Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. Be there
Speaker 1: and welcome to tech Stuff. I'm your host, Jonathan Strickland.
Speaker 1: I'm an executive producer with iHeart Podcasts. And how the
Speaker 1: tech are you now, y'all? I think I have made
Speaker 1: my thoughts pretty clear when it comes to artificial intelligence.
Speaker 1: For one thing, AI is a very broad discipline. It's huge.
Speaker 1: It's way more than just generative AI, which is a
Speaker 1: topic I feel very strongly about, and it's also the
Speaker 1: one that dominates the news cycle. But it's just one
Speaker 1: aspect of artificial intelligence. And I feel AI in general
Speaker 1: has incredible potential to augment our computing tasks if we
Speaker 1: implement it properly. Well. Recently I got the chance to
Speaker 1: work with an AI laptop and really get to grips
Speaker 1: with what that potential can be, and I'm convinced we're
Speaker 1: on another precipice, one that will transform how we interact
Speaker 1: with computing devices. Now. First, the AI powered device I
Speaker 1: used was a Lenovo Yoga Slim seven X laptop with
Speaker 1: a Snapdragon x Elite processor. It's a Copilot plus PC,
Speaker 1: which means it features Microsoft's AI Assistant. It's also got
Speaker 1: an OLED screen, and it's no joke. That's the prettiest
Speaker 1: laptop screen I have ever used. The contrast on that
Speaker 1: thing is crazy, it's amazing. It's so beautiful. Now, Snapdragon
Speaker 1: was generous in sending me this laptop so that I
Speaker 1: could actually get some hands on time with it. And
Speaker 1: I'll be talking a lot about the work Snapdragon has
Speaker 1: done to make the processor really special, but I'm saving
Speaker 1: that for a bit later. Now, I do have to
Speaker 1: say that this laptop would immediately top my holiday wish.
Speaker 1: And I'm not just saying that I could be if
Speaker 1: I had no scruples, but that's not who I am, y'all.
Speaker 1: It's legit how I feel this laptops. It's really light,
Speaker 1: it's powerful, The screen, as I mentioned, is absolutely gorgeous,
Speaker 1: but the battery life is also really impressive, particularly when
Speaker 1: you consider the power lifting this thing has to do.
Speaker 1: Even when it's running AI Enhanced applications, which y'all know AI,
Speaker 1: it requires a lot of processing power, but this laptop
Speaker 1: continued to run smoothly and I didn't have to go
Speaker 1: dashing from outlet to outlet just to keep it going.
Speaker 1: But I think the thing that really pulls me to
Speaker 1: this laptop is the fact that I can see AI
Speaker 1: enabled processors as being the tech platform of the future.
Speaker 1: So it's my belief that just as smartphones revolutionized how
Speaker 1: we interact with computing resources, you know, like apps as
Speaker 1: well as the Internet, AI is going to do the
Speaker 1: same thing. Now. Before the era of the consumer smartphone,
Speaker 1: there were very few people who were predicting a move
Speaker 1: to mobile. Now, there were a few smarty pants is
Speaker 1: out there who were ahead of the curve, but most
Speaker 1: of us didn't see it coming. Once smartphones proved their
Speaker 1: merit in the consumer marketplace, we saw a pretty rapid
Speaker 1: transition to a mobile friendly landscape. You know, Web administrators
Speaker 1: were scrambling to make sure that their websites were optimized
Speaker 1: for mobile devices lest they potentially drive away visitors who
Speaker 1: were increasingly using their phones to access the Internet rather
Speaker 1: than say, laptop and desktop computers. Meanwhile, developers began designing
Speaker 1: apps that would leverage smartphone capabilities, you know, stuff like
Speaker 1: accelerometers and touchscreens and GPS sensors, that kind of thing. Well,
Speaker 1: I believe AI is going to do much the same.
Speaker 1: We're going to see a host of new programs and
Speaker 1: apps built with AI enhanced features and devices that are
Speaker 1: capable of providing onboard AI processing are going to be
Speaker 1: way ahead of the game, while also providing ways to
Speaker 1: handle AI processing in a more secure and private approach. See,
Speaker 1: there are different ways that you can handle AI processing.
Speaker 1: One way is you offload everything to a server farm somewhere,
Speaker 1: and we hear about these a lot in the news.
Speaker 1: You know, they're massive buildings, just filled with racks of
Speaker 1: servers processing enormous amounts of data, powering AI implementations all
Speaker 1: around the world, and no doubt that will continue, that
Speaker 1: will continue to be a thing. But a complementary approach
Speaker 1: involves on device and edge computing cases in which the
Speaker 1: gadgets that we actually have our hands on can do
Speaker 1: some or all of that processing on their own without
Speaker 1: connecting to some distant server. Now it all depends upon
Speaker 1: the application, of course, but with those types of implementations,
Speaker 1: you keep the AI computations on your device, and that
Speaker 1: means you're not sharing data with some server farm that
Speaker 1: smiles away. Everything stays low, and that is a huge
Speaker 1: thing when it comes to privacy and security. Let's say
Speaker 1: that you work for a really big media company, for example,
Speaker 1: I could be using myself in this example, and you
Speaker 1: want to make sure that the work you do stays
Speaker 1: local because you're working with some sensitive information, some of
Speaker 1: it might be proprietary. You don't want to be sending
Speaker 1: that off and have it become some kernel of information
Speaker 1: that gets enveloped in a larger database somewhere. That's risky stuff.
