1 00:00:00,000 --> 00:00:03,120 Okay, so we've got this paper and the title is a mouthful. 2 00:00:03,720 --> 00:00:10,440 The dawn of GI agent, a preliminary case study with Claude 3.5 computer use. 3 00:00:10,560 --> 00:00:12,560 Yeah, it sounds pretty intense. 4 00:00:12,640 --> 00:00:17,680 So basically it's about an AI that can use a computer like just like a person would. 5 00:00:17,720 --> 00:00:20,960 And the crazy part is it's not just about typing in commands. 6 00:00:21,480 --> 00:00:26,360 Oh, OK. This AI actually interacts with the graphical user interface, you know, 7 00:00:26,360 --> 00:00:28,760 all the clicking and typing and dragging stuff. 8 00:00:28,760 --> 00:00:32,440 So like it's sitting there with a little AI mouse clicking around. 9 00:00:32,480 --> 00:00:33,400 Well, not exactly. 10 00:00:33,400 --> 00:00:36,880 It uses screenshots to see what's on the screen, kind of like taking a picture 11 00:00:36,880 --> 00:00:38,480 and then figuring it out from there. 12 00:00:38,480 --> 00:00:41,480 Wow. So how does it know what to actually do with all that information? 13 00:00:41,760 --> 00:00:43,040 That's where it gets really interesting. 14 00:00:43,040 --> 00:00:45,960 It uses something called a reasoning acting paradigm. 15 00:00:46,200 --> 00:00:48,160 OK, I'm going to need you to break that down for me. 16 00:00:48,160 --> 00:00:52,320 Basically, it looks at the screen and thinks, OK, to do this, I need to click here, 17 00:00:52,320 --> 00:00:54,960 then type that. It's like it's planning out its actions. 18 00:00:55,120 --> 00:00:58,120 So it's not just blindly following a set of rules. 19 00:00:58,120 --> 00:01:00,080 Right. It actually problem solves. 20 00:01:00,080 --> 00:01:02,360 And the researchers threw a bunch of different tasks at it. 21 00:01:02,360 --> 00:01:06,960 Everything from web searches to like working across different software. 22 00:01:07,000 --> 00:01:09,360 OK, so like what kind of tasks are we talking about here? 23 00:01:09,360 --> 00:01:10,320 Give me some examples. 24 00:01:10,480 --> 00:01:16,400 So imagine you need to like take data from a Google Sheet and put it into Excel. 25 00:01:16,680 --> 00:01:20,520 Claude can handle that whole thing moving back and forth between programs. 26 00:01:20,680 --> 00:01:22,240 Well, that would save me a ton of time. 27 00:01:22,240 --> 00:01:27,040 Yeah. And it even tackled stuff in Word and PowerPoint, like formatting documents 28 00:01:27,040 --> 00:01:28,440 and creating presentations. 29 00:01:28,600 --> 00:01:30,920 They even had it adding specific shapes and designs. 30 00:01:30,920 --> 00:01:33,120 Hold on. Is there anything this AI can't do? 31 00:01:33,680 --> 00:01:35,840 Well, yeah, there are definitely some limitations, 32 00:01:35,840 --> 00:01:38,840 especially with things like scrolling through pages 33 00:01:38,840 --> 00:01:41,320 and being super precise with its actions. 34 00:01:41,400 --> 00:01:43,800 OK, so it's not ready to take over our jobs just yet. 35 00:01:44,040 --> 00:01:46,520 Not quite, but you can see the potential, right? 36 00:01:46,520 --> 00:01:47,840 Oh, absolutely. 37 00:01:47,840 --> 00:01:50,920 It really makes you think about what AI will be able to do in the future, 38 00:01:50,920 --> 00:01:52,520 like even just a few years from now. 39 00:01:52,640 --> 00:01:54,640 And that's what's so exciting about this research. 40 00:01:54,640 --> 00:01:55,720 It's just the beginning. 