1 00:00:04,440 --> 00:00:12,400 Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. Be there 2 00:00:12,520 --> 00:00:16,520 Speaker 1: and welcome to tech Stuff. I'm your host, Jonathan Strickland. 3 00:00:16,520 --> 00:00:20,400 Speaker 1: I'm an executive producer with iHeart Podcasts. And how the 4 00:00:20,560 --> 00:00:24,080 Speaker 1: tech are you now, y'all? I think I have made 5 00:00:24,120 --> 00:00:28,080 Speaker 1: my thoughts pretty clear when it comes to artificial intelligence. 6 00:00:28,520 --> 00:00:33,600 Speaker 1: For one thing, AI is a very broad discipline. It's huge. 7 00:00:33,680 --> 00:00:37,600 Speaker 1: It's way more than just generative AI, which is a 8 00:00:37,640 --> 00:00:40,839 Speaker 1: topic I feel very strongly about, and it's also the 9 00:00:40,880 --> 00:00:43,640 Speaker 1: one that dominates the news cycle. But it's just one 10 00:00:43,680 --> 00:00:48,559 Speaker 1: aspect of artificial intelligence. And I feel AI in general 11 00:00:48,880 --> 00:00:54,400 Speaker 1: has incredible potential to augment our computing tasks if we 12 00:00:54,560 --> 00:00:58,680 Speaker 1: implement it properly. Well. Recently I got the chance to 13 00:00:58,720 --> 00:01:02,520 Speaker 1: work with an AI laptop and really get to grips 14 00:01:02,560 --> 00:01:06,240 Speaker 1: with what that potential can be, and I'm convinced we're 15 00:01:06,280 --> 00:01:10,640 Speaker 1: on another precipice, one that will transform how we interact 16 00:01:10,640 --> 00:01:14,200 Speaker 1: with computing devices. Now. First, the AI powered device I 17 00:01:14,319 --> 00:01:17,880 Speaker 1: used was a Lenovo Yoga Slim seven X laptop with 18 00:01:17,959 --> 00:01:22,200 Speaker 1: a Snapdragon x Elite processor. It's a Copilot plus PC, 19 00:01:22,440 --> 00:01:27,280 Speaker 1: which means it features Microsoft's AI Assistant. It's also got 20 00:01:27,319 --> 00:01:31,280 Speaker 1: an OLED screen, and it's no joke. That's the prettiest 21 00:01:31,480 --> 00:01:35,479 Speaker 1: laptop screen I have ever used. The contrast on that 22 00:01:35,560 --> 00:01:40,600 Speaker 1: thing is crazy, it's amazing. It's so beautiful. Now, Snapdragon 23 00:01:40,800 --> 00:01:43,720 Speaker 1: was generous in sending me this laptop so that I 24 00:01:43,760 --> 00:01:46,080 Speaker 1: could actually get some hands on time with it. And 25 00:01:46,120 --> 00:01:49,080 Speaker 1: I'll be talking a lot about the work Snapdragon has 26 00:01:49,120 --> 00:01:52,800 Speaker 1: done to make the processor really special, but I'm saving 27 00:01:52,840 --> 00:01:55,720 Speaker 1: that for a bit later. Now, I do have to 28 00:01:55,760 --> 00:01:59,880 Speaker 1: say that this laptop would immediately top my holiday wish. 29 00:02:00,520 --> 00:02:03,560 Speaker 1: And I'm not just saying that I could be if 30 00:02:03,840 --> 00:02:07,040 Speaker 1: I had no scruples, but that's not who I am, y'all. 31 00:02:07,320 --> 00:02:10,399 Speaker 1: It's legit how I feel this laptops. It's really light, 32 00:02:10,480 --> 00:02:13,600 Speaker 1: it's powerful, The screen, as I mentioned, is absolutely gorgeous, 33 00:02:13,720 --> 00:02:17,400 Speaker 1: but the battery life is also really impressive, particularly when 34 00:02:17,440 --> 00:02:19,799 Speaker 1: you consider the power lifting this thing has to do. 35 00:02:20,120 --> 00:02:24,920 Speaker 1: Even when it's running AI Enhanced applications, which y'all know AI, 36 00:02:25,240 --> 00:02:28,520 Speaker 1: it requires a lot of processing power, but this laptop 37 00:02:28,560 --> 00:02:31,400 Speaker 1: continued to run smoothly and I didn't have to go 38 00:02:31,560 --> 00:02:34,560 Speaker 1: dashing from outlet to outlet just to keep it going. 39 00:02:34,880 --> 00:02:37,480 Speaker 1: But I think the thing that really pulls me to 40 00:02:37,840 --> 00:02:40,760 Speaker 1: this laptop is the fact that I can see AI 41 00:02:40,960 --> 00:02:45,040 Speaker 1: enabled processors as being the tech platform of the future. 42 00:02:45,360 --> 00:02:49,200 Speaker 1: So it's my belief that just as smartphones revolutionized how 43 00:02:49,200 --> 00:02:52,680 Speaker 1: we interact with computing resources, you know, like apps as 44 00:02:52,720 --> 00:02:55,080 Speaker 1: well as the Internet, AI is going to do the 45 00:02:55,080 --> 00:02:58,760 Speaker 1: same thing. Now. Before the era of the consumer smartphone, 46 00:02:58,840 --> 00:03:01,320 Speaker 1: there were very few people who were predicting a move 47 00:03:01,480 --> 00:03:03,959 Speaker 1: to mobile. Now, there were a few smarty pants is 48 00:03:04,000 --> 00:03:06,359 Speaker 1: out there who were ahead of the curve, but most 49 00:03:06,360 --> 00:03:09,800 Speaker 1: of us didn't see it coming. Once smartphones proved their 50 00:03:09,880 --> 00:03:13,400 Speaker 1: merit in the consumer marketplace, we saw a pretty rapid 51 00:03:13,440 --> 00:03:18,600 Speaker 1: transition to a mobile friendly landscape. You know, Web administrators 52 00:03:18,639 --> 00:03:22,120 Speaker 1: were scrambling to make sure that their websites were optimized 53 00:03:22,160 --> 00:03:25,799 Speaker 1: for mobile devices lest they potentially drive away visitors who 54 00:03:25,800 --> 00:03:29,920 Speaker 1: were increasingly using their phones to access the Internet rather 55 00:03:30,000 --> 00:03:35,160 Speaker 1: than say, laptop and desktop computers. Meanwhile, developers began designing 56 00:03:35,240 --> 00:03:38,640 Speaker 1: apps that would leverage smartphone capabilities, you know, stuff like 57 00:03:38,720 --> 00:03:43,240 Speaker 1: accelerometers and touchscreens and GPS sensors, that kind of thing. Well, 58 00:03:43,560 --> 00:03:46,960 Speaker 1: I believe AI is going to do much the same. 59 00:03:47,080 --> 00:03:50,560 Speaker 1: We're going to see a host of new programs and 60 00:03:50,680 --> 00:03:55,320 Speaker 1: apps built with AI enhanced features and devices that are 61 00:03:55,320 --> 00:03:59,160 Speaker 1: capable of providing onboard AI processing are going to be 62 00:03:59,200 --> 00:04:01,840 Speaker 1: way ahead of the game, while also providing ways to 63 00:04:01,880 --> 00:04:06,480 Speaker 1: handle AI processing in a more secure and private approach. See, 64 00:04:06,600 --> 00:04:10,600 Speaker 1: there are different ways that you can handle AI processing. 65 00:04:10,920 --> 00:04:16,120 Speaker 1: One way is you offload everything to a server farm somewhere, 66 00:04:16,320 --> 00:04:18,520 Speaker 1: and we hear about these a lot in the news. 67 00:04:18,760 --> 00:04:21,400 Speaker 1: You know, they're massive buildings, just filled with racks of 68 00:04:21,440 --> 00:04:26,080 Speaker 1: servers processing enormous amounts of data, powering AI implementations all 69 00:04:26,120 --> 00:04:29,080 Speaker 1: around the world, and no doubt that will continue, that 70 00:04:29,120 --> 00:04:32,640 Speaker 1: will continue to be a thing. But a complementary approach 71 00:04:33,000 --> 00:04:37,599 Speaker 1: involves on device and edge computing cases in which the 72 00:04:38,080 --> 00:04:40,680 Speaker 1: gadgets that we actually have our hands on can do 73 00:04:40,880 --> 00:04:44,119 Speaker 1: some or all of that processing on their own without 74 00:04:44,200 --> 00:04:47,680 Speaker 1: connecting to some distant server. Now it all depends upon 75 00:04:47,720 --> 00:04:51,800 Speaker 1: the application, of course, but with those types of implementations, 76 00:04:51,800 --> 00:04:55,039 Speaker 1: you keep the AI computations on your device, and that 77 00:04:55,120 --> 00:04:57,840 Speaker 1: means you're not sharing data with some server farm that 78 00:04:57,920 --> 00:05:01,680 Speaker 1: smiles away. Everything stays low, and that is a huge 79 00:05:01,720 --> 00:05:04,960 Speaker 1: thing when it comes to privacy and security. Let's say 80 00:05:04,960 --> 00:05:08,799 Speaker 1: that you work for a really big media company, for example, 81 00:05:09,200 --> 00:05:11,800 Speaker 1: I could be using myself in this example, and you 82 00:05:11,839 --> 00:05:14,679 Speaker 1: want to make sure that the work you do stays 83 00:05:14,760 --> 00:05:18,800 Speaker 1: local because you're working with some sensitive information, some of 84 00:05:18,800 --> 00:05:21,400 Speaker 1: it might be proprietary. You don't want to be sending 85 00:05:21,400 --> 00:05:25,159 Speaker 1: that off and have it become some kernel of information 86 00:05:25,320 --> 00:05:29,560 Speaker 1: that gets enveloped in a larger database somewhere. That's risky stuff. 