WEBVTT - TechStuff's Top Holiday Gift for 2024 - The Lenovo Yoga Slim 7x laptop 0:00:04.440 --> 0:00:12.400 Welcome to tech Stuff, a production from iHeartRadio. Be there 0:00:12.520 --> 0:00:16.520 and welcome to tech Stuff. I'm your host, Jonathan Strickland. 0:00:16.520 --> 0:00:20.400 I'm an executive producer with iHeart Podcasts. And how the 0:00:20.560 --> 0:00:24.080 tech are you now, y'all? I think I have made 0:00:24.120 --> 0:00:28.080 my thoughts pretty clear when it comes to artificial intelligence. 0:00:28.520 --> 0:00:33.600 For one thing, AI is a very broad discipline. It's huge. 0:00:33.680 --> 0:00:37.600 It's way more than just generative AI, which is a 0:00:37.640 --> 0:00:40.839 topic I feel very strongly about, and it's also the 0:00:40.880 --> 0:00:43.640 one that dominates the news cycle. But it's just one 0:00:43.680 --> 0:00:48.559 aspect of artificial intelligence. And I feel AI in general 0:00:48.880 --> 0:00:54.400 has incredible potential to augment our computing tasks if we 0:00:54.560 --> 0:00:58.680 implement it properly. Well. Recently I got the chance to 0:00:58.720 --> 0:01:02.520 work with an AI laptop and really get to grips 0:01:02.560 --> 0:01:06.240 with what that potential can be, and I'm convinced we're 0:01:06.280 --> 0:01:10.640 on another precipice, one that will transform how we interact 0:01:10.640 --> 0:01:14.200 with computing devices. Now. First, the AI powered device I 0:01:14.319 --> 0:01:17.880 used was a Lenovo Yoga Slim seven X laptop with 0:01:17.959 --> 0:01:22.200 a Snapdragon x Elite processor. It's a Copilot plus PC, 0:01:22.440 --> 0:01:27.280 which means it features Microsoft's AI Assistant. It's also got 0:01:27.319 --> 0:01:31.280 an OLED screen, and it's no joke. That's the prettiest 0:01:31.480 --> 0:01:35.479 laptop screen I have ever used. The contrast on that 0:01:35.560 --> 0:01:40.600 thing is crazy, it's amazing. It's so beautiful. Now, Snapdragon 0:01:40.800 --> 0:01:43.720 was generous in sending me this laptop so that I 0:01:43.760 --> 0:01:46.080 could actually get some hands on time with it. And 0:01:46.120 --> 0:01:49.080 I'll be talking a lot about the work Snapdragon has 0:01:49.120 --> 0:01:52.800 done to make the processor really special, but I'm saving 0:01:52.840 --> 0:01:55.720 that for a bit later. Now, I do have to 0:01:55.760 --> 0:01:59.880 say that this laptop would immediately top my holiday wish. 0:02:00.520 --> 0:02:03.560 And I'm not just saying that I could be if 0:02:03.840 --> 0:02:07.040 I had no scruples, but that's not who I am, y'all. 0:02:07.320 --> 0:02:10.399 It's legit how I feel this laptops. It's really light, 0:02:10.480 --> 0:02:13.600 it's powerful, The screen, as I mentioned, is absolutely gorgeous, 0:02:13.720 --> 0:02:17.400 but the battery life is also really impressive, particularly when 0:02:17.440 --> 0:02:19.799 you consider the power lifting this thing has to do. 0:02:20.120 --> 0:02:24.920 Even when it's running AI Enhanced applications, which y'all know AI, 0:02:25.240 --> 0:02:28.520 it requires a lot of processing power, but this laptop 0:02:28.560 --> 0:02:31.400 continued to run smoothly and I didn't have to go 0:02:31.560 --> 0:02:34.560 dashing from outlet to outlet just to keep it going. 0:02:34.880 --> 0:02:37.480 But I think the thing that really pulls me to 0:02:37.840 --> 0:02:40.760 this laptop is the fact that I can see AI 0:02:40.960 --> 0:02:45.040 enabled processors as being the tech platform of the future. 0:02:45.360 --> 0:02:49.200 So it's my belief that just as smartphones revolutionized how 0:02:49.200 --> 0:02:52.680 we interact with computing resources, you know, like apps as 0:02:52.720 --> 0:02:55.080 well as the Internet, AI is going to do the 0:02:55.080 --> 0:02:58.760 same thing. Now. Before the era of the consumer smartphone, 0:02:58.840 --> 0:03:01.320 there were very few people who were predicting a move 0:03:01.480 --> 0:03:03.959 to mobile. Now, there were a few smarty pants is 0:03:04.000 --> 0:03:06.359 out there who were ahead of the curve, but most 0:03:06.360 --> 0:03:09.800 of us didn't see it coming. Once smartphones proved their 0:03:09.880 --> 0:03:13.400 merit in the consumer