Speaker 1: So making sure you are able to do this kind
Speaker 1: of thing locally is important for a lot of people.
Speaker 1: All right, let's talk about the overview for this whole approach.
Speaker 1: We'll get into the hows and whys a little bit later,
Speaker 1: but first I want to talk about the ways I
Speaker 1: used that Lenovo Yoga seven X laptop. So I wanted
Speaker 1: to see how an AI enhanced device could potentially help
Speaker 1: me do my work. I mean, we talk a lot
Speaker 1: about artificial intelligence augmenting our ability. I wanted to actually
Speaker 1: put that into practice. So I use the laptop while
Speaker 1: I was researching a recent episode, one that actually has
Speaker 1: already published. So I used this laptop, the Yoga laptop,
Speaker 1: specifically for that episode, and I really wanted to put
Speaker 1: it through its paces and see if it had a
Speaker 1: meaningful impact on the way I do work. Now in
Speaker 1: this episode, I referenced an extremely long research paper that
Speaker 1: was written for the journal Science and Global Security, and
Speaker 1: it was an article by Jurgen Altmann. It's actually a
Speaker 1: fantastic paper. It was incredibly readable, which is not always
Speaker 1: the case for technical papers. If you've ever tried to
Speaker 1: read one, sometimes they come across as the most stilted
Speaker 1: term paper a teacher has ever had degrade, but not
Speaker 1: this one. It's also an accessible paper. You can find
Speaker 1: it online for free, so that's great as well. But
Speaker 1: it's very long, like it's seventy pages long. Now more
Speaker 1: than ten of those pages are just notes and references,
Speaker 1: but you still have, you know, fifty nine pages of
Speaker 1: pure content there. Now, I read the full article for
Speaker 1: my research, but I need to have access to the
Speaker 1: salient points without having to thumb through all seventy pages
Speaker 1: and taking notes as I read the article. That way,
Speaker 1: I wouldn't have to thumb through a seventy page article
Speaker 1: while writing the episode. But that's not easy to do.
Speaker 1: Once you get past like twenty pages, it gets pretty cumbersome.
Speaker 1: So I use the AI assistant on the laptop to
Speaker 1: create a summarized, bulleted list of the most important notes
Speaker 1: in the paper. For quick reference. Now I'm the cautious type,
Speaker 1: and while I was using this and I wanted to
Speaker 1: really check and make sure that it worked. I also
Speaker 1: wanted to verify that the notes that were produced were
Speaker 1: accurate to the original paper, that they weren't a misinterpretation
Speaker 1: or a summary that just wasn't accurate. And obviously that
Speaker 1: added more time for me. If I had not had
Speaker 1: to do that, I would have been through pretty quickly,
Speaker 1: but I needed to check. It's not just enough to
Speaker 1: have it create this list, and it really did show
Speaker 1: that the summary was accurate to the document I was using,
Speaker 1: and that it really was the most important points in
Speaker 1: the paper that were included the summary, and it was
Speaker 1: really easy to do. It was easy to navigate to
Speaker 1: the actual source for the bullet points so that I
Speaker 1: could verify that, in fact, the information was correct, and
Speaker 1: it really made organizing my thoughts much faster than it
Speaker 1: would have been if I hadn't been able to access
Speaker 1: this tool, because while I was taking more time to
Speaker 1: verify that the bullets were accurate representations of the information
Speaker 1: in the article, it did allow me to organize the
Speaker 1: whole approach for the episode. So typically when I organized
Speaker 1: an episode, I do that by feel, and some of
Speaker 1: you might be saying, yeah, we know it's obvious, But
Speaker 1: I've been podcasts for sixteen years. I have a sense
Speaker 1: of the flow I want for an episode. Now that
Speaker 1: doesn't always mean it's the best approach, but it is
Speaker 1: the one that just feels natural to me. However, in
Speaker 1: this case, it was really nice to consult a different perspective,
Speaker 1: even an artificial perspective, to figure out how best to
Speaker 1: structure the episode. So if there was an episode in
Speaker 1: the recent past that you listened to and you thought, wow,
Speaker 1: that's more coherent than what he normally produces, well now
Speaker 1: you know why. But keep it to yourself because words
Speaker 1: can hurt y'all. One thing I didn't do but I
Speaker 1: could have done, was use real time translation tools to
Speaker 1: access information that was presented in other languages. Once upon
Speaker 1: a time, I took courses in French and in German,
Speaker 1: but I never got to the point where I was conversational,
Speaker 1: let alone fluent in those languages. And of course, over
Speaker 1: the years my skills have atrophied. So I speak two languages,
Speaker 1: English and Bad English. But I am aware that there
Speaker 1: is a wealth of information and knowledge that's captured in
Speaker 1: other languages. While there's some pretty darn good translation apps
Speaker 1: for stuff like text, the cool thing about AI powered
Speaker 1: devices is that they have the potential and the processing
Speaker 1: capability to provide real time translation for other kinds of
Speaker 1: content like audio. So one thing I could have done
Speaker 1: was watch a video that had been recorded in another language,
Speaker 1: and through using onboard AI processing capabilities, been able to
Speaker 1: read English language captions that were translating what was being
Speaker 1: said in real time. Now that opens up entire worlds
Speaker 1: of expertise that otherwise would be very difficult for me
Speaker 1: to access. And I've always said that diversity is really important.
