WEBVTT 00:00:00.000 --> 00:00:03.480 Imagine an AI that doesn't just write code for 00:00:03.480 --> 00:00:06.360 you, but actually invents completely new scientific 00:00:06.360 --> 00:00:09.419 methods. Methods that, you know, outperform human 00:00:09.419 --> 00:00:12.640 experts. Not just by a little bit, but like significantly. 00:00:12.759 --> 00:00:15.279 Yeah, and it does it in hours instead of months. 00:00:15.320 --> 00:00:16.940 It's kind of wild to think about that speed. 00:00:17.140 --> 00:00:19.359 Pretty wild acceleration, right? Welcome to the 00:00:19.359 --> 00:00:21.579 Deep Dive. Today we're looking at a whole stack 00:00:21.579 --> 00:00:24.780 of, frankly, fascinating insights into this accelerating 00:00:24.780 --> 00:00:28.460 world of AI. Our mission, like always, is to 00:00:28.460 --> 00:00:30.480 try and unpack how these systems aren't just 00:00:30.480 --> 00:00:34.420 helping us anymore, but actively redefining what's 00:00:34.420 --> 00:00:37.079 even possible in research, communication, creative 00:00:37.079 --> 00:00:38.939 stuff. That's right. So first up, we're going 00:00:38.939 --> 00:00:41.500 to dive into how Google's AI is now, well, out 00:00:41.500 --> 00:00:44.000 -researching actual researchers. It's quite something. 00:00:44.439 --> 00:00:46.299 Then we'll zip through a bunch of rapid fire 00:00:46.299 --> 00:00:49.119 updates, you know, everything from AI making 00:00:49.119 --> 00:00:51.759 films to new ways to sort out your digital life. 00:00:51.880 --> 00:00:54.219 And finally, we'll look at a potential game changer 00:00:54.219 --> 00:00:56.460 in speech recognition. Could totally reshape 00:00:56.460 --> 00:00:58.880 how we talk to machines. It's a lot to cover, 00:00:58.920 --> 00:01:01.109 so let's just jump right in. Okay, so this first 00:01:01.109 --> 00:01:03.429 big story, it really feels like it pushes the 00:01:03.429 --> 00:01:05.969 boundaries of what we thought AI could do. We're 00:01:05.969 --> 00:01:08.310 looking at this Google system, and it's not just 00:01:08.310 --> 00:01:10.769 writing essays or fixing code, it's actively 00:01:10.769 --> 00:01:14.129 inventing scientific methods. Feels like a big 00:01:14.129 --> 00:01:16.530 leap, you know? Moving AI from just an assistant 00:01:16.530 --> 00:01:20.760 into genuine discovery. It absolutely does. And 00:01:20.760 --> 00:01:22.579 what's really fascinating is how it works, the 00:01:22.579 --> 00:01:25.019 mechanics of it. You basically feed it a very 00:01:25.019 --> 00:01:27.219 specific problem. You need to clear a goal, some 00:01:27.219 --> 00:01:29.280 data to work with, and this is key, a way to 00:01:29.280 --> 00:01:32.099 score itself. Evaluation metric. Once it has 00:01:32.099 --> 00:01:34.859 that, the system starts generating initial ideas. 00:01:34.980 --> 00:01:37.079 Then it writes code based on those ideas. It's 00:01:37.079 --> 00:01:39.159 not just a one -shot deal, right? It keeps refining. 00:01:39.260 --> 00:01:42.319 Not at all. It tests thousands, literally thousands 00:01:42.319 --> 00:01:45.159 of variations of that code. It uses something 00:01:45.159 --> 00:01:48.200 called a tree search loop. which is think of 00:01:48.200 --> 00:01:49.879 it like a really sophisticated trial and error 00:01:49.879 --> 00:01:52.120 process. It systematically explores tons of possible 00:01:52.120 --> 00:01:54.640 solutions, branching out, trying different paths, 00:01:54.840 --> 00:01:57.459 always aiming for the best