marketplace, we saw a pretty rapid 0:03:13.440 --> 0:03:18.600 transition to a mobile friendly landscape. You know, Web administrators 0:03:18.639 --> 0:03:22.120 were scrambling to make sure that their websites were optimized 0:03:22.160 --> 0:03:25.799 for mobile devices lest they potentially drive away visitors who 0:03:25.800 --> 0:03:29.920 were increasingly using their phones to access the Internet rather 0:03:30.000 --> 0:03:35.160 than say, laptop and desktop computers. Meanwhile, developers began designing 0:03:35.240 --> 0:03:38.640 apps that would leverage smartphone capabilities, you know, stuff like 0:03:38.720 --> 0:03:43.240 accelerometers and touchscreens and GPS sensors, that kind of thing. Well, 0:03:43.560 --> 0:03:46.960 I believe AI is going to do much the same. 0:03:47.080 --> 0:03:50.560 We're going to see a host of new programs and 0:03:50.680 --> 0:03:55.320 apps built with AI enhanced features and devices that are 0:03:55.320 --> 0:03:59.160 capable of providing onboard AI processing are going to be 0:03:59.200 --> 0:04:01.840 way ahead of the game, while also providing ways to 0:04:01.880 --> 0:04:06.480 handle AI processing in a more secure and private approach. See, 0:04:06.600 --> 0:04:10.600 there are different ways that you can handle AI processing. 0:04:10.920 --> 0:04:16.120 One way is you offload everything to a server farm somewhere, 0:04:16.320 --> 0:04:18.520 and we hear about these a lot in the news. 0:04:18.760 --> 0:04:21.400 You know, they're massive buildings, just filled with racks of 0:04:21.440 --> 0:04:26.080 servers processing enormous amounts of data, powering AI implementations all 0:04:26.120 --> 0:04:29.080 around the world, and no doubt that will continue, that 0:04:29.120 --> 0:04:32.640 will continue to be a thing. But a complementary approach 0:04:33.000 --> 0:04:37.599 involves on device and edge computing cases in which the 0:04:38.080 --> 0:04:40.680 gadgets that we actually have our hands on can do 0:04:40.880 --> 0:04:44.119 some or all of that processing on their own without 0:04:44.200 --> 0:04:47.680 connecting to some distant server. Now it all depends upon 0:04:47.720 --> 0:04:51.800 the application, of course, but with those types of implementations, 0:04:51.800 --> 0:04:55.039 you keep the AI computations on your device, and that 0:04:55.120 --> 0:04:57.840 means you're not sharing data with some server farm that 0:04:57.920 --> 0:05:01.680 smiles away. Everything stays low, and that is a huge 0:05:01.720 --> 0:05:04.960 thing when it comes to privacy and security. Let's say 0:05:04.960 --> 0:05:08.799 that you work for a really big media company, for example, 0:05:09.200 --> 0:05:11.800 I could be using myself in this example, and you 0:05:11.839 --> 0:05:14.679 want to make sure that the work you do stays 0:05:14.760 --> 0:05:18.800 local because you're working with some sensitive information, some of 0:05:18.800 --> 0:05:21.400 it might be proprietary. You don't want to be sending 0:05:21.400 --> 0:05:25.159 that off and have it become some kernel of information 0:05:25.320 --> 0:05:29.560 that gets enveloped in a larger database somewhere. That's risky stuff. 0:05:29.839 --> 0:05:32.560 So making sure you are able to do this kind 0:05:32.600 --> 0:05:34.920 of thing locally is important for a lot of people. 0:05:35.279 --> 0:05:39.960 All right, let's talk about the overview for this whole approach. 0:05:40.040 --> 0:05:42.320 We'll get into the hows and whys a little bit later, 0:05:42.360 --> 0:05:44.479 but first I want to talk about the ways I 0:05:44.640 --> 0:05:49.119 used that Lenovo Yoga seven X laptop. So I wanted 0:05:49.160 --> 0:05:53.960 to see how an AI enhanced device could potentially help 0:05:54.040 --> 0:05:56.680 me do my work. I mean, we talk a lot 0:05:56.720 --> 0:06:01.440 about artificial intelligence augmenting our ability. I wanted to actually 0:06:01.520 --> 0:06:04.479 put that into practice. So I use the laptop while 0:06:04.520 --> 0:06:07.520 I was researching a recent episode, one that actually has 0:06:07.640 --> 0:06:11.600 already published. So I used this laptop, the Yoga laptop, 0:06:11.920 --> 0:06:15.480 specifically for that