87 00:05:29,839 --> 00:05:32,560 Speaker 1: So making sure you are able to do this kind 88 00:05:32,600 --> 00:05:34,920 Speaker 1: of thing locally is important for a lot of people. 89 00:05:35,279 --> 00:05:39,960 Speaker 1: All right, let's talk about the overview for this whole approach. 90 00:05:40,040 --> 00:05:42,320 Speaker 1: We'll get into the hows and whys a little bit later, 91 00:05:42,360 --> 00:05:44,479 Speaker 1: but first I want to talk about the ways I 92 00:05:44,640 --> 00:05:49,119 Speaker 1: used that Lenovo Yoga seven X laptop. So I wanted 93 00:05:49,160 --> 00:05:53,960 Speaker 1: to see how an AI enhanced device could potentially help 94 00:05:54,040 --> 00:05:56,680 Speaker 1: me do my work. I mean, we talk a lot 95 00:05:56,720 --> 00:06:01,440 Speaker 1: about artificial intelligence augmenting our ability. I wanted to actually 96 00:06:01,520 --> 00:06:04,479 Speaker 1: put that into practice. So I use the laptop while 97 00:06:04,520 --> 00:06:07,520 Speaker 1: I was researching a recent episode, one that actually has 98 00:06:07,640 --> 00:06:11,600 Speaker 1: already published. So I used this laptop, the Yoga laptop, 99 00:06:11,920 --> 00:06:15,480 Speaker 1: specifically for that episode, and I really wanted to put 100 00:06:15,480 --> 00:06:17,200 Speaker 1: it through its paces and see if it had a 101 00:06:17,240 --> 00:06:20,440 Speaker 1: meaningful impact on the way I do work. Now in 102 00:06:20,520 --> 00:06:25,840 Speaker 1: this episode, I referenced an extremely long research paper that 103 00:06:26,040 --> 00:06:29,880 Speaker 1: was written for the journal Science and Global Security, and 104 00:06:30,680 --> 00:06:34,200 Speaker 1: it was an article by Jurgen Altmann. It's actually a 105 00:06:34,240 --> 00:06:38,040 Speaker 1: fantastic paper. It was incredibly readable, which is not always 106 00:06:38,080 --> 00:06:40,600 Speaker 1: the case for technical papers. If you've ever tried to 107 00:06:40,640 --> 00:06:43,920 Speaker 1: read one, sometimes they come across as the most stilted 108 00:06:44,279 --> 00:06:46,919 Speaker 1: term paper a teacher has ever had degrade, but not 109 00:06:47,080 --> 00:06:50,279 Speaker 1: this one. It's also an accessible paper. You can find 110 00:06:50,279 --> 00:06:52,839 Speaker 1: it online for free, so that's great as well. But 111 00:06:53,360 --> 00:06:59,719 Speaker 1: it's very long, like it's seventy pages long. Now more 112 00:06:59,760 --> 00:07:03,120 Speaker 1: than ten of those pages are just notes and references, 113 00:07:03,160 --> 00:07:06,120 Speaker 1: but you still have, you know, fifty nine pages of 114 00:07:06,240 --> 00:07:09,760 Speaker 1: pure content there. Now, I read the full article for 115 00:07:09,880 --> 00:07:12,120 Speaker 1: my research, but I need to have access to the 116 00:07:12,160 --> 00:07:15,840 Speaker 1: salient points without having to thumb through all seventy pages 117 00:07:15,880 --> 00:07:19,480 Speaker 1: and taking notes as I read the article. That way, 118 00:07:19,560 --> 00:07:22,480 Speaker 1: I wouldn't have to thumb through a seventy page article 119 00:07:22,880 --> 00:07:28,480 Speaker 1: while writing the episode. But that's not easy to do. 120 00:07:28,560 --> 00:07:32,560 Speaker 1: Once you get past like twenty pages, it gets pretty cumbersome. 121 00:07:32,680 --> 00:07:35,760 Speaker 1: So I use the AI assistant on the laptop to 122 00:07:35,880 --> 00:07:39,640 Speaker 1: create a summarized, bulleted list of the most important notes 123 00:07:39,680 --> 00:07:44,040 Speaker 1: in the paper. For quick reference. Now I'm the cautious type, 124 00:07:44,360 --> 00:07:47,480 Speaker 1: and while I was using this and I wanted to 125 00:07:47,560 --> 00:07:50,120 Speaker 1: really check and make sure that it worked. I also 126 00:07:50,440 --> 00:07:53,920 Speaker 1: wanted to verify that the notes that were produced were 127 00:07:53,960 --> 00:07:58,080 Speaker 1: accurate to the original paper, that they weren't a misinterpretation 128 00:07:58,600 --> 00:08:03,600 Speaker 1: or a summary that just wasn't accurate. And obviously that 129 00:08:03,680 --> 00:08:06,560 Speaker 1: added more time for me. If I had not had 130 00:08:06,600 --> 00:08:08,760 Speaker 1: to do that, I would have been through pretty quickly, 131 00:08:09,000 --> 00:08:11,880 Speaker 1: but I needed to check. It's not just enough to 132 00:08:12,360 --> 00:08:15,000 Speaker 1: have it create this list, and it really did show 133 00:08:15,000 --> 00:08:17,680 Speaker 1: that the summary was accurate to the document I was using, 134 00:08:18,000 --> 00:08:21,600 Speaker 1: and that it really was the most important points in 135 00:08:21,680 --> 00:08:24,480 Speaker 1: the paper that were included the summary, and it was 136 00:08:24,520 --> 00:08:27,520 Speaker 1: really easy to do. It was easy to navigate to 137 00:08:27,560 --> 00:08:30,080 Speaker 1: the actual source for the bullet points so that I 138 00:08:30,120 --> 00:08:33,160 Speaker 1: could verify that, in fact, the information was correct, and 139 00:08:33,200 --> 00:08:35,959 Speaker 1: it really made organizing my thoughts much faster than it 140 00:08:36,000 --> 00:08:39,360 Speaker 1: would have been if I hadn't been able to access 141 00:08:39,400 --> 00:08:41,960 Speaker 1: this tool, because while I was taking more time to 142 00:08:42,040 --> 00:08:45,160 Speaker 1: verify that the bullets were accurate representations of the information 143 00:08:45,200 --> 00:08:47,840 Speaker 1: in the article, it did allow me to organize the 144 00:08:47,880 --> 00:08:52,840 Speaker 1: whole approach for the episode. So typically when I organized 145 00:08:52,840 --> 00:08:55,400 Speaker 1: an episode, I do that by feel, and some of 146 00:08:55,400 --> 00:08:59,040 Speaker 1: you might be saying, yeah, we know it's obvious, But 147 00:08:59,120 --> 00:09:02,200 Speaker 1: I've been podcasts for sixteen years. I have a sense 148 00:09:02,480 --> 00:09:05,120 Speaker 1: of the flow I want for an episode. Now that 149 00:09:05,160 --> 00:09:07,640 Speaker 1: doesn't always mean it's the best approach, but it is 150 00:09:07,679 --> 00:09:10,480 Speaker 1: the one that just feels natural to me. However, in 151 00:09:10,520 --> 00:09:13,559 Speaker 1: this case, it was really nice to consult a different perspective, 152 00:09:13,640 --> 00:09:17,400 Speaker 1: even an artificial perspective, to figure out how best to 153 00:09:17,480 --> 00:09:22,480 Speaker 1: structure the episode. So if there was an episode in 154 00:09:22,520 --> 00:09:25,040 Speaker 1: the recent past that you listened to and you thought, wow, 155 00:09:25,160 --> 00:09:28,960 Speaker 1: that's more coherent than what he normally produces, well now 156 00:09:29,000 --> 00:09:31,760 Speaker 1: you know why. But keep it to yourself because words 157 00:09:31,800 --> 00:09:35,320 Speaker 1: can hurt y'all. One thing I didn't do but I 158 00:09:35,480 --> 00:09:39,120 Speaker 1: could have done, was use real time translation tools to 159 00:09:39,200 --> 00:09:43,520 Speaker 1: access information that was presented in other languages. Once upon 160 00:09:43,559 --> 00:09:47,320 Speaker 1: a time, I took courses in French and in German, 161 00:09:47,400 --> 00:09:50,280 Speaker 1: but I never got to the point where I was conversational, 162 00:09:50,360 --> 00:09:53,120 Speaker 1: let alone fluent in those languages. And of course, over 163 00:09:53,120 --> 00:09:57,440 Speaker 1: the years my skills have atrophied. So I speak two languages, 164 00:09:57,800 --> 00:10:01,440 Speaker 1: English and Bad English. But I am aware that there 165 00:10:01,520 --> 00:10:05,160 Speaker 1: is a wealth of information and knowledge that's captured in 166 00:10:05,200 --> 00:10:08,960 Speaker 1: other languages. While there's some pretty darn good translation apps 167 00:10:08,960 --> 00:10:13,000 Speaker 1: for stuff like text, the cool thing about AI powered 168 00:10:13,000 --> 00:10:16,240 Speaker 1: devices is that they have the potential and the processing 169 00:10:16,320 --> 00:10:20,120 Speaker 1: capability to provide real time translation for other kinds of 170 00:10:20,200 --> 00:10:23,800 Speaker 1: content like audio. So one thing I could have done 171 00:10:24,200 --> 00:10:27,440 Speaker 1: was watch a video that had been recorded in another language, 172 00:10:27,440 --> 00:10:31,280 Speaker 1: and through using onboard AI processing capabilities, been able to 173 00:10:31,360 --> 00:10:34,840 Speaker 1: read English language captions that were translating what was being 174 00:10:34,920 --> 00:10:37,920 Speaker 1: said in real time. Now that opens up entire worlds 175 00:10:37,920 --> 00:10:41,200 Speaker 1: of expertise that otherwise would be very difficult for me 176 00:10:41,280 --> 00:10:45,119 Speaker 1: to access. And I've always said that diversity is really important. 