June 18, 2024

AI, Audio, and Podcasting with Emma Chillekens - Ep 7

AI, Audio, and Podcasting with Emma Chillekens - Ep 7

In Episode 7 of the Spoken Life Show Podcast, Podcast Hall of Fame host Rob Greenlee engages in a captivating conversation with Emma Chillekens, a senior producer and former actor whose career spans the performing arts, radio journalism, and podcast...

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Season 1

In Episode 7 of the Spoken Life Show Podcast, Podcast Hall of Fame host Rob Greenlee engages in a captivating conversation with Emma Chillekens, a senior producer and former actor whose career spans the performing arts, radio journalism, and podcast production. Emma's journey began in the dramatic space, where she honed her skills as an actor, dancer, and singer. She later transitioned to public radio with ABC Australia, hosting shows for eight years, and eventually moved into the podcasting space, focusing on tech and artificial intelligence. Emma is a senior producer for "Shift," a podcast exploring AI and frontier technology. In this episode, Emma discusses her unique approach to audio storytelling, emphasizing the importance of rhythm, timing, and the intention behind lines. She shares insights into the challenges and opportunities AI presents in the creative industry, reflecting on its potential to augment human creativity while posing ethical dilemmas. The conversation also delves into Emma's experiences with public radio, her audience-centric approach to content creation, and her thoughts on the evolving podcasting landscape. Emma says this about herself: "I am a bit of a mythical creature when it comes to my work in the media industry. I started my full-time career in print media in Australia with Fairfax, and I've also worked in television and online and have reported for social media. I am passionate about audio and have hosted and produced live radio at ABC Radio Audio Presenter/Host and Producer) and podcasts for seven years. I've worked on projects with ProPublica, WNYC, SpareMin, NYU, WSJ, and MIT Tech Review. Most notably, I worked on ProPublica’s Peabody, Polk, and Goldsmith award-winning maternal mortality series and the short documentary series Finding Sanctuary, which won a New York Emmy. In 2019, I changed things up and worked at the intersection of product and editorial in a role leading editorial partnerships for Barron's on its audience team. In 2020, I co-created a podcast about artificial intelligence with Jennifer Strong called Machines We Trust." The Spoken Life Show is a podcast series that explores the impact of audio on personal and professional life. It features in-depth interviews with creators, artists, and industry experts. Hosted by Rob Greenlee, the show offers valuable insights into the world of audio storytelling and its future. You can listen to the Spoken Life Show on various podcast platforms, including Spotify, Apple Podcasts, and Google Podcasts. Don't miss Episode 7 for an enlightening discussion on the intersection of creativity, technology, and the power of audio. Connect with Emma Chillekens:
- Story Editor / Voice Coach
https://podcasts.apple.com/sa/podcast/shift/id1704924524
- In Machines, We Trust podcast, Co-creator, and Senior Producer
https://www.technologyreview.com/supertopic/in-machines-we-trust/
LinkedIn: https://www.linkedin.com/in/emmacillekens/ Connect with Rob Greenlee:
Website: https://robgreenlee.com
X: https://twitter.com/robgreenlee
Instagram: @robwgreenlee
YouTube: https://YouTube.com/@robgreenlee
Email: robgreenlee @ gmail.com Apple Podcasts - https://podcasts.apple.com/us/podcast/spoken-life-show-with-rob-greenlee/id650684607
Spotify Podcasts - https://open.spotify.com/show/5OUXQ6wLZKxKIibxOQFD8t

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The inspiration of spoken word tech and connection Spoken Life

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Spoken Well.

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Welcome back to Spoken Life Show with Rob Greenley and

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I have a very special episode of the show today

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and it's in the seventh episode of this series. I'm

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honored to be joined by Emma Silicons, who is a

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senior producer former actor whose career spans the performing arts,

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radio journalism, and podcast production out of the US as

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well as down in Australia. So I'm honored to have

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her with me. So let's get on with the show.

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I'm here with Emma Silicons. It's great to be here

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with you. And you're on a trip from Australia.

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Yes, I am working on to see your projects while

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I'm here and seeing a lot of people in the States.

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I used to live here for six and a half years,

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so okay, yeah, it's great to be back.

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So I do this show mainly to talk about creators

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that are doing interesting things with audio. Right this is

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the Spoken Live Show, so I like to cover what

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people's thoughts are about how important audio has been to

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their life experience and also to where each person sees

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it going and what is next for you. So I

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don't know if you want to start, just your perspective

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on why you got involved in audio or audio, Yeah,

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what's been your experience with it?

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I my introduction to audio, I think, in the first

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instance was as a actor. So I used to be

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an actor, dancer, singer back in the day. That was

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my first career I was. I used to be a fairy,

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and then I got into films and performing on stage,

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and that's how I grew up in the craft.

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And then I think audio was always something that was

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part of.

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That environment, right, Like when you're reading lines on stage

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as an actor, you're really immersed in the intention behind

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the line and like what that line means to the audience,

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and framing it in the mouth in a way that

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lands well to the person on the other side, and

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thinking about projection and all of those sort of things.

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And then in the film space.

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Kind of bringing that sound back a little bit, and

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you've got a camera right in front of you, so

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you're thinking about how to quiet and that sound down

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a little bit. So I think that was my initial

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introduction to audio, was in that environment. So a little

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bit different to how most people might onboard towards audio.

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They might start in radio or television or something like

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that in podcasting now these days, but for me, it

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was very much in the dramatic space and also in dance,

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So audio was the rhythm of the music, like five six, seven, eight,

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so you're keeping rhythm and time.

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And so now.

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When I'm involved with podcasting and radio, I'm always thinking

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about audio from those perspectives.

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I think a lot about rhythm and timing.

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I think a lot about delivery and intentions behind lines.

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And my career's kind of almost come full circle in

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that I've done so many different things since I was

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involved in that acting world. I ended up becoming a

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print journalist for a time and then worked in radio.

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So I was a host for about eight.

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Years and then moved to New York City ended up

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getting involved here. I went to NYU as my graduate school,

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worked at the Wall Street Journal, and got involved in

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tech and artificial intelligence podcast and I've been in that

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world ever since about twenty eighteen, and now I'm in

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the podcasting space. So I went from this public radio

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background with ABC Australia, hosting shows for eight years, news,

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current affairs, all of the things that most of us

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would know of public broadcasting, and now I'm in this

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space where it's career recorded audio rather than live audio,

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and so I work with as a producer and senior producer.

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So I've taken my career, as I mentioned, full circle

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where I think deeply about intentions behind lines again because

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I coach our host on her delivery and her performance.

