June 18, 2024 • 36 mins
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
- 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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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:16):
The inspiration of spoken word tech and connection Spoken Life
Spoken Well.
Speaker 2 (00:21):
Welcome back to Spoken Life Show with Rob Greenley and
I have a very special episode of the show today
and it's in the seventh episode of this series. I'm
honored to be joined by Emma Silicons, who is a
senior producer former actor whose career spans the performing arts,
(00:41):
radio journalism, and podcast production out of the US as
well as down in Australia. So I'm honored to have
her with me. So let's get on with the show.
Speaker 3 (00:51):
I'm here with Emma Silicons. It's great to be here
with you. And you're on a trip from Australia.
Speaker 4 (00:57):
Yes, I am working on to see your projects while
I'm here and seeing a lot of people in the States.
I used to live here for six and a half years,
so okay, yeah, it's great to be back.
Speaker 3 (01:06):
So I do this show mainly to talk about creators
that are doing interesting things with audio. Right this is
the Spoken Live Show, so I like to cover what
people's thoughts are about how important audio has been to
their life experience and also to where each person sees
it going and what is next for you. So I
(01:27):
don't know if you want to start, just your perspective
on why you got involved in audio or audio, Yeah,
what's been your experience with it?
Speaker 4 (01:35):
I my introduction to audio, I think, in the first
instance was as a actor. So I used to be
an actor, dancer, singer back in the day. That was
my first career I was. I used to be a fairy,
and then I got into films and performing on stage,
and that's how I grew up in the craft.
Speaker 5 (01:52):
And then I think audio was always something that was
part of.
Speaker 4 (01:55):
That environment, right, Like when you're reading lines on stage
as an actor, you're really immersed in the intention behind
the line and like what that line means to the audience,
and framing it in the mouth in a way that
lands well to the person on the other side, and
thinking about projection and all of those sort of things.
Speaker 5 (02:11):
And then in the film space.
Speaker 4 (02:12):
Kind of bringing that sound back a little bit, and
you've got a camera right in front of you, so
you're thinking about how to quiet and that sound down
a little bit. So I think that was my initial
introduction to audio, was in that environment. So a little
bit different to how most people might onboard towards audio.
They might start in radio or television or something like
(02:34):
that in podcasting now these days, but for me, it
was very much in the dramatic space and also in dance,
So audio was the rhythm of the music, like five six, seven, eight,
so you're keeping rhythm and time.
Speaker 5 (02:48):
And so now.
Speaker 4 (02:49):
When I'm involved with podcasting and radio, I'm always thinking
about audio from those perspectives.
Speaker 5 (02:54):
I think a lot about rhythm and timing.
Speaker 4 (02:57):
I think a lot about delivery and intentions behind lines.
And my career's kind of almost come full circle in
that I've done so many different things since I was
involved in that acting world. I ended up becoming a
print journalist for a time and then worked in radio.
Speaker 5 (03:12):
So I was a host for about eight.
Speaker 4 (03:15):
Years and then moved to New York City ended up
getting involved here. I went to NYU as my graduate school,
worked at the Wall Street Journal, and got involved in
tech and artificial intelligence podcast and I've been in that
world ever since about twenty eighteen, and now I'm in
the podcasting space. So I went from this public radio
(03:35):
background with ABC Australia, hosting shows for eight years, news,
current affairs, all of the things that most of us
would know of public broadcasting, and now I'm in this
space where it's career recorded audio rather than live audio,
and so I work with as a producer and senior producer.
So I've taken my career, as I mentioned, full circle
(03:56):
where I think deeply about intentions behind lines again because
I coach our host on her delivery and her performance.
But I'm also thinking about the intention of complete piece
that we're putting together because I work as a story editor,
so I'm always thinking deeply about like, where are we
coming from for this piece, what experience are we trying
to give our audience, what journey do we want to
(04:17):
take them on? And I think I draw a lot
from my background as an actor, dancer and singer from
my past to do my job today, which is as
a scene of producer of Shift, which is a new
podcast that's come out in the last six months. So
looking specifically at artificial intelligence and frontier tech and it's
impact on our lives.
Speaker 3 (04:37):
It kind of seems like the ar official intelligence aspect
of what you've been doing is a little bit of
a contrast to what you've actually been doing with your career,
and is that the whole performance and creative element of
how do you see AI? Do you think it's going
to harm that or do you think that it's going
to be able to add to it to get better?
Speaker 4 (04:58):
Big conversation, Oh, I think AI is very complicated when
it comes to the creative space. Now we're seeing AI
be able to generate its own music, be able to
generate its own art, but I think one of the
big problems in this space is that we're seeing it
piggyback off the creative talent of artists and not necessarily
(05:21):
credit those artists. So you'll notice mid journey and different
types of artistic platforms, they'll pull from artists, and you'll
even sometimes see the artist's signature on the work that
is developed by AI, but there's no crediting that original
artist for that work.
Speaker 5 (05:38):
So it's just pulling, as all AI does, pulling.
Speaker 4 (05:41):
From big data from a data set and putting that
together in a different form and format. So AI is
only as smart as the data that it's fed and
it's only going to do what it's creator's talent to do.
So until the creators start being more transparent with these
black box algorithms and start actually building in fail safes
(06:03):
to protect artists, we won't really see equity or fairness
in this space. I think there's a lot of unfairness
right now, especially towards creatives in this area. But I
also think that AI can create beautiful things as well,
and if it's used responsibly, can do amazing things.
Speaker 5 (06:20):
And there's an artist that I follow on Instagram.
Speaker 4 (06:23):
I can't think of their at handle at the moment,
but they're an AI artist and they work with dancers
and do this amazing kind of folding imagery that folds
in on itself and follows the movement of a dancer.
And so something of that beauty couldn't necessarily be created
just to the naked eye. This is something that's like
(06:44):
graphics and dance and art all rolled into one. And
so that's an artist taking something and creating something new.
So I think the creative space and AI is very complicated.
I don't think AI is enough to eradicate.
Speaker 5 (07:02):
The human condition of what we're doing.
Speaker 4 (07:04):
We were just talking off air a little bit earlier,
Rob about narrow intelligence versus wide intelligence, and how AI
is very good at doing one specific thing and training
on that thing and getting deeper and deeper on that
particular thing, but not so good at when you take
those blinders off and try to have it be more
generally intelligent. That's something that is still a very complicated
(07:26):
place for AI right now and somewhere that it's not
even close to general intelligence.
Speaker 3 (07:31):
And I've seen some flaws in it here recently. I've
seen some work that I've generated with.
Speaker 4 (07:38):
Have you seen the five fingers or six Normally normal
normal fingers have five fingers, but it has a sixth
finger that's popping out, or there's a third eye on
the face, and you're like, the human would never make
that mistake.
Speaker 3 (07:52):
Rod work that i've seen get generated that it's almost
exactly the scene with multiple users, like I saw just today,
I saw a post by a podcaster that posted an
image that was part of his LinkedIn post okay that
looked exactly like an image that I generated from chat GPT,
(08:15):
just like maybe a month ago. Wow, So it does.
I guess if the prompt is you're saying, then it
should generate the same thing. But I didn't really give
it that much of a prompt when I generate, I
just gave it some really broad parameters.
Speaker 5 (08:30):
So we're now talking about.
Speaker 3 (08:31):
So it went out and created the same image, but
it was my impression that chat GPT would never create
the same image. Tie.
Speaker 4 (08:40):
I've only used it in the text format, so things
like it'sprucing up cover letters for jobs and those type
of things. A being quite generalized in your prompt around
please write a professional cover letter for the audio field
with this selection criteria. Please include this job description and
(09:00):
please include my resume items here, or please include my
bio and just like iterating on that and just plugging
in a lot of data. I think AI is very
good at taking a lot of data and sorting that out,
but what you get on the other end may not
necessarily be something that's cohesive.
Speaker 2 (09:17):
Yeah.
Speaker 3 (09:17):
Yeah, because it's increasingly starting to feel like one of
the needs for a detailed prompt is that you don't
want to give too much license to the AI, because.
Speaker 5 (09:26):
You know, you want to keep it very controlled.
Speaker 3 (09:29):
Because it will do what I just gave an example of,
it'll create the same image. Right, So if you make
a real simple prom.