177 00:10:45,240 --> 00:10:49,160 Speaker 1: It means you get multiple perspectives providing information, and you 178 00:10:49,200 --> 00:10:52,880 Speaker 1: can view the world from different perspectives, including ones that 179 00:10:53,040 --> 00:10:56,720 Speaker 1: you might not have ever even considered otherwise. Now, you 180 00:10:56,800 --> 00:11:00,000 Speaker 1: might ultimately not agree with this other point of view, 181 00:11:00,160 --> 00:11:03,560 Speaker 1: but being able to access it is important. Otherwise you 182 00:11:03,679 --> 00:11:07,000 Speaker 1: just remain ignorant of it. So from a research perspective, 183 00:11:07,400 --> 00:11:11,840 Speaker 1: real time translation is an enormous benefit, and I imagine 184 00:11:11,840 --> 00:11:15,120 Speaker 1: we'll see this technology continue to evolve as well. Tools 185 00:11:15,200 --> 00:11:18,199 Speaker 1: can be pretty good at doing things like translating word 186 00:11:18,320 --> 00:11:22,760 Speaker 1: for word, but in future implementations. I imagine AI translation 187 00:11:22,920 --> 00:11:26,319 Speaker 1: will also have to handle stuff like syntax and idioms 188 00:11:26,360 --> 00:11:29,280 Speaker 1: really well, so that we don't just understand the actual 189 00:11:29,360 --> 00:11:34,160 Speaker 1: words being spoken, but what the speaker means when they 190 00:11:34,240 --> 00:11:38,120 Speaker 1: say those things. If someone uses like an idiom like 191 00:11:38,160 --> 00:11:42,280 Speaker 1: a regional saying, or they're using really complex phrasing that 192 00:11:42,320 --> 00:11:46,160 Speaker 1: doesn't easily translate to English, I can imagine future AI 193 00:11:46,280 --> 00:11:51,040 Speaker 1: translation tools handling that and providing a relatable translation to 194 00:11:51,160 --> 00:11:54,640 Speaker 1: avoid ambiguity, unless, of course, ambiguity was the intent in 195 00:11:54,679 --> 00:11:57,680 Speaker 1: the first place. Sometimes it is another thing that I 196 00:11:57,720 --> 00:11:59,840 Speaker 1: could have done at the end of the whole episode 197 00:12:00,080 --> 00:12:03,040 Speaker 1: because I have used tools like this before, is use 198 00:12:03,080 --> 00:12:07,160 Speaker 1: AI to generate show notes. So, y'all, Podcasting is a 199 00:12:07,160 --> 00:12:10,920 Speaker 1: lot of work, particularly if you have a small team. 200 00:12:11,280 --> 00:12:15,480 Speaker 1: In my case, the team is me and super producer Tari, 201 00:12:15,640 --> 00:12:18,920 Speaker 1: who also works on other shows. There are a lot 202 00:12:18,920 --> 00:12:22,120 Speaker 1: of steps in making a podcast. You know, you have 203 00:12:22,280 --> 00:12:25,880 Speaker 1: pre prep. You've got prep, you've got research, you've got 204 00:12:25,960 --> 00:12:31,200 Speaker 1: writing and recording and editing and publishing. One post production 205 00:12:31,400 --> 00:12:33,600 Speaker 1: step that a lot of shows will skip is the 206 00:12:33,600 --> 00:12:36,520 Speaker 1: production of show notes. So why do so many shows 207 00:12:36,559 --> 00:12:39,840 Speaker 1: just skip show notes? Well, I can't speak for everyone, 208 00:12:40,360 --> 00:12:43,160 Speaker 1: But in my case it comes down to being a 209 00:12:43,200 --> 00:12:46,720 Speaker 1: mental block. When I finish an episode, after I've done 210 00:12:47,040 --> 00:12:50,480 Speaker 1: speaking my amazing words into a microphone and then shipping 211 00:12:50,480 --> 00:12:53,600 Speaker 1: off the file to my producer extraordinaire Tari, I'm ready 212 00:12:53,640 --> 00:12:56,320 Speaker 1: to move on. My brain has effectively said, welp. That 213 00:12:56,400 --> 00:12:59,760 Speaker 1: closes that chapter, dusts off its hands and whistles as 214 00:12:59,760 --> 00:13:02,480 Speaker 1: a walks into the sunset. So it can be really 215 00:13:02,520 --> 00:13:06,040 Speaker 1: hard to stop and reflect on what I just created 216 00:13:06,360 --> 00:13:09,520 Speaker 1: and then distill that into useful notes for listeners. But 217 00:13:09,800 --> 00:13:13,760 Speaker 1: AI tools can do that automatically. Of course, after creating 218 00:13:13,840 --> 00:13:16,520 Speaker 1: the notes, then I would review them to make sure 219 00:13:16,600 --> 00:13:20,320 Speaker 1: that again they accurately reflect the episode. But that's one 220 00:13:20,440 --> 00:13:23,560 Speaker 1: step in the podcasting process that I would be happy 221 00:13:23,600 --> 00:13:26,679 Speaker 1: to hand over to an AI enabled tool, as it 222 00:13:26,760 --> 00:13:29,360 Speaker 1: is a step that I otherwise find really tedious and 223 00:13:29,400 --> 00:13:32,760 Speaker 1: it actually discourages me from doing my job. You know, 224 00:13:32,840 --> 00:13:36,680 Speaker 1: I will find any excuse. I will invent excuses to 225 00:13:36,679 --> 00:13:39,440 Speaker 1: put off doing that kind of thing. Now, while all 226 00:13:39,520 --> 00:13:42,640 Speaker 1: my research and writing and recording was going on, I 227 00:13:42,760 --> 00:13:46,720 Speaker 1: also was using the assistant to keep me up to 228 00:13:46,800 --> 00:13:52,439 Speaker 1: speed on my daily schedule. I'm somewhat notorious for missing 229 00:13:52,480 --> 00:13:57,559 Speaker 1: things like important emails and meetings and that sort of thing. 230 00:13:57,800 --> 00:13:59,880 Speaker 1: I have kind of made it an art formed to 231 00:14:00,160 --> 00:14:03,839 Speaker 1: make it difficult to reach me because I find it 232 00:14:03,880 --> 00:14:07,520 Speaker 1: creates an environment that allows me to focus right. I 233 00:14:07,559 --> 00:14:09,840 Speaker 1: want to really focus on what I'm doing, and that 234 00:14:09,920 --> 00:14:13,280 Speaker 1: means I need to filter out distractions because otherwise I 235 00:14:13,480 --> 00:14:16,880 Speaker 1: will stop whatever it is I'm working on, and that 236 00:14:17,000 --> 00:14:20,840 Speaker 1: just ruins my whole flow. Getting back into that is 237 00:14:20,920 --> 00:14:23,840 Speaker 1: hard to do. I found using the AI assistant to 238 00:14:23,880 --> 00:14:27,200 Speaker 1: help block out my time so that I had specific 239 00:14:27,320 --> 00:14:30,800 Speaker 1: blocks of time where I was doing specific activities made 240 00:14:30,840 --> 00:14:34,400 Speaker 1: me overall more efficient and effective. I actually one of 241 00:14:34,400 --> 00:14:36,520 Speaker 1: the In fact, the very first thing I asked my 242 00:14:36,600 --> 00:14:39,280 Speaker 1: AI assistant to do was to help me create a 243 00:14:39,720 --> 00:14:43,280 Speaker 1: working schedule and it did, and it even built in 244 00:14:43,400 --> 00:14:46,360 Speaker 1: things like breaks and stuff, and I followed it. I 245 00:14:46,400 --> 00:14:48,480 Speaker 1: was like, this is a real experiment. I am going 246 00:14:48,520 --> 00:14:51,960 Speaker 1: to follow the schedule that's been created for me, and 247 00:14:52,040 --> 00:14:54,880 Speaker 1: I found that it was incredibly helpful. It really added 248 00:14:54,920 --> 00:14:57,320 Speaker 1: structure to my day, something that I haven't had a 249 00:14:57,320 --> 00:15:01,280 Speaker 1: lot of because I work remotely and mostly on my own, 250 00:15:01,600 --> 00:15:05,720 Speaker 1: so structure is something that I have to create and 251 00:15:05,760 --> 00:15:09,000 Speaker 1: I'm not great at doing that. So using this tool 252 00:15:09,080 --> 00:15:13,080 Speaker 1: to help me to augment my abilities and to take 253 00:15:13,120 --> 00:15:17,480 Speaker 1: on a workload that I otherwise would find difficult to do, 254 00:15:17,720 --> 00:15:20,720 Speaker 1: that was incredibly helpful and it was a really nice change. 