Speaker 1: It means you get multiple perspectives providing information, and you
Speaker 1: can view the world from different perspectives, including ones that
Speaker 1: you might not have ever even considered otherwise. Now, you
Speaker 1: might ultimately not agree with this other point of view,
Speaker 1: but being able to access it is important. Otherwise you
Speaker 1: just remain ignorant of it. So from a research perspective,
Speaker 1: real time translation is an enormous benefit, and I imagine
Speaker 1: we'll see this technology continue to evolve as well. Tools
Speaker 1: can be pretty good at doing things like translating word
Speaker 1: for word, but in future implementations. I imagine AI translation
Speaker 1: will also have to handle stuff like syntax and idioms
Speaker 1: really well, so that we don't just understand the actual
Speaker 1: words being spoken, but what the speaker means when they
Speaker 1: say those things. If someone uses like an idiom like
Speaker 1: a regional saying, or they're using really complex phrasing that
Speaker 1: doesn't easily translate to English, I can imagine future AI
Speaker 1: translation tools handling that and providing a relatable translation to
Speaker 1: avoid ambiguity, unless, of course, ambiguity was the intent in
Speaker 1: the first place. Sometimes it is another thing that I
Speaker 1: could have done at the end of the whole episode
Speaker 1: because I have used tools like this before, is use
Speaker 1: AI to generate show notes. So, y'all, Podcasting is a
Speaker 1: lot of work, particularly if you have a small team.
Speaker 1: In my case, the team is me and super producer Tari,
Speaker 1: who also works on other shows. There are a lot
Speaker 1: of steps in making a podcast. You know, you have
Speaker 1: pre prep. You've got prep, you've got research, you've got
Speaker 1: writing and recording and editing and publishing. One post production
Speaker 1: step that a lot of shows will skip is the
Speaker 1: production of show notes. So why do so many shows
Speaker 1: just skip show notes? Well, I can't speak for everyone,
Speaker 1: But in my case it comes down to being a
Speaker 1: mental block. When I finish an episode, after I've done
Speaker 1: speaking my amazing words into a microphone and then shipping
Speaker 1: off the file to my producer extraordinaire Tari, I'm ready
Speaker 1: to move on. My brain has effectively said, welp. That
Speaker 1: closes that chapter, dusts off its hands and whistles as
Speaker 1: a walks into the sunset. So it can be really
Speaker 1: hard to stop and reflect on what I just created
Speaker 1: and then distill that into useful notes for listeners. But
Speaker 1: AI tools can do that automatically. Of course, after creating
Speaker 1: the notes, then I would review them to make sure
Speaker 1: that again they accurately reflect the episode. But that's one
Speaker 1: step in the podcasting process that I would be happy
Speaker 1: to hand over to an AI enabled tool, as it
Speaker 1: is a step that I otherwise find really tedious and
Speaker 1: it actually discourages me from doing my job. You know,
Speaker 1: I will find any excuse. I will invent excuses to
Speaker 1: put off doing that kind of thing. Now, while all
Speaker 1: my research and writing and recording was going on, I
Speaker 1: also was using the assistant to keep me up to
Speaker 1: speed on my daily schedule. I'm somewhat notorious for missing
Speaker 1: things like important emails and meetings and that sort of thing.
Speaker 1: I have kind of made it an art formed to
Speaker 1: make it difficult to reach me because I find it
Speaker 1: creates an environment that allows me to focus right. I
Speaker 1: want to really focus on what I'm doing, and that
Speaker 1: means I need to filter out distractions because otherwise I
Speaker 1: will stop whatever it is I'm working on, and that
Speaker 1: just ruins my whole flow. Getting back into that is
Speaker 1: hard to do. I found using the AI assistant to
Speaker 1: help block out my time so that I had specific
Speaker 1: blocks of time where I was doing specific activities made
Speaker 1: me overall more efficient and effective. I actually one of
Speaker 1: the In fact, the very first thing I asked my
Speaker 1: AI assistant to do was to help me create a
Speaker 1: working schedule and it did, and it even built in
Speaker 1: things like breaks and stuff, and I followed it. I
Speaker 1: was like, this is a real experiment. I am going
Speaker 1: to follow the schedule that's been created for me, and
Speaker 1: I found that it was incredibly helpful. It really added
Speaker 1: structure to my day, something that I haven't had a
Speaker 1: lot of because I work remotely and mostly on my own,
Speaker 1: so structure is something that I have to create and
Speaker 1: I'm not great at doing that. So using this tool
Speaker 1: to help me to augment my abilities and to take
Speaker 1: on a workload that I otherwise would find difficult to do,
Speaker 1: that was incredibly helpful and it was a really nice change.