Speaker 4 (09:36):
For the same cover letter, I'm sure there's now people
reading a lot of cover letters that are very much
in the same vein because they're being spat up by
the same program.
Speaker 3 (09:46):
Right, Because the AI has to come up with a
model of those very kind of outputs that are generated
based on a very generic problem.
Speaker 4 (09:54):
I think it's good to use AI as inspiration, So
use it as a kind of phone a friend fee
like you might in the past have done a cover
letter and send it to your friend and got them
to spruce it up. Instead, you're going to chat GBT
if you're using three or four or Gemini or some
other AI platform, and you're now using that as your
(10:14):
sort of inspiration and getting help, but not necessarily relying
on it. I think even things like driverless cars, we
thought that they would be a lot further than they are,
or the promise was they'd be a lot further than
they are today, and that we'd have all these fleets
of driverless cars just driving people around, and we're not
at that point yet. They're still, while very smart in
(10:36):
some ways, very dumb in other ways. I mean, if
a bird flies passed a vehicle, it's very quickly thinking
that's a hazard and like putting on the brakes, or
it's just it's very interesting, not all that reliable.
Speaker 3 (10:49):
Yes, I think the big takeaway for podcasters is that
it's still early days for that stuff.
Speaker 4 (10:54):
And well even when you hear the AI editing. Have
you heard in descript and different processes that you might
listen to. It cuts off parts of words, So people
are relying on something and not doing the craft, which
is sitting down in audition or sitting down in pro
tools and actually editing top to tail. But these AI
softwares are cutting off actual words like humans, if they
(11:15):
were editing, they wouldn't cut off a word, right, You
wouldn't cut off part of a word. You wouldn't cut
it that tight in your editing, whereas like AI will.
Speaker 3 (11:25):
Yeah, it can actually do that, right, especially if there's
any kind of cross talk. I've seen that. Yeah, I
used the descript tool as well, and I see these
things come up on a regular basis as I try
different capabilities. Yeah, it's an interesting time with the podcasting space,
and increasingly as you were talking about your experience with
(11:45):
podcasting and how you approach it more from a performance perspective.
Speaker 4 (11:51):
Yeah, I would say that's important performance in the past,
that's definitely it still shapes how I think about things.
But I would say, if I had to give you
one thing that is the driving force behind what I do,
it's always audience. So it's the same in a performance space.
You're always performing to an audience, right. I think when
(12:12):
we're making podcasts, or when we're making anything that is art,
we're doing that for a particular audience. So I try
to do the to kill a mockingbird thing and put
myself in their shoes. What is it that the audience
wants to listen to, what is it that they want
to feel?
Speaker 5 (12:29):
How do I move them? How do I take them
somewhere with me?
Speaker 4 (12:32):
So I tend to have a very audience centric approach
to how I create content. I'm always thinking about what
piece of audio would be the one that would knock
them over the head and be the most interesting, or
what would paint a picture for them. I'm not thinking
of it from a me perspective of performance, as in
I am out there performing. I'm thinking of it as
what can I give the audience? What gift can I
(12:55):
give the audience out of what I'm creating.
Speaker 3 (12:57):
Yeah, it's an interesting contrast to my experience with the
very early days of podcasting, because the example that you
are giving is a creator thinks about creating content for
a type of an audience or a demographic of an audience.
Versus I think the early days of podcasting, I think,
but a lot of podcasts started. They would do a
(13:20):
show that they had a passion for. They did it
in a very authentic and real way.
Speaker 4 (13:26):
But it was about them and not about the audience.
Speaker 3 (13:29):
See that's the line here. It's different is that oftentimes
the creator thought of themselves first, so it was what
they wanted to talk about or their passions were. And
what you're doing is you're self selecting your audience based
on connection with those interest areas versus trying to come
(13:49):
up with a piece of content that maybe not coming
out of who you are as a person. But it's
like a concept to be able to reach a certain audience.
Speaker 4 (14:00):
And it's interesting. I think I've this audience centric approach
has also been born out of the fact that I
grew up in public radio, and so we had to
get out of the way, or did that, We.
Speaker 5 (14:09):
Had to get out of the way. We couldn't be
the star.
Speaker 4 (14:12):
If we're using that performance analogy in my past, we
couldn't be the star that everyone was looking at. It's
not about you, it's about the story and serving the
story and serving the audience.
Speaker 5 (14:21):
So in public radio, there was no place for egos.
Speaker 4 (14:25):
You had to remove yourself from a story and be
completely objective in the way that you were doing it,
because that's the whole form and format of public radio, right, Yeah.
Speaker 3 (14:35):
Yeah, I think it is. And also I think we're
talking about also the contrast between natural conversations or like
a back and forth versus a very scripted performance. And
I done both, So I think we have to draw
a little bit of a distinction there that oftentimes these
performative to a certain audience are usually oftentimes scripted or
(14:59):
pay the outline. I don't know, what's your experience with
the tension around that, around outlines versus more kind of
conversational bit.
Speaker 4 (15:07):
Yeah, I think I've done both. I think my public
radio up until last year, I was on air with ABC,
which is the public broadcast in Australia. So I was
doing breakfast radio programs, and I think that those are
much more free and much more conversational. There's a topic
for a particular day, and you're talking about a particular subject,
and so you're talking around that subject. So say, I
(15:30):
don't know, for example, let me draw from an example
from about ten years ago when I was on air
there's a gas explosion that happens while you're on air,
and you later find out it's a double murdered suicide.
Speaker 5 (15:39):
So you're talking about this on air as it happens.
Speaker 4 (15:43):
You hear the sound in town, and it's very conversational
and very free because.
Speaker 5 (15:48):
You're moving in that moment.
Speaker 4 (15:50):
You're ringing police, you're ringing emergency services, you're bringing them
on live. The story is developing over time because you're
learning more and more information. Then you might go out
to the scene and then that becomes pre recorded content
because you're now out of the scene, you're no longer
live on air, and that's when you're learning that.
Speaker 5 (16:08):
And this gets a little bit dark.
Speaker 4 (16:09):
But there's body parts of children on the road, there's
awful things that have happened, and you're gathering more and
more data, and so that story develops as you go along,
and then you learn, okay, this was not an accident,
this was something that was a double murder suicide. That's
more information that comes to lights that might come over days,
and in talking to those different people that are a
(16:32):
part of it, you might talk to For example, I
ended up speaking to the sister of the woman whose
children had died, and so those type of things, those stories,
the conversational type stories, they develop over time in radio
and in live whereas in pre recorder do you have
all of the information there to begin with, Right, So
you've gone and you sought all of your interviews, you
(16:54):
sought all of your facts and figures. You might have
ten interviews, and then you sit there and you plow
through those ten interviews and you do what we call
a best of, and you listen to the best of
the best audio from that, and then you decide from
that best of how many minutes of each of those
people will be in a five part series. So you
might have one hundred hours of content, and you might
(17:15):
be passing through that, and then once you've decided the
best of the best, then you're putting it into a
script format and you're going, Okay, this person says something
similar to this person, so let's place those together in
this audio series that we're developing. And this gets more
investigative in nature, because there's a lot of fact finding,
there's a lot of story seeking, and you go, okay,
this narrative might mix nicely. We've got two people saying this,
(17:39):
who's the person that's coming in and being the voice
of reason. Here are we talking to someone about the
legislation around AI? Can we bring them in and be
the opposing voice here? Okay, so we'll bring them in
this episode. And I think the difference in the story
quality is both have their place, but one is developed
over time and in chunks and the other one is
(18:01):
the development space. Yeah, happening in real time is definitely
your live element, and you're learning your story as it
goes along, and you're bringing that audience on a journey.
The hard thing about narrative podcasting is that you have
to still bring that audience along on that journey. But
you already know what the whole journey is. Ro do
you know what I mean?
Speaker 3 (18:22):
Just sharing it?
Speaker 4 (18:23):
You're just sharing it. So then you have to think
about you asked about tension. Where do we build in
that tension? How do we build in that tension? Because
we already know all the information. And one thing that
I see podcasting do a lot, and it absolutely frustrates
the shit out of me, is like this is a table.
Oh that's a table, This is a table, and oh
(18:43):
that was a table. You hear people like saying that
this is a thing. Then you hear the audio about
the thing, and then they backing ounce the thing and
say the thing again. I don't know if you've noticed
that in podcasting, it seems to be a style that
some people do, and it takes away that magic and
that joy of just discovering something when you don't have
to pre announce that it's a table.