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But I'm also thinking about the intention of complete piece

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that we're putting together because I work as a story editor,

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so I'm always thinking deeply about like, where are we

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coming from for this piece, what experience are we trying

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to give our audience, what journey do we want to

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take them on? And I think I draw a lot

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from my background as an actor, dancer and singer from

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my past to do my job today, which is as

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a scene of producer of Shift, which is a new

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podcast that's come out in the last six months. So

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looking specifically at artificial intelligence and frontier tech and it's

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impact on our lives.

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It kind of seems like the ar official intelligence aspect

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of what you've been doing is a little bit of

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a contrast to what you've actually been doing with your career,

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and is that the whole performance and creative element of

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how do you see AI? Do you think it's going

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to harm that or do you think that it's going

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to be able to add to it to get better?

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Big conversation, Oh, I think AI is very complicated when

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it comes to the creative space. Now we're seeing AI

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be able to generate its own music, be able to

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generate its own art, but I think one of the

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big problems in this space is that we're seeing it

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piggyback off the creative talent of artists and not necessarily

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credit those artists. So you'll notice mid journey and different

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types of artistic platforms, they'll pull from artists, and you'll

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even sometimes see the artist's signature on the work that

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is developed by AI, but there's no crediting that original

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artist for that work.

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So it's just pulling, as all AI does, pulling.

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From big data from a data set and putting that

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together in a different form and format. So AI is

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only as smart as the data that it's fed and

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it's only going to do what it's creator's talent to do.

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So until the creators start being more transparent with these

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black box algorithms and start actually building in fail safes

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to protect artists, we won't really see equity or fairness

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in this space. I think there's a lot of unfairness

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right now, especially towards creatives in this area. But I

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also think that AI can create beautiful things as well,

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and if it's used responsibly, can do amazing things.

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And there's an artist that I follow on Instagram.

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I can't think of their at handle at the moment,

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but they're an AI artist and they work with dancers

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and do this amazing kind of folding imagery that folds

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in on itself and follows the movement of a dancer.

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And so something of that beauty couldn't necessarily be created

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just to the naked eye. This is something that's like

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graphics and dance and art all rolled into one. And

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so that's an artist taking something and creating something new.

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So I think the creative space and AI is very complicated.

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I don't think AI is enough to eradicate.

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The human condition of what we're doing.

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We were just talking off air a little bit earlier,

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Rob about narrow intelligence versus wide intelligence, and how AI

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is very good at doing one specific thing and training

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on that thing and getting deeper and deeper on that

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particular thing, but not so good at when you take

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those blinders off and try to have it be more

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generally intelligent. That's something that is still a very complicated

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place for AI right now and somewhere that it's not

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even close to general intelligence.

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And I've seen some flaws in it here recently. I've

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seen some work that I've generated with.

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Have you seen the five fingers or six Normally normal

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normal fingers have five fingers, but it has a sixth

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finger that's popping out, or there's a third eye on

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the face, and you're like, the human would never make

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that mistake.

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Rod work that i've seen get generated that it's almost

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exactly the scene with multiple users, like I saw just today,

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I saw a post by a podcaster that posted an

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image that was part of his LinkedIn post okay that

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looked exactly like an image that I generated from chat GPT,

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just like maybe a month ago. Wow, So it does.

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I guess if the prompt is you're saying, then it

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should generate the same thing. But I didn't really give

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it that much of a prompt when I generate, I

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just gave it some really broad parameters.

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So we're now talking about.

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So it went out and created the same image, but

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it was my impression that chat GPT would never create

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the same image. Tie.

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I've only used it in the text format, so things

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like it'sprucing up cover letters for jobs and those type

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of things. A being quite generalized in your prompt around

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please write a professional cover letter for the audio field

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with this selection criteria. Please include this job description and

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please include my resume items here, or please include my

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bio and just like iterating on that and just plugging

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in a lot of data. I think AI is very

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good at taking a lot of data and sorting that out,

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but what you get on the other end may not

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necessarily be something that's cohesive.

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Yeah.

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Yeah, because it's increasingly starting to feel like one of

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the needs for a detailed prompt is that you don't

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want to give too much license to the AI, because.

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You know, you want to keep it very controlled.

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Because it will do what I just gave an example of,

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it'll create the same image. Right, So if you make

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a real simple prom.

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For the same cover letter, I'm sure there's now people

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reading a lot of cover letters that are very much

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in the same vein because they're being spat up by

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the same program.

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Right, Because the AI has to come up with a

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model of those very kind of outputs that are generated

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based on a very generic problem.

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I think it's good to use AI as inspiration, So

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use it as a kind of phone a friend fee

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like you might in the past have done a cover

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letter and send it to your friend and got them

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to spruce it up. Instead, you're going to chat GBT

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if you're using three or four or Gemini or some

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other AI platform, and you're now using that as your

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sort of inspiration and getting help, but not necessarily relying

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on it. I think even things like driverless cars, we

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thought that they would be a lot further than they are,

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or the promise was they'd be a lot further than

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they are today, and that we'd have all these fleets

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of driverless cars just driving people around, and we're not

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at that point yet. They're still, while very smart in

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some ways, very dumb in other ways. I mean, if

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a bird flies passed a vehicle, it's very quickly thinking

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that's a hazard and like putting on the brakes, or

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it's just it's very interesting, not all that reliable.

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Yes, I think the big takeaway for podcasters is that

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it's still early days for that stuff.

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And well even when you hear the AI editing. Have

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you heard in descript and different processes that you might

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listen to. It cuts off parts of words, So people

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are relying on something and not doing the craft, which

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is sitting down in audition or sitting down in pro

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tools and actually editing top to tail. But these AI

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softwares are cutting off actual words like humans, if they

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were editing, they wouldn't cut off a word, right, You

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wouldn't cut off part of a word. You wouldn't cut

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it that tight in your editing, whereas like AI will.

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Yeah, it can actually do that, right, especially if there's

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any kind of cross talk. I've seen that. Yeah, I

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used the descript tool as well, and I see these

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things come up on a regular basis as I try

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different capabilities. Yeah, it's an interesting time with the podcasting space,

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and increasingly as you were talking about your experience with

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podcasting and how you approach it more from a performance perspective.

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Yeah, I would say that's important performance in the past,

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that's definitely it still shapes how I think about things.

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But I would say, if I had to give you

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one thing that is the driving force behind what I do,

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it's always audience. So it's the same in a performance space.

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You're always performing to an audience, right. I think when

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we're making podcasts, or when we're making anything that is art,

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we're doing that for a particular audience. So I try

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to do the to kill a mockingbird thing and put

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myself in their shoes. What is it that the audience

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wants to listen to, what is it that they want

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to feel?

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How do I move them? How do I take them

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somewhere with me?