41 00:01:55,720 --> 00:01:58,920 There's so much more to explore and figure out. 42 00:01:59,240 --> 00:02:04,280 All right. So we've established this AI can do some pretty incredible things, 43 00:02:05,000 --> 00:02:06,360 but it's not perfect. Yeah. 44 00:02:06,360 --> 00:02:08,960 Tell me more about those limitations. Where did it struggle? 45 00:02:09,320 --> 00:02:11,080 One of the big things was scrolling. 46 00:02:11,080 --> 00:02:14,760 It often relied on those page up and page down keys, 47 00:02:14,800 --> 00:02:18,120 like flipping through a book instead of smoothly scrolling like we do. 48 00:02:18,160 --> 00:02:21,000 So it might miss important information if it's not scrolling properly. 49 00:02:21,000 --> 00:02:22,080 Yeah, exactly. 50 00:02:22,080 --> 00:02:26,800 And that highlights one of the key areas for improvement, teaching these AIs 51 00:02:26,800 --> 00:02:30,680 to understand the flow of information, how to navigate content more naturally. 52 00:02:30,840 --> 00:02:32,520 Right. Makes sense. 53 00:02:32,520 --> 00:02:36,440 What other areas did the researchers point out as needing work? 54 00:02:36,680 --> 00:02:38,080 Precision was another one. 55 00:02:38,080 --> 00:02:40,960 So, for example, when it was editing a resume in Word, 56 00:02:41,360 --> 00:02:43,800 sometimes it only replaced part of the text 57 00:02:43,800 --> 00:02:45,960 because it didn't select everything accurately. 58 00:02:45,960 --> 00:02:48,120 Oh, that's a big deal, especially on a job application. 59 00:02:48,480 --> 00:02:50,440 It's not just understanding what's on the screen, 60 00:02:50,440 --> 00:02:53,080 but interacting with it at a really detailed level. 61 00:02:53,080 --> 00:02:56,640 Gotcha. And then there's the issue of knowing if it's done something correctly or not. 62 00:02:56,880 --> 00:02:58,600 Right. Like self-evaluation. 63 00:02:58,600 --> 00:03:02,560 There were times where Claude thought it had nailed a task, 64 00:03:02,560 --> 00:03:05,160 but it was only partially done or there were mistakes. 65 00:03:05,160 --> 00:03:07,560 So it needs to be able to judge its own work better. 66 00:03:07,840 --> 00:03:09,040 Yeah, exactly. 67 00:03:09,040 --> 00:03:12,600 Like being able to say, wait, I messed that up and figuring out how to fix it. 68 00:03:12,880 --> 00:03:14,120 This is also fascinating. 69 00:03:14,120 --> 00:03:17,440 So we've got this AI that can handle pretty complex tasks, 70 00:03:17,880 --> 00:03:19,280 but it's got room to grow. 71 00:03:19,280 --> 00:03:20,800 That's a great way to put it. 72 00:03:20,800 --> 00:03:22,520 The research is super promising, 73 00:03:22,520 --> 00:03:26,200 but it also shows just how tricky it is to develop AI systems 74 00:03:26,200 --> 00:03:28,800 that are truly reliable and robust. 75 00:03:28,800 --> 00:03:30,680 Well, I'm definitely hooked. 76 00:03:30,680 --> 00:03:33,680 In part two, let's dive deeper into those specific areas 77 00:03:33,680 --> 00:03:36,680 where Claude really shined and where it still needs some work. 78 00:03:37,080 --> 00:03:39,720 And we'll talk about what all of this means for the future of AI. 79 00:03:39,960 --> 00:03:40,840 Looking forward to it. 80 00:03:40,840 --> 00:03:43,520 We've just scratched the surface of what GUI agents can do. 81 00:03:43,520 --> 00:03:45,280 There's so much more to uncover. 82 00:03:45,280 --> 00:03:46,440 All right, welcome back. 83 00:03:46,440 --> 00:03:50,400 So let's dig into some more of what Claude 3.5 computer use can do. 84 00:03:50,480 --> 00:03:53,680 Yeah, you mentioned earlier that it could handle those multi-step workflows. 85 00:03:54,080 --> 00:03:56,200 What really stood out to the researchers there? 