situations. 167 00:07:19,680 --> 00:07:21,840 What about other code generation models? 168 00:07:21,840 --> 00:07:25,200 Are there any that like Excel where Codex falls short? 169 00:07:25,200 --> 00:07:26,560 Yeah, there are actually. 170 00:07:26,560 --> 00:07:28,960 Each has its own like strengths and weaknesses. 171 00:07:28,960 --> 00:07:30,960 So for example, there's Code T5. 172 00:07:30,960 --> 00:07:31,960 Code T5. 173 00:07:31,960 --> 00:07:32,960 Okay. 174 00:07:32,960 --> 00:07:33,960 It takes a slightly different approach. 175 00:07:33,960 --> 00:07:38,400 It's designed to be really accurate, but for specific code generation tasks like, you 176 00:07:38,400 --> 00:07:43,280 know, translating instructions into SQL queries or generating Python code. 177 00:07:43,280 --> 00:07:48,120 So if Codex is like the generalist, Code T5 is the specialist focused on certain things. 178 00:07:48,120 --> 00:07:49,120 Exactly. 179 00:07:49,120 --> 00:07:50,120 Yeah. 180 00:07:50,120 --> 00:07:53,200 It might not be as flexible as Codex in a like real time coding environment, but for 181 00:07:53,200 --> 00:07:57,000 those very specific tasks, the accuracy is incredible. 182 00:07:57,000 --> 00:07:58,000 Really cool. 183 00:07:58,000 --> 00:08:01,000 We've got the all arounder, the specialist. 184 00:08:01,000 --> 00:08:02,240 Who else is out there? 185 00:08:02,240 --> 00:08:04,240 So one that stands out is Graph Codebird. 186 00:08:04,240 --> 00:08:05,240 Graph Toadbird. 187 00:08:05,240 --> 00:08:06,240 All right. 188 00:08:06,240 --> 00:08:10,640 What makes this one unique is its ability to understand like the structure and dependencies 189 00:08:10,640 --> 00:08:15,200 within code, which is really important for those larger, more complex projects where 190 00:08:15,200 --> 00:08:16,760 there are lots of moving pieces. 191 00:08:16,760 --> 00:08:17,760 Okay. 192 00:08:17,760 --> 00:08:19,480 I'm going to need a little more explanation on that one. 193 00:08:19,480 --> 00:08:23,120 What do you mean by like structure and dependencies? 194 00:08:23,120 --> 00:08:24,520 Code can get pretty intricate. 195 00:08:24,520 --> 00:08:25,520 Right. 196 00:08:25,520 --> 00:08:29,680 So if you look about a big software project with tons of files all interconnected and 197 00:08:29,680 --> 00:08:30,880 referencing each other. 198 00:08:30,880 --> 00:08:31,880 Okay. 199 00:08:31,880 --> 00:08:32,880 Yeah. 200 00:08:32,880 --> 00:08:36,480 You got to understand those relationships to write good code that doesn't break other 201 00:08:36,480 --> 00:08:38,200 parts of the system. 202 00:08:38,200 --> 00:08:42,440 Graph Codebird is really good at that kind of analysis, which is why it's so good at 203 00:08:42,440 --> 00:08:44,280 generating that complex code. 204 00:08:44,280 --> 00:08:47,160 So it's like Graph Codebird is the architect. 205 00:08:47,160 --> 00:08:51,720 It sees the big picture and can generate code that works seamlessly within the system. 206 00:08:51,720 --> 00:08:52,720 Yeah. 207 00:08:52,720 --> 00:08:53,720 That's a great analogy. 208 00:08:53,720 --> 00:08:58,560 Because of this deep understanding, it's used for those like really tough coding projects 209 00:08:58,560 --> 00:09:02,400 where getting all those dependencies right is critical. 210 00:09:02,400 --> 00:09:03,400 Sounds powerful. 211 00:09:03,400 --> 00:09:05,400 But I'm guessing there's a trade-off. 212 00:09:05,400 --> 00:09:06,400 Yeah. 213 00:09:06,400 --> 00:09:10,080 It can be a bit slower than some of the other models just because it's doing so much analysis. 214 00:09:10,080 --> 00:09:13,840 So it's that classic trade-off, speed versus depth of understanding. 215 00:09:13,840 --> 00:09:14,840 Exactly. 