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So I tend to have a very audience centric approach

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to how I create content. I'm always thinking about what

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piece of audio would be the one that would knock

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them over the head and be the most interesting, or

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what would paint a picture for them. I'm not thinking

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of it from a me perspective of performance, as in

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I am out there performing. I'm thinking of it as

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what can I give the audience? What gift can I

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give the audience out of what I'm creating.

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Yeah, it's an interesting contrast to my experience with the

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very early days of podcasting, because the example that you

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are giving is a creator thinks about creating content for

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a type of an audience or a demographic of an audience.

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Versus I think the early days of podcasting, I think,

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but a lot of podcasts started. They would do a

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show that they had a passion for. They did it

253
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in a very authentic and real way.

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But it was about them and not about the audience.

255
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See that's the line here. It's different is that oftentimes

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the creator thought of themselves first, so it was what

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they wanted to talk about or their passions were. And

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what you're doing is you're self selecting your audience based

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on connection with those interest areas versus trying to come

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up with a piece of content that maybe not coming

261
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out of who you are as a person. But it's

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like a concept to be able to reach a certain audience.

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And it's interesting. I think I've this audience centric approach

264
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has also been born out of the fact that I

265
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grew up in public radio, and so we had to

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get out of the way, or did that, We.

267
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Had to get out of the way. We couldn't be

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the star.

269
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If we're using that performance analogy in my past, we

270
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couldn't be the star that everyone was looking at. It's

271
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not about you, it's about the story and serving the

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story and serving the audience.

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So in public radio, there was no place for egos.

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You had to remove yourself from a story and be

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completely objective in the way that you were doing it,

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because that's the whole form and format of public radio, right, Yeah.

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Yeah, I think it is. And also I think we're

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talking about also the contrast between natural conversations or like

279
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a back and forth versus a very scripted performance. And

280
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I done both, So I think we have to draw

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a little bit of a distinction there that oftentimes these

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performative to a certain audience are usually oftentimes scripted or

283
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pay the outline. I don't know, what's your experience with

284
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the tension around that, around outlines versus more kind of

285
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conversational bit.

286
00:15:07.919 --> 00:15:09.960
Yeah, I think I've done both. I think my public

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radio up until last year, I was on air with ABC,

288
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which is the public broadcast in Australia. So I was

289
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doing breakfast radio programs, and I think that those are

290
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much more free and much more conversational. There's a topic

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for a particular day, and you're talking about a particular subject,

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and so you're talking around that subject. So say, I

293
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don't know, for example, let me draw from an example

294
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from about ten years ago when I was on air

295
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there's a gas explosion that happens while you're on air,

296
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and you later find out it's a double murdered suicide.

297
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So you're talking about this on air as it happens.

298
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You hear the sound in town, and it's very conversational

299
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and very free because.

300
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You're moving in that moment.

301
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You're ringing police, you're ringing emergency services, you're bringing them

302
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on live. The story is developing over time because you're

303
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learning more and more information. Then you might go out

304
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to the scene and then that becomes pre recorded content

305
00:16:03.440 --> 00:16:05.600
because you're now out of the scene, you're no longer

306
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live on air, and that's when you're learning that.

307
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And this gets a little bit dark.

308
00:16:09.440 --> 00:16:12.000
But there's body parts of children on the road, there's

309
00:16:12.039 --> 00:16:15.120
awful things that have happened, and you're gathering more and

310
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more data, and so that story develops as you go along,

311
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and then you learn, okay, this was not an accident,

312
00:16:21.919 --> 00:16:25.279
this was something that was a double murder suicide. That's

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more information that comes to lights that might come over days,

314
00:16:28.879 --> 00:16:32.559
and in talking to those different people that are a

315
00:16:32.639 --> 00:16:34.639
part of it, you might talk to For example, I

316
00:16:34.679 --> 00:16:37.000
ended up speaking to the sister of the woman whose

317
00:16:37.120 --> 00:16:40.879
children had died, and so those type of things, those stories,

318
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the conversational type stories, they develop over time in radio

319
00:16:45.279 --> 00:16:47.919
and in live whereas in pre recorder do you have

320
00:16:48.440 --> 00:16:51.200
all of the information there to begin with, Right, So

321
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you've gone and you sought all of your interviews, you

322
00:16:54.039 --> 00:16:56.639
sought all of your facts and figures. You might have

323
00:16:56.720 --> 00:17:00.159
ten interviews, and then you sit there and you plow

324
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through those ten interviews and you do what we call

325
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a best of, and you listen to the best of

326
00:17:05.240 --> 00:17:08.000
the best audio from that, and then you decide from

327
00:17:08.000 --> 00:17:10.799
that best of how many minutes of each of those

328
00:17:10.839 --> 00:17:12.920
people will be in a five part series. So you

329
00:17:13.000 --> 00:17:15.720
might have one hundred hours of content, and you might

330
00:17:15.799 --> 00:17:18.839
be passing through that, and then once you've decided the

331
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best of the best, then you're putting it into a

332
00:17:21.079 --> 00:17:24.279
script format and you're going, Okay, this person says something

333
00:17:24.359 --> 00:17:27.119
similar to this person, so let's place those together in

334
00:17:27.200 --> 00:17:29.880
this audio series that we're developing. And this gets more

335
00:17:29.880 --> 00:17:33.079
investigative in nature, because there's a lot of fact finding,

336
00:17:33.119 --> 00:17:35.640
there's a lot of story seeking, and you go, okay,

337
00:17:35.640 --> 00:17:39.000
this narrative might mix nicely. We've got two people saying this,

338
00:17:39.079 --> 00:17:41.079
who's the person that's coming in and being the voice

339
00:17:41.119 --> 00:17:43.720
of reason. Here are we talking to someone about the

340
00:17:43.839 --> 00:17:47.799
legislation around AI? Can we bring them in and be

341
00:17:47.920 --> 00:17:50.640
the opposing voice here? Okay, so we'll bring them in

342
00:17:50.680 --> 00:17:54.640
this episode. And I think the difference in the story

343
00:17:54.799 --> 00:17:58.720
quality is both have their place, but one is developed

344
00:17:58.759 --> 00:18:01.440
over time and in chunks and the other one is

345
00:18:01.920 --> 00:18:05.720
the development space. Yeah, happening in real time is definitely

346
00:18:05.759 --> 00:18:09.400
your live element, and you're learning your story as it

347
00:18:09.440 --> 00:18:11.799
goes along, and you're bringing that audience on a journey.

348
00:18:12.200 --> 00:18:15.200
The hard thing about narrative podcasting is that you have

349
00:18:15.240 --> 00:18:18.559
to still bring that audience along on that journey. But

350
00:18:18.640 --> 00:18:20.960
you already know what the whole journey is. Ro do

351
00:18:20.960 --> 00:18:21.519
you know what I mean?