one. This whole approach, 00:01:57.519 --> 00:01:59.579 by the way, it's inspired by AlphaZero, you know, 00:01:59.599 --> 00:02:03.019 the AI that mastered Go and chess. So the system 00:02:03.019 --> 00:02:05.540 keeps rewriting its code, constantly trying to 00:02:05.540 --> 00:02:08.020 improve those scores, and it only keeps the very 00:02:08.020 --> 00:02:10.219 best solutions as it goes along. Okay, and this 00:02:10.219 --> 00:02:12.280 is where the results get. Well, you called it 00:02:12.280 --> 00:02:14.340 wild earlier. Yeah. Pretty staggering stuff. 00:02:14.580 --> 00:02:16.620 The system didn't just match human performance. 00:02:16.699 --> 00:02:19.939 It blew past it in several areas, like genomics. 00:02:20.060 --> 00:02:22.939 It generated 40 new methods for RNA -seq integration, 00:02:23.240 --> 00:02:25.719 and the best one, 14 % better than the top tool 00:02:25.719 --> 00:02:28.580 made by human experts. That's a serious jump. 00:02:28.860 --> 00:02:32.539 And it wasn't just one field for COVID -19 forecasting. 00:02:33.189 --> 00:02:36.750 He came up with 14 new models. And those models 00:02:36.750 --> 00:02:39.430 outperformed the CDC's own gold standard system, 00:02:39.629 --> 00:02:42.169 the COVID Hub Ensemble. That's directly relevant 00:02:42.169 --> 00:02:44.229 to public health planning. Right. And neuroscience, 00:02:44.409 --> 00:02:46.490 too, predicting neuron activity in zebrafish. 00:02:46.750 --> 00:02:49.169 Yeah, 70 ,000 neurons. More accurately than any 00:02:49.169 --> 00:02:51.189 of the baseline models they compared it to. Wow. 00:02:51.490 --> 00:02:55.289 And math. It solved hard integrals. Solved 17 00:02:55.289 --> 00:02:57.669 out of 19 tough integrals where the standard 00:02:57.669 --> 00:03:00.770 methods just failed. Couldn't do it. Yeah. And 00:03:00.770 --> 00:03:03.009 for time series data, it basically built its 00:03:03.009 --> 00:03:05.629 own forecasting library from scratch, testing 00:03:05.629 --> 00:03:08.150 it across 28 different data sets. The sheer breadth 00:03:08.150 --> 00:03:11.669 of that is. Wow. It really is. Whoa. Just imagine 00:03:11.669 --> 00:03:14.490 scaling that, scaling that kind of exploration 00:03:14.490 --> 00:03:17.629 to, I don't know, a billion queries across every 00:03:17.629 --> 00:03:19.810 single scientific field. The amount of new knowledge 00:03:19.810 --> 00:03:23.509 just pouring out. It would be. genuinely transformative. 00:03:23.909 --> 00:03:25.509 And that's why it matters so much. It signals 00:03:25.509 --> 00:03:27.810 this fundamental shift, right? Most coding AIs 00:03:27.810 --> 00:03:29.610 we talk about, they focus on getting the syntax 00:03:29.610 --> 00:03:31.430 right, making sure the code runs without errors. 00:03:31.550 --> 00:03:33.189 But this Google system, it's different. It's 00:03:33.189 --> 00:03:35.629 idea generating and it's performance maximizing. 00:03:35.689 --> 00:03:37.330 It's not just following orders. It's actively 00:03:37.330 --> 00:03:40.669 finding new, better ways to do things. And it 00:03:40.669 --> 00:03:43.050 never gets tired. It never stops exploring that 00:03:43.050 --> 00:03:46.469 kind of relentless search. It pushes entire fields 00:03:46.469 --> 00:03:50.310 forward at, well, warp speed. So thinking of 00:03:50.310 --> 00:03:53.000 the immediate future. What's the biggest single 00:03:53.000 --> 00:03:55.960 impact this could have on scientific discovery, 00:03:56.120 --> 00:03:58.740 like right now? Yeah, it just shortens those 00:03:58.740 --> 00:04:01.060 discovery cycles like crazy, accelerates breakthroughs. 