episode, and I really wanted to put 0:06:15.480 --> 0:06:17.200 it through its paces and see if it had a 0:06:17.240 --> 0:06:20.440 meaningful impact on the way I do work. Now in 0:06:20.520 --> 0:06:25.840 this episode, I referenced an extremely long research paper that 0:06:26.040 --> 0:06:29.880 was written for the journal Science and Global Security, and 0:06:30.680 --> 0:06:34.200 it was an article by Jurgen Altmann. It's actually a 0:06:34.240 --> 0:06:38.040 fantastic paper. It was incredibly readable, which is not always 0:06:38.080 --> 0:06:40.600 the case for technical papers. If you've ever tried to 0:06:40.640 --> 0:06:43.920 read one, sometimes they come across as the most stilted 0:06:44.279 --> 0:06:46.919 term paper a teacher has ever had degrade, but not 0:06:47.080 --> 0:06:50.279 this one. It's also an accessible paper. You can find 0:06:50.279 --> 0:06:52.839 it online for free, so that's great as well. But 0:06:53.360 --> 0:06:59.719 it's very long, like it's seventy pages long. Now more 0:06:59.760 --> 0:07:03.120 than ten of those pages are just notes and references, 0:07:03.160 --> 0:07:06.120 but you still have, you know, fifty nine pages of 0:07:06.240 --> 0:07:09.760 pure content there. Now, I read the full article for 0:07:09.880 --> 0:07:12.120 my research, but I need to have access to the 0:07:12.160 --> 0:07:15.840 salient points without having to thumb through all seventy pages 0:07:15.880 --> 0:07:19.480 and taking notes as I read the article. That way, 0:07:19.560 --> 0:07:22.480 I wouldn't have to thumb through a seventy page article 0:07:22.880 --> 0:07:28.480 while writing the episode. But that's not easy to do. 0:07:28.560 --> 0:07:32.560 Once you get past like twenty pages, it gets pretty cumbersome. 0:07:32.680 --> 0:07:35.760 So I use the AI assistant on the laptop to 0:07:35.880 --> 0:07:39.640 create a summarized, bulleted list of the most important notes 0:07:39.680 --> 0:07:44.040 in the paper. For quick reference. Now I'm the cautious type, 0:07:44.360 --> 0:07:47.480 and while I was using this and I wanted to 0:07:47.560 --> 0:07:50.120 really check and make sure that it worked. I also 0:07:50.440 --> 0:07:53.920 wanted to verify that the notes that were produced were 0:07:53.960 --> 0:07:58.080 accurate to the original paper, that they weren't a misinterpretation 0:07:58.600 --> 0:08:03.600 or a summary that just wasn't accurate. And obviously that 0:08:03.680 --> 0:08:06.560 added more time for me. If I had not had 0:08:06.600 --> 0:08:08.760 to do that, I would have been through pretty quickly, 0:08:09.000 --> 0:08:11.880 but I needed to check. It's not just enough to 0:08:12.360 --> 0:08:15.000 have it create this list, and it really did show 0:08:15.000 --> 0:08:17.680 that the summary was accurate to the document I was using, 0:08:18.000 --> 0:08:21.600 and that it really was the most important points in 0:08:21.680 --> 0:08:24.480 the paper that were included the summary, and it was 0:08:24.520 --> 0:08:27.520 really easy to do. It was easy to navigate to 0:08:27.560 --> 0:08:30.080 the actual source for the bullet points so that I 0:08:30.120 --> 0:08:33.160 could verify that, in fact, the information was correct, and 0:08:33.200 --> 0:08:35.959 it really made organizing my thoughts much faster than it 0:08:36.000 --> 0:08:39.360 would have been if I hadn't been able to access 0:08:39.400 --> 0:08:41.960 this tool, because while I was taking more time to 0:08:42.040 --> 0:08:45.160 verify that the bullets were accurate representations of the information 0:08:45.200 --> 0:08:47.840 in the article, it did allow me to organize the 0:08:47.880 --> 0:08:52.840 whole approach for the episode. So typically when I organized 0:08:52.840 --> 0:08:55.400 an episode, I do that by feel, and some of 0:08:55.400 --> 0:08:59.040 you might be saying, yeah, we know it's obvious, But 0:08:59.120 --> 0:09:02.200 I've been podcasts for sixteen years. I have a sense 0:09:02.480 --> 0:09:05.120 of the flow I want for an episode. Now that 0:09:05.160 --> 0:09:07.640 doesn't always mean it's the best approach, but it is 0:09:07.679 --> 0:09:10.480 the one that just feels natural to me. However, in 0:09:10.520 --> 0:09:13.559 this case, it was really nice to consult a different perspective, 0:09:13.640 --> 0:09:17.400 even an artificial perspective, to figure out how best to 0:09:17.480 --> 0:09:22.480 structure the episode. So if there was an episode in 0:09:22.520 --> 0:09:25.040 the recent past that you listened to and you thought, wow, 0:09:25.160 --> 0:09:28.960 that's more coherent than what he normally produces, well now 0:09:29.000 --> 0:09:31.760 you know why. But keep it to yourself because words 0:09:31.800 --> 0:09:35.320 can hurt y'all. One thing I didn't do but I 0:09:35.480 --> 0:09:39.120 could have done, was use real time translation tools to 0:09:39.200 --> 0:09:43.520 access information that was presented in other languages. Once upon 0:09:43.559 --> 0:09:47.320 a time, I took courses in French and in German, 0:09:47.400 --> 0:09:50.280 but I never got to the point where I was conversational, 0:09:50.360 --> 0:09:53.120 let alone fluent in those languages. And of course, over 0:09:53.120 --> 0:09:57.440 the years my skills have atrophied. So I speak two languages, 0:09:57.800 --> 0:10:01.440 English and Bad English. But I am aware that there 0:10:01.520 --> 0:10:05.160 is a wealth of information and knowledge that's captured in 0:10:05.200 --> 0:10:08.960 other languages. While there's some pretty darn good translation apps 0:10:08.960 --> 0:10:13.000 for stuff like text, the cool thing about AI powered 0:10:13.000 --> 0:10:16.240 devices is that they have the potential and the processing 0:10:16.320 --> 0:10:20.120 capability to provide real time translation for other kinds of 0:10:20.200 --> 0:10:23.800 content like audio. So one thing I could have done 0:10:24.200 --> 0:10:27.440 was watch a video that had been recorded in another language, 0:10:27.440 --> 0:10:31.280 and through using onboard AI processing capabilities, been able to 0:10:31.360 --> 0:10:34.840 read English language captions that were translating what was being 0:10:34.920 --> 0:10:37.920 said in real time. Now that opens up entire worlds 0:10:37.920 --> 0:10:41.200 of expertise that otherwise would be very difficult for me 0:10:41.280 --> 0:10:45.119 to access. And I've always said that diversity is really important. 0:10:45.240 --> 0:10:49.160 It means you get multiple perspectives providing information, and you 0:10:49.200 --> 0:10:52.880 can view the world from different perspectives, including ones that 0:10:53.040 --> 0:10:56.720 you might not have ever even considered otherwise. Now, you 0:10:56.800 --> 0:11:00.000 might ultimately not agree with this other point of view, 0:11:00.160 --> 0:11:03.560 but being able to access it is important. Otherwise you 0:11:03.679 --> 0:11:07.000 just remain ignorant of it. So from a research perspective, 0:11:07.400 --> 0:11:11.840 real time translation is an enormous benefit, and I imagine 0:11:11.840 --> 0:11:15.120 we'll see this technology continue to evolve as well. Tools 0:11:15.200 --> 0:11:18.199 can be pretty good at doing things like translating word 0:11:18.320 --> 0:11:22.760 for word, but in future implementations. I imagine AI translation 0:11:22.920 --> 0:11:26.319 will also have to handle stuff like syntax and idioms 0:11:26.360 --> 0:11:29.280 really well, so that we don't just understand the actual 0:11:29.360 --> 0:11:34.160 words being spoken, but what the speaker means when they 0:11:34.240 --> 0:11:38.120 say those things. If someone uses like an idiom like 0:11:38.160 --> 0:11:42.280 a regional saying, or they're using really complex phrasing that 0:11:42.320 --> 0:11:46.160 doesn't easily translate to English, I can imagine future AI 0:11:46.280 --> 0:11:51.040 translation tools handling that and providing a relatable translation to 0:11:51.160 --> 0:11:54.640 avoid ambiguity, unless, of course, ambiguity was the intent in 0:11:54.679 --> 0:11:57.680 the first place. Sometimes it is another thing that I 0:11:57.720 --> 0:11:59.840 could have done at the end of the whole episode 0:12:00.080 --> 0:12:03.040 because I have used tools like this before, is use 0:12:03.080 --> 0:12:07.160 AI to generate show notes. So, y'all, Podcasting is a 0:12:07.160 --> 0:12:10.920 lot of work, particularly