352
00:18:22.160 --> 00:18:23.480
Just sharing it?

353
00:18:23.720 --> 00:18:25.640
You're just sharing it. So then you have to think

354
00:18:25.640 --> 00:18:28.279
about you asked about tension. Where do we build in

355
00:18:28.319 --> 00:18:30.880
that tension? How do we build in that tension? Because

356
00:18:31.200 --> 00:18:34.039
we already know all the information. And one thing that

357
00:18:34.079 --> 00:18:37.559
I see podcasting do a lot, and it absolutely frustrates

358
00:18:37.599 --> 00:18:40.720
the shit out of me, is like this is a table.

359
00:18:41.000 --> 00:18:43.720
Oh that's a table, This is a table, and oh

360
00:18:43.759 --> 00:18:46.519
that was a table. You hear people like saying that

361
00:18:46.599 --> 00:18:48.720
this is a thing. Then you hear the audio about

362
00:18:48.720 --> 00:18:50.920
the thing, and then they backing ounce the thing and

363
00:18:50.960 --> 00:18:53.400
say the thing again. I don't know if you've noticed

364
00:18:53.440 --> 00:18:55.640
that in podcasting, it seems to be a style that

365
00:18:55.680 --> 00:18:58.440
some people do, and it takes away that magic and

366
00:18:58.480 --> 00:19:01.519
that joy of just discovering something when you don't have

367
00:19:01.559 --> 00:19:03.160
to pre announce that it's a table.

368
00:19:03.440 --> 00:19:06.079
We could just hear the grab about all the select

369
00:19:06.160 --> 00:19:06.839
depending on what.

370
00:19:06.759 --> 00:19:09.200
Country you're in and what you call audio, you just

371
00:19:09.279 --> 00:19:11.559
hear this select about the table and you go, oh, wow,

372
00:19:12.079 --> 00:19:14.319
that's a table, and then you hear, yeah, that was

373
00:19:14.400 --> 00:19:16.559
Rob talking about the table. But you don't have to

374
00:19:16.599 --> 00:19:19.279
say table again because we know you're talking about the table.

375
00:19:19.680 --> 00:19:22.240
So one thing I've noticed in podcasting, and this would

376
00:19:22.279 --> 00:19:26.359
be I think one of my criticisms of the craft

377
00:19:26.359 --> 00:19:29.039
where you do know the whole story beforehand, is there's

378
00:19:29.920 --> 00:19:32.680
sometimes a lot of over explaining that happens, and you

379
00:19:32.759 --> 00:19:35.519
lose a little bit of that joy as an audience

380
00:19:35.559 --> 00:19:38.640
member of discovery, of being surprised of.

381
00:19:39.039 --> 00:19:41.680
That element of learning discovery.

382
00:19:44.680 --> 00:19:45.400
And I don't think.

383
00:19:47.519 --> 00:19:49.960
I don't think we need to tell all of the story.

384
00:19:50.000 --> 00:19:55.319
I think we can allow people the agency to make

385
00:19:55.400 --> 00:19:57.880
up their own minds a little bit. And I think

386
00:19:58.440 --> 00:20:01.039
live radio is very good at that because they have

387
00:20:01.079 --> 00:20:04.000
to be, because it's happening as things develop, so you're

388
00:20:04.039 --> 00:20:07.359
coming along on the journey, Whereas I think podcasting some

389
00:20:07.400 --> 00:20:09.400
people are very good at it and some shows are

390
00:20:09.440 --> 00:20:10.440
not so good at that.

391
00:20:10.359 --> 00:20:14.119
And radio tends to be shorter. Right, So you've done

392
00:20:14.160 --> 00:20:15.440
a short chunk of.

393
00:20:15.440 --> 00:20:18.839
Time, just seven minute packages, yeah, ten minutes.

394
00:20:18.559 --> 00:20:21.039
Yeah, versus a podcast where you just discal.

395
00:20:20.839 --> 00:20:24.119
Ramble or you don't ramble. The type of work that

396
00:20:24.160 --> 00:20:26.519
we do, we shift and in machines we trust, and

397
00:20:27.160 --> 00:20:29.440
the future of everything. It's These are the podcasts that

398
00:20:29.480 --> 00:20:32.160
I've worked on over the past five six years. They've

399
00:20:32.200 --> 00:20:36.839
been heavily produced, so a lot of soonification from our

400
00:20:37.279 --> 00:20:41.359
composers and audio engineers, so really deep thought about how

401
00:20:41.359 --> 00:20:43.920
do we create a mood with that music that we're

402
00:20:43.960 --> 00:20:47.880
placing around what we're doing. And then sometimes if it's

403
00:20:47.880 --> 00:20:51.359
an investigation hundreds of hours of going through hundreds of

404
00:20:51.359 --> 00:20:54.759
hours of audio, that ends up being one sixty minute

405
00:20:54.799 --> 00:20:59.839
series across five episodes, right, yeah, So it just really depends,

406
00:21:00.079 --> 00:21:04.079
and I think different people have different ways of inserting

407
00:21:04.119 --> 00:21:07.079
that tension. But for me, it's about not telling the

408
00:21:07.119 --> 00:21:12.799
whole story and allowing the listener to discover things for themselves,

409
00:21:12.839 --> 00:21:16.039
and also just using the gold as well, just taking

410
00:21:16.720 --> 00:21:19.759
Rather than thinking about what is the story I want

411
00:21:19.759 --> 00:21:23.519
to tell, I think about what audio do I have first,

412
00:21:24.000 --> 00:21:26.119
and where is the gold? And I dig out all

413
00:21:26.160 --> 00:21:28.759
of that gold and then I try to put that

414
00:21:29.119 --> 00:21:31.039
in front of me and then I use that to

415
00:21:31.119 --> 00:21:34.839
guide the story. And archival audio as well helps bring

416
00:21:35.400 --> 00:21:37.960
something to life, like a moment in time or a

417
00:21:37.960 --> 00:21:40.839
particular thing that happened, or a news event, like using

418
00:21:42.000 --> 00:21:44.920
archival audio from that time, I think helps paint a picture.

419
00:21:44.960 --> 00:21:46.880
I'm always thinking about how am I painting a picture

420
00:21:46.880 --> 00:21:47.480
with words?

421
00:21:47.920 --> 00:21:50.480
They say a picture tells a thousand words, but I

422
00:21:50.519 --> 00:21:55.039
think sometimes audio can do the same thing. It's experiential,

423
00:21:55.200 --> 00:21:57.119
like to hear the sound of a place and you're

424
00:21:57.200 --> 00:21:59.400
just if you've got it. It's quite intimate if you've

425
00:21:59.440 --> 00:22:01.119
got it in your ear as you've just taken there,

426
00:22:01.160 --> 00:22:03.759
And like, how do we I don't know, how do

427
00:22:03.880 --> 00:22:07.240
we serve that intimate space with that people are giving

428
00:22:07.319 --> 00:22:09.759
us and make sure that we're giving them something that's

429
00:22:09.799 --> 00:22:12.559
interesting to listen to where they can learn something and

430
00:22:12.559 --> 00:22:13.359
take away something.