Speaker 5 (19:03):
We could just hear the grab about all the select
depending on what.
Speaker 4 (19:06):
Country you're in and what you call audio, you just
hear this select about the table and you go, oh, wow,
that's a table, and then you hear, yeah, that was
Rob talking about the table. But you don't have to
say table again because we know you're talking about the table.
So one thing I've noticed in podcasting, and this would
be I think one of my criticisms of the craft
where you do know the whole story beforehand, is there's
(19:29):
sometimes a lot of over explaining that happens, and you
lose a little bit of that joy as an audience
member of discovery, of being surprised of.
Speaker 5 (19:39):
That element of learning discovery.
Speaker 4 (19:44):
And I don't think.
Speaker 5 (19:47):
I don't think we need to tell all of the story.
Speaker 4 (19:50):
I think we can allow people the agency to make
up their own minds a little bit. And I think
live radio is very good at that because they have
to be, because it's happening as things develop, so you're
coming along on the journey, Whereas I think podcasting some
people are very good at it and some shows are
not so good at that.
Speaker 3 (20:10):
And radio tends to be shorter. Right, So you've done
a short chunk of.
Speaker 4 (20:15):
Time, just seven minute packages, yeah, ten minutes.
Speaker 3 (20:18):
Yeah, versus a podcast where you just discal.
Speaker 4 (20:20):
Ramble or you don't ramble. The type of work that
we do, we shift and in machines we trust, and
the future of everything. It's These are the podcasts that
I've worked on over the past five six years. They've
been heavily produced, so a lot of soonification from our
composers and audio engineers, so really deep thought about how
(20:41):
do we create a mood with that music that we're
placing around what we're doing. And then sometimes if it's
an investigation hundreds of hours of going through hundreds of
hours of audio, that ends up being one sixty minute
series across five episodes, right, yeah, So it just really depends,
and I think different people have different ways of inserting
(21:04):
that tension. But for me, it's about not telling the
whole story and allowing the listener to discover things for themselves,
and also just using the gold as well, just taking
Rather than thinking about what is the story I want
to tell, I think about what audio do I have first,
and where is the gold? And I dig out all
(21:26):
of that gold and then I try to put that
in front of me and then I use that to
guide the story. And archival audio as well helps bring
something to life, like a moment in time or a
particular thing that happened, or a news event, like using
archival audio from that time, I think helps paint a picture.
Speaker 5 (21:44):
I'm always thinking about how am I painting a picture
with words?
Speaker 4 (21:47):
They say a picture tells a thousand words, but I
think sometimes audio can do the same thing. It's experiential,
like to hear the sound of a place and you're
just if you've got it. It's quite intimate if you've
got it in your ear as you've just taken there,
And like, how do we I don't know, how do
we serve that intimate space with that people are giving
us and make sure that we're giving them something that's
(22:09):
interesting to listen to where they can learn something and
take away something.
Speaker 3 (22:13):
Yeah, it's interesting. The whole experiential part of it is.
Speaker 4 (22:17):
We want to take people somewhere, do you know what
I mean, or help them learn something, or guide them
along a journey where they're learning something new. Right.
Speaker 3 (22:26):
A lot of podcasting is it's just conversational banter back
and forth. And I know a lot of terms get
thrown around to refer to different types of formats of podcasts,
and this whole term of quality podcasts or high quality podcasts.
Speaker 5 (22:43):
And long form narrative podcasts.
Speaker 4 (22:46):
Yeah, mostly in yes.
Speaker 3 (22:49):
Jess, how do you think about this concept of drawing
distinction between what's a quality podcast and maybe what is
equality podcast?
Speaker 4 (22:58):
I think quality is in the ear of the beholder, right, Yeah,
it is. It depends on to some degree.
Speaker 5 (23:05):
Exactly, but it depends on what you're interested in.
Speaker 4 (23:08):
And you might love those double hitter two people talking
heads podcasts backwards and forwards. That might be your jam.
So who am I to say that's not quality? It's
not heavily produced. I think we can talk about a
light production value and a heavy production value. What I
tend to make are things with more heavy production values.
(23:28):
When I was in live radio, that was less heavily produced, right,
So that was more a talking heads and you could
turn those into podcasts quite easily, top and tail and
off you go. And those don't have hundreds of hours
put into them, whereas something that got the soonification, something
that's composed, something that works with sound designers, something that
(23:49):
takes an investigative look at something and has say hundreds
of hours of audio and might take that down to
a five part series that are twenty minutes each. That's
a higher it's a high quality of audio, but it's
a high production value. So I don't think that I'm
the one that can tell people what they should and
shouldn't listen to or what isn't high quality.
Speaker 5 (24:11):
I just know that there's a lot more time money, and.
Speaker 3 (24:16):
There's a spectrum.
Speaker 4 (24:17):
Yeah, time and money I would say put into those
higher production value podcasts. And sadly that's where we're seeing
a lot of the space drinking at the moment podcast
world right now, Like just there's no money, there's no
ad sale dollars. We've got two waws that are happening
(24:38):
globally that have meant that companies are tightening their belts
when it comes to ad sale dollars, and it's sad.
Speaker 5 (24:44):
We're seeing a lot of really good podcasts.
Speaker 4 (24:47):
Die and a lot of them are those ones that
cost a lot more to make with higher production value.
And that's sad because that's the type of stuff that
really I think engages people and stands the test of time. Well,
it's not just the shelf life of a day or
a week. It's something that lasts a lot longer than that.
Like you can go back now and listen to our
(25:07):
series on AI and hiring if you're looking for a
job to learn about where artificial intelligence is inserted in
the hiring process, and that work is still as relevant
today as it was. I think it was three years ago,
or it might even be four years ago that we
made that series within Machines we Trust with MIT Technology Review,
and we were talking about where there were issues in
(25:30):
the process with AI and hiring and resume passes and
people that were being like resumes that were being left
on the cutting room law and never making it to
hiring the managers because they might not be a male name,
or they might not have played lacrosse. So certain resume
passes and different sorting software meant that like they were
(25:50):
never seen by the people that needed to see them,
and so we're still seeing people deal with this today.
I'm an immigrant. You can hear from my voice that
I am not from America. I'm Australian and I don't
know about you. But when you fill out you might
not have noticed the box. But when you're filling out
job ads in the US, there's a will you ever
now or in the future require sponsorship box. If you
(26:11):
click yes, a lot of the time you're automatically discounted
from the hiring process. Your resume never makes it to
the hiring manager. So you either learn to get really
good at sponsoring your own visa, which is what I've
gotten really good at, so I can say no, I
don't require this, or you say yes and you have
to knock on the door of the hiring manager to
(26:32):
get your resume scene because you're never going to get
seen if it's up to the algorithm, because you are
a problem child. You're causing extra work for hiring managers,
or at least so they think. Some of these international
visas like an E three are actually quite easy. And
if you learn to self sponsor an O visa, you
know that's something that hiring managers never even have to
worry about.
Speaker 5 (26:52):
But it's a complicating factor.
Speaker 4 (26:55):
And I find that in the AI and hiring space,
these complicating factors mean that people are less likely, Like
being a woman is a complicating factor. If you're a male,
you're much more likely with certain software, and we prove
this in our investigation to get hired than you are
as a woman. Wow, So it's just according to some
(27:15):
resume sca vrapers with some organization. So also, if you
speak in a really confident voice like this, you're much
more likely to get hired. Even if you do your
job interview in German or in Mandarin Chinese, you're much
more likely to get hired or to be seen as
a good candidate by a particular AI that's looking at
your voice than if you were speaking English in a
(27:38):
softer tone and something that didn't sound as confident like
Sometimes it has nothing to do with the content, which
is absolutely god we found when we were doing our investigation,
Like these types of software, these types of AI that
are used sometimes aren't as smart as we think they are.
They're just literally looking at how confident does your voice sound?
But it can't tell the difference between an artificial voice
(28:01):
and your own voice. We actually trained an AI algorithm
to have a fake voice of our host on this series,
and it couldn't tell the difference, and it actually rated
the AI the fake voice as more engaging than the
real voice at times. I can't remember exactly what.
Speaker 3 (28:17):
The criteria were a little bit more, I.