if you have a small team. 0:12:11.280 --> 0:12:15.480 In my case, the team is me and super producer Tari, 0:12:15.640 --> 0:12:18.920 who also works on other shows. There are a lot 0:12:18.920 --> 0:12:22.120 of steps in making a podcast. You know, you have 0:12:22.280 --> 0:12:25.880 pre prep. You've got prep, you've got research, you've got 0:12:25.960 --> 0:12:31.200 writing and recording and editing and publishing. One post production 0:12:31.400 --> 0:12:33.600 step that a lot of shows will skip is the 0:12:33.600 --> 0:12:36.520 production of show notes. So why do so many shows 0:12:36.559 --> 0:12:39.840 just skip show notes? Well, I can't speak for everyone, 0:12:40.360 --> 0:12:43.160 But in my case it comes down to being a 0:12:43.200 --> 0:12:46.720 mental block. When I finish an episode, after I've done 0:12:47.040 --> 0:12:50.480 speaking my amazing words into a microphone and then shipping 0:12:50.480 --> 0:12:53.600 off the file to my producer extraordinaire Tari, I'm ready 0:12:53.640 --> 0:12:56.320 to move on. My brain has effectively said, welp. That 0:12:56.400 --> 0:12:59.760 closes that chapter, dusts off its hands and whistles as 0:12:59.760 --> 0:13:02.480 a walks into the sunset. So it can be really 0:13:02.520 --> 0:13:06.040 hard to stop and reflect on what I just created 0:13:06.360 --> 0:13:09.520 and then distill that into useful notes for listeners. But 0:13:09.800 --> 0:13:13.760 AI tools can do that automatically. Of course, after creating 0:13:13.840 --> 0:13:16.520 the notes, then I would review them to make sure 0:13:16.600 --> 0:13:20.320 that again they accurately reflect the episode. But that's one 0:13:20.440 --> 0:13:23.560 step in the podcasting process that I would be happy 0:13:23.600 --> 0:13:26.679 to hand over to an AI enabled tool, as it 0:13:26.760 --> 0:13:29.360 is a step that I otherwise find really tedious and 0:13:29.400 --> 0:13:32.760 it actually discourages me from doing my job. You know, 0:13:32.840 --> 0:13:36.680 I will find any excuse. I will invent excuses to 0:13:36.679 --> 0:13:39.440 put off doing that kind of thing. Now, while all 0:13:39.520 --> 0:13:42.640 my research and writing and recording was going on, I 0:13:42.760 --> 0:13:46.720 also was using the assistant to keep me up to 0:13:46.800 --> 0:13:52.439 speed on my daily schedule. I'm somewhat notorious for missing 0:13:52.480 --> 0:13:57.559 things like important emails and meetings and that sort of thing. 0:13:57.800 --> 0:13:59.880 I have kind of made it an art formed to 0:14:00.160 --> 0:14:03.839 make it difficult to reach me because I find it 0:14:03.880 --> 0:14:07.520 creates an environment that allows me to focus right. I 0:14:07.559 --> 0:14:09.840 want to really focus on what I'm doing, and that 0:14:09.920 --> 0:14:13.280 means I need to filter out distractions because otherwise I 0:14:13.480 --> 0:14:16.880 will stop whatever it is I'm working on, and that 0:14:17.000 --> 0:14:20.840 just ruins my whole flow. Getting back into that is 0:14:20.920 --> 0:14:23.840 hard to do. I found using the AI assistant to 0:14:23.880 --> 0:14:27.200 help block out my time so that I had specific 0:14:27.320 --> 0:14:30.800 blocks of time where I was doing specific activities made 0:14:30.840 --> 0:14:34.400 me overall more efficient and effective. I actually one of 0:14:34.400 --> 0:14:36.520 the In fact, the very first thing I asked my 0:14:36.600 --> 0:14:39.280 AI assistant to do was to help me create a 0:14:39.720 --> 0:14:43.280 working schedule and it did, and it even built in 0:14:43.400 --> 0:14:46.360 things like breaks and stuff, and I followed it. I 0:14:46.400 --> 0:14:48.480 was like, this is a real experiment. I am going 0:14:48.520 --> 0:14:51.960 to follow the schedule that's been created for me, and 0:14:52.040 --> 0:14:54.880 I found that it was incredibly helpful. It really added 0:14:54.920 --> 0:14:57.320 structure to my day, something that I haven't had a 0:14:57.320 --> 0:15:01.280 lot of because I work remotely and mostly on my own, 0:15:01.600 --> 0:15:05.720 so structure is something that I have to create and 0:15:05.760 --> 0:15:09.000 I'm not great at doing that. So using this tool 0:15:09.080 --> 0:15:13.080 to help me to augment my abilities and to take 0:15:13.120 --> 0:15:17.480 on a workload that I otherwise would find difficult to do, 0:15:17.720 --> 0:15:20.720 that was incredibly helpful and it was a really nice change. 