255 00:15:21,000 --> 00:15:23,960 Speaker 1: Now I can envision other uses of AI as well, 256 00:15:24,080 --> 00:15:26,600 Speaker 1: though I didn't use them for that particular episode. So 257 00:15:26,720 --> 00:15:32,240 Speaker 1: for example creating audiograms, and I can already use AI 258 00:15:32,360 --> 00:15:34,920 Speaker 1: to do this. I have used AI tools to do this, 259 00:15:35,000 --> 00:15:38,440 Speaker 1: but they were cloud based. And what I'm talking about 260 00:15:38,480 --> 00:15:43,480 Speaker 1: here is using AI to identify interesting passages in an episode, 261 00:15:43,520 --> 00:15:47,600 Speaker 1: like a section that's really compelling. And you could do 262 00:15:47,680 --> 00:15:49,760 Speaker 1: this in different ways, Like you could use AI to 263 00:15:49,880 --> 00:15:53,960 Speaker 1: analyze the written work that you create, like in my case, 264 00:15:54,000 --> 00:15:56,760 Speaker 1: I write out episodes, right, so I could actually use 265 00:15:56,800 --> 00:16:00,560 Speaker 1: AI to analyze what I've written and to identify, oh, 266 00:16:00,720 --> 00:16:05,320 Speaker 1: this is a particularly compelling section. Or you can use 267 00:16:05,360 --> 00:16:08,640 Speaker 1: it to analyze the recorded audio. Because I also go 268 00:16:08,720 --> 00:16:11,360 Speaker 1: off book a lot. I don't just have a script 269 00:16:11,360 --> 00:16:15,120 Speaker 1: that I read. I extemporize like crazy. If you read 270 00:16:15,360 --> 00:16:18,200 Speaker 1: what I wrote and compared it to what I say, 271 00:16:18,600 --> 00:16:21,239 Speaker 1: you would notice that there are a lot of departures. 272 00:16:21,800 --> 00:16:26,720 Speaker 1: So using AI, I could analyze the recorded program and 273 00:16:26,760 --> 00:16:30,520 Speaker 1: create audiograms, which are those excerpts you sometimes come across 274 00:16:30,560 --> 00:16:34,080 Speaker 1: on various social platforms. These are ones that play not 275 00:16:34,360 --> 00:16:37,480 Speaker 1: just an audio clip from a show. Typically they'll also 276 00:16:37,520 --> 00:16:41,320 Speaker 1: include stuff like real time captions that will help emphasize 277 00:16:41,360 --> 00:16:46,000 Speaker 1: the point being expressed. I've had some experience using AI 278 00:16:46,120 --> 00:16:49,560 Speaker 1: to generate these, and that includes matching text to spoken 279 00:16:49,600 --> 00:16:52,080 Speaker 1: words automatically, so that you don't have to do it 280 00:16:52,120 --> 00:16:54,280 Speaker 1: on your own, like you don't have to create an 281 00:16:54,320 --> 00:16:57,360 Speaker 1: animation or anything. It does it for you. Now, the 282 00:16:57,400 --> 00:17:01,360 Speaker 1: tools I've used, they're not perfect, but really good. Ones 283 00:17:01,400 --> 00:17:04,919 Speaker 1: typically include a pretty easy way to edit the text 284 00:17:05,240 --> 00:17:07,960 Speaker 1: so that if you're like me, let's say you have 285 00:17:08,119 --> 00:17:11,720 Speaker 1: a little bit of a dialect that occasionally creeps through 286 00:17:11,760 --> 00:17:14,720 Speaker 1: your spoken words, then you can review and fix the 287 00:17:14,760 --> 00:17:18,760 Speaker 1: little goofs that the transcription might make when you maybe 288 00:17:18,760 --> 00:17:23,080 Speaker 1: get a little too southern or whatever. For the individual creator, 289 00:17:23,200 --> 00:17:27,159 Speaker 1: these kinds of tools are phenomenally useful. They simplify the 290 00:17:27,200 --> 00:17:30,520 Speaker 1: process of promoting your work, and they help creators make 291 00:17:30,760 --> 00:17:34,760 Speaker 1: bite sized pieces of their output that are ideal for 292 00:17:34,880 --> 00:17:38,560 Speaker 1: social platforms to promote and to send people back to 293 00:17:39,080 --> 00:17:41,960 Speaker 1: a full episode. For example, now I have the luxury 294 00:17:42,000 --> 00:17:44,639 Speaker 1: of working at a major media company, and so in 295 00:17:44,680 --> 00:17:47,919 Speaker 1: certain situations, I can actually lean on other people to 296 00:17:48,000 --> 00:17:52,240 Speaker 1: help me create these kinds of social assets. But even 297 00:17:52,400 --> 00:17:55,000 Speaker 1: in my case, my resources have their limits. I mean, 298 00:17:55,040 --> 00:17:58,200 Speaker 1: those departments are supporting tons of other shows, they may 299 00:17:58,200 --> 00:18:01,840 Speaker 1: not have the capacity to work with me, and most 300 00:18:01,920 --> 00:18:04,320 Speaker 1: other creators don't even have that kind of help at 301 00:18:04,320 --> 00:18:07,080 Speaker 1: their disposal to start with. Being able to lean on 302 00:18:07,119 --> 00:18:10,760 Speaker 1: AI powered tools can give a creator more opportunities to 303 00:18:10,880 --> 00:18:14,800 Speaker 1: find their audience, to stand out in a crowded field. Okay, 304 00:18:14,840 --> 00:18:18,240 Speaker 1: back to my personal experiences. One of the big bonuses 305 00:18:18,520 --> 00:18:22,560 Speaker 1: of using this Yoga Slim seven X laptop is as 306 00:18:22,600 --> 00:18:27,600 Speaker 1: the name suggests, the laptop is extremely portable. It is lightweight, 307 00:18:27,800 --> 00:18:30,800 Speaker 1: it has an incredibly thin form factor, and if I 308 00:18:30,840 --> 00:18:34,439 Speaker 1: felt myself getting restless while I was working in my office, literally, 309 00:18:34,520 --> 00:18:37,720 Speaker 1: I could just know, save my progress, shut the laptop 310 00:18:37,920 --> 00:18:40,200 Speaker 1: and carry it upstairs to the living room and then 311 00:18:40,240 --> 00:18:42,679 Speaker 1: work on the couch. And my dog found that to 312 00:18:42,720 --> 00:18:45,760 Speaker 1: be a fantastic change of pace. And while he's not 313 00:18:46,000 --> 00:18:49,239 Speaker 1: quite as good at keeping me on task as the 314 00:18:49,440 --> 00:18:53,240 Speaker 1: AI assistant is, I definitely appreciated the change of scenery. 315 00:18:53,720 --> 00:18:56,280 Speaker 1: You know, We're gonna take a quick break to thank 316 00:18:56,320 --> 00:19:08,840 Speaker 1: our sponsor, but we'll be right back. So let's talk 317 00:19:08,840 --> 00:19:13,400 Speaker 1: about this processor for a moment, because that's really ultimately 318 00:19:13,440 --> 00:19:15,960 Speaker 1: what makes this experience possible in the first place. So, 319 00:19:16,280 --> 00:19:19,919 Speaker 1: first off, Snapdragon obviously has a very long history of 320 00:19:19,960 --> 00:19:23,760 Speaker 1: developing processors for mobile platforms, and I believe that gives 321 00:19:23,840 --> 00:19:27,880 Speaker 1: Snapdragon some distinct advantages because the engineers and designers are 322 00:19:27,960 --> 00:19:31,720 Speaker 1: used to working within tight limitations. I'm talking about tight 323 00:19:31,760 --> 00:19:35,159 Speaker 1: limitations when it comes to the actual form factor the space. 324 00:19:35,200 --> 00:19:37,880 Speaker 1: You have to work in tight limitations on how much 325 00:19:38,040 --> 00:19:40,600 Speaker 1: power you're going to have access to, how much heat 326 00:19:40,800 --> 00:19:43,239 Speaker 1: you can generate because it is a mobile device and 327 00:19:43,280 --> 00:19:46,560 Speaker 1: you're not going to have access to like massive fans 328 00:19:46,680 --> 00:19:50,560 Speaker 1: or water cooling systems. You know, you still need to 329 00:19:50,600 --> 00:19:54,359 Speaker 1: get all the processing power as well. So all of 330 00:19:54,359 --> 00:19:56,760 Speaker 1: this sounds like it could be a bad thing, but 331 00:19:56,880 --> 00:20:01,280 Speaker 1: in my experience, when you are set with tight limitations, 332 00:20:01,680 --> 00:20:05,960 Speaker 1: it can really inspire innovation and creativity because you still 333 00:20:06,000 --> 00:20:08,879 Speaker 1: have a goal that you're working toward, right, and then 334 00:20:08,920 --> 00:20:11,159 Speaker 1: you just have to think, well, how do I achieve 335 00:20:11,240 --> 00:20:13,840 Speaker 1: this goal? And if you've got those limitations, it means 336 00:20:13,840 --> 00:20:16,520 Speaker 1: that certain avenues are just cut off, and you have 337 00:20:16,600 --> 00:20:19,480 Speaker 1: to really focus on what is possible and then push 338 00:20:19,560 --> 00:20:22,840 Speaker 1: the boundary as hard as you can. And how you 339 00:20:23,359 --> 00:20:26,840 Speaker 1: create a processor that provides the compute power needed while 340 00:20:27,200 --> 00:20:32,280 Speaker 1: maintaining battery life ends up becoming kind of this guiding principle. 341 00:20:32,560 --> 00:20:35,840 Speaker 1: Mobile devices in particular need to conserve battery power, right. 