216 00:09:14,840 --> 00:09:15,840 Got it. 217 00:09:15,840 --> 00:09:18,160 But overall it sounds like a really valuable tool. 218 00:09:18,160 --> 00:09:22,040 Are there any other cool code generation models that are making waves? 219 00:09:22,040 --> 00:09:23,040 Yeah. 220 00:09:23,040 --> 00:09:27,480 There are a couple of others, the paper highlights like Find and DeepSeat Coder. 221 00:09:27,480 --> 00:09:28,480 Okay. 222 00:09:28,480 --> 00:09:29,480 Let's hear about those. 223 00:09:29,480 --> 00:09:30,480 What makes them stand out? 224 00:09:30,480 --> 00:09:35,400 So Find has been getting a lot of attention lately because of its performance in benchmark 225 00:09:35,400 --> 00:09:36,480 tests. 226 00:09:36,480 --> 00:09:41,360 It's really accurate when it comes to generating code from specific prompts, like the kinds 227 00:09:41,360 --> 00:09:44,520 of challenges you'd find in a coding interview or competition. 228 00:09:44,520 --> 00:09:45,680 Wow. 229 00:09:45,680 --> 00:09:48,680 So it's been training for the coding exams that we all dread. 230 00:09:48,680 --> 00:09:49,680 Right. 231 00:09:49,680 --> 00:09:54,200 It's a really great tool for students or anyone prepping for technical interviews. 232 00:09:54,200 --> 00:09:55,200 Absolutely. 233 00:09:55,200 --> 00:09:57,320 So Find, good for the exams. 234 00:09:57,320 --> 00:09:59,000 What about DeepSeat Coder? 235 00:09:59,000 --> 00:10:04,160 DeepSeat Coder is really interesting because it can predict and complete unfinished code 236 00:10:04,160 --> 00:10:05,480 snippets. 237 00:10:05,480 --> 00:10:07,960 And it does this across multiple programming languages. 238 00:10:07,960 --> 00:10:08,960 Oh, wow. 239 00:10:08,960 --> 00:10:13,040 So it's like having a coding partner who can read your mind and knows what you're going 240 00:10:13,040 --> 00:10:14,040 to do next. 241 00:10:14,040 --> 00:10:15,040 Yeah, pretty much. 242 00:10:15,040 --> 00:10:18,280 It can help you finish your thought even if you're switching between different languages. 243 00:10:18,280 --> 00:10:19,280 That's incredible. 244 00:10:19,280 --> 00:10:20,520 It's a huge time saver. 245 00:10:20,520 --> 00:10:21,520 Right. 246 00:10:21,520 --> 00:10:24,120 Especially for developers who work with multiple languages. 247 00:10:24,120 --> 00:10:25,120 Absolutely. 248 00:10:25,120 --> 00:10:27,840 But I mean, I'm sure there are limitations there too, right? 249 00:10:27,840 --> 00:10:31,920 Its performance can vary depending on the programming language you're using. 250 00:10:31,920 --> 00:10:38,080 And it might not have the same level of accuracy for less commonly used languages. 251 00:10:38,080 --> 00:10:40,480 So it's like choosing the right tool for the job. 252 00:10:40,480 --> 00:10:41,480 Yeah. 253 00:10:41,480 --> 00:10:44,800 You've got your generalist, your specialist, your up and comers. 254 00:10:44,800 --> 00:10:47,840 And it really depends on what you need it to do. 255 00:10:47,840 --> 00:10:48,840 Absolutely. 256 00:10:48,840 --> 00:10:51,920 And what's exciting is that this field is moving so quickly. 257 00:10:51,920 --> 00:10:55,280 What's cutting edge today might be commonplace tomorrow. 258 00:10:55,280 --> 00:10:56,280 Exactly. 259 00:10:56,280 --> 00:10:59,840 It's really amazing to think about how AI is transforming software development. 260 00:10:59,840 --> 00:11:02,400 It feels like we're just scratching the surface of what's possible. 261 00:11:02,400 --> 00:11:04,080 Yeah, it really does. 262 00:11:04,080 --> 00:11:08,400 But before we get too carried away with the possibilities, I think it's important to acknowledge 263 00:11:08,400 --> 00:11:10,440 the elephant in the room. 264 00:11:10,440 --> 00:11:12,160 What are some of the limitations of AI? 265 00:11:12,160 --> 00:11:16,440 What are some of the downsides or the risks that we need to be thinking about? 