0:15:21.000 --> 0:15:23.960 Now I can envision other uses of AI as well, 0:15:24.080 --> 0:15:26.600 though I didn't use them for that particular episode. So 0:15:26.720 --> 0:15:32.240 for example creating audiograms, and I can already use AI 0:15:32.360 --> 0:15:34.920 to do this. I have used AI tools to do this, 0:15:35.000 --> 0:15:38.440 but they were cloud based. And what I'm talking about 0:15:38.480 --> 0:15:43.480 here is using AI to identify interesting passages in an episode, 0:15:43.520 --> 0:15:47.600 like a section that's really compelling. And you could do 0:15:47.680 --> 0:15:49.760 this in different ways, Like you could use AI to 0:15:49.880 --> 0:15:53.960 analyze the written work that you create, like in my case, 0:15:54.000 --> 0:15:56.760 I write out episodes, right, so I could actually use 0:15:56.800 --> 0:16:00.560 AI to analyze what I've written and to identify, oh, 0:16:00.720 --> 0:16:05.320 this is a particularly compelling section. Or you can use 0:16:05.360 --> 0:16:08.640 it to analyze the recorded audio. Because I also go 0:16:08.720 --> 0:16:11.360 off book a lot. I don't just have a script 0:16:11.360 --> 0:16:15.120 that I read. I extemporize like crazy. If you read 0:16:15.360 --> 0:16:18.200 what I wrote and compared it to what I say, 0:16:18.600 --> 0:16:21.239 you would notice that there are a lot of departures. 0:16:21.800 --> 0:16:26.720 So using AI, I could analyze the recorded program and 0:16:26.760 --> 0:16:30.520 create audiograms, which are those excerpts you sometimes come across 0:16:30.560 --> 0:16:34.080 on various social platforms. These are ones that play not 0:16:34.360 --> 0:16:37.480 just an audio clip from a show. Typically they'll also 0:16:37.520 --> 0:16:41.320 include stuff like real time captions that will help emphasize 0:16:41.360 --> 0:16:46.000 the point being expressed. I've had some experience using AI 0:16:46.120 --> 0:16:49.560 to generate these, and that includes matching text to spoken 0:16:49.600 --> 0:16:52.080 words automatically, so that you don't have to do it 0:16:52.120 --> 0:16:54.280 on your own, like you don't have to create an 0:16:54.320 --> 0:16:57.360 animation or anything. It does it for you. Now, the 0:16:57.400 --> 0:17:01.360 tools I've used, they're not perfect, but really good. Ones 0:17:01.400 --> 0:17:04.919 typically include a pretty easy way to edit the text 0:17:05.240 --> 0:17:07.960 so that if you're like me, let's say you have 0:17:08.119 --> 0:17:11.720 a little bit of a dialect that occasionally creeps through 0:17:11.760 --> 0:17:14.720 your spoken words, then you can review and fix the 0:17:14.760 --> 0:17:18.760 little goofs that the transcription might make when you maybe 0:17:18.760 --> 0:17:23.080 get a little too southern or whatever. For the individual creator, 0:17:23.200 --> 0:17:27.159 these kinds of tools are phenomenally useful. They simplify the 0:17:27.200 --> 0:17:30.520 process of promoting your work, and they help creators make 0:17:30.760 --> 0:17:34.760 bite sized pieces of their output that are ideal for 0:17:34.880 --> 0:17:38.560 social platforms to promote and to send people back to 0:17:39.080 --> 0:17:41.960 a full episode. For example, now I have the luxury 0:17:42.000 --> 0:17:44.639 of working at a major media company, and so in 0:17:44.680 --> 0:17:47.919 certain situations, I can actually lean on other people to 0:17:48.000 --> 0:17:52.240 help me create these kinds of social assets. But even 0:17:52.400 --> 0:17:55.000 in my case, my resources have their limits. I mean, 0:17:55.040 --> 0:17:58.200 those departments are supporting tons of other shows, they may 0:17:58.200 --> 0:18:01.840 not have the capacity to work with me, and most 0:18:01.920 --> 0:18:04.320 other creators don't even have that kind of help at 0:18:04.320 --> 0:18:07.080 their disposal to start with. Being able to lean on 0:18:07.119 --> 0:18:10.760 AI powered tools can give a creator more opportunities