Speaker 5 (28:20):
Can't even remember exactly what criteria we've placed around them,
but the voice, yeah, in.
Speaker 4 (28:25):
Certain areas it performed better than the real voice of
Jennifer Strong, who is my host that I work with.
And we actually went through some of these higher view
and clear view not clearview, higher view I think it
was called. It's been a while since I've done that investigation.
But you'd have to jump in and have a look.
If you type in AI plus hiring plus in machines,
we trust you'd pretty quickly find the series. And it's
(28:47):
a really interesting series and like that type of content.
I know that I've told a long story to get
around is just to say it's just as relevant today,
even though they've come further in developing these type of technologies.
Clear View AI is a completely different technology that I
got confused with. That's has to do with another world
of things that we can discuss another day, but Higher
(29:07):
View is one of the ones that I was thinking
of is the type of software. But there's so many
different ones out there. But if you jump online and
have a look at the investigation and listen to it,
you'll be just as brushed up on that, if not
more than I have because I haven't listened to it
for a couple of years. But it's just as relevant today.
And so that type of content, like those hundreds of
hours that we use to make that at the time
(29:29):
were well invested because it has that longest shelf life,
whereas like something that you and I do on the
presidential election, right, if we were to talk about the
race for president, if we were to talk about the
race for president, I've just knocked my coffee cup in
front of me.
Speaker 5 (29:42):
Rob, I don't know if you could probably hear about
in the background.
Speaker 4 (29:44):
That type of content might be dead in a week
or two, because it's very relevant to today. So I think,
going back to that phrase that the beauty of a
piece or the quality of a piece is in the
era of the beholder, is such that it's really up
to the person listening and what their fascinating are and
what they're interested in as to I guess what tickles
their fancy and what commands their attention. And I'm really
(30:08):
not qualified to tell other people what they should and
shouldn't listen to.
Speaker 3 (30:12):
Yeah, I think if you look at the overall numbers
of where audience consumes content, what types of content that
they consume in larger quantities, I think they do tend
to be programs that are higher production quality type content.
And I think that may be.
Speaker 4 (30:28):
I think the data would speak to that.
Speaker 3 (30:29):
Yeah. Yeah, I think there are some evidence of that.
That's not to say that there aren't successful shows that
are not what we in that upper spectrum of production
quality that are successful. I think that there are showings
that are very conversational and not highly produced. But I
do think it's putting a lot of pressure on the
industry of podcasting to do more with less.
Speaker 4 (30:52):
Right now, with the crunch on ads shrinking of the industry. Yeah,
it's just a really challenging time right now. Yeah.
Speaker 3 (31:00):
So instead of having six people on your staff on
a podcasting project, where we can have multiple producers, multiple
hosts and stuff like that, I think projects are being
created if they're being even funded.
Speaker 4 (31:12):
I was going to say, a lot of them killed.
Speaker 5 (31:14):
A lot of them have been killed.
Speaker 3 (31:15):
Right, or smallerutines, maybe one host, maybe a couple of producers,
and that's about it.
Speaker 4 (31:20):
And if that keeps happening, we're going to have to
go back to more of that two heads having a
conversation like similar to what we're producing here today, because
it doesn't take as much time and effort as an
investigation like one that we're working on at the moment.
It's going to be hundreds of hours. Yeah, But I.
Speaker 3 (31:34):
Also think that AI will augment this as well. I
know I heard a lot of people this was like
a year ago, talk about how a lot of the
startup investors are increasingly thinking as they think of AI,
is that it may be possible in the next couple
of years to have a billion dollar startup that is
(31:55):
run by three people.
Speaker 4 (31:56):
Well, times are certainly a changing, and the proof will
be in the pudding, right, the same as that conversation
about driverless cars earlier, that we thought that there would
be so much further along than they are now, but
that narrow intelligence hasn't widened into general intelligence. So to
wait and see whether this billion dollar company run by
three people will actually exist or whether it's just something
(32:18):
that we think might exist.
Speaker 3 (32:20):
I think right now it is a projection of right
people think might happen if the AI gets powerful enough.
Speaker 4 (32:27):
Will be very interesting. I think it's like cracking that
lot of general intelligence. And that's actually what a series
that we're working on right now is about. We're looking
at the race for super intelligence, and we've got some
Pulitza funding to work on a series about that. Yeah,
it should be interesting on this amid this backdrop of
generative AI and everything that we're seeing with things like chat,
(32:48):
GPT three, Gemini and others. And yeah, we're looking at
this narrow intelligence that currently exists and where we are
in this race for super intelligence, talking to people like
Gary Marcus and other luminaries. So it's an interesting series
and we'll certainly keep you informed on what happens with
that one. Then.
Speaker 3 (33:06):
Yeah, I've been increasingly thinking about this whole concept that
maybe in ten years or whatever, I could acquire five
or ten robots that can do a bunch of work
for me out there to make an income for me
or something like that.
Speaker 2 (33:20):
Yeah, can we.
Speaker 5 (33:21):
Get the cryptominas onto it? You already have that happening now.
Speaker 3 (33:25):
Yeah, or I think you can. I think there might
be an opportunity for people that go out and buy
low costs, use Tesla's right now, and maybe eventually turn
them into robotaxis just in a couple of years, right
when you start making an income based on these robots
going on and doing things in the world.
Speaker 4 (33:44):
One thing is for sure is that the world is
a very interesting place right now, and that we haven't
even conceptualized half of the things that will exist into
the future. And I think we're living in a very
exciting time.
Speaker 2 (33:57):
Yeah.
Speaker 3 (33:57):
I just wonder how it's all going to impact media
like an our career.
Speaker 5 (34:02):
It will be very interesting to see.
Speaker 4 (34:03):
I think it's a shrinking space and has been for
a number of years. And I hope that artificial intelligence
has a place, but not too much of a place
in that it gets rid of people like you and I,
Rob right.
Speaker 3 (34:16):
They probably will replace us. Actually, I'm already accepting the
fact that it probably will.
Speaker 4 (34:20):
I guess I'm not ready for it.
Speaker 3 (34:22):
To the questions, how am I going to respond to that?
Am I going to.
Speaker 4 (34:27):
I don't think that as you are part of it?
Speaker 3 (34:29):
Or am I going to I don't.
Speaker 4 (34:30):
Think an AI can do what we can do in
this conversation here and have nuance and have a conversation
like what we've just done. So I don't know if
we can completely be replaced, but certainly functions of our job,
like editing, I think might get replaced, and then we'll
find that time and.
Speaker 5 (34:47):
Allocate that time to something else.
Speaker 4 (34:49):
Wouldn't it be great if we had them the ais
doing all the chop work, and we were able to
invest more time into investigations.
Speaker 5 (34:56):
And doing actual the.
Speaker 4 (34:58):
Creative sound and really, I don't know, really investing it
in the areas that brought good to the world.
Speaker 3 (35:04):
If the robots can go off and do all the
dangerous work, all of the work that nobody likes to.
Speaker 4 (35:08):
Do, send them out as war reporters rather than journalists.
Speaker 3 (35:11):
Just they walk out with a microphone. Their task is
to capture the audio.
Speaker 4 (35:16):
From somebody exactly.
Speaker 3 (35:17):
Yeah, yeah, it's too interesting.
Speaker 4 (35:19):
Send them out via a drone, you drop them in
the field, or even attached the microphone to the drone.
Speaker 5 (35:24):
But I'm sure things like that are already happening.
Speaker 3 (35:27):
It's certainly technically classible.
Speaker 4 (35:28):
Yeah, absolutely, yeah, But I guess we'll just have to
wait to see.
Speaker 3 (35:32):
Yeah, that must have been great talking to you.
Speaker 4 (35:34):
Yeah, it's been lovely. It's been a bit of fun
and trying out this new software, this new recording equipment
that you've got here today as well. It's very exciting.
Speaker 3 (35:43):
Yeah, it's another example of the changes and technology far
things have come.
Speaker 4 (35:48):
Yeah.
Speaker 5 (35:49):
Absolutely, I look forward to hearing the finished product.
Speaker 3 (35:51):
Okay, all right, thanks.
Speaker 1 (36:00):
Mm hmmm hmmmm. The inspiration spoken word, tech and connection,
Spoken like boken, spoken Light, Spoken Light, Spoken Lights with
Rob Greenley with Rob Greenley with Rob green
Speaker 4 (36:19):
Mm hmm
The inspiration of spoken word tech and connection Spoken Life
Spoken Well.