86 00:03:56,320 --> 00:04:00,120 Well, one example that really jumped out was its ability to download a Google 87 00:04:00,120 --> 00:04:04,080 sheet, open it up in Excel and then like actually enable editing. 88 00:04:04,280 --> 00:04:06,640 OK, so it's not just hopping between programs. 89 00:04:06,880 --> 00:04:09,720 It's understanding what needs to happen in each one. 90 00:04:09,800 --> 00:04:14,880 Exactly. And remember, this is all happening within the graphical user interface. 91 00:04:14,880 --> 00:04:19,200 So the AI has to understand the layout of each program, the buttons, 92 00:04:19,200 --> 00:04:20,840 the menus, all of that. 93 00:04:20,840 --> 00:04:24,880 Like it's learning how to get around a new city, figuring out the streets and landmarks. 94 00:04:25,000 --> 00:04:27,240 That's a great way to put it. It's not just seeing images. 95 00:04:27,240 --> 00:04:30,200 It's understanding how they work together, how to use them. 96 00:04:30,360 --> 00:04:32,360 OK, so complex workflows, check. 97 00:04:32,880 --> 00:04:35,080 What other things did Claude really excel at? 98 00:04:35,160 --> 00:04:37,520 It was also pretty impressive with office tasks. 99 00:04:37,520 --> 00:04:42,040 Like think about formatting a Word document, creating a PowerPoint presentation 100 00:04:42,040 --> 00:04:45,600 with a specific background, even adding shapes to a slide. 101 00:04:45,760 --> 00:04:49,440 So are we talking basic formatting here or can it handle more advanced stuff? 102 00:04:49,440 --> 00:04:51,000 Oh, it goes beyond the basics. 103 00:04:51,000 --> 00:04:54,840 They had it creating presentations with gradient backgrounds, adding shapes 104 00:04:54,840 --> 00:04:57,280 like triangles, positioning them precisely. 105 00:04:57,520 --> 00:05:00,680 Wow, it's really starting to sound like this AI could be a huge help at work. 106 00:05:01,200 --> 00:05:04,400 You know, taking care of all those little tasks that eat up so much time. 107 00:05:04,440 --> 00:05:07,000 Exactly. And that's one of the big takeaways here. 108 00:05:07,440 --> 00:05:11,400 GUI agents have the potential to completely change how we interact with 109 00:05:11,400 --> 00:05:14,360 technology. We're not just talking about voice commands or typing anymore. 110 00:05:14,360 --> 00:05:17,720 It's like the AI is right there with us using the computer, too. 111 00:05:17,880 --> 00:05:19,560 And it's just the beginning. 112 00:05:19,560 --> 00:05:23,240 Who knows what other applications will discover as this technology develops? 113 00:05:23,480 --> 00:05:24,720 This is all pretty mind blowing. 114 00:05:24,720 --> 00:05:27,280 But let's get back to those limitations for a second. 115 00:05:28,040 --> 00:05:31,760 You mentioned scrolling and precision as areas for improvement. 116 00:05:32,240 --> 00:05:34,600 What are the researchers doing to address those? 117 00:05:35,080 --> 00:05:40,480 So with scrolling, one of the big focuses is developing better mechanisms, 118 00:05:40,480 --> 00:05:44,960 you know, moving away from those clunky page up and down keys. 119 00:05:45,000 --> 00:05:47,880 And teaching it to scroll more like a person would smoothly. 120 00:05:47,920 --> 00:05:51,040 Right. It's about understanding the flow of information on a page, 121 00:05:51,040 --> 00:05:52,640 not just the individual elements. 122 00:05:52,640 --> 00:05:53,560 And how about precision? 123 00:05:53,560 --> 00:05:56,640 So precision is all about that fine grained control. 124 00:05:56,920 --> 00:06:00,240 It's the difference between selecting an entire text field versus 125 00:06:00,240 --> 00:06:02,080 accidentally only replacing part of it. 126 00:06:02,360 --> 00:06:06,000 So the AI needs a better understanding of like the spatial relationships 127 00:06:06,000 --> 00:06:07,000 between things on the screen. 