to 0:18:10.880 --> 0:18:14.800 find their audience, to stand out in a crowded field. Okay, 0:18:14.840 --> 0:18:18.240 back to my personal experiences. One of the big bonuses 0:18:18.520 --> 0:18:22.560 of using this Yoga Slim seven X laptop is as 0:18:22.600 --> 0:18:27.600 the name suggests, the laptop is extremely portable. It is lightweight, 0:18:27.800 --> 0:18:30.800 it has an incredibly thin form factor, and if I 0:18:30.840 --> 0:18:34.439 felt myself getting restless while I was working in my office, literally, 0:18:34.520 --> 0:18:37.720 I could just know, save my progress, shut the laptop 0:18:37.920 --> 0:18:40.200 and carry it upstairs to the living room and then 0:18:40.240 --> 0:18:42.679 work on the couch. And my dog found that to 0:18:42.720 --> 0:18:45.760 be a fantastic change of pace. And while he's not 0:18:46.000 --> 0:18:49.239 quite as good at keeping me on task as the 0:18:49.440 --> 0:18:53.240 AI assistant is, I definitely appreciated the change of scenery. 0:18:53.720 --> 0:18:56.280 You know, We're gonna take a quick break to thank 0:18:56.320 --> 0:19:08.840 our sponsor, but we'll be right back. So let's talk 0:19:08.840 --> 0:19:13.400 about this processor for a moment, because that's really ultimately 0:19:13.440 --> 0:19:15.960 what makes this experience possible in the first place. So, 0:19:16.280 --> 0:19:19.919 first off, Snapdragon obviously has a very long history of 0:19:19.960 --> 0:19:23.760 developing processors for mobile platforms, and I believe that gives 0:19:23.840 --> 0:19:27.880 Snapdragon some distinct advantages because the engineers and designers are 0:19:27.960 --> 0:19:31.720 used to working within tight limitations. I'm talking about tight 0:19:31.760 --> 0:19:35.159 limitations when it comes to the actual form factor the space. 0:19:35.200 --> 0:19:37.880 You have to work in tight limitations on how much 0:19:38.040 --> 0:19:40.600 power you're going to have access to, how much heat 0:19:40.800 --> 0:19:43.239 you can generate because it is a mobile device and 0:19:43.280 --> 0:19:46.560 you're not going to have access to like massive fans 0:19:46.680 --> 0:19:50.560 or water cooling systems. You know, you still need to 0:19:50.600 --> 0:19:54.359 get all the processing power as well. So all of 0:19:54.359 --> 0:19:56.760 this sounds like it could be a bad thing, but 0:19:56.880 --> 0:20:01.280 in my experience, when you are set with tight limitations, 0:20:01.680 --> 0:20:05.960 it can really inspire innovation and creativity because you still 0:20:06.000 --> 0:20:08.879 have a goal that you're working toward, right, and then 0:20:08.920 --> 0:20:11.159 you just have to think, well, how do I achieve 0:20:11.240 --> 0:20:13.840 this goal? And if you've got those limitations, it means 0:20:13.840 --> 0:20:16.520 that certain avenues are just cut off, and you have 0:20:16.600 --> 0:20:19.480 to really focus on what is possible and then push 0:20:19.560 --> 0:20:22.840 the boundary as hard as you can. And how you 0:20:23.359 --> 0:20:26.840 create a processor that provides the compute power needed while 0:20:27.200 --> 0:20:32.280 maintaining battery life ends up becoming kind of this guiding principle. 0:20:32.560 --> 0:20:35.840 Mobile devices in particular need to conserve battery power, right. 0:20:35.880 --> 0:20:38.480 I mean, you don't want to have a smartphone that 0:20:38.840 --> 0:20:41.240 has three hours of useful life in it and then 0:20:41.320 --> 0:20:43.600 you need to recharge it. But you also need to 0:20:43.640 --> 0:20:46.080 make sure that it can actually handle the computational jobs 0:20:46.119 --> 0:20:48.440 being thrown at it or else everything's going to feel 0:20:48.480 --> 0:20:52.439 sluggish and that's not a good user experience either. So 0:20:52.760 --> 0:20:57.399 Snapdragon's approach has been to incorporate different kinds of processors 0:20:57.480 --> 0:20:59.920 all on a single chip. So you've got your CPU, 0:21:00.000 --> 0:21:02.320 so that's your central processing unit. I think we're all 0:21:02.359 --> 0:21:05.560 familiar with those. The microprocessor kind of acts like the 0:21:05.560 --> 0:21:09.720 brains of the operation. CPUs traditionally