00:04:01.080 --> 00:04:03.439 Okay, all right. Let's pivot a bit from that 00:04:03.439 --> 00:04:06.120 really deep foundational research to just the 00:04:06.120 --> 00:04:10.180 sheer whirlwind of daily AI stuff happening. 00:04:10.259 --> 00:04:12.039 It feels like there's so much going on, it's 00:04:12.039 --> 00:04:14.509 almost impossible to keep up. What's caught your 00:04:14.509 --> 00:04:16.629 eye recently? What feels like a sign of where 00:04:16.629 --> 00:04:18.310 things are headed? It really is a whirlwind, 00:04:18.350 --> 00:04:20.189 yeah. Let's maybe hit some rapid -fire highlights 00:04:20.189 --> 00:04:22.870 just to show the range. Notebook LM, for instance. 00:04:22.949 --> 00:04:25.269 That's really leveled up. Users can now auto 00:04:25.269 --> 00:04:29.149 -generate custom reports, full blog posts, flashcards, 00:04:29.250 --> 00:04:32.230 quizzes, even video summaries. And it works in 00:04:32.230 --> 00:04:34.290 over 80 languages. So it's not just organizing 00:04:34.290 --> 00:04:37.389 info anymore. It's actively synthesizing it into 00:04:37.389 --> 00:04:39.410 new things for you. That sounds incredibly useful, 00:04:39.629 --> 00:04:41.430 especially if you're drowning in information. 00:04:42.160 --> 00:04:44.540 And on the creative side, OpenAI is getting into 00:04:44.540 --> 00:04:47.259 movies. Yeah, they are. They're backing a film 00:04:47.259 --> 00:04:49.300 called Critters. It's supposed to be an entirely 00:04:49.300 --> 00:04:52.980 AI -generated animated movie slated for con in 00:04:52.980 --> 00:04:55.740 2026. Apparently, yeah. They even showed a short 00:04:55.740 --> 00:04:58.879 video bit back in 2023. So, yeah, they're showing 00:04:58.879 --> 00:05:01.399 early signs of AI storytelling on a bigger scale. 00:05:02.319 --> 00:05:03.839 And talking about creating content, Claude's 00:05:03.839 --> 00:05:05.740 making some practical moves too, right? And like 00:05:05.740 --> 00:05:08.459 office tools. Totally. Claude can now create 00:05:08.459 --> 00:05:12.259 and edit PDFs, slide decks, reports with actual... 00:05:12.459 --> 00:05:15.199 visual charts, right inside the chat. You can 00:05:15.199 --> 00:05:16.959 just feed it raw data, maybe from a spreadsheet, 00:05:17.160 --> 00:05:19.560 and it pulls together a whole write -up. That's 00:05:19.560 --> 00:05:22.500 a huge time saver for anyone doing business reports 00:05:22.500 --> 00:05:24.939 or presentations. It really feels like AI becoming 00:05:24.939 --> 00:05:27.959 a direct co -pilot for office work. Very practical. 00:05:28.220 --> 00:05:30.579 And then for the younger crowd, Google and Stanford 00:05:30.579 --> 00:05:33.540 launched AI Quests. Right. It's a game series 00:05:33.540 --> 00:05:36.060 for middle schoolers. Kids get to role play as 00:05:36.060 --> 00:05:37.939 Google researchers. It's kind of edutainment 00:05:37.939 --> 00:05:40.899 for the AI age, right? Making tricky AI concepts 00:05:40.899 --> 00:05:43.259 interactive, fun, maybe inspires the next generation. 