Episode 32 with Jeremy Tuttle: Balancing Innovation and Practicality when using AI in Instructional Design

Show Notes

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Unlock the secrets to harnessing the power of generative AI in instructional design with our special guest, Jeremy Tuttle, Director of Learning Design at Niche Academy. Jeremy shares his firsthand experiences using tools like ChatGPT and Adobe Illustrator, revealing practical insights on their applications and limitations. Learn why creating effective learning materials still demands significant human intervention for editing and fact-checking, and discover the challenges of aligning AI-generated images with specific design needs.

As we explore the broader landscape, we draw intriguing parallels between the current AI integration process and the early days of UX/UI design. Hear about the balance required between pushing the boundaries of innovation and maintaining practicality, underscored by a nostalgic anecdote about the evolution from MySpace's customizable pages to today's streamlined interfaces. We also delve into future trends such as learner personalization, chatbots, and the critical role user feedback plays in refining AI applications.

Finally, we tackle the ethical dimensions of using AI in creative fields. From the necessity of artists' consent and fair compensation to the complexities of AI training pools, we leave no stone unturned. Drawing a provocative analogy to the Napster era, we question the implications of AI-generated art on the value of creativity. This episode is a compelling exploration of the intersection between technology and artistry, advocating for stronger protections to ensure art continues to thrive in the age of AI. Join us for an essential conversation that challenges and enlightens.

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What is Demystifying Instructional Design?

Instructional designers specializes in the creation of learning solutions. In this podcast, Rebecca J Hogue interviews instructional designers from a variety of different fields, with the goal of gaining a better understanding of what instructional designers do. The role of instructional designers varies across contexts including formal education, corporate sector, healthcare, non-profit, military, government, and more. Further the types of learning solutions created by instructional designers varies including learning programs, semester length courses, short workshops, eLearning, and job aids. Join Rebecca as she demystifies instructional design.

Rebecca Hogue: Welcome to
Demystifying Instructional

Design.

This is the new season and this
season we're going to focus on

the use of generative AI, in
particular, in instructional

design.

Welcome, jeremy.

Can you start by introducing
yourself?

Jeremy Tuttle: Yes, First of all
, thanks for having me back.

I was in the last season and I
had a great time and I'm so

thankful for thanks for having
me back.

I was in the last season and I
had a great time and I'm so

thankful for you to invite me
back.

I'm Jeremy Tuttle, I'm the
Director of Learning Design for

Niche Academy and I lead a
fantastic team in the creation

of many, many tutorials.

Rebecca Hogue: When we last
talked, chat GPT was just

starting and you said you were
going to wait until chat GPT-4

came out before you tried it out
.

My question is how did that go?

Did you try it out?

Do you use it?

Jeremy Tuttle: Yeah, yeah, we
tried it out and it did a great

job with the text generation
side of things that we were

looking at, but it didn't quite
meet our needs in the way that

we thought it would.

We have a very particular style
in which we write and we have

very specific goals that we want
to hit as we're putting

together our learning material,
and when we asked ChatGPT to

spit out something to help us in
our process, we found that we

were spending more time editing
what had been spat out to bring

it into our style and then also
spending more time fact-checking

the information than if we had
just done it ourselves.

So we don't use it to generate
learning material, but we have

used it to generate scenarios.

You know, if we're going to ask
a learner to think about a

certain situation that they
might be put in in regards to

the topic at hand, an easy
example would be you're working

with an angry customer and you
need to do X, y, z as part of

the process.

Instead of trying to envision
what an angry customer could be,

you could just ask ChatGPT
write up a script of a customer

who's angry about this thing and
then it'll spit out three or

four paragraphs describing how
this angry customer is angry and

that has saved us time.

Rebecca Hogue: That's
interesting because I heard that

as well, the idea of scenarios,
but I wasn't quite sure how you

would use it.

I have Notions I pay for the
intelligent version of Notion

with the AI in it, and I asked
it what questions to ask, and

the first question it gave me
was how has AI influenced your

approach to instructional design
?

Jeremy Tuttle: Good question.

As of right now, it hasn't,
because I see AI as something

that's alongside me, not
something that's leading me, so

my process hasn't really changed
.

I just now occasionally go to
it for help with ideas or as a

sounding board, more than
something that's going to

dramatically change my process.

Rebecca Hogue: And we talked a
little bit about the text

generation.

Do you use it for any image
generation?