Speaker 1: Now I can envision other uses of AI as well,
Speaker 1: though I didn't use them for that particular episode. So
Speaker 1: for example creating audiograms, and I can already use AI
Speaker 1: to do this. I have used AI tools to do this,
Speaker 1: but they were cloud based. And what I'm talking about
Speaker 1: here is using AI to identify interesting passages in an episode,
Speaker 1: like a section that's really compelling. And you could do
Speaker 1: this in different ways, Like you could use AI to
Speaker 1: analyze the written work that you create, like in my case,
Speaker 1: I write out episodes, right, so I could actually use
Speaker 1: AI to analyze what I've written and to identify, oh,
Speaker 1: this is a particularly compelling section. Or you can use
Speaker 1: it to analyze the recorded audio. Because I also go
Speaker 1: off book a lot. I don't just have a script
Speaker 1: that I read. I extemporize like crazy. If you read
Speaker 1: what I wrote and compared it to what I say,
Speaker 1: you would notice that there are a lot of departures.
Speaker 1: So using AI, I could analyze the recorded program and
Speaker 1: create audiograms, which are those excerpts you sometimes come across
Speaker 1: on various social platforms. These are ones that play not
Speaker 1: just an audio clip from a show. Typically they'll also
Speaker 1: include stuff like real time captions that will help emphasize
Speaker 1: the point being expressed. I've had some experience using AI
Speaker 1: to generate these, and that includes matching text to spoken
Speaker 1: words automatically, so that you don't have to do it
Speaker 1: on your own, like you don't have to create an
Speaker 1: animation or anything. It does it for you. Now, the
Speaker 1: tools I've used, they're not perfect, but really good. Ones
Speaker 1: typically include a pretty easy way to edit the text
Speaker 1: so that if you're like me, let's say you have
Speaker 1: a little bit of a dialect that occasionally creeps through
Speaker 1: your spoken words, then you can review and fix the
Speaker 1: little goofs that the transcription might make when you maybe
Speaker 1: get a little too southern or whatever. For the individual creator,
Speaker 1: these kinds of tools are phenomenally useful. They simplify the
Speaker 1: process of promoting your work, and they help creators make
Speaker 1: bite sized pieces of their output that are ideal for
Speaker 1: social platforms to promote and to send people back to
Speaker 1: a full episode. For example, now I have the luxury
Speaker 1: of working at a major media company, and so in
Speaker 1: certain situations, I can actually lean on other people to
Speaker 1: help me create these kinds of social assets. But even
Speaker 1: in my case, my resources have their limits. I mean,
Speaker 1: those departments are supporting tons of other shows, they may
Speaker 1: not have the capacity to work with me, and most
Speaker 1: other creators don't even have that kind of help at
Speaker 1: their disposal to start with. Being able to lean on
Speaker 1: AI powered tools can give a creator more opportunities to
Speaker 1: find their audience, to stand out in a crowded field. Okay,
Speaker 1: back to my personal experiences. One of the big bonuses
Speaker 1: of using this Yoga Slim seven X laptop is as
Speaker 1: the name suggests, the laptop is extremely portable. It is lightweight,
Speaker 1: it has an incredibly thin form factor, and if I
Speaker 1: felt myself getting restless while I was working in my office, literally,
Speaker 1: I could just know, save my progress, shut the laptop
Speaker 1: and carry it upstairs to the living room and then
Speaker 1: work on the couch. And my dog found that to
Speaker 1: be a fantastic change of pace. And while he's not
Speaker 1: quite as good at keeping me on task as the
Speaker 1: AI assistant is, I definitely appreciated the change of scenery.
Speaker 1: You know, We're gonna take a quick break to thank
Speaker 1: our sponsor, but we'll be right back. So let's talk
Speaker 1: about this processor for a moment, because that's really ultimately
Speaker 1: what makes this experience possible in the first place. So,
Speaker 1: first off, Snapdragon obviously has a very long history of
Speaker 1: developing processors for mobile platforms, and I believe that gives
Speaker 1: Snapdragon some distinct advantages because the engineers and designers are
Speaker 1: used to working within tight limitations. I'm talking about tight
Speaker 1: limitations when it comes to the actual form factor the space.
Speaker 1: You have to work in tight limitations on how much
Speaker 1: power you're going to have access to, how much heat
Speaker 1: you can generate because it is a mobile device and
Speaker 1: you're not going to have access to like massive fans
Speaker 1: or water cooling systems. You know, you still need to
Speaker 1: get all the processing power as well. So all of
Speaker 1: this sounds like it could be a bad thing, but
Speaker 1: in my experience, when you are set with tight limitations,
Speaker 1: it can really inspire innovation and creativity because you still
Speaker 1: have a goal that you're working toward, right, and then
Speaker 1: you just have to think, well, how do I achieve
Speaker 1: this goal? And if you've got those limitations, it means
Speaker 1: that certain avenues are just cut off, and you have
Speaker 1: to really focus on what is possible and then push
Speaker 1: the boundary as hard as you can. And how you
Speaker 1: create a processor that provides the compute power needed while
Speaker 1: maintaining battery life ends up becoming kind of this guiding principle.
Speaker 1: Mobile devices in particular need to conserve battery power, right.