00:05:43.540 --> 00:05:46.000 Clever way to introduce those ideas early. And 00:05:46.000 --> 00:05:48.199 on a later note, apparently you shouldn't call 00:05:48.199 --> 00:05:51.579 ChatGPT dumb anymore. Slight chuckle. Yeah, seems 00:05:51.579 --> 00:05:53.980 like you'll get roasted. Not by the AI, but by 00:05:53.980 --> 00:05:57.100 other humans defending it. Someone actually asked 00:05:57.100 --> 00:05:58.819 if that makes them a bad person for criticizing 00:05:58.819 --> 00:06:01.379 it. Kind of highlights the weirdly social, almost 00:06:01.379 --> 00:06:03.240 emotional connection people are forming with 00:06:03.240 --> 00:06:05.079 these tools. That's a funny little community 00:06:05.079 --> 00:06:08.199 quirk. Shows how embedded they're becoming. But 00:06:08.199 --> 00:06:10.040 back to content. Yeah. There's this company, 00:06:10.180 --> 00:06:14.100 Inception Point, making waves or maybe causing 00:06:14.100 --> 00:06:16.600 a bit of a stir. Definitely causing a stir. Their 00:06:16.600 --> 00:06:20.740 plan, produce 5 ,000 AI -generated podcasts per 00:06:20.740 --> 00:06:24.680 week with 3 ,000 new episodes weekly. And get 00:06:24.680 --> 00:06:27.959 this. For just $1 per episode. So yeah, critics 00:06:27.959 --> 00:06:30.300 are pretty understandably screaming soulless 00:06:30.300 --> 00:06:32.720 content at that kind of volume. Raises huge questions 00:06:32.720 --> 00:06:35.660 about authenticity, market saturation, the future 00:06:35.660 --> 00:06:37.720 of stuff made by actual people. It certainly 00:06:37.720 --> 00:06:40.079 does. And on the business end, some really eye 00:06:40.079 --> 00:06:41.740 -popping fundraising numbers are coming out. 00:06:41.779 --> 00:06:43.939 Shows massive investor confidence. Oh, absolutely. 00:06:44.160 --> 00:06:46.500 Cognition AI, for example. They just raised, 00:06:46.519 --> 00:06:49.339 what, $400 million. Reached a $10 .2 billion 00:06:49.339 --> 00:06:52.360 valuation in just 18 months. And their tool, 00:06:52.480 --> 00:06:54.689 Devin. Its annual recurring revenue jumped from 00:06:54.689 --> 00:06:57.670 $1 million to $73 million. They've got clients 00:06:57.670 --> 00:07:00.230 like Goldman Sachs, Cisco already using it, that 00:07:00.230 --> 00:07:02.730 kind of growth. Yeah. It's just astronomical. 00:07:02.930 --> 00:07:05.410 Shows the huge demand for these specialized AI 00:07:05.410 --> 00:07:07.189 tools. Okay, so with all this happening just 00:07:07.189 --> 00:07:10.750 incredibly fast, what's maybe one... key challenge 00:07:10.750 --> 00:07:13.230 these rapid advances are creating that we really 00:07:13.230 --> 00:07:15.189 need to watch. Yeah, I'd say maintaining content 00:07:15.189 --> 00:07:18.050 authenticity. Yeah. Especially with that AI generated 00:07:18.050 --> 00:07:21.230 volume exploding. Gotcha. Okay, let's keep this 00:07:21.230 --> 00:07:23.310 pace. Let's do some AI quick hits. Just a quick 00:07:23.310 --> 00:07:25.509 rundown of smaller but still important announcements 00:07:25.509 --> 00:07:28.029 that maybe show subtle shifts. Microsoft, Azure, 00:07:28.129 --> 00:07:30.410 OpenAI, they're offering new conversational AI 00:07:30.410 --> 00:07:32.670 shopping experiences now. Yeah. It could genuinely 00:07:32.670 --> 00:07:34.930 change how we interact with online stores, you 00:07:34.930 --> 00:07:37.170 know, making shopping assistants feel more personal, 00:07:37.230 --> 00:07:39.350 maybe more proactive. That's a big step for customers. 00:07:39.370 --> 00:07:41.529 Customer service. Yeah. And definitely a sign 00:07:41.529 --> 00:07:44.470 of AI getting deeper into everyday commerce. 00:07:45.250 --> 00:07:47.370 And also Microsoft, they announced they'll be 00:07:47.370 --> 00:07:49.490 using Clots on it 4. That's a pretty big shift 00:07:49.490 --> 00:07:52.230 from relying so heavily on OpenAI's GPT models. 