Jeremy Tuttle: So that's another
thing where we have a very

specific style that we have and
the files that we create for our

imagery are then used for
animation.

So they have to have layers in
specific spots so that our

animators can find the thing
that they need to animate and

then use it effectively.

If you generate an item in
Adobe Illustrator, it might look

good, but when you dig into the
layers it's a mess.

Rebecca Hogue: Okay, I was going
to ask you about Adobe

Illustrator because, yes,
chatgpt4 can create images.

I use it to create feature
images for some of my blogs, but

in instructional design, we're
more likely to be using

illustrations and using Adobe
Illustrator, and they have this

new beta adding in backgrounds
as well as icons, and so you're

saying that the backgrounds that
they add are just sort of a

mess.

Jeremy Tuttle: Oh.

So the way I like to describe
it is that if you want to take

what it provides you wholesale,
it's great.

If you just want to take what
you see and run with it, it does

its job.

But if you need to edit it in
any capacity the way that the

file is built, the way that the
layers are organized, the way

that the groups are grouped
together it becomes a real

hassle to try to get the image
to look the way you want it to

look.

For the example, this wasn't as
part of my work, but just a side

project that I was doing.

I was in a play and we needed a
logo for a sporting goods store

.

So I was thinking this play is
set in Minnesota.

People like to fish.

Why not have this sporting
goods store be like a

fisherman's and a hunting
sporting goods store?

So I asked Illustrator to
generate a logo that would

include a fish in some capacity,
and it spat out this beautiful

I believe it was a bass jumping
out of the water.

It had lovely splash marks, it
looked good, but it was far too

busy to be believable as a logo.

It had way too much detail.

So I wanted to go in and just
remove a little bit of the

detail.

So I click into layer one and
there are, out of 200 layers or

so, group one, 200 layers, and
so I have to go find this shape,

delete that shape, this shape,
delete that shape.

But they're these little tiny
specks everywhere.

So, try, trying to bring it
down to be a more simple one,

and even if I prompted it to
give me simplify it, simplify it

, simplify it, didn't matter.

I still had that issue.

So if I want to create
something that I'm going to work

with and on, I feel it's still
easier to start from scratch

than it is to take something and
try to mold it and push it into

something that I feel I need.

Rebecca Hogue: And have you
tried any of the video creator

stuff?

Jeremy Tuttle: So I know in
Adobe Premiere they have a

couple really good tools like
the Remix tool.

If you're working on video and
you're in Adobe Premiere and

you're going to put music down
on your video, use the Rem remix

tool.

What it does is you can stretch
a song longer or you can shrink

a song shorter, but it will
always end on the end of the

song and it will find ways to
stitch the song at good specific

spots so that as you're
listening to it, you can't tell

that there was a transition
applied within the music as you

shrunk it or stretched it.

Rebecca Hogue: That sounds super
handy.

Jeremy Tuttle: It is incredibly
handy.

One of the things when I was
doing video editing a lot in my

prior job, I was really good at
stitching music together in that

in that way, I play a lot of
musical instruments.

I love music, so I could find
the beat.

I could cut on the beat, pull
it together.

If it didn't sound quite right,
I could add a little bit of a

crossfade so that it blended the
cut a little bit and it took me

probably five minutes five to
10 minutes per video doing that.

But with the remix tool you
just go.

I wanted this long.

Then it processes for maybe 15
seconds and it's done and all

the ones that I've checked it
sounds immaculate.

So highly recommend the remix
tool.

There are other tools.

I have a list up on my other
screen so I don't forget.

They have other tools like auto
reframe, which, if you're

filming your, your training,
that could be helpful, but it's

not that difficult to reframe
yourself, so adjusting the scale

of the image and then changing
the position to sit where you

want it to be.

Rebecca Hogue: Yeah, I don't
find that a particularly

difficult task.

Jeremy Tuttle: Removing the
background was an interesting

one yeah, and that's been
available in After Effects for

quite some time Using keying
effects.

Traditionally, you would auto
key using the color green or the

color blue.

I'm probably diving way too
deep into video production here

for this audience, but taking
that same technology, you don't

always have to do green, you
don't always have to do blue.

So is now that we have uh,
what's it called?

I can't remember what it's
called, but it it can detect

lines in pixels, so the shape of
my head produces a line

compared to the background.

As long as the system can
identify where that line is, it

can easily pull everything else
out.

So it's a good thing that
they're adding it to Premiere.