342 00:20:35,880 --> 00:20:38,480 Speaker 1: I mean, you don't want to have a smartphone that 343 00:20:38,840 --> 00:20:41,240 Speaker 1: has three hours of useful life in it and then 344 00:20:41,320 --> 00:20:43,600 Speaker 1: you need to recharge it. But you also need to 345 00:20:43,640 --> 00:20:46,080 Speaker 1: make sure that it can actually handle the computational jobs 346 00:20:46,119 --> 00:20:48,440 Speaker 1: being thrown at it or else everything's going to feel 347 00:20:48,480 --> 00:20:52,439 Speaker 1: sluggish and that's not a good user experience either. So 348 00:20:52,760 --> 00:20:57,399 Speaker 1: Snapdragon's approach has been to incorporate different kinds of processors 349 00:20:57,480 --> 00:20:59,920 Speaker 1: all on a single chip. So you've got your CPU, 350 00:21:00,000 --> 00:21:02,320 Speaker 1: so that's your central processing unit. I think we're all 351 00:21:02,359 --> 00:21:05,560 Speaker 1: familiar with those. The microprocessor kind of acts like the 352 00:21:05,560 --> 00:21:09,720 Speaker 1: brains of the operation. CPUs traditionally are very good at handling, 353 00:21:09,840 --> 00:21:14,440 Speaker 1: you know, like sequential problems, ones that are consecutive problems 354 00:21:14,480 --> 00:21:17,800 Speaker 1: where the solution to one calculation feeds directly into the next. 355 00:21:18,119 --> 00:21:21,720 Speaker 1: But then you've got your GPU, your graphics processing unit. Again, 356 00:21:21,800 --> 00:21:23,600 Speaker 1: I feel like most of us have a handle on 357 00:21:23,760 --> 00:21:26,880 Speaker 1: these these days. I remember, I'm old enough to remember 358 00:21:27,240 --> 00:21:30,600 Speaker 1: when GPUs didn't exist. They weren't a thing. You would 359 00:21:30,600 --> 00:21:34,400 Speaker 1: occasionally get a graphics chip, but it wasn't called a GPU. 360 00:21:34,560 --> 00:21:37,119 Speaker 1: That didn't happen until you get up into the nineties. Really, 361 00:21:37,480 --> 00:21:41,680 Speaker 1: and initially these were built to, as the name suggests, 362 00:21:41,760 --> 00:21:44,959 Speaker 1: handle graphics processing. But GPUs have really come into their 363 00:21:45,000 --> 00:21:47,480 Speaker 1: own in recent years and have proven to be extremely 364 00:21:47,520 --> 00:21:51,960 Speaker 1: powerful when handling parallel processing jobs. So those are computational 365 00:21:51,960 --> 00:21:56,280 Speaker 1: problems that can split into different tasks that a processor 366 00:21:56,359 --> 00:22:01,800 Speaker 1: can potentially handle concurrently rather than consecutive, so they solve 367 00:22:02,080 --> 00:22:05,520 Speaker 1: parts of problems all at the same time. Now, that 368 00:22:05,560 --> 00:22:08,760 Speaker 1: doesn't work for every type of computational problem, but for 369 00:22:08,840 --> 00:22:12,639 Speaker 1: that particular subset, GPUs are pretty darn good. But the 370 00:22:12,720 --> 00:22:18,000 Speaker 1: snap Dragon x Elite processors also incorporate an NPU, and 371 00:22:18,040 --> 00:22:22,440 Speaker 1: that's a relatively new technology. The NPU is the neural 372 00:22:22,600 --> 00:22:25,560 Speaker 1: processing unit, and that sounds a bit like science fiction, 373 00:22:25,760 --> 00:22:29,199 Speaker 1: but in reality, it's a processor that's optimized to handle 374 00:22:29,280 --> 00:22:34,280 Speaker 1: AI related workloads. So think of a highly specialized processor 375 00:22:34,480 --> 00:22:38,959 Speaker 1: that is ideal. It is optimized for AI operations. It 376 00:22:39,040 --> 00:22:42,480 Speaker 1: handles those kinds of operations that speeds that even powerful 377 00:22:42,560 --> 00:22:46,800 Speaker 1: GPUs can't match because they weren't built to handle those 378 00:22:46,880 --> 00:22:50,640 Speaker 1: kinds of problems. And a good in PU, a well 379 00:22:50,640 --> 00:22:54,920 Speaker 1: designed in PU, can do this with incredible power efficiency. 380 00:22:55,520 --> 00:22:58,679 Speaker 1: So an NPU at a very basic level has an 381 00:22:58,800 --> 00:23:03,480 Speaker 1: architecture that is inspired by the network of neurons that 382 00:23:03,560 --> 00:23:06,919 Speaker 1: you have in that old gray matter up in your noggin. 383 00:23:07,280 --> 00:23:12,000 Speaker 1: The component of the processor is great for another subset 384 00:23:12,000 --> 00:23:15,000 Speaker 1: of computational problems, the ones relating to AI. It doesn't 385 00:23:15,080 --> 00:23:19,600 Speaker 1: replace the CPU, it doesn't replace the GPU. It enhances 386 00:23:19,680 --> 00:23:23,040 Speaker 1: the capabilities of the processor as a whole. And I 387 00:23:23,080 --> 00:23:25,679 Speaker 1: think of that as being the ideal use case of 388 00:23:25,800 --> 00:23:29,800 Speaker 1: artificial intelligence in general. It's good for an enhancement, it's 389 00:23:29,840 --> 00:23:33,240 Speaker 1: good for augmentation. Now, we have heard lots of stories 390 00:23:33,240 --> 00:23:36,760 Speaker 1: about AI potentially replacing people, and in some cases not 391 00:23:36,840 --> 00:23:42,760 Speaker 1: potentially actually leaders choosing AI to replace staff, and trust me, 392 00:23:43,080 --> 00:23:45,560 Speaker 1: I know these aren't just stories, and I think that 393 00:23:45,560 --> 00:23:48,639 Speaker 1: that is a very human problem, and specifically a human 394 00:23:48,680 --> 00:23:52,800 Speaker 1: problem that originates at leadership levels at some organizations. But 395 00:23:52,880 --> 00:23:56,679 Speaker 1: I think the real sweet spot for artificial intelligence isn't 396 00:23:56,720 --> 00:23:58,880 Speaker 1: in replacing humans, and I think a lot of those 397 00:23:58,880 --> 00:24:01,760 Speaker 1: companies are finding that out too. Instead, I think it's 398 00:24:02,040 --> 00:24:05,320 Speaker 1: augmenting what people can do so that they can do 399 00:24:05,400 --> 00:24:08,680 Speaker 1: the things they already do well even better. But they 400 00:24:08,680 --> 00:24:12,000 Speaker 1: can also lean on AI to help them with tasks 401 00:24:12,080 --> 00:24:15,600 Speaker 1: that they themselves find challenging. Maybe it's the stuff they 402 00:24:15,720 --> 00:24:18,159 Speaker 1: don't do so well but still kind of part of 403 00:24:18,200 --> 00:24:21,560 Speaker 1: their daily tasks. So let me give another example. I'm 404 00:24:21,600 --> 00:24:24,720 Speaker 1: a writer and i'm a podcaster, but I am not 405 00:24:24,840 --> 00:24:28,399 Speaker 1: a graphics designer. In fact, I find design to be 406 00:24:28,920 --> 00:24:34,159 Speaker 1: almost impenetrable. I recognize great design when I see it, 407 00:24:34,480 --> 00:24:38,120 Speaker 1: like I can see great design and say, wow, that's incredible, 408 00:24:38,359 --> 00:24:40,680 Speaker 1: But if I were looking at a blank page, it's 409 00:24:40,720 --> 00:24:43,080 Speaker 1: like a prison cell to me. I have fallen victim 410 00:24:43,119 --> 00:24:45,800 Speaker 1: as well to the trap of reducing the work of 411 00:24:45,920 --> 00:24:49,679 Speaker 1: real artists as just an element that they have something 412 00:24:49,680 --> 00:24:52,840 Speaker 1: that I lack. Right, Like, there's some spark or gift 413 00:24:53,040 --> 00:24:55,320 Speaker 1: that those people have and I don't have it. You're 414 00:24:55,359 --> 00:25:00,000 Speaker 1: either born with it or you're not. That is harmfully reductive. 