00:07:52.389 --> 00:07:54.069 Yeah. Tells you a lot about the competitive scene, 00:07:54.149 --> 00:07:56.470 right? And how companies are diversifying, looking 00:07:56.470 --> 00:07:59.029 for the best AI for specific jobs, not just one 00:07:59.029 --> 00:08:01.509 size fits all. Indeed. Yeah. Feels like a strategic 00:08:01.509 --> 00:08:04.230 move, leveraging different AI strengths. Meanwhile, 00:08:04.629 --> 00:08:07.040 Apple. They're quietly working on something called 00:08:07.040 --> 00:08:09.540 World Knowledge Answers. Sounds like a potential 00:08:09.540 --> 00:08:12.319 chat GPT rival. Always interesting to see what 00:08:12.319 --> 00:08:13.879 Apple's cooking up in the background with their 00:08:13.879 --> 00:08:17.240 resources. Very true. It really highlights how 00:08:17.240 --> 00:08:20.100 every major tech player is scrambling to build 00:08:20.100 --> 00:08:23.399 their own powerful conversational AI. And kind 00:08:23.399 --> 00:08:25.259 of an interesting side note, within the AI research 00:08:25.259 --> 00:08:27.259 community itself, apparently researchers often 00:08:27.259 --> 00:08:29.459 highlight well -cited papers and certain big 00:08:29.459 --> 00:08:32.120 names. It's a bit of a human thing, right? Recognizing 00:08:32.120 --> 00:08:35.110 who influenced what, even in this super - automated 00:08:35.110 --> 00:08:37.809 field. A little nod to the foundational work. 00:08:37.929 --> 00:08:41.769 Huh. Bit of academic. Self -reinforcement, maybe. 00:08:42.289 --> 00:08:45.029 And speaking of Apple, one detail that kind of 00:08:45.029 --> 00:08:49.210 surprised me, their new iPhone 17 devices still 00:08:49.210 --> 00:08:51.529 apparently don't have a fully AI -powered Siri 00:08:51.529 --> 00:08:54.049 yet. That's kind of surprising given everything 00:08:54.049 --> 00:08:55.570 we're talking about. Maybe suggest they're being 00:08:55.570 --> 00:08:57.909 more careful, slower integration, perhaps. It 00:08:57.909 --> 00:09:00.409 is surprising, yeah. Maybe suggest they're taking 00:09:00.409 --> 00:09:02.799 a more cautious route. Or perhaps a more deeply 00:09:02.799 --> 00:09:04.740 integrated approach for their on -device AI, 00:09:04.879 --> 00:09:07.259 rather than just rushing out something superficial. 00:09:07.600 --> 00:09:09.600 So how quickly do you think these quick hits, 00:09:09.679 --> 00:09:12.820 these smaller things, become just mainstream 00:09:12.820 --> 00:09:15.799 reality for us? Well, many are already in beta 00:09:15.799 --> 00:09:18.480 testing, so mass adoption. It feels pretty imminent, 00:09:18.539 --> 00:09:21.179 actually. Mid -roll sponsor Reed Placeholder 00:09:21.179 --> 00:09:23.240 will be provided separately. All right, let's 00:09:23.240 --> 00:09:24.919 switch gears again. Let's look at another area 00:09:24.919 --> 00:09:27.779 that feels ripe for a big transformation, speech 00:09:27.779 --> 00:09:30.320 recognition. Alibaba just put out something called 00:09:30.320 --> 00:09:33.039 QEN3 ASR Flash. It's a new speech recognition 00:09:33.039 --> 00:09:35.980 model built on their QEN3 Omni Foundation, but 00:09:35.980 --> 00:09:38.240 with some really clever, unique tricks. Okay, 00:09:38.279 --> 00:09:41.340 so what can it actually do that's different besides 00:09:41.340 --> 00:09:43.759 just, you know, turning speech into text? Well, 00:09:43.799 --> 00:09:45.940 first off, performance -wise, it outperforms 00:09:45.940 --> 00:09:48.299 rivals in Chinese, English, and nine other languages. 