266 00:11:16,440 --> 00:11:17,440 Right. 267 00:11:17,440 --> 00:11:20,680 And we'll definitely dive into those in more detail in the next segment. 268 00:11:20,680 --> 00:11:21,680 Awesome. 269 00:11:21,680 --> 00:11:22,680 Looking forward to it. 270 00:11:22,680 --> 00:11:23,680 Stay tuned, everyone. 271 00:11:23,680 --> 00:11:27,000 So, yeah, we were talking about all the amazing things that AI can do with code before the 272 00:11:27,000 --> 00:11:28,000 break. 273 00:11:28,000 --> 00:11:29,000 Right. 274 00:11:29,000 --> 00:11:30,000 It's really mind blowing stuff. 275 00:11:30,000 --> 00:11:32,920 But like any new tool, there are limitations. 276 00:11:32,920 --> 00:11:34,240 Things to be careful of. 277 00:11:34,240 --> 00:11:39,920 What are some of those potential risks or downsides that developers should be aware of? 278 00:11:39,920 --> 00:11:40,920 Yeah. 279 00:11:40,920 --> 00:11:46,800 So one of the biggest ones is the accuracy and reliability of the AI-generated code. 280 00:11:46,800 --> 00:11:49,520 And the repairs. 281 00:11:49,520 --> 00:11:52,480 These models are getting better and better all the time, but they're not perfect. 282 00:11:52,480 --> 00:11:53,800 Oh, it's not, yeah. 283 00:11:53,800 --> 00:11:59,440 They can still misinterpret instructions or miss really subtle details in the code. 284 00:11:59,440 --> 00:12:02,440 Or worst case, introduce new errors. 285 00:12:02,440 --> 00:12:03,680 Yeah, that's a scary thought. 286 00:12:03,680 --> 00:12:09,000 So it's not just a case of throwing the problem at the AI and then just trusting that it's 287 00:12:09,000 --> 00:12:10,520 going to spit out perfect code. 288 00:12:10,520 --> 00:12:11,520 Right, exactly. 289 00:12:11,520 --> 00:12:13,040 Human oversight is still super important. 290 00:12:13,040 --> 00:12:15,320 Yeah, you got to double check the work. 291 00:12:15,320 --> 00:12:20,040 Think of it like having an intern, a really good intern who can code, but you still got 292 00:12:20,040 --> 00:12:22,960 to check their work before you push it live. 293 00:12:22,960 --> 00:12:23,960 Exactly. 294 00:12:23,960 --> 00:12:26,520 It's all about that balance. 295 00:12:26,520 --> 00:12:30,120 Leveraging the power of AI, but still maintaining that human oversight. 296 00:12:30,120 --> 00:12:31,120 Right. 297 00:12:31,120 --> 00:12:33,400 And making sure things are secure and high quality. 298 00:12:33,400 --> 00:12:34,400 Absolutely. 299 00:12:34,400 --> 00:12:35,400 Absolutely. 300 00:12:35,400 --> 00:12:39,440 What other limitations should we be mindful of? 301 00:12:39,440 --> 00:12:42,680 Another challenge is what we call context sensitivity. 302 00:12:42,680 --> 00:12:43,680 Context sensitivity, okay. 303 00:12:43,680 --> 00:12:48,200 AI models can sometimes struggle to understand the bigger picture. 304 00:12:48,200 --> 00:12:53,320 Like a complex code base with all its dependencies. 305 00:12:53,320 --> 00:12:58,400 They might make a change that works in isolation, but then it ends up breaking something else. 306 00:12:58,400 --> 00:12:59,400 Right. 307 00:12:59,400 --> 00:13:01,240 Like fixing one thing and breaking 10 other things. 308 00:13:01,240 --> 00:13:02,240 Exactly. 309 00:13:02,240 --> 00:13:03,240 Classic. 310 00:13:03,240 --> 00:13:06,600 So that's where the human developers come in with their understanding of the software 311 00:13:06,600 --> 00:13:07,880 architecture, the business logic. 312 00:13:07,880 --> 00:13:12,920 They've got to make sure those AI generated changes don't have unintended consequences. 