415 00:25:00,560 --> 00:25:02,600 Speaker 1: And I've actually had a good friend of mine, a 416 00:25:02,720 --> 00:25:06,199 Speaker 1: talented artist, take me aside to talk about this and 417 00:25:06,240 --> 00:25:10,280 Speaker 1: set me straight. He was very direct but polite about it, 418 00:25:10,320 --> 00:25:14,000 Speaker 1: and he described to me his experiences learning art and 419 00:25:14,040 --> 00:25:18,000 Speaker 1: developing his craft and practicing his skills, and he explained 420 00:25:18,000 --> 00:25:21,479 Speaker 1: that reducing art to some sort of almost mystical gift 421 00:25:21,720 --> 00:25:25,280 Speaker 1: is an insult considering the countless hours he and other 422 00:25:25,400 --> 00:25:28,400 Speaker 1: artists have poured into their work in order to get 423 00:25:28,440 --> 00:25:31,400 Speaker 1: to where they are. That really struck me and made 424 00:25:31,400 --> 00:25:33,480 Speaker 1: me look at what he did in a different way. 425 00:25:33,760 --> 00:25:35,800 Speaker 1: But at the end of the day, I don't have 426 00:25:35,880 --> 00:25:40,200 Speaker 1: those skills. Now. Potentially I could develop such skills if 427 00:25:40,200 --> 00:25:43,680 Speaker 1: I gave the skills enough time and effort and practice 428 00:25:43,720 --> 00:25:46,679 Speaker 1: to develop them. But let's be realistic. There are a 429 00:25:46,720 --> 00:25:49,040 Speaker 1: limited number of hours in the day, and I have 430 00:25:49,119 --> 00:25:52,359 Speaker 1: responsibilities that I have to meet. The likelihood that I 431 00:25:52,400 --> 00:25:55,600 Speaker 1: can make the time to practice a new skill and 432 00:25:55,680 --> 00:25:58,880 Speaker 1: reach a level of skill that would be considered professional, 433 00:25:59,200 --> 00:26:02,359 Speaker 1: that's pretty love. Oh, I need help, but I don't 434 00:26:02,400 --> 00:26:05,080 Speaker 1: have an assistant. I don't have a graphics department that 435 00:26:05,240 --> 00:26:08,000 Speaker 1: specifically reports to me. I have one that I can 436 00:26:08,200 --> 00:26:11,280 Speaker 1: share with everybody else, which means they don't always have 437 00:26:11,760 --> 00:26:14,080 Speaker 1: availability for me. So what if I need to put 438 00:26:14,080 --> 00:26:17,520 Speaker 1: together a presentation. Well, I could use a standard format 439 00:26:17,600 --> 00:26:21,080 Speaker 1: in a presentation software package. That's kind of a dead giveaway, 440 00:26:21,359 --> 00:26:24,560 Speaker 1: right if anyone has ever sat through a presentation and 441 00:26:24,600 --> 00:26:28,280 Speaker 1: they said, oh, I recognize that layout immediately, like I 442 00:26:28,359 --> 00:26:32,680 Speaker 1: know exactly which default layout you used. Plus, while I'm 443 00:26:32,720 --> 00:26:35,399 Speaker 1: a decent writer, boiling things down into slides is not 444 00:26:35,480 --> 00:26:38,320 Speaker 1: my strong suit. This is an area where an AI 445 00:26:38,480 --> 00:26:42,240 Speaker 1: powered assist would be incredibly valuable to me. I would 446 00:26:42,280 --> 00:26:45,000 Speaker 1: still be doing the work. Keep it that in mind. 447 00:26:45,040 --> 00:26:48,680 Speaker 1: I'm not laying the work onto the AI. I've created 448 00:26:48,720 --> 00:26:51,840 Speaker 1: all this work. Putting together the content of my presentation 449 00:26:52,200 --> 00:26:54,840 Speaker 1: was the main part of the job. But the AI 450 00:26:54,960 --> 00:26:58,320 Speaker 1: assistant can help me lay out a presentation and design 451 00:26:58,359 --> 00:27:02,480 Speaker 1: it so that it looks great, flows well, and most importantly, 452 00:27:02,520 --> 00:27:04,600 Speaker 1: that my key points are summarized in a way that 453 00:27:04,760 --> 00:27:08,040 Speaker 1: is effective on the screen. No one wants to sit 454 00:27:08,080 --> 00:27:11,040 Speaker 1: down to a presentation only to see a slide that 455 00:27:11,119 --> 00:27:13,720 Speaker 1: looks like it's a dissertation. I'm sure you have all 456 00:27:13,760 --> 00:27:16,159 Speaker 1: done that where you've gone in and one slide is 457 00:27:16,560 --> 00:27:21,119 Speaker 1: just a wall of text that stinks. It's not good design, 458 00:27:21,280 --> 00:27:24,240 Speaker 1: it's missing the point entirely. It's using the presentation for 459 00:27:24,320 --> 00:27:26,879 Speaker 1: the wrong reason. And I think the key to using 460 00:27:26,960 --> 00:27:30,960 Speaker 1: AI in an ethical way is all about boosting your 461 00:27:31,000 --> 00:27:34,240 Speaker 1: own abilities, not fabricating something out of thin air. The 462 00:27:34,400 --> 00:27:36,600 Speaker 1: art still has to come from the artist, The content 463 00:27:36,720 --> 00:27:39,360 Speaker 1: still needs to come from the creator. The words need 464 00:27:39,440 --> 00:27:42,679 Speaker 1: to come from the writer. The AI's job is to 465 00:27:42,680 --> 00:27:47,040 Speaker 1: add some polish and to help organize thoughts and help 466 00:27:47,080 --> 00:27:50,440 Speaker 1: the human make stuff that has the most powerful impact 467 00:27:50,840 --> 00:27:54,760 Speaker 1: upon their intended audience. Now, on a personal side, I 468 00:27:54,800 --> 00:27:57,480 Speaker 1: am starting to dip my toe into stuff like AI 469 00:27:57,640 --> 00:28:01,199 Speaker 1: powered photo tools. So I like taking pictures of my 470 00:28:01,320 --> 00:28:05,480 Speaker 1: aforementioned dog. His name is Timbolt, and he's a joy. 471 00:28:06,000 --> 00:28:08,480 Speaker 1: But it could be something of a challenge to get 472 00:28:08,520 --> 00:28:11,560 Speaker 1: a really good photo of Timbolt while I'm walking him, 473 00:28:11,760 --> 00:28:14,600 Speaker 1: because he's always on a leash, which means I always 474 00:28:14,600 --> 00:28:17,119 Speaker 1: have one hand holding the other end of that leash, 475 00:28:17,320 --> 00:28:20,119 Speaker 1: and meanwhile I'm fumbling with my smartphone in an effort 476 00:28:20,160 --> 00:28:21,800 Speaker 1: to take a photo of them. I can't tell you 477 00:28:21,840 --> 00:28:23,920 Speaker 1: how many times I've taken a shot that I thought 478 00:28:24,000 --> 00:28:26,439 Speaker 1: at the time it was gonna look really good, but 479 00:28:26,520 --> 00:28:29,480 Speaker 1: then the framing is off, or I caught my dog 480 00:28:29,680 --> 00:28:32,119 Speaker 1: just as he was looking the other way. You know, 481 00:28:32,240 --> 00:28:35,240 Speaker 1: just after he was looking at me, or the ding 482 00:28:35,400 --> 00:28:38,720 Speaker 1: dang dern leash is ruining everything and it's in the 483 00:28:38,760 --> 00:28:41,880 Speaker 1: way of photos or video that I'm trying to take. Now, 484 00:28:41,920 --> 00:28:44,240 Speaker 1: some of that can be fixed with AI enhanced photo 485 00:28:44,320 --> 00:28:47,800 Speaker 1: taking tools. For example, imagine that you open up your 486 00:28:47,840 --> 00:28:50,920 Speaker 1: camera app and you go to take a photo of 487 00:28:50,960 --> 00:28:52,680 Speaker 1: your pet, and you call it to your pet and 488 00:28:52,720 --> 00:28:55,200 Speaker 1: you're trying to get its attention, and it looks around 489 00:28:55,680 --> 00:28:59,320 Speaker 1: and briefly as it's looking around at glances at you 490 00:28:59,400 --> 00:29:03,200 Speaker 1: before it bounds off to another pet adventure or whatever. 491 00:29:03,640 --> 00:29:05,720 Speaker 1: One cool feature I want to play with in the 492 00:29:05,760 --> 00:29:09,480 Speaker 1: future is one I saw at a Snapdragon presentation recently 493 00:29:09,560 --> 00:29:12,560 Speaker 1: for the Snapdragon eight Elite processor, and it's a tool 494 00:29:12,760 --> 00:29:15,680 Speaker 1: that will snap a picture when your pet is actually 495 00:29:16,000 --> 00:29:19,480 Speaker 1: looking at you, so you get that great eye contact. 496 00:29:19,720 --> 00:29:22,800 Speaker 1: It does like a burst photo mode where it'll take 497 00:29:22,840 --> 00:29:26,200 Speaker 1: a bunch of pictures, it will select the best one, 498 00:29:26,520 --> 00:29:30,000 Speaker 1: and it'll even do a little AI enhancement for fur management, 499 00:29:30,040 --> 00:29:35,000 Speaker 1: which again I love. Or imagine using it to capture 500 00:29:35,040 --> 00:29:37,640 Speaker 1: the perfect moment as your dog is catching a frisbee 501 00:29:37,720 --> 00:29:39,840 Speaker 1: or your cat is leaping in the air to play 502 00:29:39,880 --> 00:29:41,680 Speaker 1: with a toy. You don't have to count on your 503 00:29:41,680 --> 00:29:44,600 Speaker 1: own reflexes to snap the photo. I really like that, 504 00:29:44,920 --> 00:29:46,640 Speaker 1: and I look forward to getting a phone that can 505 00:29:46,720 --> 00:29:49,720 Speaker 1: actually do this in the future. For now, I guess 506 00:29:49,760 --> 00:29:52,120 Speaker 1: I'll continue fumbling, but I know something is better right 507 00:29:52,160 --> 00:29:55,600 Speaker 1: around the corner now. With photo editing, I like having 508 00:29:55,640 --> 00:29:58,640 Speaker 1: options to do things like remove objects from the frame 509 00:29:58,720 --> 00:30:03,200 Speaker 1: of photos and video like that darn leash. It's not 510 00:30:03,520 --> 00:30:06,840 Speaker 1: altering the photo in a fundamental way. It's just removing 511 00:30:07,000 --> 00:30:09,480 Speaker 1: something that I considered to be a distraction. Now that's 512 00:30:09,480 --> 00:30:13,520 Speaker 1: something that I potentially could do myself with photo editing 513 00:30:13,560 --> 00:30:16,760 Speaker 1: tools if I developed the skill set to do it. 514 