00:09:48.919 --> 00:09:51.460 broad capability. But here's where it gets really 00:09:51.460 --> 00:09:54.659 interesting. It can accurately transcribe rap 00:09:54.659 --> 00:09:58.740 music and songs, even with complex background 00:09:58.740 --> 00:10:01.700 music playing. That's a huge leap handling really 00:10:01.700 --> 00:10:05.259 nuanced, noisy audio like that. And here's another 00:10:05.259 --> 00:10:07.740 potential game changer. You can feed it keywords 00:10:07.740 --> 00:10:09.960 or whole documents or even just sort of general 00:10:09.960 --> 00:10:13.039 text to bias its results without any complex 00:10:13.039 --> 00:10:15.700 pre -processing. It's kind of like prompt engineering, 00:10:15.879 --> 00:10:18.970 but specifically for speech recognition. You 00:10:18.970 --> 00:10:20.909 mean like giving the automatic speech recognition, 00:10:21.129 --> 00:10:24.070 the ASR system, specific instructions or examples 00:10:24.070 --> 00:10:26.370 to guide how it understands things and what it 00:10:26.370 --> 00:10:28.590 writes out. Exactly that. It's about giving the 00:10:28.590 --> 00:10:30.809 AI context, telling it what's important to you 00:10:30.809 --> 00:10:33.210 without needing to rebuild the whole model. And 00:10:33.210 --> 00:10:35.230 apparently it works really well under noisy conditions. 00:10:35.429 --> 00:10:37.610 It's like cars, crowd noise, even when people 00:10:37.610 --> 00:10:39.820 are code switching. missing languages in one 00:10:39.820 --> 00:10:42.200 conversation, it just automatically detects the 00:10:42.200 --> 00:10:44.320 language being spoken and intelligently ignores 00:10:44.320 --> 00:10:46.259 background noise or silence. It makes it seem 00:10:46.259 --> 00:10:48.659 extremely robust. Okay, this is where the why 00:10:48.659 --> 00:10:51.519 it matters really clicks for me. Most ASR models, 00:10:51.759 --> 00:10:53.740 they're pretty good, sure, but they can be quite 00:10:53.740 --> 00:10:56.500 rigid. They often struggle to adapt to your specific 00:10:56.500 --> 00:10:59.139 context, your industry jargon, your company names 00:10:59.139 --> 00:11:01.360 without a lot of engineering work or complex 00:11:01.360 --> 00:11:05.259 fine -tuning. QEN 3 ASR sounds like it completely 00:11:05.259 --> 00:11:07.600 changes that dynamic. Like you want transcripts 00:11:07.600 --> 00:11:09.620 to always get your brand name right or pick up 00:11:09.620 --> 00:11:11.919 specific technical terms. You just feed it the 00:11:11.919 --> 00:11:15.279 relevant info once and boom, it's set. I'll admit 00:11:15.279 --> 00:11:17.720 I still wrestle with prompt drift myself sometimes 00:11:17.720 --> 00:11:19.580 trying to keep an AI focused and consistent. 00:11:19.980 --> 00:11:23.440 So this biasing feature. That sounds like a huge 00:11:23.440 --> 00:11:25.980 leap in control, real precision. It really is. 00:11:26.019 --> 00:11:28.860 It potentially elevates ASR from just a generic 00:11:28.860 --> 00:11:31.519 transcription tool to something much more like 00:11:31.519 --> 00:11:33.759 a highly customized intelligent assistant, one 00:11:33.759 --> 00:11:35.840 that actually adapts to your specific vocabulary 00:11:35.840 --> 00:11:38.700 and context. Right now, OpenAI's Whisper, that 00:11:38.700 --> 00:11:41.659 really dominates the open source ASR world. But 00:11:41.659 --> 00:11:45.179 this Quen 3 ASR, it feels like Alibaba's direct 00:11:45.179 --> 00:11:47.860 enterprise -ready challenger. Its ability to 00:11:47.860 --> 00:11:49.820 handle singing, different accents, plus that 00:11:49.820 --> 00:11:52.299 unique biasing feature, that's a major differentiator. 