Speaker 1: I mean, you don't want to have a smartphone that
Speaker 1: has three hours of useful life in it and then
Speaker 1: you need to recharge it. But you also need to
Speaker 1: make sure that it can actually handle the computational jobs
Speaker 1: being thrown at it or else everything's going to feel
Speaker 1: sluggish and that's not a good user experience either. So
Speaker 1: Snapdragon's approach has been to incorporate different kinds of processors
Speaker 1: all on a single chip. So you've got your CPU,
Speaker 1: so that's your central processing unit. I think we're all
Speaker 1: familiar with those. The microprocessor kind of acts like the
Speaker 1: brains of the operation. CPUs traditionally are very good at handling,
Speaker 1: you know, like sequential problems, ones that are consecutive problems
Speaker 1: where the solution to one calculation feeds directly into the next.
Speaker 1: But then you've got your GPU, your graphics processing unit. Again,
Speaker 1: I feel like most of us have a handle on
Speaker 1: these these days. I remember, I'm old enough to remember
Speaker 1: when GPUs didn't exist. They weren't a thing. You would
Speaker 1: occasionally get a graphics chip, but it wasn't called a GPU.
Speaker 1: That didn't happen until you get up into the nineties. Really,
Speaker 1: and initially these were built to, as the name suggests,
Speaker 1: handle graphics processing. But GPUs have really come into their
Speaker 1: own in recent years and have proven to be extremely
Speaker 1: powerful when handling parallel processing jobs. So those are computational
Speaker 1: problems that can split into different tasks that a processor
Speaker 1: can potentially handle concurrently rather than consecutive, so they solve
Speaker 1: parts of problems all at the same time. Now, that
Speaker 1: doesn't work for every type of computational problem, but for
Speaker 1: that particular subset, GPUs are pretty darn good. But the
Speaker 1: snap Dragon x Elite processors also incorporate an NPU, and
Speaker 1: that's a relatively new technology. The NPU is the neural
Speaker 1: processing unit, and that sounds a bit like science fiction,
Speaker 1: but in reality, it's a processor that's optimized to handle
Speaker 1: AI related workloads. So think of a highly specialized processor
Speaker 1: that is ideal. It is optimized for AI operations. It
Speaker 1: handles those kinds of operations that speeds that even powerful
Speaker 1: GPUs can't match because they weren't built to handle those
Speaker 1: kinds of problems. And a good in PU, a well
Speaker 1: designed in PU, can do this with incredible power efficiency.
Speaker 1: So an NPU at a very basic level has an
Speaker 1: architecture that is inspired by the network of neurons that
Speaker 1: you have in that old gray matter up in your noggin.
Speaker 1: The component of the processor is great for another subset
Speaker 1: of computational problems, the ones relating to AI. It doesn't
Speaker 1: replace the CPU, it doesn't replace the GPU. It enhances
Speaker 1: the capabilities of the processor as a whole. And I
Speaker 1: think of that as being the ideal use case of
Speaker 1: artificial intelligence in general. It's good for an enhancement, it's
Speaker 1: good for augmentation. Now, we have heard lots of stories
Speaker 1: about AI potentially replacing people, and in some cases not
Speaker 1: potentially actually leaders choosing AI to replace staff, and trust me,
Speaker 1: I know these aren't just stories, and I think that
Speaker 1: that is a very human problem, and specifically a human
Speaker 1: problem that originates at leadership levels at some organizations. But
Speaker 1: I think the real sweet spot for artificial intelligence isn't
Speaker 1: in replacing humans, and I think a lot of those
Speaker 1: companies are finding that out too. Instead, I think it's
Speaker 1: augmenting what people can do so that they can do
Speaker 1: the things they already do well even better. But they
Speaker 1: can also lean on AI to help them with tasks
Speaker 1: that they themselves find challenging. Maybe it's the stuff they
Speaker 1: don't do so well but still kind of part of
Speaker 1: their daily tasks. So let me give another example. I'm
Speaker 1: a writer and i'm a podcaster, but I am not
Speaker 1: a graphics designer. In fact, I find design to be
Speaker 1: almost impenetrable. I recognize great design when I see it,
Speaker 1: like I can see great design and say, wow, that's incredible,
Speaker 1: But if I were looking at a blank page, it's
Speaker 1: like a prison cell to me. I have fallen victim
Speaker 1: as well to the trap of reducing the work of
Speaker 1: real artists as just an element that they have something
Speaker 1: that I lack. Right, Like, there's some spark or gift
Speaker 1: that those people have and I don't have it. You're
Speaker 1: either born with it or you're not. That is harmfully reductive.
Speaker 1: And I've actually had a good friend of mine, a
Speaker 1: talented artist, take me aside to talk about this and
Speaker 1: set me straight. He was very direct but polite about it,
Speaker 1: and he described to me his experiences learning art and
Speaker 1: developing his craft and practicing his skills, and he explained
Speaker 1: that reducing art to some sort of almost mystical gift
Speaker 1: is an insult considering the countless hours he and other
Speaker 1: artists have poured into their work in order to get
Speaker 1: to where they are. That really struck me and made
Speaker 1: me look at what he did in a different way.