00:30:17,080 --> 00:30:19,320 Speaker 1: But it's not something I could do right now. I 515 00:30:19,320 --> 00:30:22,440 Speaker 1: mean I could try, but it would look terrible. You'd 516 00:30:22,440 --> 00:30:25,560 Speaker 1: say something like, well, yeah, you got rid of the leash, 517 00:30:25,600 --> 00:30:28,880 Speaker 1: but what the heck is this band of blurry pixels 518 00:30:28,920 --> 00:30:31,520 Speaker 1: doing throughout your photo because I would have done a 519 00:30:31,560 --> 00:30:34,880 Speaker 1: bad job. With tools like a video object eraser, I 520 00:30:34,920 --> 00:30:38,360 Speaker 1: could do this and have it automatically remove the leash 521 00:30:38,400 --> 00:30:42,040 Speaker 1: even with videos, all with the processing that's happening native 522 00:30:42,520 --> 00:30:45,840 Speaker 1: to the device I'm using now. To be clear, these capabilities, again, 523 00:30:45,880 --> 00:30:48,200 Speaker 1: they've been around for a bit, but they have almost 524 00:30:48,240 --> 00:30:53,600 Speaker 1: always relied upon cloud processing, and that slows everything down, 525 00:30:53,840 --> 00:30:55,840 Speaker 1: and that means fewer people are going to use it 526 00:30:55,880 --> 00:30:58,440 Speaker 1: and be able to take advantage of it, moving that 527 00:30:58,600 --> 00:31:02,200 Speaker 1: compute power to the actual device. By having these AI 528 00:31:02,320 --> 00:31:06,040 Speaker 1: enabled processors, it not only speeds things up, but again 529 00:31:06,080 --> 00:31:08,440 Speaker 1: it means you're not sending your data up to some 530 00:31:08,640 --> 00:31:12,560 Speaker 1: server farms somewhere in the process. Tools like co Creator 531 00:31:12,680 --> 00:31:14,920 Speaker 1: end up giving me options that I would otherwise be 532 00:31:15,040 --> 00:31:18,040 Speaker 1: too intimidated to try on my own because I'd be 533 00:31:18,120 --> 00:31:21,400 Speaker 1: worried I'd just ruin the photo. One application I haven't 534 00:31:21,440 --> 00:31:23,440 Speaker 1: had a chance to play with yet, but I'm really 535 00:31:23,440 --> 00:31:28,440 Speaker 1: interested in is AI enhanced Digital Audio workstation programs or 536 00:31:28,480 --> 00:31:32,320 Speaker 1: apps if you prefer. I'm old, so for me, everything's programs, 537 00:31:32,480 --> 00:31:36,480 Speaker 1: but I recognize that the terminology these days really tends 538 00:31:36,520 --> 00:31:40,560 Speaker 1: to be apps. So during the lockdown era of the pandemic, 539 00:31:40,800 --> 00:31:44,280 Speaker 1: which really wasn't that long ago but feels like a lifetime, I, 540 00:31:44,440 --> 00:31:46,560 Speaker 1: like a lot of other people, picked up a new hobby, 541 00:31:46,720 --> 00:31:49,920 Speaker 1: and for me it was learning guitar. Also side note, 542 00:31:49,960 --> 00:31:53,080 Speaker 1: it's true what they say buying your first guitar ends 543 00:31:53,120 --> 00:31:56,880 Speaker 1: up being a gateway. Because now I own three electric guitars, 544 00:31:57,160 --> 00:32:02,360 Speaker 1: one acoustic guitar, one electric bass, and two cigar box guitars. 545 00:32:02,880 --> 00:32:05,160 Speaker 1: I do have a problem, and I'm not even gonna 546 00:32:05,160 --> 00:32:08,520 Speaker 1: talk to you about the ukuleles anyway. While my guitar 547 00:32:08,560 --> 00:32:11,880 Speaker 1: collection has been growing, one thing I haven't really explored 548 00:32:12,000 --> 00:32:16,280 Speaker 1: are things like effects pedals. I have one effects pedal 549 00:32:16,320 --> 00:32:19,160 Speaker 1: and I haven't really played with it that much, but 550 00:32:19,240 --> 00:32:23,000 Speaker 1: I love hearing the output of different effects pedals when 551 00:32:23,040 --> 00:32:27,880 Speaker 1: I watch videos online. But like photo editing, I don't 552 00:32:28,040 --> 00:32:32,240 Speaker 1: really have any experience with using these kind of pedals, 553 00:32:32,280 --> 00:32:36,320 Speaker 1: and I find it intimidating to even dive into that world. 554 00:32:36,360 --> 00:32:38,600 Speaker 1: I'm worried that I would just buy something that wouldn't 555 00:32:38,640 --> 00:32:41,280 Speaker 1: really work for what I was trying to achieve. Well, 556 00:32:41,440 --> 00:32:45,960 Speaker 1: Snapdragon and Microsoft have been working to create low latency 557 00:32:46,120 --> 00:32:51,360 Speaker 1: AZEO ASIO that actually stands for audio stream input output 558 00:32:51,520 --> 00:32:54,959 Speaker 1: drivers for musicians. All Right, So this is gonna get 559 00:32:55,000 --> 00:32:58,240 Speaker 1: really nerdy from both a technical and a musical side. 560 00:32:58,360 --> 00:33:01,600 Speaker 1: So pardon me as I geek out about this because 561 00:33:01,640 --> 00:33:04,960 Speaker 1: it's the convergence of two worlds that I love very much. 562 00:33:05,240 --> 00:33:09,719 Speaker 1: So there are USB audio interface devices that are already 563 00:33:09,760 --> 00:33:13,360 Speaker 1: out there on the market, And what these devices do 564 00:33:13,560 --> 00:33:18,800 Speaker 1: is they accept inputs from stuff like musical instruments or microphones. 565 00:33:19,000 --> 00:33:22,200 Speaker 1: So you plug your instrument or your microphone into this 566 00:33:22,360 --> 00:33:25,840 Speaker 1: audio device. Then you connect the audio device to a 567 00:33:25,920 --> 00:33:29,640 Speaker 1: computer using a USB port in this particular case, and 568 00:33:29,680 --> 00:33:33,680 Speaker 1: that lets you capture or manipulate the audio coming from 569 00:33:33,720 --> 00:33:37,880 Speaker 1: your device directly into your computer. Now, essentially it's a 570 00:33:37,920 --> 00:33:41,400 Speaker 1: way to set up a recording studio that's pretty darn portable, 571 00:33:41,760 --> 00:33:45,960 Speaker 1: whether you're doing music or podcasting or whatever. Now any 572 00:33:46,200 --> 00:33:50,240 Speaker 1: olden days, which honestly that wasn't that long ago, to 573 00:33:50,280 --> 00:33:53,960 Speaker 1: get the most out of an ASIO interface, you had 574 00:33:54,000 --> 00:33:59,320 Speaker 1: to specialize, so they were largely device specific interfaces, and 575 00:33:59,360 --> 00:34:03,080 Speaker 1: this was to optimize for the purposes of capturing audio. 576 00:34:03,480 --> 00:34:08,120 Speaker 1: So you would have to have multiple ASIO interfaces if 577 00:34:08,160 --> 00:34:10,880 Speaker 1: you wanted to work with different types of instruments and 578 00:34:11,320 --> 00:34:15,560 Speaker 1: microphones and such. A general purpose USB audio interface just 579 00:34:15,680 --> 00:34:19,120 Speaker 1: wasn't realistic for a long time because you would see 580 00:34:19,120 --> 00:34:22,920 Speaker 1: a decline in performance or you would have latency issues, 581 00:34:22,920 --> 00:34:25,120 Speaker 1: both of which are not good news. Like, if you 582 00:34:25,120 --> 00:34:28,239 Speaker 1: have latency problems, I can't express to you how hard 583 00:34:28,280 --> 00:34:31,120 Speaker 1: it is to cope for that. Because if you're playing 584 00:34:31,160 --> 00:34:34,920 Speaker 1: something and you're hearing what you're playing after you're actually 585 00:34:34,960 --> 00:34:37,360 Speaker 1: strumming the string and you're moving on to the next chord. 