00:11:52.620 --> 00:11:55.279 This looks like Alibaba making a serious play 00:11:55.279 --> 00:11:57.559 to own how the world talks to AI. Seriously. 00:11:57.899 --> 00:12:00.639 And if they actually nail this, voice interaction 00:12:00.639 --> 00:12:03.019 could be the place where Quinn finally leapfrogs 00:12:03.019 --> 00:12:05.600 some of the Western models in terms of adoption 00:12:05.600 --> 00:12:07.980 and capability. It's a direct challenge for market 00:12:07.980 --> 00:12:10.299 leadership in a really critical area. So to thinking 00:12:10.299 --> 00:12:11.679 about that, what's maybe the most surprising 00:12:11.679 --> 00:12:13.679 real world application you could see for this 00:12:13.679 --> 00:12:16.559 new kind of speech AI? Maybe capturing really 00:12:16.559 --> 00:12:20.379 nuanced legal discussions or diverse global meetings 00:12:20.379 --> 00:12:22.840 with perfect clarity, finally getting all the 00:12:22.840 --> 00:12:26.080 specific terms right. That could be huge. So, 00:12:26.120 --> 00:12:28.299 OK, let's try and pull this all together. What 00:12:28.299 --> 00:12:30.799 does this all mean for you listening right now? 00:12:31.179 --> 00:12:33.440 Today's deep dive really shows us this landscape 00:12:33.440 --> 00:12:36.399 where AI isn't just a tool anymore. It's increasingly 00:12:36.399 --> 00:12:40.019 a collaborator, an inventor even, and a global 00:12:40.019 --> 00:12:42.299 competitor. We've definitely moved beyond simple 00:12:42.299 --> 00:12:45.429 automation into... genuine creation, sophisticated 00:12:45.429 --> 00:12:47.710 understanding. And it's happening everywhere 00:12:47.710 --> 00:12:49.870 all at once. Absolutely. We're seeing this really 00:12:49.870 --> 00:12:52.509 profound shift. AI isn't just processing information 00:12:52.509 --> 00:12:55.610 anymore. It's generating new knowledge, new capabilities. 00:12:56.460 --> 00:12:58.779 And at a speed that was, frankly, previously 00:12:58.779 --> 00:13:00.480 unimaginable. It's kind of like, you know, stacking 00:13:00.480 --> 00:13:02.500 Lego blocks of data, but the blocks are also 00:13:02.500 --> 00:13:04.399 building themselves faster than we can quite 00:13:04.399 --> 00:13:07.220 grasp sometimes. And as these systems keep evolving, 00:13:07.440 --> 00:13:09.759 they really challenge our basic definitions of 00:13:09.759 --> 00:13:12.559 human ingenuity. They accelerate progress across 00:13:12.559 --> 00:13:15.340 pretty much every field you can think of. So 00:13:15.340 --> 00:13:19.200 how do you see AI reshaping your world in the 00:13:19.200 --> 00:13:21.940 next few years? What new possibilities does all 00:13:21.940 --> 00:13:24.679 this spark for you? personally, professionally. 00:13:25.039 --> 00:13:27.299 Yeah, think about that. The speed is just incredible. 00:13:27.559 --> 00:13:29.980 It really makes you wonder, how are we actually 00:13:29.980 --> 00:13:31.799 going to collaborate with these intelligent systems 00:13:31.799 --> 00:13:34.519 tomorrow? What will that look like? That is definitely 00:13:34.519 --> 00:13:36.700 a deep dive for another day, perhaps. Thank you 00:13:36.700 --> 00:13:38.399 for joining us. Yeah, thanks for tuning in. We 00:13:38.399 --> 00:13:39.279 always appreciate your curiosity.