Speaker 1: But at the end of the day, I don't have
Speaker 1: those skills. Now. Potentially I could develop such skills if
Speaker 1: I gave the skills enough time and effort and practice
Speaker 1: to develop them. But let's be realistic. There are a
Speaker 1: limited number of hours in the day, and I have
Speaker 1: responsibilities that I have to meet. The likelihood that I
Speaker 1: can make the time to practice a new skill and
Speaker 1: reach a level of skill that would be considered professional,
Speaker 1: that's pretty love. Oh, I need help, but I don't
Speaker 1: have an assistant. I don't have a graphics department that
Speaker 1: specifically reports to me. I have one that I can
Speaker 1: share with everybody else, which means they don't always have
Speaker 1: availability for me. So what if I need to put
Speaker 1: together a presentation. Well, I could use a standard format
Speaker 1: in a presentation software package. That's kind of a dead giveaway,
Speaker 1: right if anyone has ever sat through a presentation and
Speaker 1: they said, oh, I recognize that layout immediately, like I
Speaker 1: know exactly which default layout you used. Plus, while I'm
Speaker 1: a decent writer, boiling things down into slides is not
Speaker 1: my strong suit. This is an area where an AI
Speaker 1: powered assist would be incredibly valuable to me. I would
Speaker 1: still be doing the work. Keep it that in mind.
Speaker 1: I'm not laying the work onto the AI. I've created
Speaker 1: all this work. Putting together the content of my presentation
Speaker 1: was the main part of the job. But the AI
Speaker 1: assistant can help me lay out a presentation and design
Speaker 1: it so that it looks great, flows well, and most importantly,
Speaker 1: that my key points are summarized in a way that
Speaker 1: is effective on the screen. No one wants to sit
Speaker 1: down to a presentation only to see a slide that
Speaker 1: looks like it's a dissertation. I'm sure you have all
Speaker 1: done that where you've gone in and one slide is
Speaker 1: just a wall of text that stinks. It's not good design,
Speaker 1: it's missing the point entirely. It's using the presentation for
Speaker 1: the wrong reason. And I think the key to using
Speaker 1: AI in an ethical way is all about boosting your
Speaker 1: own abilities, not fabricating something out of thin air. The
Speaker 1: art still has to come from the artist, The content
Speaker 1: still needs to come from the creator. The words need
Speaker 1: to come from the writer. The AI's job is to
Speaker 1: add some polish and to help organize thoughts and help
Speaker 1: the human make stuff that has the most powerful impact
Speaker 1: upon their intended audience. Now, on a personal side, I
Speaker 1: am starting to dip my toe into stuff like AI
Speaker 1: powered photo tools. So I like taking pictures of my
Speaker 1: aforementioned dog. His name is Timbolt, and he's a joy.
Speaker 1: But it could be something of a challenge to get
Speaker 1: a really good photo of Timbolt while I'm walking him,
Speaker 1: because he's always on a leash, which means I always
Speaker 1: have one hand holding the other end of that leash,
Speaker 1: and meanwhile I'm fumbling with my smartphone in an effort
Speaker 1: to take a photo of them. I can't tell you
Speaker 1: how many times I've taken a shot that I thought
Speaker 1: at the time it was gonna look really good, but
Speaker 1: then the framing is off, or I caught my dog
Speaker 1: just as he was looking the other way. You know,
Speaker 1: just after he was looking at me, or the ding
Speaker 1: dang dern leash is ruining everything and it's in the
Speaker 1: way of photos or video that I'm trying to take. Now,
Speaker 1: some of that can be fixed with AI enhanced photo
Speaker 1: taking tools. For example, imagine that you open up your
Speaker 1: camera app and you go to take a photo of
Speaker 1: your pet, and you call it to your pet and
Speaker 1: you're trying to get its attention, and it looks around
Speaker 1: and briefly as it's looking around at glances at you
Speaker 1: before it bounds off to another pet adventure or whatever.
Speaker 1: One cool feature I want to play with in the
Speaker 1: future is one I saw at a Snapdragon presentation recently
Speaker 1: for the Snapdragon eight Elite processor, and it's a tool
Speaker 1: that will snap a picture when your pet is actually
Speaker 1: looking at you, so you get that great eye contact.
Speaker 1: It does like a burst photo mode where it'll take
Speaker 1: a bunch of pictures, it will select the best one,
Speaker 1: and it'll even do a little AI enhancement for fur management,
Speaker 1: which again I love. Or imagine using it to capture
Speaker 1: the perfect moment as your dog is catching a frisbee
Speaker 1: or your cat is leaping in the air to play
Speaker 1: with a toy. You don't have to count on your
Speaker 1: own reflexes to snap the photo. I really like that,
Speaker 1: and I look forward to getting a phone that can
Speaker 1: actually do this in the future. For now, I guess
Speaker 1: I'll continue fumbling, but I know something is better right
Speaker 1: around the corner now. With photo editing, I like having
Speaker 1: options to do things like remove objects from the frame
Speaker 1: of photos and video like that darn leash. It's not
Speaker 1: altering the photo in a fundamental way. It's just removing
Speaker 1: something that I considered to be a distraction. Now that's
Speaker 1: something that I potentially could do myself with photo editing
Speaker 1: tools if I developed the skill set to do it.