Rebecca Hogue: Yeah, and
actually if you're recording on

things like Zoom, you just ask
your audience or you're

recording people to use a green
screen background, which I

thought was a very interesting
thing.

You just use, change the
background to green and you have

a green screen.

Jeremy Tuttle: Yep, there you go
, there you go, done.

Rebecca Hogue: So can you
provide some examples of

projects where AI was
particularly useful?

Jeremy Tuttle: Yes, one of our
instructional designers was

working on a project for working
safer hours.

It's part of our OSHA series
making sure that people

understand what safe work hours
is and this is one of the times

where she could not quite
envision a specific scenario

where a manager who's in charge
of setting a schedule could look

at the situation and then react
to it.

So she asked chat gpt uh,
provide me a scenario where an

employee is working unsafe work
hours.

Just give me that.

And she was given a story.

She was able to change some of
the details, but it, she said,

it saved her three hours worth
of time and effort trying,

because she just felt like she
was hitting a wall.

She didn't have the creative
spark at that moment in time to

put that kind of a story
together, so that that's one

instance where it came in handy.

Rebecca Hogue: It sounds like
that is the most common use from

instructional designers right
now is in that, creating

dialogue or creating scenarios
that you know.

Act as that other person and
tell me what you think.

Jeremy Tuttle: Right right.

Rebecca Hogue: Have you had
challenges in integrating AI

into your processes?

Jeremy Tuttle: That's a good
question and I think it comes

down to what.

Do I consider the threshold as
a challenge.

I don't think we've tried to
implement it as earnestly as

others have.

We have a very specific process
that we follow and we've

considered it at different
points along the process.

And if it hasn't worked in that
point of contact of the process

, we haven't tried to force it
in some other capacity.

And some people might say
that's not innovative.

Other people might say you're
doing the right thing.

From from my perspective, I
need to keep my production line

moving and I'm not willing to
halt the presses long enough to

really dig in and see if it
would make a difference.

And it's not impacting our ROI
to not put it in with fidelity,

so I'm not incentivized.

Rebecca Hogue: Do you have any
thoughts on how it will change?

What do you need generative AI
to do to be useful for you?

Jeremy Tuttle: That is a really
good question and I recently

spoke at the Al Polly Humboldt
Innovation Summit and with this

group.

They were not concerned.

They were interested in
innovative ed tech and how

people are approaching AI and as
part of that presentation I

went into user experience, user
interface, ux, ui stuff and I

think that generative AI is
following an extremely similar

trajectory as UX UI faced a
decade ago.

So I'll give the example that I
gave there, which is and all of

a sudden my brain just turns
into presenter mode and I was

about to ask the room raise your
hand, If you ever had raise

your hand If you ever had a
MySpace page.

Rebecca Hogue: Oh, can't say
that one.

No, I did not have a MySpace
page.

Jeremy Tuttle: But are you aware
of what MySpace looked like?

Yes, yeah.

So everybody on MySpace had the
capacity to change how their

page looked.

You could put a plain
background.

You could put an image as your
background.

You could desolate an image of
you and five friends to be your

background.

So that background could be
incredibly simple or incredibly

busy.

You have the capacity to change
the font color, to change other

bounding box color.

So if you were so inclined, you
could put a bright orange text

on a slightly less bright orange
background and you weren't

stopped.

So if people came to your page
you'd have to squint really hard

or get out your at mantis
shrimp yeah, notoriously bad for

that yeah.

So in thinking about that, uxui
and in its nascent stage had a

whole world of open
possibilities.

You could design a web page
however you wanted, no matter

how bad compared to modern day
standards it is.

Think of the space jam web page
, where it's got sparkly

sparkles going on in the
background.

And now we know that if
somebody is going to interact

with a website and needs to be
clear what is interactable and

what isn't interactable, we need
to remove noise from those

things that we want them to
engage with, whether it's text,

video, a button and if we remove
that noise, people are.

They feel better in the digital
environment.

Another example would be the
three line icon.

You know the three horizontal
lines Seven years ago?

Well, today that's called the
menu icon.

You go to a website.

You see the three lines or
hamburger icon.

Right, yeah, you see that.

You click it.

It opens a menu and it turns
into a little X and then, if you

want to leave that menu, you
click the X.

The menu swivels back up into
itself.

But seven years ago, when I was
building tutorials here for

Niche Academy, I had to call it
the three line icon because if I

didn't, people didn't know what
to click.

Today I can just call it the
menu icon, and I get zero

negative feedback from the
learners of those tutorials

saying I don't know what a menu
icon is, I don't know where to

click.