431
00:22:13.680 --> 00:22:18.039
Yeah, it's interesting. The whole experiential part of it is.

432
00:22:17.400 --> 00:22:18.960
We want to take people somewhere, do you know what

433
00:22:19.000 --> 00:22:21.880
I mean, or help them learn something, or guide them

434
00:22:21.880 --> 00:22:26.119
along a journey where they're learning something new. Right.

435
00:22:26.680 --> 00:22:31.519
A lot of podcasting is it's just conversational banter back

436
00:22:31.559 --> 00:22:34.039
and forth. And I know a lot of terms get

437
00:22:34.359 --> 00:22:38.359
thrown around to refer to different types of formats of podcasts,

438
00:22:38.480 --> 00:22:44.440
and this whole term of quality podcasts or high quality podcasts.

439
00:22:43.640 --> 00:22:46.000
And long form narrative podcasts.

440
00:22:46.079 --> 00:22:49.559
Yeah, mostly in yes.

441
00:22:49.240 --> 00:22:52.160
Jess, how do you think about this concept of drawing

442
00:22:52.200 --> 00:22:56.799
distinction between what's a quality podcast and maybe what is

443
00:22:56.960 --> 00:22:57.960
equality podcast?

444
00:22:58.000 --> 00:23:03.079
I think quality is in the ear of the beholder, right, Yeah,

445
00:23:03.200 --> 00:23:05.880
it is. It depends on to some degree.

446
00:23:05.880 --> 00:23:08.000
Exactly, but it depends on what you're interested in.

447
00:23:08.119 --> 00:23:12.920
And you might love those double hitter two people talking

448
00:23:12.960 --> 00:23:15.960
heads podcasts backwards and forwards. That might be your jam.

449
00:23:16.359 --> 00:23:19.119
So who am I to say that's not quality? It's

450
00:23:19.160 --> 00:23:22.359
not heavily produced. I think we can talk about a

451
00:23:22.480 --> 00:23:25.519
light production value and a heavy production value. What I

452
00:23:25.559 --> 00:23:28.440
tend to make are things with more heavy production values.

453
00:23:28.759 --> 00:23:32.039
When I was in live radio, that was less heavily produced, right,

454
00:23:32.160 --> 00:23:34.559
So that was more a talking heads and you could

455
00:23:34.599 --> 00:23:37.400
turn those into podcasts quite easily, top and tail and

456
00:23:37.440 --> 00:23:40.920
off you go. And those don't have hundreds of hours

457
00:23:40.960 --> 00:23:44.599
put into them, whereas something that got the soonification, something

458
00:23:44.640 --> 00:23:48.680
that's composed, something that works with sound designers, something that

459
00:23:49.039 --> 00:23:53.039
takes an investigative look at something and has say hundreds

460
00:23:53.079 --> 00:23:55.599
of hours of audio and might take that down to

461
00:23:55.799 --> 00:23:58.680
a five part series that are twenty minutes each. That's

462
00:23:58.720 --> 00:24:02.319
a higher it's a high quality of audio, but it's

463
00:24:02.359 --> 00:24:06.480
a high production value. So I don't think that I'm

464
00:24:06.519 --> 00:24:08.720
the one that can tell people what they should and

465
00:24:08.720 --> 00:24:11.759
shouldn't listen to or what isn't high quality.

466
00:24:11.880 --> 00:24:16.480
I just know that there's a lot more time money, and.

467
00:24:16.519 --> 00:24:17.319
There's a spectrum.

468
00:24:17.640 --> 00:24:22.119
Yeah, time and money I would say put into those

469
00:24:22.279 --> 00:24:27.839
higher production value podcasts. And sadly that's where we're seeing

470
00:24:28.400 --> 00:24:32.480
a lot of the space drinking at the moment podcast

471
00:24:32.519 --> 00:24:34.960
world right now, Like just there's no money, there's no

472
00:24:35.079 --> 00:24:38.359
ad sale dollars. We've got two waws that are happening

473
00:24:38.400 --> 00:24:41.880
globally that have meant that companies are tightening their belts

474
00:24:41.920 --> 00:24:44.680
when it comes to ad sale dollars, and it's sad.

475
00:24:44.720 --> 00:24:47.480
We're seeing a lot of really good podcasts.

476
00:24:47.039 --> 00:24:50.599
Die and a lot of them are those ones that

477
00:24:50.680 --> 00:24:53.240
cost a lot more to make with higher production value.

478
00:24:53.839 --> 00:24:56.119
And that's sad because that's the type of stuff that

479
00:24:56.200 --> 00:25:00.279
really I think engages people and stands the test of time. Well,

480
00:25:00.279 --> 00:25:02.200
it's not just the shelf life of a day or

481
00:25:02.240 --> 00:25:04.319
a week. It's something that lasts a lot longer than that.

482
00:25:04.400 --> 00:25:07.079
Like you can go back now and listen to our

483
00:25:07.680 --> 00:25:10.319
series on AI and hiring if you're looking for a

484
00:25:10.440 --> 00:25:14.720
job to learn about where artificial intelligence is inserted in

485
00:25:14.720 --> 00:25:18.680
the hiring process, and that work is still as relevant

486
00:25:18.720 --> 00:25:21.279
today as it was. I think it was three years ago,

487
00:25:21.359 --> 00:25:22.839
or it might even be four years ago that we

488
00:25:22.960 --> 00:25:27.000
made that series within Machines we Trust with MIT Technology Review,

489
00:25:27.160 --> 00:25:29.960
and we were talking about where there were issues in

490
00:25:30.000 --> 00:25:34.039
the process with AI and hiring and resume passes and

491
00:25:34.079 --> 00:25:37.039
people that were being like resumes that were being left

492
00:25:37.039 --> 00:25:39.079
on the cutting room law and never making it to

493
00:25:39.279 --> 00:25:43.079
hiring the managers because they might not be a male name,

494
00:25:43.200 --> 00:25:46.440
or they might not have played lacrosse. So certain resume

495
00:25:46.559 --> 00:25:50.880
passes and different sorting software meant that like they were

496
00:25:50.880 --> 00:25:53.160
never seen by the people that needed to see them,

497
00:25:53.200 --> 00:25:55.640
and so we're still seeing people deal with this today.