Speaker 1: But it's not something I could do right now. I
Speaker 1: mean I could try, but it would look terrible. You'd
Speaker 1: say something like, well, yeah, you got rid of the leash,
Speaker 1: but what the heck is this band of blurry pixels
Speaker 1: doing throughout your photo because I would have done a
Speaker 1: bad job. With tools like a video object eraser, I
Speaker 1: could do this and have it automatically remove the leash
Speaker 1: even with videos, all with the processing that's happening native
Speaker 1: to the device I'm using now. To be clear, these capabilities, again,
Speaker 1: they've been around for a bit, but they have almost
Speaker 1: always relied upon cloud processing, and that slows everything down,
Speaker 1: and that means fewer people are going to use it
Speaker 1: and be able to take advantage of it, moving that
Speaker 1: compute power to the actual device. By having these AI
Speaker 1: enabled processors, it not only speeds things up, but again
Speaker 1: it means you're not sending your data up to some
Speaker 1: server farms somewhere in the process. Tools like co Creator
Speaker 1: end up giving me options that I would otherwise be
Speaker 1: too intimidated to try on my own because I'd be
Speaker 1: worried I'd just ruin the photo. One application I haven't
Speaker 1: had a chance to play with yet, but I'm really
Speaker 1: interested in is AI enhanced Digital Audio workstation programs or
Speaker 1: apps if you prefer. I'm old, so for me, everything's programs,
Speaker 1: but I recognize that the terminology these days really tends
Speaker 1: to be apps. So during the lockdown era of the pandemic,
Speaker 1: which really wasn't that long ago but feels like a lifetime, I,
Speaker 1: like a lot of other people, picked up a new hobby,
Speaker 1: and for me it was learning guitar. Also side note,
Speaker 1: it's true what they say buying your first guitar ends
Speaker 1: up being a gateway. Because now I own three electric guitars,
Speaker 1: one acoustic guitar, one electric bass, and two cigar box guitars.
Speaker 1: I do have a problem, and I'm not even gonna
Speaker 1: talk to you about the ukuleles anyway. While my guitar
Speaker 1: collection has been growing, one thing I haven't really explored
Speaker 1: are things like effects pedals. I have one effects pedal
Speaker 1: and I haven't really played with it that much, but
Speaker 1: I love hearing the output of different effects pedals when
Speaker 1: I watch videos online. But like photo editing, I don't
Speaker 1: really have any experience with using these kind of pedals,
Speaker 1: and I find it intimidating to even dive into that world.
Speaker 1: I'm worried that I would just buy something that wouldn't
Speaker 1: really work for what I was trying to achieve. Well,
Speaker 1: Snapdragon and Microsoft have been working to create low latency
Speaker 1: AZEO ASIO that actually stands for audio stream input output
Speaker 1: drivers for musicians. All Right, So this is gonna get
Speaker 1: really nerdy from both a technical and a musical side.
Speaker 1: So pardon me as I geek out about this because
Speaker 1: it's the convergence of two worlds that I love very much.
Speaker 1: So there are USB audio interface devices that are already
Speaker 1: out there on the market, And what these devices do
Speaker 1: is they accept inputs from stuff like musical instruments or microphones.
Speaker 1: So you plug your instrument or your microphone into this
Speaker 1: audio device. Then you connect the audio device to a
Speaker 1: computer using a USB port in this particular case, and
Speaker 1: that lets you capture or manipulate the audio coming from
Speaker 1: your device directly into your computer. Now, essentially it's a
Speaker 1: way to set up a recording studio that's pretty darn portable,
Speaker 1: whether you're doing music or podcasting or whatever. Now any
Speaker 1: olden days, which honestly that wasn't that long ago, to
Speaker 1: get the most out of an ASIO interface, you had
Speaker 1: to specialize, so they were largely device specific interfaces, and
Speaker 1: this was to optimize for the purposes of capturing audio.
Speaker 1: So you would have to have multiple ASIO interfaces if
Speaker 1: you wanted to work with different types of instruments and
Speaker 1: microphones and such. A general purpose USB audio interface just
Speaker 1: wasn't realistic for a long time because you would see
Speaker 1: a decline in performance or you would have latency issues,
Speaker 1: both of which are not good news. Like, if you
Speaker 1: have latency problems, I can't express to you how hard
Speaker 1: it is to cope for that. Because if you're playing
Speaker 1: something and you're hearing what you're playing after you're actually
Speaker 1: strumming the string and you're moving on to the next chord.