Speaker 2 (00:21):
Welcome back to Spoken Life Show with Rob Greenley and
I have a very special episode of the show today
and it's in the seventh episode of this series. I'm
honored to be joined by Emma Silicons, who is a
senior producer former actor whose career spans the performing arts,
(00:41):
radio journalism, and podcast production out of the US as
well as down in Australia. So I'm honored to have
her with me. So let's get on with the show.
Speaker 3 (00:51):
I'm here with Emma Silicons. It's great to be here
with you. And you're on a trip from Australia.
Speaker 4 (00:57):
Yes, I am working on to see your projects while
I'm here and seeing a lot of people in the States.
I used to live here for six and a half years,
so okay, yeah, it's great to be back.
Speaker 3 (01:06):
So I do this show mainly to talk about creators
that are doing interesting things with audio. Right this is
the Spoken Live Show, so I like to cover what
people's thoughts are about how important audio has been to
their life experience and also to where each person sees
it going and what is next for you. So I
(01:27):
don't know if you want to start, just your perspective
on why you got involved in audio or audio, Yeah,
what's been your experience with it?
Speaker 4 (01:35):
I my introduction to audio, I think, in the first
instance was as a actor. So I used to be
an actor, dancer, singer back in the day. That was
my first career I was. I used to be a fairy,
and then I got into films and performing on stage,
and that's how I grew up in the craft.
Speaker 5 (01:52):
And then I think audio was always something that was
part of.
Speaker 4 (01:55):
That environment, right, Like when you're reading lines on stage
as an actor, you're really immersed in the intention behind
the line and like what that line means to the audience,
and framing it in the mouth in a way that
lands well to the person on the other side, and
thinking about projection and all of those sort of things.
Speaker 5 (02:11):
And then in the film space.
Speaker 4 (02:12):
Kind of bringing that sound back a little bit, and
you've got a camera right in front of you, so
you're thinking about how to quiet and that sound down
a little bit. So I think that was my initial
introduction to audio, was in that environment. So a little
bit different to how most people might onboard towards audio.
They might start in radio or television or something like
(02:34):
that in podcasting now these days, but for me, it
was very much in the dramatic space and also in dance,
So audio was the rhythm of the music, like five six, seven, eight,
so you're keeping rhythm and time.
Speaker 5 (02:48):
And so now.
Speaker 4 (02:49):
When I'm involved with podcasting and radio, I'm always thinking
about audio from those perspectives.
Speaker 5 (02:54):
I think a lot about rhythm and timing.
Speaker 4 (02:57):
I think a lot about delivery and intentions behind lines.
And my career's kind of almost come full circle in
that I've done so many different things since I was
involved in that acting world. I ended up becoming a
print journalist for a time and then worked in radio.
Speaker 5 (03:12):
So I was a host for about eight.
Speaker 4 (03:15):
Years and then moved to New York City ended up
getting involved here. I went to NYU as my graduate school,
worked at the Wall Street Journal, and got involved in
tech and artificial intelligence podcast and I've been in that
world ever since about twenty eighteen, and now I'm in
the podcasting space. So I went from this public radio
(03:35):
background with ABC Australia, hosting shows for eight years, news,
current affairs, all of the things that most of us
would know of public broadcasting, and now I'm in this
space where it's career recorded audio rather than live audio,
and so I work with as a producer and senior producer.
So I've taken my career, as I mentioned, full circle
(03:56):
where I think deeply about intentions behind lines again because
I coach our host on her delivery and her performance.
But I'm also thinking about the intention of complete piece
that we're putting together because I work as a story editor,
so I'm always thinking deeply about like, where are we
coming from for this piece, what experience are we trying
to give our audience, what journey do we want to
(04:17):
take them on? And I think I draw a lot
from my background as an actor, dancer and singer from
my past to do my job today, which is as
a scene of producer of Shift, which is a new
podcast that's come out in the last six months. So
looking specifically at artificial intelligence and frontier tech and it's
impact on our lives.
Speaker 3 (04:37):
It kind of seems like the ar official intelligence aspect
of what you've been doing is a little bit of
a contrast to what you've actually been doing with your career,
and is that the whole performance and creative element of
how do you see AI? Do you think it's going
to harm that or do you think that it's going
to be able to add to it to get better?
Speaker 4 (04:58):
Big conversation, Oh, I think AI is very complicated when
it comes to the creative space. Now we're seeing AI
be able to generate its own music, be able to
generate its own art, but I think one of the
big problems in this space is that we're seeing it
piggyback off the creative talent of artists and not necessarily
(05:21):
credit those artists. So you'll notice mid journey and different
types of artistic platforms, they'll pull from artists, and you'll
even sometimes see the artist's signature on the work that
is developed by AI, but there's no crediting that original
artist for that work.
Speaker 5 (05:38):
So it's just pulling, as all AI does, pulling.
Speaker 4 (05:41):
From big data from a data set and putting that
together in a different form and format. So AI is
only as smart as the data that it's fed and
it's only going to do what it's creator's talent to do.
So until the creators start being more transparent with these
black box algorithms and start actually building in fail safes
(06:03):
to protect artists, we won't really see equity or fairness
in this space. I think there's a lot of unfairness
right now, especially towards creatives in this area. But I
also think that AI can create beautiful things as well,
and if it's used responsibly, can do amazing things.
Speaker 5 (06:20):
And there's an artist that I follow on Instagram.
Speaker 4 (06:23):
I can't think of their at handle at the moment,
but they're an AI artist and they work with dancers
and do this amazing kind of folding imagery that folds
in on itself and follows the movement of a dancer.
And so something of that beauty couldn't necessarily be created
just to the naked eye. This is something that's like
(06:44):
graphics and dance and art all rolled into one. And
so that's an artist taking something and creating something new.
So I think the creative space and AI is very complicated.
I don't think AI is enough to eradicate.
Speaker 5 (07:02):
The human condition of what we're doing.
Speaker 4 (07:04):
We were just talking off air a little bit earlier,
Rob about narrow intelligence versus wide intelligence, and how AI
is very good at doing one specific thing and training
on that thing and getting deeper and deeper on that
particular thing, but not so good at when you take
those blinders off and try to have it be more
generally intelligent. That's something that is still a very complicated
(07:26):
place for AI right now and somewhere that it's not
even close to general intelligence.
Speaker 3 (07:31):
And I've seen some flaws in it here recently. I've
seen some work that I've generated with.
Speaker 4 (07:38):
Have you seen the five fingers or six Normally normal
normal fingers have five fingers, but it has a sixth
finger that's popping out, or there's a third eye on
the face, and you're like, the human would never make
that mistake.
Speaker 3 (07:52):
Rod work that i've seen get generated that it's almost
exactly the scene with multiple users, like I saw just today,
I saw a post by a podcaster that posted an
image that was part of his LinkedIn post okay that
looked exactly like an image that I generated from chat GPT,
(08:15):
just like maybe a month ago. Wow, So it does.
I guess if the prompt is you're saying, then it
should generate the same thing. But I didn't really give
it that much of a prompt when I generate, I
just gave it some really broad parameters.
Speaker 5 (08:30):
So we're now talking about.
Speaker 3 (08:31):
So it went out and created the same image, but
it was my impression that chat GPT would never create
the same image. Tie.
Speaker 4 (08:40):
I've only used it in the text format, so things
like it'sprucing up cover letters for jobs and those type
of things. A being quite generalized in your prompt around
please write a professional cover letter for the audio field
with this selection criteria. Please include this job description and
(09:00):
please include my resume items here, or please include my
bio and just like iterating on that and just plugging
in a lot of data. I think AI is very
good at taking a lot of data and sorting that out,
but what you get on the other end may not
necessarily be something that's cohesive.
Speaker 2 (09:17):
Yeah.
Speaker 3 (09:17):
Yeah, because it's increasingly starting to feel like one of
the needs for a detailed prompt is that you don't
want to give too much license to the AI, because.
Speaker 5 (09:26):
You know, you want to keep it very controlled.
Speaker 3 (09:29):
Because it will do what I just gave an example of,
it'll create the same image. Right, So if you make
a real simple prom.
Speaker 4 (09:36):
For the same cover letter, I'm sure there's now people
reading a lot of cover letters that are very much
in the same vein because they're being spat up by
the same program.