128 00:06:07,000 --> 00:06:07,760 Exactly. 129 00:06:07,760 --> 00:06:11,240 And that level of detail is super important for things like editing 130 00:06:11,240 --> 00:06:13,440 documents or even playing video games. 131 00:06:13,480 --> 00:06:16,880 Oh, yeah. You mentioned earlier that they tested Claude on some games. 132 00:06:16,880 --> 00:06:17,680 How did that go? 133 00:06:17,680 --> 00:06:21,240 Yeah, they used Hearthstone, which is a card game, and Hongkai, 134 00:06:21,600 --> 00:06:24,080 Starrail, which is a more visual role playing game. 135 00:06:24,120 --> 00:06:27,120 So different types of games, different skills required. 136 00:06:27,760 --> 00:06:29,640 Did Claude hold its own? 137 00:06:29,640 --> 00:06:32,600 It had some pretty impressive wins, especially with Hearthstone. 138 00:06:32,640 --> 00:06:37,240 Like it could create a new deck of cards, rename it based on instructions, 139 00:06:37,240 --> 00:06:40,160 even pull off complex in-game actions. 140 00:06:40,240 --> 00:06:43,680 So it's adapting to a changing environment, making decisions on the fly. 141 00:06:43,800 --> 00:06:45,680 That's what's so cool about this research. 142 00:06:45,680 --> 00:06:47,560 We're not just talking about automating tasks. 143 00:06:47,560 --> 00:06:51,120 It's about AI that can understand and interact with complex systems, 144 00:06:51,120 --> 00:06:55,080 like whether it's a spreadsheet, a presentation, or even a virtual world. 145 00:06:55,320 --> 00:06:59,720 It sounds like we're really on the verge of a major shift in how we use computers. 146 00:06:59,800 --> 00:07:01,400 Yeah. I think that's a good way to put it. 147 00:07:01,560 --> 00:07:05,400 GUI agents like Claude represent a whole new frontier for AI. 148 00:07:05,400 --> 00:07:09,320 But as with any new tech, there are definitely challenges. 149 00:07:09,480 --> 00:07:13,760 All right. So we've explored the strengths, how Claude handles complex workflows, 150 00:07:13,960 --> 00:07:15,560 even tackles video games. 151 00:07:15,960 --> 00:07:18,800 And we've touched on those areas where it needs some fine tuning, 152 00:07:18,840 --> 00:07:20,640 like scrolling and precision. 153 00:07:20,880 --> 00:07:24,840 And let's not forget about the crucial element of self-evaluation. 154 00:07:24,840 --> 00:07:27,240 We'll dive into that and some of the other challenges, 155 00:07:27,400 --> 00:07:31,640 as well as what all this means for the future of AI when we come back for part three. 156 00:07:31,880 --> 00:07:33,960 So we've talked about all the cool things Claude can do, 157 00:07:33,960 --> 00:07:37,000 but you also mentioned this idea of self-evaluation. 158 00:07:37,000 --> 00:07:39,760 Like it needs to get better at judging its own work. 159 00:07:39,920 --> 00:07:42,600 Yeah. And that's where this critic function comes into play. 160 00:07:42,600 --> 00:07:45,040 A critic function. Okay. Explain that one to me. 161 00:07:45,240 --> 00:07:47,880 So think of it like an inner editor. 162 00:07:48,040 --> 00:07:49,960 You know how we can look at our own work and be like, 163 00:07:49,960 --> 00:07:52,320 oh, I messed that up or that could be better? 164 00:07:52,440 --> 00:07:55,200 That's kind of what we want Claude to be able to do. 165 00:07:55,320 --> 00:07:58,400 So it's not enough to just follow instructions and complete the task. 166 00:07:58,600 --> 00:08:00,880 It also needs to be able to say, did I do this right? 167 00:08:00,880 --> 00:08:01,800 Exactly. 