498
00:25:55.799 --> 00:25:57.720
I'm an immigrant. You can hear from my voice that

499
00:25:57.880 --> 00:26:01.119
I am not from America. I'm Australian and I don't

500
00:26:01.160 --> 00:26:03.160
know about you. But when you fill out you might

501
00:26:03.200 --> 00:26:05.079
not have noticed the box. But when you're filling out

502
00:26:05.319 --> 00:26:08.000
job ads in the US, there's a will you ever

503
00:26:08.359 --> 00:26:11.799
now or in the future require sponsorship box. If you

504
00:26:11.880 --> 00:26:14.960
click yes, a lot of the time you're automatically discounted

505
00:26:15.000 --> 00:26:17.880
from the hiring process. Your resume never makes it to

506
00:26:17.880 --> 00:26:22.160
the hiring manager. So you either learn to get really

507
00:26:22.200 --> 00:26:24.400
good at sponsoring your own visa, which is what I've

508
00:26:24.440 --> 00:26:26.279
gotten really good at, so I can say no, I

509
00:26:26.319 --> 00:26:30.240
don't require this, or you say yes and you have

510
00:26:30.279 --> 00:26:32.079
to knock on the door of the hiring manager to

511
00:26:32.119 --> 00:26:34.519
get your resume scene because you're never going to get

512
00:26:34.559 --> 00:26:37.160
seen if it's up to the algorithm, because you are

513
00:26:37.200 --> 00:26:41.279
a problem child. You're causing extra work for hiring managers,

514
00:26:41.359 --> 00:26:44.279
or at least so they think. Some of these international

515
00:26:44.319 --> 00:26:46.440
visas like an E three are actually quite easy. And

516
00:26:46.480 --> 00:26:48.960
if you learn to self sponsor an O visa, you

517
00:26:48.960 --> 00:26:51.759
know that's something that hiring managers never even have to

518
00:26:51.799 --> 00:26:52.400
worry about.

519
00:26:52.839 --> 00:26:55.279
But it's a complicating factor.

520
00:26:55.720 --> 00:26:58.519
And I find that in the AI and hiring space,

521
00:26:58.599 --> 00:27:03.000
these complicating factors mean that people are less likely, Like

522
00:27:03.039 --> 00:27:05.880
being a woman is a complicating factor. If you're a male,

523
00:27:06.200 --> 00:27:09.279
you're much more likely with certain software, and we prove

524
00:27:09.359 --> 00:27:11.599
this in our investigation to get hired than you are

525
00:27:11.640 --> 00:27:15.720
as a woman. Wow, So it's just according to some

526
00:27:15.759 --> 00:27:20.440
resume sca vrapers with some organization. So also, if you

527
00:27:20.519 --> 00:27:23.359
speak in a really confident voice like this, you're much

528
00:27:23.359 --> 00:27:25.559
more likely to get hired. Even if you do your

529
00:27:25.640 --> 00:27:29.359
job interview in German or in Mandarin Chinese, you're much

530
00:27:29.400 --> 00:27:31.640
more likely to get hired or to be seen as

531
00:27:31.680 --> 00:27:35.319
a good candidate by a particular AI that's looking at

532
00:27:35.359 --> 00:27:38.640
your voice than if you were speaking English in a

533
00:27:38.680 --> 00:27:41.960
softer tone and something that didn't sound as confident like

534
00:27:42.039 --> 00:27:44.880
Sometimes it has nothing to do with the content, which

535
00:27:44.920 --> 00:27:48.799
is absolutely god we found when we were doing our investigation,

536
00:27:49.039 --> 00:27:52.519
Like these types of software, these types of AI that

537
00:27:52.559 --> 00:27:55.279
are used sometimes aren't as smart as we think they are.

538
00:27:55.279 --> 00:27:58.119
They're just literally looking at how confident does your voice sound?

539
00:27:58.440 --> 00:28:00.960
But it can't tell the difference between an artificial voice

540
00:28:01.000 --> 00:28:03.799
and your own voice. We actually trained an AI algorithm

541
00:28:04.200 --> 00:28:07.079
to have a fake voice of our host on this series,

542
00:28:07.519 --> 00:28:10.720
and it couldn't tell the difference, and it actually rated

543
00:28:11.119 --> 00:28:14.319
the AI the fake voice as more engaging than the

544
00:28:14.319 --> 00:28:17.319
real voice at times. I can't remember exactly what.

545
00:28:17.920 --> 00:28:20.240
The criteria were a little bit more, I.

546
00:28:20.200 --> 00:28:23.079
Can't even remember exactly what criteria we've placed around them,

547
00:28:23.119 --> 00:28:25.359
but the voice, yeah, in.

548
00:28:25.359 --> 00:28:28.400
Certain areas it performed better than the real voice of

549
00:28:28.480 --> 00:28:30.640
Jennifer Strong, who is my host that I work with.

550
00:28:30.720 --> 00:28:33.240
And we actually went through some of these higher view

551
00:28:33.319 --> 00:28:36.559
and clear view not clearview, higher view I think it

552
00:28:36.599 --> 00:28:39.240
was called. It's been a while since I've done that investigation.

553
00:28:39.279 --> 00:28:40.599
But you'd have to jump in and have a look.

554
00:28:40.599 --> 00:28:43.960
If you type in AI plus hiring plus in machines,

555
00:28:44.000 --> 00:28:47.519
we trust you'd pretty quickly find the series. And it's

556
00:28:47.559 --> 00:28:50.559
a really interesting series and like that type of content.

557
00:28:50.599 --> 00:28:52.319
I know that I've told a long story to get

558
00:28:52.319 --> 00:28:54.640
around is just to say it's just as relevant today,

559
00:28:55.240 --> 00:28:58.480
even though they've come further in developing these type of technologies.

560
00:28:59.000 --> 00:29:01.880
Clear View AI is a completely different technology that I

561
00:29:02.119 --> 00:29:05.200
got confused with. That's has to do with another world

562
00:29:05.200 --> 00:29:07.720
of things that we can discuss another day, but Higher

563
00:29:07.799 --> 00:29:09.160
View is one of the ones that I was thinking

564
00:29:09.240 --> 00:29:12.400
of is the type of software. But there's so many

565
00:29:12.480 --> 00:29:15.119
different ones out there. But if you jump online and

566
00:29:15.160 --> 00:29:17.880
have a look at the investigation and listen to it,

567
00:29:17.920 --> 00:29:19.599
you'll be just as brushed up on that, if not

568
00:29:19.640 --> 00:29:21.400
more than I have because I haven't listened to it

569
00:29:21.400 --> 00:29:24.160
for a couple of years. But it's just as relevant today.