Speaker 3 (09:46):
Right, Because the AI has to come up with a
model of those very kind of outputs that are generated
based on a very generic problem.
Speaker 4 (09:54):
I think it's good to use AI as inspiration, So
use it as a kind of phone a friend fee
like you might in the past have done a cover
letter and send it to your friend and got them
to spruce it up. Instead, you're going to chat GBT
if you're using three or four or Gemini or some
other AI platform, and you're now using that as your
(10:14):
sort of inspiration and getting help, but not necessarily relying
on it. I think even things like driverless cars, we
thought that they would be a lot further than they are,
or the promise was they'd be a lot further than
they are today, and that we'd have all these fleets
of driverless cars just driving people around, and we're not
at that point yet. They're still, while very smart in
(10:36):
some ways, very dumb in other ways. I mean, if
a bird flies passed a vehicle, it's very quickly thinking
that's a hazard and like putting on the brakes, or
it's just it's very interesting, not all that reliable.
Speaker 3 (10:49):
Yes, I think the big takeaway for podcasters is that
it's still early days for that stuff.
Speaker 4 (10:54):
And well even when you hear the AI editing. Have
you heard in descript and different processes that you might
listen to. It cuts off parts of words, So people
are relying on something and not doing the craft, which
is sitting down in audition or sitting down in pro
tools and actually editing top to tail. But these AI
softwares are cutting off actual words like humans, if they
(11:15):
were editing, they wouldn't cut off a word, right, You
wouldn't cut off part of a word. You wouldn't cut
it that tight in your editing, whereas like AI will.
Speaker 3 (11:25):
Yeah, it can actually do that, right, especially if there's
any kind of cross talk. I've seen that. Yeah, I
used the descript tool as well, and I see these
things come up on a regular basis as I try
different capabilities. Yeah, it's an interesting time with the podcasting space,
and increasingly as you were talking about your experience with
(11:45):
podcasting and how you approach it more from a performance perspective.
Speaker 4 (11:51):
Yeah, I would say that's important performance in the past,
that's definitely it still shapes how I think about things.
But I would say, if I had to give you
one thing that is the driving force behind what I do,
it's always audience. So it's the same in a performance space.
You're always performing to an audience, right. I think when
(12:12):
we're making podcasts, or when we're making anything that is art,
we're doing that for a particular audience. So I try
to do the to kill a mockingbird thing and put
myself in their shoes. What is it that the audience
wants to listen to, what is it that they want
to feel?
Speaker 5 (12:29):
How do I move them? How do I take them
somewhere with me?
Speaker 4 (12:32):
So I tend to have a very audience centric approach
to how I create content. I'm always thinking about what
piece of audio would be the one that would knock
them over the head and be the most interesting, or
what would paint a picture for them. I'm not thinking
of it from a me perspective of performance, as in
I am out there performing. I'm thinking of it as
what can I give the audience? What gift can I
(12:55):
give the audience out of what I'm creating.
Speaker 3 (12:57):
Yeah, it's an interesting contrast to my experience with the
very early days of podcasting, because the example that you
are giving is a creator thinks about creating content for
a type of an audience or a demographic of an audience.
Versus I think the early days of podcasting, I think,
but a lot of podcasts started. They would do a
(13:20):
show that they had a passion for. They did it
in a very authentic and real way.
Speaker 4 (13:26):
But it was about them and not about the audience.
Speaker 3 (13:29):
See that's the line here. It's different is that oftentimes
the creator thought of themselves first, so it was what
they wanted to talk about or their passions were. And
what you're doing is you're self selecting your audience based
on connection with those interest areas versus trying to come
(13:49):
up with a piece of content that maybe not coming
out of who you are as a person. But it's
like a concept to be able to reach a certain audience.
Speaker 4 (14:00):
And it's interesting. I think I've this audience centric approach
has also been born out of the fact that I
grew up in public radio, and so we had to
get out of the way, or did that, We.
Speaker 5 (14:09):
Had to get out of the way. We couldn't be
the star.
Speaker 4 (14:12):
If we're using that performance analogy in my past, we
couldn't be the star that everyone was looking at. It's
not about you, it's about the story and serving the
story and serving the audience.
Speaker 5 (14:21):
So in public radio, there was no place for egos.
Speaker 4 (14:25):
You had to remove yourself from a story and be
completely objective in the way that you were doing it,
because that's the whole form and format of public radio, right, Yeah.
Speaker 3 (14:35):
Yeah, I think it is. And also I think we're
talking about also the contrast between natural conversations or like
a back and forth versus a very scripted performance. And
I done both, So I think we have to draw
a little bit of a distinction there that oftentimes these
performative to a certain audience are usually oftentimes scripted or
(14:59):
pay the outline. I don't know, what's your experience with
the tension around that, around outlines versus more kind of
conversational bit.
Speaker 4 (15:07):
Yeah, I think I've done both. I think my public
radio up until last year, I was on air with ABC,
which is the public broadcast in Australia. So I was
doing breakfast radio programs, and I think that those are
much more free and much more conversational. There's a topic
for a particular day, and you're talking about a particular subject,
and so you're talking around that subject. So say, I
(15:30):
don't know, for example, let me draw from an example
from about ten years ago when I was on air
there's a gas explosion that happens while you're on air,
and you later find out it's a double murdered suicide.
Speaker 5 (15:39):
So you're talking about this on air as it happens.
Speaker 4 (15:43):
You hear the sound in town, and it's very conversational
and very free because.
Speaker 5 (15:48):
You're moving in that moment.
Speaker 4 (15:50):
You're ringing police, you're ringing emergency services, you're bringing them
on live. The story is developing over time because you're
learning more and more information. Then you might go out
to the scene and then that becomes pre recorded content
because you're now out of the scene, you're no longer
live on air, and that's when you're learning that.
Speaker 5 (16:08):
And this gets a little bit dark.
Speaker 4 (16:09):
But there's body parts of children on the road, there's
awful things that have happened, and you're gathering more and
more data, and so that story develops as you go along,
and then you learn, okay, this was not an accident,
this was something that was a double murder suicide. That's
more information that comes to lights that might come over days,
and in talking to those different people that are a
(16:32):
part of it, you might talk to For example, I
ended up speaking to the sister of the woman whose
children had died, and so those type of things, those stories,
the conversational type stories, they develop over time in radio
and in live whereas in pre recorder do you have
all of the information there to begin with, Right, So
you've gone and you sought all of your interviews, you
(16:54):
sought all of your facts and figures. You might have
ten interviews, and then you sit there and you plow
through those ten interviews and you do what we call
a best of, and you listen to the best of
the best audio from that, and then you decide from
that best of how many minutes of each of those
people will be in a five part series. So you
might have one hundred hours of content, and you might
(17:15):
be passing through that, and then once you've decided the
best of the best, then you're putting it into a
script format and you're going, Okay, this person says something
similar to this person, so let's place those together in
this audio series that we're developing. And this gets more
investigative in nature, because there's a lot of fact finding,
there's a lot of story seeking, and you go, okay,
this narrative might mix nicely. We've got two people saying this,
(17:39):
who's the person that's coming in and being the voice
of reason. Here are we talking to someone about the
legislation around AI? Can we bring them in and be
the opposing voice here? Okay, so we'll bring them in
this episode. And I think the difference in the story
quality is both have their place, but one is developed
over time and in chunks and the other one is
(18:01):
the development space. Yeah, happening in real time is definitely
your live element, and you're learning your story as it
goes along, and you're bringing that audience on a journey.
The hard thing about narrative podcasting is that you have
to still bring that audience along on that journey. But
you already know what the whole journey is. Ro do
you know what I mean?
Speaker 3 (18:22):
Just sharing it?
Speaker 4 (18:23):
You're just sharing it. So then you have to think
about you asked about tension. Where do we build in
that tension? How do we build in that tension? Because
we already know all the information. And one thing that
I see podcasting do a lot, and it absolutely frustrates
the shit out of me, is like this is a table.
Oh that's a table, This is a table, and oh
(18:43):
that was a table. You hear people like saying that
this is a thing. Then you hear the audio about
the thing, and then they backing ounce the thing and
say the thing again. I don't know if you've noticed
that in podcasting, it seems to be a style that
some people do, and it takes away that magic and
that joy of just discovering something when you don't have
to pre announce that it's a table.