are very good at handling, 0:21:09.840 --> 0:21:14.440 you know, like sequential problems, ones that are consecutive problems 0:21:14.480 --> 0:21:17.800 where the solution to one calculation feeds directly into the next. 0:21:18.119 --> 0:21:21.720 But then you've got your GPU, your graphics processing unit. Again, 0:21:21.800 --> 0:21:23.600 I feel like most of us have a handle on 0:21:23.760 --> 0:21:26.880 these these days. I remember, I'm old enough to remember 0:21:27.240 --> 0:21:30.600 when GPUs didn't exist. They weren't a thing. You would 0:21:30.600 --> 0:21:34.400 occasionally get a graphics chip, but it wasn't called a GPU. 0:21:34.560 --> 0:21:37.119 That didn't happen until you get up into the nineties. Really, 0:21:37.480 --> 0:21:41.680 and initially these were built to, as the name suggests, 0:21:41.760 --> 0:21:44.959 handle graphics processing. But GPUs have really come into their 0:21:45.000 --> 0:21:47.480 own in recent years and have proven to be extremely 0:21:47.520 --> 0:21:51.960 powerful when handling parallel processing jobs. So those are computational 0:21:51.960 --> 0:21:56.280 problems that can split into different tasks that a processor 0:21:56.359 --> 0:22:01.800 can potentially handle concurrently rather than consecutive, so they solve 0:22:02.080 --> 0:22:05.520 parts of problems all at the same time. Now, that 0:22:05.560 --> 0:22:08.760 doesn't work for every type of computational problem, but for 0:22:08.840 --> 0:22:12.639 that particular subset, GPUs are pretty darn good. But the 0:22:12.720 --> 0:22:18.000 snap Dragon x Elite processors also incorporate an NPU, and 0:22:18.040 --> 0:22:22.440 that's a relatively new technology. The NPU is the neural 0:22:22.600 --> 0:22:25.560 processing unit, and that sounds a bit like science fiction, 0:22:25.760 --> 0:22:29.199 but in reality, it's a processor that's optimized to handle 0:22:29.280 --> 0:22:34.280 AI related workloads. So think of a highly specialized processor 0:22:34.480 --> 0:22:38.959 that is ideal. It is optimized for AI operations. It 0:22:39.040 --> 0:22:42.480 handles those kinds of operations that speeds that even powerful 0:22:42.560 --> 0:22:46.800 GPUs can't match because they weren't built to handle those 0:22:46.880 --> 0:22:50.640 kinds of problems. And a good in PU, a well 0:22:50.640 --> 0:22:54.920 designed in PU, can do this with incredible power efficiency. 0:22:55.520 --> 0:22:58.679 So an NPU at a very basic level has an 0:22:58.800 --> 0:23:03.480 architecture that is inspired by the network of neurons that 0:23:03.560 --> 0:23:06.919 you have in that old gray matter up in your noggin. 0:23:07.280 --> 0:23:12.000 The component of the processor is great for another subset 0:23:12.000 --> 0:23:15.000 of computational problems, the ones relating to AI. It doesn't 0:23:15.080 --> 0:23:19.600 replace the CPU, it doesn't replace the GPU. It enhances 0:23:19.680 --> 0:23:23.040 the capabilities of the processor as a whole. And I 0:23:23.080 --> 0:23:25.679 think of that as being the ideal use case of 0:23:25.800 --> 0:23:29.800 artificial intelligence in general. It's good for an enhancement, it's 0:23:29.840 --> 0:23:33.240 good for augmentation. Now, we have heard lots of stories 0:23:33.240 --> 0:23:36.760 about AI potentially replacing people, and in some cases not 0:23:36.840 --> 0:23:42.760 potentially actually leaders choosing AI to replace staff, and trust me, 0:23:43.080 --> 0:23:45.560 I know these aren't just stories, and I think that 0:23:45.560 --> 0:23:48.639 that is a very human problem, and specifically a human 0:23:48.680 --> 0:23:52.800 problem that originates at leadership levels at some organizations. But 0:23:52.880 --> 0:23:56.679 I think the real sweet spot for artificial intelligence isn't 0:23:56.720 --> 0:23:58.880 in replacing humans, and I think a lot of those 0:23:58.880 --> 0:24:01.760 companies are finding that out too. Instead, I think it's 0:24:02.040 --> 0:24:05.320 augmenting what people can do so that they can do 0:24:05.400 --> 0:24:08.680 the things they already do well even better. But they 0:24:08.680 --> 0:24:12.000 can also lean on AI to help them with tasks 0:24:12.080 --> 0:24:15.600