Speaker 1: So what Snapdragon and Microsoft, along with Yamaha have done
Speaker 1: is create a driver that leverages the Snapdragon processing power
Speaker 1: to provide high quality and low latency capture support. With
Speaker 1: the appropriate DAW program. DAW stands for Digital Audio Workspace,
Speaker 1: you could play guitar directly into your PC for capture,
Speaker 1: or use tools to create all sorts of effects that
Speaker 1: you might otherwise only manage. If you had an entire
Speaker 1: panel of pedals at your disposal. You could even create
Speaker 1: the effects of different types of amps. So maybe there's
Speaker 1: a specific kind of amp that gives an output that
Speaker 1: you really want, Well, there are digital Audio workstation programs
Speaker 1: out there that can simulate those amps as well as
Speaker 1: various pedals. Obviously, the features that you have access to
Speaker 1: are going to depend entirely upon which DAW program you're
Speaker 1: actually using. But the point I'm trying to make is
Speaker 1: that this technology enables that kind of feature, that processing
Speaker 1: power where it does cut down on latency while ensuring
Speaker 1: high fidelity audio quality. That's what makes it possible. In fact,
Speaker 1: really that's the key to my whole point of view
Speaker 1: about the AI enabled processors. They provide opportunities for developers
Speaker 1: to tap into incredible processing power in order to achieve
Speaker 1: unprecedented results. And it's hard to talk about what these
Speaker 1: apps will be able to do because anything I say
Speaker 1: is likely to not even come close to what people
Speaker 1: already have in mind. Another program I learned about that
Speaker 1: haven't used yet but I am eager to is one
Speaker 1: that reminds me of the video object removal tools I
Speaker 1: mentioned earlier, except you could do it for audio. So
Speaker 1: remember how I was talking about how an AI enhanced
Speaker 1: video editing tool could potentially remove unwanted elements from a video.
Speaker 1: Let's say you had a shot of acute video of
Speaker 1: your dog barking at Halloween decorations. But let's say the
Speaker 1: video also has these annoying homeowners in the background that
Speaker 1: are just scowling at your dog. Not that I'm speaking
Speaker 1: from actual personal experience that I had not very long ago. Well,
Speaker 1: with the AI enhanced video editing capabilities I had talked about,
Speaker 1: you could just remove those sourpusses and focus on how
Speaker 1: adorable your dog was. But now imagined something similar except
Speaker 1: for audio tracks. So let's say you've got a song file,
Speaker 1: but maybe the baseline just isn't doing it for you.
Speaker 1: So you use a tool it's called DJ neural Mix,
Speaker 1: and you identify and remove the baseline and it's just gone.
Speaker 1: Like everything else is untouched, but the baseline is gone. Now.
Speaker 1: Typically to do this you would need access to like
Speaker 1: master recordings in order to be able to remove a
Speaker 1: specific track, right, Like the bass would be recorded to
Speaker 1: one track and you would just bring that track down.
Speaker 1: But usually you don't have access to the master recordings.
Speaker 1: Usually you get a mixed file, right, it's already been
Speaker 1: mixed together, and it's not like you can easily unmix it.
Speaker 1: Typically not without the power of AI anyway. But with AI,
Speaker 1: the DJ Neural Mixed tool can isolate, say the baseline
Speaker 1: and separate it from the rest, and you could do
Speaker 1: that with anything. It wouldn't just be the baseline. You
Speaker 1: could do it with the vocals or the drums, whatever
Speaker 1: it might be, which means you could also use this
Speaker 1: tool to do what DJs do, namely remix music and
Speaker 1: create new works. It's at the very heart of the
Speaker 1: transformative nature that is DJing. That's a powerful capability, and
Speaker 1: again that's one that would be really hard to do
Speaker 1: without the AI component, or you know, access to those
Speaker 1: master recordings of somehow you have the magic keys, so
Speaker 1: it gives DJs a lot of freedom to experiment with
Speaker 1: different mixes. So maybe you think the drum track from
Speaker 1: one song would actually sound amazing against the guitars and
Speaker 1: vocals of a totally different song. Well, you could use
Speaker 1: a tool like this one to isolate all those components
Speaker 1: and then remix them together and maybe you would end
Speaker 1: up with a really awesome groove, or maybe it would
Speaker 1: be a big mess and you need to go back
Speaker 1: to the drawing board. With me, it's more likely to
Speaker 1: be the second one. But you know, you get the idea.
Speaker 1: There's so much more I could cover here. The Yoga
Speaker 1: seven X laptop experience I had was impressive, But the
Speaker 1: crazy thing is I see it as just the starting point.
Speaker 1: I think the real aha moment for a lot of
Speaker 1: people out there will hit when they get a chance
Speaker 1: to see how AI enabled devices will enhance what they
Speaker 1: are already doing, whether that's work or planning out a
Speaker 1: vacation or editing and organizing photos and videos. Or accessing
Speaker 1: a new generation of apps that function unlike anything we've
Speaker 1: experienced before. The creativity is still going to originate with
Speaker 1: the person who's behind the keyboard. That's where the heart
Speaker 1: of all of this comes from, is the person. I
Speaker 1: still firmly believe that AI is never going to be
Speaker 1: a replacement for human ingenuity. The genius resides in you,
Speaker 1: the user. But I do think that AI can help
Speaker 1: each person unlock options that otherwise would just remain out
Speaker 1: of reach, and to me, that's the most exciting thing.
Speaker 1: So yeah, I guess I'm saying my experience with the
Speaker 1: Snapdragon ex Elite processor and the Yoga seven X laptop
Speaker 1: really impressed me, and I can't wait to see what's next.
Speaker 1: That's it for this episode of tech Stuff. I hope
Speaker 1: all of you are well, and I'll talk to you
Speaker 1: again really soon. Stuff is an iHeartRadio production. For more
Speaker 1: podcasts from iHeartRadio, visit the iHeartRadio app, Apple Podcasts, or
Speaker 1: wherever you listen to your favorite shows.