So this progression in UX, ui
and understanding how people

want to and need to interact in
a digital space has vastly

improved.

I think the same can be said
for generative AI, in that right

now we're sitting in the
MySpace era.

Everything and anything is
possible.

You can ask it to do all the
things and in five years from

now, we're going to look back at
today and go man, there were a

lot of bad practices going on
back then.

So do I know what those bad
practices are in this moment?

No, I don't.

I don't have the ability to
jump ahead five years and then

look back, but I do think that
what is the entire possibility

of what's happening right now is
not going to be the case in

five years from now.

It's going to be more limited
scope as we learn what end

learners and users for just
general use cases, as they

decide what feels good and what
meets needs.

Rebecca Hogue: And just figuring
out what that is and getting

yeah, because again you think
about the hamburger menu and you

know, yeah, it took a little
while for everyone to know what

it means, but now everyone does
Right, it's the same kind of

yeah, what can we do?

Have you done any learner
personalization?

Do you do any of that?

Have you done any learner
personalization?

Jeremy Tuttle: Do you do any of
that?

I do not.

So the training that my team
creates and provides is meant to

work in any organization and
therefore be very broad, and

then we hand that training off
to our customers and then

customers can customize it to
their unique needs.

So for that sort of
personalization it would have to

be done on the customer side,
not ours.

Rebecca Hogue: Okay, and do you
do?

Have you done any chatbots?

Jeremy Tuttle: We do have a semi
chatbot on our website.

It pulls from the knowledge
base that we have for our

product, but it's very limited
and we do that very

intentionally.

Rebecca Hogue: And so that's
just more on your marketing side

than not really on your
training and creation side.

Jeremy Tuttle: Correct, correct.

Yeah, we don't do a chat bot on
the training creation side.

Rebecca Hogue: Yeah, I think
that's think that is potentially

an interesting future practice.

Whether it turns into being a
future bad practice or future

good practice, the jury's still
out on that one.

Jeremy Tuttle: Yeah.

So I'm glad you mentioned
because one of the aspects of

generative AI that I'm very
interested in is the concept of

authority and expertise.

So, if you don't mind me taking
a minute or two to help define

those terms so that we're
running on the the same

vocabulary somebody who is an
expert is a person who has

extensive knowledge, practice
experience, research on a very

specific topic, and authority is
constructed around expertise

and it's dependent on the
situation and context in which

that information is being used.

So I'm going to give you a very
disparate, very extreme example

of expert information versus
authoritative information.

So we have a dietician,
somebody who specializes in

diets, in nutrition, in getting
people to eat food in a specific

way to meet their end goals.

Then we have you.

You are an expert in what you
eat.

If I asked you what you had for
breakfast, could you tell me

what you had for breakfast?

Coffee, yes, coffee, perfect,
beautiful.

Who would I go to to ask what
you had for breakfast?

Should I go to you or should I
go to the dietician?

If I wanted my question
answered, I would come to you.

You are an authority on what you
eat, but a dietitian has spent

way more time thinking about
nutritional value and diet plans

and whatnot, right.

So even though the dietitian is
an expert, they are not an

authority on your diet.

Using that kind of thought
process, let's put that in

context with generative AI.

Generative AI is pulling from
expert sources.

Is it pulling from all the
expert sources?

Could be debated.

What we'll assume for the sake
of this, this discussion, that

it's pulling from at least one
expert source.

Is that expert source that it's
pulling from authoritative for

your needs?

I'll pull a more recent example
.

I believe it was air canada
that lost ruling because their

chatbot on their website
guaranteed something yeah yeah,

person very sad, her husband
died on a flight.

She went to the chatbot saying,
hey, this happened, I need to be

refunded.

And the chatbot said, well, as
long as you've done this within

90 days, we can refund you.

So she followed that procedure
and then she got a message back

from Air Canada human, not Air
Canada chatbot saying, actually,

our policy is that doesn't
happen at all, sorry.

And she took him to court and
she won.

Air Canada lost and she won,
she won, she won, yeah.

Rebecca Hogue: Because chatbots
yeah, that was actually quite a

remarkable thing.

So the chatbot is considered an
authority.

Jeremy Tuttle: Exactly so.

It's expert information was not
the policies of the company.

The expert information was just
whatever it had back in its

repository.

If we take that in context for
our learners, if we release

responsibility of the company
content that we put in front of

our learners, they are going to
assume it is authoritative

information and they're going to
run with it.