570
00:29:24.240 --> 00:29:27.440
And so that type of content, like those hundreds of

571
00:29:27.440 --> 00:29:29.319
hours that we use to make that at the time

572
00:29:29.680 --> 00:29:32.359
were well invested because it has that longest shelf life,

573
00:29:32.359 --> 00:29:34.480
whereas like something that you and I do on the

574
00:29:34.480 --> 00:29:37.240
presidential election, right, if we were to talk about the

575
00:29:37.359 --> 00:29:39.400
race for president, if we were to talk about the

576
00:29:39.480 --> 00:29:41.759
race for president, I've just knocked my coffee cup in

577
00:29:41.759 --> 00:29:42.160
front of me.

578
00:29:42.240 --> 00:29:43.880
Rob, I don't know if you could probably hear about

579
00:29:43.920 --> 00:29:44.559
in the background.

580
00:29:44.839 --> 00:29:46.839
That type of content might be dead in a week

581
00:29:46.920 --> 00:29:50.319
or two, because it's very relevant to today. So I think,

582
00:29:50.440 --> 00:29:52.440
going back to that phrase that the beauty of a

583
00:29:52.519 --> 00:29:54.519
piece or the quality of a piece is in the

584
00:29:54.559 --> 00:29:57.119
era of the beholder, is such that it's really up

585
00:29:57.160 --> 00:30:00.799
to the person listening and what their fascinating are and

586
00:30:00.839 --> 00:30:04.240
what they're interested in as to I guess what tickles

587
00:30:04.240 --> 00:30:07.640
their fancy and what commands their attention. And I'm really

588
00:30:08.039 --> 00:30:10.240
not qualified to tell other people what they should and

589
00:30:10.240 --> 00:30:11.079
shouldn't listen to.

590
00:30:12.000 --> 00:30:15.000
Yeah, I think if you look at the overall numbers

591
00:30:15.200 --> 00:30:18.960
of where audience consumes content, what types of content that

592
00:30:19.000 --> 00:30:22.480
they consume in larger quantities, I think they do tend

593
00:30:22.519 --> 00:30:27.079
to be programs that are higher production quality type content.

594
00:30:27.119 --> 00:30:28.200
And I think that may be.

595
00:30:28.119 --> 00:30:29.359
I think the data would speak to that.

596
00:30:29.440 --> 00:30:31.880
Yeah. Yeah, I think there are some evidence of that.

597
00:30:32.240 --> 00:30:34.720
That's not to say that there aren't successful shows that

598
00:30:34.839 --> 00:30:38.799
are not what we in that upper spectrum of production

599
00:30:39.279 --> 00:30:42.279
quality that are successful. I think that there are showings

600
00:30:42.319 --> 00:30:46.319
that are very conversational and not highly produced. But I

601
00:30:46.359 --> 00:30:48.079
do think it's putting a lot of pressure on the

602
00:30:48.119 --> 00:30:52.440
industry of podcasting to do more with less.

603
00:30:52.680 --> 00:30:57.680
Right now, with the crunch on ads shrinking of the industry. Yeah,

604
00:30:57.720 --> 00:31:00.279
it's just a really challenging time right now. Yeah.

605
00:31:00.279 --> 00:31:02.720
So instead of having six people on your staff on

606
00:31:02.759 --> 00:31:07.400
a podcasting project, where we can have multiple producers, multiple

607
00:31:07.440 --> 00:31:09.839
hosts and stuff like that, I think projects are being

608
00:31:10.200 --> 00:31:12.160
created if they're being even funded.

609
00:31:12.440 --> 00:31:14.119
I was going to say, a lot of them killed.

610
00:31:14.200 --> 00:31:15.559
A lot of them have been killed.

611
00:31:15.359 --> 00:31:19.400
Right, or smallerutines, maybe one host, maybe a couple of producers,

612
00:31:19.440 --> 00:31:20.039
and that's about it.

613
00:31:20.079 --> 00:31:21.720
And if that keeps happening, we're going to have to

614
00:31:21.759 --> 00:31:24.160
go back to more of that two heads having a

615
00:31:24.200 --> 00:31:27.359
conversation like similar to what we're producing here today, because

616
00:31:27.559 --> 00:31:29.519
it doesn't take as much time and effort as an

617
00:31:29.599 --> 00:31:31.759
investigation like one that we're working on at the moment.

618
00:31:31.880 --> 00:31:34.640
It's going to be hundreds of hours. Yeah, But I.

619
00:31:34.599 --> 00:31:38.440
Also think that AI will augment this as well. I

620
00:31:38.480 --> 00:31:40.799
know I heard a lot of people this was like

621
00:31:40.839 --> 00:31:43.880
a year ago, talk about how a lot of the

622
00:31:44.000 --> 00:31:48.039
startup investors are increasingly thinking as they think of AI,

623
00:31:48.319 --> 00:31:51.480
is that it may be possible in the next couple

624
00:31:51.440 --> 00:31:55.200
of years to have a billion dollar startup that is

625
00:31:55.279 --> 00:31:56.240
run by three people.

626
00:31:56.400 --> 00:31:59.400
Well, times are certainly a changing, and the proof will

627
00:31:59.400 --> 00:32:01.519
be in the pudding, right, the same as that conversation

628
00:32:01.559 --> 00:32:04.599
about driverless cars earlier, that we thought that there would

629
00:32:04.640 --> 00:32:07.240
be so much further along than they are now, but

630
00:32:07.319 --> 00:32:10.880
that narrow intelligence hasn't widened into general intelligence. So to

631
00:32:11.480 --> 00:32:15.119
wait and see whether this billion dollar company run by

632
00:32:15.119 --> 00:32:18.079
three people will actually exist or whether it's just something

633
00:32:18.119 --> 00:32:20.599
that we think might exist.

634
00:32:20.799 --> 00:32:23.680
I think right now it is a projection of right

635
00:32:23.799 --> 00:32:27.720
people think might happen if the AI gets powerful enough.

636
00:32:27.799 --> 00:32:30.400
Will be very interesting. I think it's like cracking that

637
00:32:30.519 --> 00:32:33.200
lot of general intelligence. And that's actually what a series

638
00:32:33.240 --> 00:32:35.799
that we're working on right now is about. We're looking

639
00:32:35.880 --> 00:32:39.160
at the race for super intelligence, and we've got some

640
00:32:39.279 --> 00:32:42.799
Pulitza funding to work on a series about that. Yeah,

641
00:32:42.799 --> 00:32:45.440
it should be interesting on this amid this backdrop of

642
00:32:45.559 --> 00:32:48.160
generative AI and everything that we're seeing with things like chat,

643
00:32:48.240 --> 00:32:52.119
GPT three, Gemini and others. And yeah, we're looking at

644
00:32:52.160 --> 00:32:55.559
this narrow intelligence that currently exists and where we are

645
00:32:55.839 --> 00:32:58.559
in this race for super intelligence, talking to people like

646
00:32:58.960 --> 00:33:02.319
Gary Marcus and other luminaries. So it's an interesting series

647
00:33:02.359 --> 00:33:05.079
and we'll certainly keep you informed on what happens with

648
00:33:05.160 --> 00:33:05.720
that one. Then.