586 00:34:37,719 --> 00:34:42,000 Speaker 1: So what Snapdragon and Microsoft, along with Yamaha have done 587 00:34:42,600 --> 00:34:46,400 Speaker 1: is create a driver that leverages the Snapdragon processing power 588 00:34:46,440 --> 00:34:49,759 Speaker 1: to provide high quality and low latency capture support. With 589 00:34:49,840 --> 00:34:55,359 Speaker 1: the appropriate DAW program. DAW stands for Digital Audio Workspace, 590 00:34:56,160 --> 00:34:59,280 Speaker 1: you could play guitar directly into your PC for capture, 591 00:34:59,560 --> 00:35:02,239 Speaker 1: or use tools to create all sorts of effects that 592 00:35:02,360 --> 00:35:05,280 Speaker 1: you might otherwise only manage. If you had an entire 593 00:35:05,440 --> 00:35:08,480 Speaker 1: panel of pedals at your disposal. You could even create 594 00:35:08,520 --> 00:35:11,759 Speaker 1: the effects of different types of amps. So maybe there's 595 00:35:11,800 --> 00:35:14,319 Speaker 1: a specific kind of amp that gives an output that 596 00:35:14,400 --> 00:35:18,239 Speaker 1: you really want, Well, there are digital Audio workstation programs 597 00:35:18,239 --> 00:35:22,279 Speaker 1: out there that can simulate those amps as well as 598 00:35:22,680 --> 00:35:25,719 Speaker 1: various pedals. Obviously, the features that you have access to 599 00:35:25,760 --> 00:35:29,120 Speaker 1: are going to depend entirely upon which DAW program you're 600 00:35:29,239 --> 00:35:31,319 Speaker 1: actually using. But the point I'm trying to make is 601 00:35:31,320 --> 00:35:34,960 Speaker 1: that this technology enables that kind of feature, that processing 602 00:35:35,040 --> 00:35:39,719 Speaker 1: power where it does cut down on latency while ensuring 603 00:35:39,920 --> 00:35:44,279 Speaker 1: high fidelity audio quality. That's what makes it possible. In fact, 604 00:35:44,520 --> 00:35:46,359 Speaker 1: really that's the key to my whole point of view 605 00:35:46,440 --> 00:35:50,360 Speaker 1: about the AI enabled processors. They provide opportunities for developers 606 00:35:50,360 --> 00:35:54,160 Speaker 1: to tap into incredible processing power in order to achieve 607 00:35:54,360 --> 00:35:57,399 Speaker 1: unprecedented results. And it's hard to talk about what these 608 00:35:57,440 --> 00:36:00,160 Speaker 1: apps will be able to do because anything I say 609 00:36:00,200 --> 00:36:02,320 Speaker 1: is likely to not even come close to what people 610 00:36:02,400 --> 00:36:05,880 Speaker 1: already have in mind. Another program I learned about that 611 00:36:06,080 --> 00:36:09,759 Speaker 1: haven't used yet but I am eager to is one 612 00:36:09,760 --> 00:36:12,960 Speaker 1: that reminds me of the video object removal tools I 613 00:36:13,000 --> 00:36:16,160 Speaker 1: mentioned earlier, except you could do it for audio. So 614 00:36:16,719 --> 00:36:19,840 Speaker 1: remember how I was talking about how an AI enhanced 615 00:36:19,880 --> 00:36:24,360 Speaker 1: video editing tool could potentially remove unwanted elements from a video. 616 00:36:24,680 --> 00:36:28,319 Speaker 1: Let's say you had a shot of acute video of 617 00:36:28,360 --> 00:36:31,960 Speaker 1: your dog barking at Halloween decorations. But let's say the 618 00:36:32,040 --> 00:36:35,040 Speaker 1: video also has these annoying homeowners in the background that 619 00:36:35,080 --> 00:36:38,080 Speaker 1: are just scowling at your dog. Not that I'm speaking 620 00:36:38,160 --> 00:36:43,000 Speaker 1: from actual personal experience that I had not very long ago. Well, 621 00:36:43,080 --> 00:36:46,480 Speaker 1: with the AI enhanced video editing capabilities I had talked about, 622 00:36:46,520 --> 00:36:49,359 Speaker 1: you could just remove those sourpusses and focus on how 623 00:36:49,360 --> 00:36:53,360 Speaker 1: adorable your dog was. But now imagined something similar except 624 00:36:53,600 --> 00:36:58,080 Speaker 1: for audio tracks. So let's say you've got a song file, 625 00:36:58,239 --> 00:37:01,319 Speaker 1: but maybe the baseline just isn't doing it for you. 626 00:37:01,680 --> 00:37:05,560 Speaker 1: So you use a tool it's called DJ neural Mix, 627 00:37:05,880 --> 00:37:09,480 Speaker 1: and you identify and remove the baseline and it's just gone. 628 00:37:09,680 --> 00:37:13,239 Speaker 1: Like everything else is untouched, but the baseline is gone. Now. 629 00:37:13,280 --> 00:37:15,680 Speaker 1: Typically to do this you would need access to like 630 00:37:15,840 --> 00:37:18,640 Speaker 1: master recordings in order to be able to remove a 631 00:37:18,680 --> 00:37:21,640 Speaker 1: specific track, right, Like the bass would be recorded to 632 00:37:21,920 --> 00:37:24,640 Speaker 1: one track and you would just bring that track down. 633 00:37:25,120 --> 00:37:28,080 Speaker 1: But usually you don't have access to the master recordings. 634 00:37:28,719 --> 00:37:32,440 Speaker 1: Usually you get a mixed file, right, it's already been 635 00:37:32,440 --> 00:37:36,359 Speaker 1: mixed together, and it's not like you can easily unmix it. 636 00:37:36,520 --> 00:37:40,320 Speaker 1: Typically not without the power of AI anyway. But with AI, 637 00:37:40,800 --> 00:37:44,879 Speaker 1: the DJ Neural Mixed tool can isolate, say the baseline 638 00:37:44,920 --> 00:37:47,000 Speaker 1: and separate it from the rest, and you could do 639 00:37:47,040 --> 00:37:48,840 Speaker 1: that with anything. It wouldn't just be the baseline. You 640 00:37:48,840 --> 00:37:51,320 Speaker 1: could do it with the vocals or the drums, whatever 641 00:37:51,360 --> 00:37:53,360 Speaker 1: it might be, which means you could also use this 642 00:37:53,400 --> 00:37:57,239 Speaker 1: tool to do what DJs do, namely remix music and 643 00:37:57,320 --> 00:38:01,040 Speaker 1: create new works. It's at the very heart of the 644 00:38:01,120 --> 00:38:06,560 Speaker 1: transformative nature that is DJing. That's a powerful capability, and 645 00:38:06,600 --> 00:38:08,759 Speaker 1: again that's one that would be really hard to do 646 00:38:08,840 --> 00:38:12,560 Speaker 1: without the AI component, or you know, access to those 647 00:38:12,560 --> 00:38:15,319 Speaker 1: master recordings of somehow you have the magic keys, so 648 00:38:15,400 --> 00:38:17,960 Speaker 1: it gives DJs a lot of freedom to experiment with 649 00:38:18,000 --> 00:38:20,719 Speaker 1: different mixes. So maybe you think the drum track from 650 00:38:20,760 --> 00:38:24,080 Speaker 1: one song would actually sound amazing against the guitars and 651 00:38:24,160 --> 00:38:26,759 Speaker 1: vocals of a totally different song. Well, you could use 652 00:38:26,760 --> 00:38:29,200 Speaker 1: a tool like this one to isolate all those components 653 00:38:29,360 --> 00:38:31,920 Speaker 1: and then remix them together and maybe you would end 654 00:38:32,000 --> 00:38:34,319 Speaker 1: up with a really awesome groove, or maybe it would 655 00:38:34,320 --> 00:38:35,759 Speaker 1: be a big mess and you need to go back 656 00:38:35,760 --> 00:38:37,640 Speaker 1: to the drawing board. With me, it's more likely to 657 00:38:37,680 --> 00:38:40,040 Speaker 1: be the second one. But you know, you get the idea. 658 00:38:40,160 --> 00:38:42,640 Speaker 1: There's so much more I could cover here. The Yoga 659 00:38:42,680 --> 00:38:46,680 Speaker 1: seven X laptop experience I had was impressive, But the 660 00:38:46,719 --> 00:38:49,440 Speaker 1: crazy thing is I see it as just the starting point. 661 00:38:49,680 --> 00:38:51,880 Speaker 1: I think the real aha moment for a lot of 662 00:38:51,880 --> 00:38:54,439 Speaker 1: people out there will hit when they get a chance 663 00:38:54,480 --> 00:38:58,600 Speaker 1: to see how AI enabled devices will enhance what they 664 00:38:58,640 --> 00:39:01,920 Speaker 1: are already doing, whether that's work or planning out a 665 00:39:02,000 --> 00:39:06,480 Speaker 1: vacation or editing and organizing photos and videos. Or accessing 666 00:39:06,520 --> 00:39:09,600 Speaker 1: a new generation of apps that function unlike anything we've 667 00:39:09,640 --> 00:39:13,279 Speaker 1: experienced before. The creativity is still going to originate with 668 00:39:13,360 --> 00:39:16,279 Speaker 1: the person who's behind the keyboard. That's where the heart 669 00:39:16,320 --> 00:39:19,080 Speaker 1: of all of this comes from, is the person. I 670 00:39:19,120 --> 00:39:21,600 Speaker 1: still firmly believe that AI is never going to be 671 00:39:21,640 --> 00:39:25,880 Speaker 1: a replacement for human ingenuity. The genius resides in you, 672 00:39:26,480 --> 00:39:29,080 Speaker 1: the user. But I do think that AI can help 673 00:39:29,120 --> 00:39:32,520 Speaker 1: each person unlock options that otherwise would just remain out 674 00:39:32,600 --> 00:39:35,879 Speaker 1: of reach, and to me, that's the most exciting thing. 675 00:39:36,280 --> 00:39:38,640 Speaker 1: So yeah, I guess I'm saying my experience with the 676 00:39:38,680 --> 00:39:42,280 Speaker 1: Snapdragon ex Elite processor and the Yoga seven X laptop 677 00:39:42,800 --> 00:39:45,960 Speaker 1: really impressed me, and I can't wait to see what's next. 678 00:39:46,560 --> 00:39:49,520 Speaker 1: That's it for this episode of tech Stuff. I hope 679 00:39:49,640 --> 00:39:51,880 Speaker 1: all of you are well, and I'll talk to you 680 00:39:51,960 --> 00:40:02,879 Speaker 1: again really soon. Stuff is an iHeartRadio production. For more 681 00:40:02,960 --> 00:40:07,680 Speaker 1: podcasts from iHeartRadio, visit the iHeartRadio app, Apple Podcasts, or 682 00:40:07,719 --> 00:40:09,640 Speaker 1: wherever you listen to your favorite shows.