Speaker 5 (19:03):
We could just hear the grab about all the select
depending on what.
Speaker 4 (19:06):
Country you're in and what you call audio, you just
hear this select about the table and you go, oh, wow,
that's a table, and then you hear, yeah, that was
Rob talking about the table. But you don't have to
say table again because we know you're talking about the table.
So one thing I've noticed in podcasting, and this would
be I think one of my criticisms of the craft
where you do know the whole story beforehand, is there's
(19:29):
sometimes a lot of over explaining that happens, and you
lose a little bit of that joy as an audience
member of discovery, of being surprised of.
Speaker 5 (19:39):
That element of learning discovery.
Speaker 4 (19:44):
And I don't think.
Speaker 5 (19:47):
I don't think we need to tell all of the story.
Speaker 4 (19:50):
I think we can allow people the agency to make
up their own minds a little bit. And I think
live radio is very good at that because they have
to be, because it's happening as things develop, so you're
coming along on the journey, Whereas I think podcasting some
people are very good at it and some shows are
not so good at that.
Speaker 3 (20:10):
And radio tends to be shorter. Right, So you've done
a short chunk of.
Speaker 4 (20:15):
Time, just seven minute packages, yeah, ten minutes.
Speaker 3 (20:18):
Yeah, versus a podcast where you just discal.
Speaker 4 (20:20):
Ramble or you don't ramble. The type of work that
we do, we shift and in machines we trust, and
the future of everything. It's These are the podcasts that
I've worked on over the past five six years. They've
been heavily produced, so a lot of soonification from our
composers and audio engineers, so really deep thought about how
(20:41):
do we create a mood with that music that we're
placing around what we're doing. And then sometimes if it's
an investigation hundreds of hours of going through hundreds of
hours of audio, that ends up being one sixty minute
series across five episodes, right, yeah, So it just really depends,
and I think different people have different ways of inserting
(21:04):
that tension. But for me, it's about not telling the
whole story and allowing the listener to discover things for themselves,
and also just using the gold as well, just taking
Rather than thinking about what is the story I want
to tell, I think about what audio do I have first,
and where is the gold? And I dig out all
(21:26):
of that gold and then I try to put that
in front of me and then I use that to
guide the story. And archival audio as well helps bring
something to life, like a moment in time or a
particular thing that happened, or a news event, like using
archival audio from that time, I think helps paint a picture.
Speaker 5 (21:44):
I'm always thinking about how am I painting a picture
with words?
Speaker 4 (21:47):
They say a picture tells a thousand words, but I
think sometimes audio can do the same thing. It's experiential,
like to hear the sound of a place and you're
just if you've got it. It's quite intimate if you've
got it in your ear as you've just taken there,
And like, how do we I don't know, how do
we serve that intimate space with that people are giving
us and make sure that we're giving them something that's
(22:09):
interesting to listen to where they can learn something and
take away something.
Speaker 3 (22:13):
Yeah, it's interesting. The whole experiential part of it is.
Speaker 4 (22:17):
We want to take people somewhere, do you know what
I mean, or help them learn something, or guide them
along a journey where they're learning something new. Right.
Speaker 3 (22:26):
A lot of podcasting is it's just conversational banter back
and forth. And I know a lot of terms get
thrown around to refer to different types of formats of podcasts,
and this whole term of quality podcasts or high quality podcasts.
Speaker 5 (22:43):
And long form narrative podcasts.
Speaker 4 (22:46):
Yeah, mostly in yes.
Speaker 3 (22:49):
Jess, how do you think about this concept of drawing
distinction between what's a quality podcast and maybe what is
equality podcast?
Speaker 4 (22:58):
I think quality is in the ear of the beholder, right, Yeah,
it is. It depends on to some degree.
Speaker 5 (23:05):
Exactly, but it depends on what you're interested in.
Speaker 4 (23:08):
And you might love those double hitter two people talking
heads podcasts backwards and forwards. That might be your jam.
So who am I to say that's not quality? It's
not heavily produced. I think we can talk about a
light production value and a heavy production value. What I
tend to make are things with more heavy production values.
(23:28):
When I was in live radio, that was less heavily produced, right,
So that was more a talking heads and you could
turn those into podcasts quite easily, top and tail and
off you go. And those don't have hundreds of hours
put into them, whereas something that got the soonification, something
that's composed, something that works with sound designers, something that
(23:49):
takes an investigative look at something and has say hundreds
of hours of audio and might take that down to
a five part series that are twenty minutes each. That's
a higher it's a high quality of audio, but it's
a high production value. So I don't think that I'm
the one that can tell people what they should and
shouldn't listen to or what isn't high quality.
Speaker 5 (24:11):
I just know that there's a lot more time money, and.
Speaker 3 (24:16):
There's a spectrum.
Speaker 4 (24:17):
Yeah, time and money I would say put into those
higher production value podcasts. And sadly that's where we're seeing
a lot of the space drinking at the moment podcast
world right now, Like just there's no money, there's no
ad sale dollars. We've got two waws that are happening
(24:38):
globally that have meant that companies are tightening their belts
when it comes to ad sale dollars, and it's sad.
Speaker 5 (24:44):
We're seeing a lot of really good podcasts.
Speaker 4 (24:47):
Die and a lot of them are those ones that
cost a lot more to make with higher production value.
And that's sad because that's the type of stuff that
really I think engages people and stands the test of time. Well,
it's not just the shelf life of a day or
a week. It's something that lasts a lot longer than that.
Like you can go back now and listen to our
(25:07):
series on AI and hiring if you're looking for a
job to learn about where artificial intelligence is inserted in
the hiring process, and that work is still as relevant
today as it was. I think it was three years ago,
or it might even be four years ago that we
made that series within Machines we Trust with MIT Technology Review,
and we were talking about where there were issues in
(25:30):
the process with AI and hiring and resume passes and
people that were being like resumes that were being left
on the cutting room law and never making it to
hiring the managers because they might not be a male name,
or they might not have played lacrosse. So certain resume
passes and different sorting software meant that like they were
(25:50):
never seen by the people that needed to see them,
and so we're still seeing people deal with this today.
I'm an immigrant. You can hear from my voice that
I am not from America. I'm Australian and I don't
know about you. But when you fill out you might
not have noticed the box. But when you're filling out
job ads in the US, there's a will you ever
now or in the future require sponsorship box. If you
(26:11):
click yes, a lot of the time you're automatically discounted
from the hiring process. Your resume never makes it to
the hiring manager. So you either learn to get really
good at sponsoring your own visa, which is what I've
gotten really good at, so I can say no, I
don't require this, or you say yes and you have
to knock on the door of the hiring manager to
(26:32):
get your resume scene because you're never going to get
seen if it's up to the algorithm, because you are
a problem child. You're causing extra work for hiring managers,
or at least so they think. Some of these international
visas like an E three are actually quite easy. And
if you learn to self sponsor an O visa, you
know that's something that hiring managers never even have to
worry about.
Speaker 5 (26:52):
But it's a complicating factor.
Speaker 4 (26:55):
And I find that in the AI and hiring space,
these complicating factors mean that people are less likely, Like
being a woman is a complicating factor. If you're a male,
you're much more likely with certain software, and we prove
this in our investigation to get hired than you are
as a woman. Wow, So it's just according to some
(27:15):
resume sca vrapers with some organization. So also, if you
speak in a really confident voice like this, you're much
more likely to get hired. Even if you do your
job interview in German or in Mandarin Chinese, you're much
more likely to get hired or to be seen as
a good candidate by a particular AI that's looking at
your voice than if you were speaking English in a
(27:38):
softer tone and something that didn't sound as confident like
Sometimes it has nothing to do with the content, which
is absolutely god we found when we were doing our investigation,
Like these types of software, these types of AI that
are used sometimes aren't as smart as we think they are.
They're just literally looking at how confident does your voice sound?
But it can't tell the difference between an artificial voice
(28:01):
and your own voice. We actually trained an AI algorithm
to have a fake voice of our host on this series,
and it couldn't tell the difference, and it actually rated
the AI the fake voice as more engaging than the
real voice at times. I can't remember exactly what.
Speaker 3 (28:17):
The criteria were a little bit more, I.
Speaker 5 (28:20):
Can't even remember exactly what criteria we've placed around them,
but the voice, yeah, in.