313 00:13:12,920 --> 00:13:17,920 Yeah, because AI is great at those very localized tasks, but it's still learning to think about 314 00:13:17,920 --> 00:13:18,920 the bigger picture. 315 00:13:18,920 --> 00:13:19,920 You know what I mean? 316 00:13:19,920 --> 00:13:20,920 Yeah, that's a good way to put it. 317 00:13:20,920 --> 00:13:22,440 But the researchers are working on it. 318 00:13:22,440 --> 00:13:25,720 And one of the promising areas is developing multimodal models. 319 00:13:25,720 --> 00:13:26,720 Oh, right. 320 00:13:26,720 --> 00:13:28,560 We talked about those briefly before. 321 00:13:28,560 --> 00:13:32,760 Remind me, how do those work and how could they help with this whole context sensitivity 322 00:13:32,760 --> 00:13:33,760 thing? 323 00:13:33,760 --> 00:13:37,320 So, multimodal models, they're really cool because they don't just look at the code 324 00:13:37,320 --> 00:13:38,320 itself. 325 00:13:38,320 --> 00:13:39,320 Right. 326 00:13:39,320 --> 00:13:42,160 They can actually incorporate comments, documentation, even log files. 327 00:13:42,160 --> 00:13:45,400 So they get a much more holistic understanding of the code base. 328 00:13:45,400 --> 00:13:46,400 Wow. 329 00:13:46,400 --> 00:13:49,520 Like giving them more puzzle pieces so they can see the whole picture better. 330 00:13:49,520 --> 00:13:50,520 Exactly. 331 00:13:50,520 --> 00:13:51,520 That's really smart. 332 00:13:51,520 --> 00:13:54,080 But we talked about this before too. 333 00:13:54,080 --> 00:13:57,240 Not every developer has access to the latest hardware. 334 00:13:57,240 --> 00:14:02,160 So how does that affect the accessibility of these AI tools? 335 00:14:02,160 --> 00:14:03,160 Yeah. 336 00:14:03,160 --> 00:14:05,640 Resource overhead is a big consideration. 337 00:14:05,640 --> 00:14:09,880 You know, some of these tools, especially for like dynamic analysis or training those 338 00:14:09,880 --> 00:14:11,400 large language models. 339 00:14:11,400 --> 00:14:13,120 They need a lot of computing power. 340 00:14:13,120 --> 00:14:14,120 Yeah. 341 00:14:14,120 --> 00:14:16,240 Lots of processing power, lots of memory. 342 00:14:16,240 --> 00:14:20,160 And that can be a barrier, especially for developers working on smaller projects or, 343 00:14:20,160 --> 00:14:21,320 you know, with limited hardware. 344 00:14:21,320 --> 00:14:22,320 Right. 345 00:14:22,320 --> 00:14:26,200 So there's a risk of kind of widening the digital divide where only people with all 346 00:14:26,200 --> 00:14:30,000 this fancy equipment can actually benefit from these new advancements. 347 00:14:30,000 --> 00:14:31,000 Right. 348 00:14:31,000 --> 00:14:32,000 And that's definitely a concern. 349 00:14:32,000 --> 00:14:37,000 The AI community needs to be thinking about how to make these tools more accessible and 350 00:14:37,000 --> 00:14:39,200 affordable so everyone can benefit. 351 00:14:39,200 --> 00:14:40,200 Yeah. 352 00:14:40,200 --> 00:14:41,720 That's an important point. 353 00:14:41,720 --> 00:14:45,360 But even with those limitations, the research is moving so fast. 354 00:14:45,360 --> 00:14:46,480 What are you most excited about? 355 00:14:46,480 --> 00:14:51,880 What are some of the like the next frontiers in AI driven code repair and generation? 356 00:14:51,880 --> 00:14:55,040 I'm really excited about explainable AI or XAI. 357 00:14:55,040 --> 00:14:56,040 XAI, okay. 358 00:14:56,040 --> 00:14:57,040 Yeah. 359 00:14:57,040 --> 00:15:00,680 So it's all about making the AI's decision making process more transparent. 360 00:15:00,680 --> 00:15:02,400 You know, something humans can understand. 361 00:15:02,400 --> 00:15:03,400 Oh, interesting. 