168 00:08:01,800 --> 00:08:05,200 Recognizing errors, understanding why they happened, 169 00:08:05,200 --> 00:08:07,280 and then figuring out how to fix them. 170 00:08:07,280 --> 00:08:08,520 Okay. That makes sense. 171 00:08:08,720 --> 00:08:13,320 But how do you even begin to teach an AI to be self-critical? 172 00:08:13,720 --> 00:08:17,120 Well, it's definitely a tough challenge and researchers are trying different things. 173 00:08:17,120 --> 00:08:21,320 One approach is to like give the AI feedback on its performance. 174 00:08:21,320 --> 00:08:24,360 Oh, so it's like giving a student feedback on their homework. 175 00:08:24,360 --> 00:08:25,400 Yeah, exactly. 176 00:08:25,400 --> 00:08:28,520 Helping it learn what it did well and where it needs to improve. 177 00:08:28,520 --> 00:08:31,000 This whole critic function thing is really fascinating. 178 00:08:31,000 --> 00:08:36,120 It seems like a crucial step in developing AI that's more independent and reliable. 179 00:08:36,120 --> 00:08:36,880 It really is. 180 00:08:36,880 --> 00:08:39,320 If an AI can accurately judge its own work, 181 00:08:39,320 --> 00:08:42,320 it can catch and fix mistakes without us having to step in. 182 00:08:42,320 --> 00:08:45,560 Which would make them way more efficient and trustworthy. 183 00:08:45,560 --> 00:08:46,520 Exactly. 184 00:08:46,520 --> 00:08:50,360 Imagine AI systems that can manage projects, analyze data, 185 00:08:50,360 --> 00:08:55,560 even write code, all while double checking their own work to make sure it's accurate. 186 00:08:55,560 --> 00:08:59,080 It's pretty mind-blowing and maybe a little bit scary to think about AI 187 00:08:59,080 --> 00:09:01,080 becoming that advanced. 188 00:09:01,080 --> 00:09:03,080 It's powerful technology for sure, 189 00:09:03,080 --> 00:09:05,080 and we need to be thoughtful about how we develop it. 190 00:09:05,080 --> 00:09:07,080 Well, we've covered a lot of ground in this deep dive. 191 00:09:07,080 --> 00:09:12,080 We've seen what Claude 3.5 computer use is capable of, 192 00:09:12,080 --> 00:09:14,080 talked about its limitations, 193 00:09:14,080 --> 00:09:17,080 even got a little philosophical with this whole critic function idea. 194 00:09:17,080 --> 00:09:20,080 And it really highlights the incredible progress that's being made in AI. 195 00:09:20,080 --> 00:09:24,080 We're seeing a real shift in how we interact with technology. 196 00:09:24,080 --> 00:09:25,080 And this is just the beginning. 197 00:09:25,080 --> 00:09:27,080 There's so much more to learn and explore. 198 00:09:27,080 --> 00:09:30,080 But one thing's for sure, GUI agents like Claude 199 00:09:30,080 --> 00:09:32,080 are pushing the boundaries of what's possible. 200 00:09:32,080 --> 00:09:36,080 The potential applications are, well, pretty much endless. 201 00:09:36,080 --> 00:09:38,080 It's both exciting and a little daunting, right? 202 00:09:38,080 --> 00:09:40,080 Like, where is this all going to lead? 203 00:09:40,080 --> 00:09:43,080 It's an incredible time to be following this field. 204 00:09:43,080 --> 00:09:45,080 Who knows what the future holds, 205 00:09:45,080 --> 00:09:48,080 but it's clear that AI is going to play an even bigger role in our lives. 206 00:09:48,080 --> 00:09:49,080 Absolutely. 207 00:09:49,080 --> 00:09:53,080 And it's up to us to stay informed and make sure it's developed and used responsibly. 208 00:09:53,080 --> 00:09:55,080 Well said. 209 00:09:55,080 --> 00:10:00,080 This deep dive into Claude 3.5 computer use has been a wild ride. 210 00:10:00,080 --> 00:10:29,080 And I can't wait to see what comes next.