Speaker 4 (28:25):
Certain areas it performed better than the real voice of
Jennifer Strong, who is my host that I work with.
And we actually went through some of these higher view
and clear view not clearview, higher view I think it
was called. It's been a while since I've done that investigation.
But you'd have to jump in and have a look.
If you type in AI plus hiring plus in machines,
we trust you'd pretty quickly find the series. And it's
(28:47):
a really interesting series and like that type of content.
I know that I've told a long story to get
around is just to say it's just as relevant today,
even though they've come further in developing these type of technologies.
Clear View AI is a completely different technology that I
got confused with. That's has to do with another world
of things that we can discuss another day, but Higher
(29:07):
View is one of the ones that I was thinking
of is the type of software. But there's so many
different ones out there. But if you jump online and
have a look at the investigation and listen to it,
you'll be just as brushed up on that, if not
more than I have because I haven't listened to it
for a couple of years. But it's just as relevant today.
And so that type of content, like those hundreds of
hours that we use to make that at the time
(29:29):
were well invested because it has that longest shelf life,
whereas like something that you and I do on the
presidential election, right, if we were to talk about the
race for president, if we were to talk about the
race for president, I've just knocked my coffee cup in
front of me.
Speaker 5 (29:42):
Rob, I don't know if you could probably hear about
in the background.
Speaker 4 (29:44):
That type of content might be dead in a week
or two, because it's very relevant to today. So I think,
going back to that phrase that the beauty of a
piece or the quality of a piece is in the
era of the beholder, is such that it's really up
to the person listening and what their fascinating are and
what they're interested in as to I guess what tickles
their fancy and what commands their attention. And I'm really
(30:08):
not qualified to tell other people what they should and
shouldn't listen to.
Speaker 3 (30:12):
Yeah, I think if you look at the overall numbers
of where audience consumes content, what types of content that
they consume in larger quantities, I think they do tend
to be programs that are higher production quality type content.
And I think that may be.
Speaker 4 (30:28):
I think the data would speak to that.
Speaker 3 (30:29):
Yeah. Yeah, I think there are some evidence of that.
That's not to say that there aren't successful shows that
are not what we in that upper spectrum of production
quality that are successful. I think that there are showings
that are very conversational and not highly produced. But I
do think it's putting a lot of pressure on the
industry of podcasting to do more with less.
Speaker 4 (30:52):
Right now, with the crunch on ads shrinking of the industry. Yeah,
it's just a really challenging time right now. Yeah.
Speaker 3 (31:00):
So instead of having six people on your staff on
a podcasting project, where we can have multiple producers, multiple
hosts and stuff like that, I think projects are being
created if they're being even funded.
Speaker 4 (31:12):
I was going to say, a lot of them killed.
Speaker 5 (31:14):
A lot of them have been killed.
Speaker 3 (31:15):
Right, or smallerutines, maybe one host, maybe a couple of producers,
and that's about it.
Speaker 4 (31:20):
And if that keeps happening, we're going to have to
go back to more of that two heads having a
conversation like similar to what we're producing here today, because
it doesn't take as much time and effort as an
investigation like one that we're working on at the moment.
It's going to be hundreds of hours. Yeah, But I.
Speaker 3 (31:34):
Also think that AI will augment this as well. I
know I heard a lot of people this was like
a year ago, talk about how a lot of the
startup investors are increasingly thinking as they think of AI,
is that it may be possible in the next couple
of years to have a billion dollar startup that is
(31:55):
run by three people.
Speaker 4 (31:56):
Well, times are certainly a changing, and the proof will
be in the pudding, right, the same as that conversation
about driverless cars earlier, that we thought that there would
be so much further along than they are now, but
that narrow intelligence hasn't widened into general intelligence. So to
wait and see whether this billion dollar company run by
three people will actually exist or whether it's just something
(32:18):
that we think might exist.
Speaker 3 (32:20):
I think right now it is a projection of right
people think might happen if the AI gets powerful enough.
Speaker 4 (32:27):
Will be very interesting. I think it's like cracking that
lot of general intelligence. And that's actually what a series
that we're working on right now is about. We're looking
at the race for super intelligence, and we've got some
Pulitza funding to work on a series about that. Yeah,
it should be interesting on this amid this backdrop of
generative AI and everything that we're seeing with things like chat,
(32:48):
GPT three, Gemini and others. And yeah, we're looking at
this narrow intelligence that currently exists and where we are
in this race for super intelligence, talking to people like
Gary Marcus and other luminaries. So it's an interesting series
and we'll certainly keep you informed on what happens with
that one. Then.
Speaker 3 (33:06):
Yeah, I've been increasingly thinking about this whole concept that
maybe in ten years or whatever, I could acquire five
or ten robots that can do a bunch of work
for me out there to make an income for me
or something like that.
Speaker 2 (33:20):
Yeah, can we.
Speaker 5 (33:21):
Get the cryptominas onto it? You already have that happening now.
Speaker 3 (33:25):
Yeah, or I think you can. I think there might
be an opportunity for people that go out and buy
low costs, use Tesla's right now, and maybe eventually turn
them into robotaxis just in a couple of years, right
when you start making an income based on these robots
going on and doing things in the world.
Speaker 4 (33:44):
One thing is for sure is that the world is
a very interesting place right now, and that we haven't
even conceptualized half of the things that will exist into
the future. And I think we're living in a very
exciting time.
Speaker 2 (33:57):
Yeah.
Speaker 3 (33:57):
I just wonder how it's all going to impact media
like an our career.
Speaker 5 (34:02):
It will be very interesting to see.
Speaker 4 (34:03):
I think it's a shrinking space and has been for
a number of years. And I hope that artificial intelligence
has a place, but not too much of a place
in that it gets rid of people like you and I,
Rob right.
Speaker 3 (34:16):
They probably will replace us. Actually, I'm already accepting the
fact that it probably will.
Speaker 4 (34:20):
I guess I'm not ready for it.
Speaker 3 (34:22):
To the questions, how am I going to respond to that?
Am I going to.
Speaker 4 (34:27):
I don't think that as you are part of it?
Speaker 3 (34:29):
Or am I going to I don't.
Speaker 4 (34:30):
Think an AI can do what we can do in
this conversation here and have nuance and have a conversation
like what we've just done. So I don't know if
we can completely be replaced, but certainly functions of our job,
like editing, I think might get replaced, and then we'll
find that time and.
Speaker 5 (34:47):
Allocate that time to something else.
Speaker 4 (34:49):
Wouldn't it be great if we had them the ais
doing all the chop work, and we were able to
invest more time into investigations.
Speaker 5 (34:56):
And doing actual the.
Speaker 4 (34:58):
Creative sound and really, I don't know, really investing it
in the areas that brought good to the world.
Speaker 3 (35:04):
If the robots can go off and do all the
dangerous work, all of the work that nobody likes to.
Speaker 4 (35:08):
Do, send them out as war reporters rather than journalists.
Speaker 3 (35:11):
Just they walk out with a microphone. Their task is
to capture the audio.
Speaker 4 (35:16):
From somebody exactly.
Speaker 3 (35:17):
Yeah, yeah, it's too interesting.
Speaker 4 (35:19):
Send them out via a drone, you drop them in
the field, or even attached the microphone to the drone.
Speaker 5 (35:24):
But I'm sure things like that are already happening.
Speaker 3 (35:27):
It's certainly technically classible.
Speaker 4 (35:28):
Yeah, absolutely, yeah, But I guess we'll just have to
wait to see.
Speaker 3 (35:32):
Yeah, that must have been great talking to you.
Speaker 4 (35:34):
Yeah, it's been lovely. It's been a bit of fun
and trying out this new software, this new recording equipment
that you've got here today as well. It's very exciting.
Speaker 3 (35:43):
Yeah, it's another example of the changes and technology far
things have come.
Speaker 4 (35:48):
Yeah.
Speaker 5 (35:49):
Absolutely, I look forward to hearing the finished product.
Speaker 3 (35:51):
Okay, all right, thanks.
Speaker 1 (36:00):
Mm hmmm hmmmm. The inspiration spoken word, tech and connection,
Spoken like boken, spoken Light, Spoken Light, Spoken Lights with
Rob Greenley with Rob Greenley with Rob green
Speaker 4 (36:19):
Mm hmm
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