362 00:15:03,400 --> 00:15:09,360 So imagine the AI suggests a really complex code fix, but you have no idea why it shows 363 00:15:09,360 --> 00:15:10,360 that solution. 364 00:15:10,360 --> 00:15:11,360 Would you trust it? 365 00:15:11,360 --> 00:15:12,360 Probably not, no. 366 00:15:12,360 --> 00:15:13,360 Right. 367 00:15:13,360 --> 00:15:15,960 So XAI is all about giving us insight into the AI's reasoning. 368 00:15:15,960 --> 00:15:20,280 So it's not just about the AI solving the problem, but actually being able to understand 369 00:15:20,280 --> 00:15:23,320 how and why it arrived at that solution. 370 00:15:23,320 --> 00:15:24,320 That builds trust. 371 00:15:24,320 --> 00:15:25,320 Exactly. 372 00:15:25,320 --> 00:15:26,760 And it's about empowerment too. 373 00:15:26,760 --> 00:15:30,560 If developers can understand how the AI thinks, they can actually learn from it. 374 00:15:30,560 --> 00:15:31,560 Oh, I see. 375 00:15:31,560 --> 00:15:32,560 Yeah, that makes sense. 376 00:15:32,560 --> 00:15:34,800 It becomes a learning opportunity, not just a black box. 377 00:15:34,800 --> 00:15:35,800 Right. 378 00:15:35,800 --> 00:15:37,040 So yeah, XAI is a big one. 379 00:15:37,040 --> 00:15:41,800 And then another area I'm keeping my eye on is AI models that can actually reason about 380 00:15:41,800 --> 00:15:43,000 code security. 381 00:15:43,000 --> 00:15:44,400 Oh, wow. 382 00:15:44,400 --> 00:15:50,600 So imagine an AI that can not only find vulnerabilities, but also suggest fixes that are, you know, 383 00:15:50,600 --> 00:15:52,120 based on best practices. 384 00:15:52,120 --> 00:15:55,760 Like having an AI security consultant built into your development environment. 385 00:15:55,760 --> 00:15:56,760 Exactly. 386 00:15:56,760 --> 00:16:01,120 And I think we're going to see big advances in this area as the models get more sophisticated. 387 00:16:01,120 --> 00:16:05,840 It really does feel like, you know, the potential for AI to change how we do software development 388 00:16:05,840 --> 00:16:10,720 from fixing bugs to generating code to, like you said, even analyzing security. 389 00:16:10,720 --> 00:16:11,720 It's just, it's incredible. 390 00:16:11,720 --> 00:16:12,720 It really is. 391 00:16:12,720 --> 00:16:15,080 It's such an exciting time to be a developer. 392 00:16:15,080 --> 00:16:18,520 But before we get too carried away, let's take a step back and think about what we've 393 00:16:18,520 --> 00:16:20,020 learned today. 394 00:16:20,020 --> 00:16:24,440 You know, we've talked about the incredible things AI can do, but also the limitations, 395 00:16:24,440 --> 00:16:26,080 the things we need to be aware of. 396 00:16:26,080 --> 00:16:27,080 Yeah, definitely. 397 00:16:27,080 --> 00:16:31,160 We've covered a lot, both the potential and the challenges. 398 00:16:31,160 --> 00:16:32,400 Absolutely. 399 00:16:32,400 --> 00:16:36,320 And in the last segment, we'll wrap things up and talk about, you know, where we go from 400 00:16:36,320 --> 00:16:37,320 here, what are the next steps? 401 00:16:37,320 --> 00:16:39,880 What are some of the big questions we still need to answer? 402 00:16:39,880 --> 00:16:40,880 Right. 403 00:16:40,880 --> 00:16:44,400 It's a rapidly evolving field and there's still so much to explore. 404 00:16:44,400 --> 00:16:46,000 Welcome back to the deep dive. 405 00:16:46,000 --> 00:16:48,720 So before the break, we asked you a big question. 406 00:16:48,720 --> 00:16:56,320 Will AI ever be able to write and debug code entirely on its own, like completely without 407 00:16:56,320 --> 00:16:57,320 human help? 408 00:16:57,320 --> 00:16:58,320 Yeah, a big question indeed. 409 00:16:58,320 --> 00:17:01,920 I mean, you see these AI tools getting more powerful every day. 410 00:17:01,920 --> 00:17:04,360 It's easy to imagine that future, right? 