649
00:33:06.279 --> 00:33:09.920
Yeah, I've been increasingly thinking about this whole concept that

650
00:33:10.359 --> 00:33:13.440
maybe in ten years or whatever, I could acquire five

651
00:33:13.519 --> 00:33:16.720
or ten robots that can do a bunch of work

652
00:33:16.799 --> 00:33:19.079
for me out there to make an income for me

653
00:33:19.440 --> 00:33:20.119
or something like that.

654
00:33:20.359 --> 00:33:21.680
Yeah, can we.

655
00:33:21.799 --> 00:33:25.160
Get the cryptominas onto it? You already have that happening now.

656
00:33:25.119 --> 00:33:27.640
Yeah, or I think you can. I think there might

657
00:33:27.680 --> 00:33:29.640
be an opportunity for people that go out and buy

658
00:33:30.440 --> 00:33:35.160
low costs, use Tesla's right now, and maybe eventually turn

659
00:33:35.200 --> 00:33:38.319
them into robotaxis just in a couple of years, right

660
00:33:38.319 --> 00:33:41.640
when you start making an income based on these robots

661
00:33:41.680 --> 00:33:44.400
going on and doing things in the world.

662
00:33:44.759 --> 00:33:46.680
One thing is for sure is that the world is

663
00:33:46.720 --> 00:33:49.559
a very interesting place right now, and that we haven't

664
00:33:49.559 --> 00:33:52.920
even conceptualized half of the things that will exist into

665
00:33:52.920 --> 00:33:55.559
the future. And I think we're living in a very

666
00:33:55.799 --> 00:33:56.720
exciting time.

667
00:33:57.039 --> 00:33:57.240
Yeah.

668
00:33:57.279 --> 00:33:59.960
I just wonder how it's all going to impact media

669
00:34:00.400 --> 00:34:01.839
like an our career.

670
00:34:02.079 --> 00:34:03.759
It will be very interesting to see.

671
00:34:03.839 --> 00:34:06.240
I think it's a shrinking space and has been for

672
00:34:06.279 --> 00:34:10.119
a number of years. And I hope that artificial intelligence

673
00:34:10.280 --> 00:34:13.480
has a place, but not too much of a place

674
00:34:13.519 --> 00:34:15.480
in that it gets rid of people like you and I,

675
00:34:15.599 --> 00:34:16.440
Rob right.

676
00:34:16.320 --> 00:34:19.440
They probably will replace us. Actually, I'm already accepting the

677
00:34:19.440 --> 00:34:20.559
fact that it probably will.

678
00:34:20.840 --> 00:34:22.039
I guess I'm not ready for it.

679
00:34:22.119 --> 00:34:25.840
To the questions, how am I going to respond to that?

680
00:34:26.159 --> 00:34:27.039
Am I going to.

681
00:34:27.719 --> 00:34:29.199
I don't think that as you are part of it?

682
00:34:29.320 --> 00:34:31.000
Or am I going to I don't.

683
00:34:30.880 --> 00:34:33.559
Think an AI can do what we can do in

684
00:34:33.599 --> 00:34:36.760
this conversation here and have nuance and have a conversation

685
00:34:36.960 --> 00:34:38.639
like what we've just done. So I don't know if

686
00:34:38.639 --> 00:34:41.960
we can completely be replaced, but certainly functions of our job,

687
00:34:42.079 --> 00:34:46.239
like editing, I think might get replaced, and then we'll

688
00:34:46.679 --> 00:34:47.760
find that time and.

689
00:34:47.719 --> 00:34:49.280
Allocate that time to something else.

690
00:34:49.320 --> 00:34:51.880
Wouldn't it be great if we had them the ais

691
00:34:51.960 --> 00:34:53.800
doing all the chop work, and we were able to

692
00:34:53.840 --> 00:34:56.880
invest more time into investigations.

693
00:34:56.039 --> 00:34:58.599
And doing actual the.

694
00:34:58.559 --> 00:35:01.800
Creative sound and really, I don't know, really investing it

695
00:35:01.840 --> 00:35:03.920
in the areas that brought good to the world.

696
00:35:04.119 --> 00:35:05.719
If the robots can go off and do all the

697
00:35:05.800 --> 00:35:08.440
dangerous work, all of the work that nobody likes to.

698
00:35:08.320 --> 00:35:11.440
Do, send them out as war reporters rather than journalists.

699
00:35:11.480 --> 00:35:14.719
Just they walk out with a microphone. Their task is

700
00:35:14.760 --> 00:35:16.199
to capture the audio.

701
00:35:16.000 --> 00:35:17.280
From somebody exactly.

702
00:35:17.440 --> 00:35:19.079
Yeah, yeah, it's too interesting.

703
00:35:19.320 --> 00:35:21.480
Send them out via a drone, you drop them in

704
00:35:21.480 --> 00:35:24.719
the field, or even attached the microphone to the drone.

705
00:35:24.800 --> 00:35:26.800
But I'm sure things like that are already happening.

706
00:35:27.159 --> 00:35:28.760
It's certainly technically classible.

707
00:35:28.880 --> 00:35:31.719
Yeah, absolutely, yeah, But I guess we'll just have to

708
00:35:31.760 --> 00:35:32.239
wait to see.

709
00:35:32.679 --> 00:35:34.360
Yeah, that must have been great talking to you.

710
00:35:34.480 --> 00:35:36.400
Yeah, it's been lovely. It's been a bit of fun

711
00:35:36.440 --> 00:35:40.800
and trying out this new software, this new recording equipment

712
00:35:40.800 --> 00:35:42.920
that you've got here today as well. It's very exciting.

713
00:35:43.199 --> 00:35:48.039
Yeah, it's another example of the changes and technology far

714
00:35:48.159 --> 00:35:48.719
things have come.

715
00:35:48.840 --> 00:35:49.320
Yeah.

716
00:35:49.400 --> 00:35:51.440
Absolutely, I look forward to hearing the finished product.

717
00:35:51.599 --> 00:35:54.239
Okay, all right, thanks.

718
00:36:00.079 --> 00:36:07.719
Mm hmmm hmmmm. The inspiration spoken word, tech and connection,

719
00:36:08.440 --> 00:36:14.760
Spoken like boken, spoken Light, Spoken Light, Spoken Lights with

720
00:36:15.119 --> 00:36:17.559
Rob Greenley with Rob Greenley with Rob green

721
00:36:19.480 --> 00:36:19.960
Mm hmm