411 00:17:04,360 --> 00:17:05,880 Robots taking over the coding world. 412 00:17:05,880 --> 00:17:06,880 Yeah. 413 00:17:06,880 --> 00:17:10,600 But you know, I think human developers will still be essential, at least for the foreseeable 414 00:17:10,600 --> 00:17:11,600 future. 415 00:17:11,600 --> 00:17:16,040 There's still something about human intuition and creativity that AI just hasn't quite 416 00:17:16,040 --> 00:17:17,320 grasped yet. 417 00:17:17,320 --> 00:17:18,320 That's a relief to hear. 418 00:17:18,320 --> 00:17:20,120 But what does that future look like? 419 00:17:20,120 --> 00:17:24,800 Will it be this, you know, collaboration between humans and AI or will one eventually 420 00:17:24,800 --> 00:17:27,360 just like replace the other? 421 00:17:27,360 --> 00:17:29,360 I think it'll be more of a partnership, you know? 422 00:17:29,360 --> 00:17:35,240 Imagine AI handling all those tedious, repetitive tasks, the things that take up so much time 423 00:17:35,240 --> 00:17:39,920 that frees up human developers to focus on the really interesting stuff, the creative 424 00:17:39,920 --> 00:17:42,360 problem solving, the strategic thinking. 425 00:17:42,360 --> 00:17:43,960 So it's not about replacement. 426 00:17:43,960 --> 00:17:47,840 It's about AI augmenting human capabilities. 427 00:17:47,840 --> 00:17:49,200 Exactly. 428 00:17:49,200 --> 00:17:52,040 We can achieve so much more when we work together. 429 00:17:52,040 --> 00:17:55,560 And this paper, it really highlights that potential. 430 00:17:55,560 --> 00:18:00,000 What are some of the research areas that are like really pushing the boundaries of what 431 00:18:00,000 --> 00:18:02,120 AI can do in this field? 432 00:18:02,120 --> 00:18:06,360 Like we talked about before, the development of AI models that can reason about security 433 00:18:06,360 --> 00:18:08,080 is huge. 434 00:18:08,080 --> 00:18:14,440 Imagine AI that can not only identify vulnerabilities, but also suggest fixes based on best practices. 435 00:18:14,440 --> 00:18:15,760 That would be a game changer. 436 00:18:15,760 --> 00:18:18,080 It's like having an AI security expert on your team. 437 00:18:18,080 --> 00:18:19,080 Right. 438 00:18:19,080 --> 00:18:22,320 And as these AI models get smarter and are trained on more and more data, I think we'll 439 00:18:22,320 --> 00:18:24,880 see huge leaps forward in that area. 440 00:18:24,880 --> 00:18:25,880 That's really exciting. 441 00:18:25,880 --> 00:18:28,320 So much potential, but also so much we still don't know. 442 00:18:28,320 --> 00:18:30,160 Yeah, definitely still early days. 443 00:18:30,160 --> 00:18:33,240 We got to approach this new technology carefully. 444 00:18:33,240 --> 00:18:35,760 Recognize the possibilities, but also the limitations. 445 00:18:35,760 --> 00:18:36,760 Right. 446 00:18:36,760 --> 00:18:37,900 More research is needed. 447 00:18:37,900 --> 00:18:43,080 We need to really understand how these AI models work so we can use them responsibly. 448 00:18:43,080 --> 00:18:44,080 Absolutely. 449 00:18:44,080 --> 00:18:46,200 Well, this has been a fascinating deep dive. 450 00:18:46,200 --> 00:18:50,720 Thanks for joining us on this journey into the world of AI and software development. 451 00:18:50,720 --> 00:18:51,720 My pleasure. 452 00:18:51,720 --> 00:18:53,480 Lots of exciting stuff to think about. 453 00:18:53,480 --> 00:18:56,920 If you want to learn more, we've got links to the research paper and other resources in 454 00:18:56,920 --> 00:18:58,240 the episode description. 455 00:18:58,240 --> 00:18:59,400 Be sure to check those out. 456 00:18:59,400 --> 00:19:26,960 And until next time, happy coding, everyone.