But if I, as a trainer, as a
leader, as a manager, as a

supervisor, do not verify that
that is authoritative

information, that it is what the
learner should be doing, then

that learner is going to be
running down a path that they

shouldn't be and they're going
to be going down it faster if

they're using the AI as their
source because the AI spits it

out faster and if they're not
thinking critically about what

that information is, where it's
coming from, what the sources of

that generated GISA information
comes from, they're going to

keep going and they're going to
keep going.

And the next time I as a
manager, supervisor, trainer,

check in, I'm going to go Whoa,
how did you get all the way

there when all I put was was
here, and then you got to

backtrack, you got to retrain,
you got to.

Rebecca Hogue: Well, and yeah,
it's worse.

Right, yeah, there's nothing
worse than training wrong.

It's like documentation, right,
wrong documentation is worse

than no documentation.

Jeremy Tuttle: Absolutely.

Rebecca Hogue: And yeah, I can
see that from a training

perspective as well.

And so where does the AI?

Where is AI going to learn
authority?

Absolutely.

And yeah, I can see that from a
training perspective as well.

And so where does the AI?

Where is AI?

Jeremy Tuttle: going to learn
authority.

It doesn't, because authority
is contextual.

You need to know the situation
in which it's being presented

and what the intended outcome is
.

For that to happen, but AI,
unless extremely specifically

prompted, cannot do that.

Rebecca Hogue: And then, in
order to do that prompting at

least today you spend more time
figuring out the prompt than if

you would have just done it
yourself.

Jeremy Tuttle: Absolutely,
absolutely.

Rebecca Hogue: So what are some
of the tools that you use

regularly that involve AI?

We've talked a little bit about
Adobe, creative Cloud tools and

ChatGPT.

Is there any other?

Jeremy Tuttle: There aren't any
other tools that I use right now

.

One tool that both intrigues me
and concerns me is Sora.

It's the film generative AI.

You can ask it to say a woman
walks down a Japanese or a

street in Tokyo with neon lights
, and it'll produce a very

convincing woman walking down
the street in Tokyo with neon

lights going on around her.

Rebecca Hogue: In three arms
because you know the image stuff

, you can't get human and you
can't get text.

Jeremy Tuttle: Sora is pretty
good, and that's why I find it

both intriguing and concerning.

I concerning on the ethical
side making sure that you're not

generating politician doing
this insane act but on the

instruction side, there is the
potential to say I need to

demonstrate this dangerous
situation without putting an

actual human at risk.

Can I prompt this film
generator to put a fake person

in this dangerous situation so
that you can help somebody

identify what's going wrong?

So is it at that point?

Yet I don't think so.

Is it cost effective?

Yet?

Definitely not, but at some
point in the future I think it

will be.

Rebecca Hogue: That sounds like
a very useful use case for

generative AI.

Is that danger case where you
don't want to put, or you can't

put, somebody in danger, but you
need to demonstrate what that

danger looks like?

Yeah, yeah, that's actually
really, and we talk about that

as a reason to use simulation
rather than being able to do the

actual in the training context.

Right, you have to simulate
because it's not safe or it's

not cost-effective, right?

Those are big reasons for
simulation, and so that sounds

like it could be a very.

That's where video could, that
generative video could compete

in that realm.

That's an interesting idea.

How do you effectively evaluate
, or how do you evaluate, the

effectiveness of AI in your
projects?

Now, you've talked a little bit
about how some of them are just

not time effective.

Jeremy Tuttle: Yes, the other is
the intended outcome with the

generative text side of things.

Chatgpt is good at grammar, but
tone, even if you ask it to

switch tone, is it really the
tone that you want or is it the

tone that they're giving you?

Same thing for Grammarly, right
.

In Grammarly as you're writing,
you can get suggested changes

to how you're writing to improve
it in some capacity.

Grammarly as you're writing,
you can get suggested changes to

how you're writing to improve
it in some capacity, whether

it's for brevity, for fluidity.

Rebecca Hogue: And that's
actually a great example.

Grammarly is one of the earlier
AI examples that people don't

necessarily know that.

Yeah, that's AI that work in
the background giving you those

things.

But yeah, tone, that's a good
point.

Jeremy Tuttle: So understanding
what I want out of this

situation and if I'm getting it.

That takes the mental effort.

It takes the mental effort.

It takes the mental effort, the
cognitive load of that creative

experience which, in my opinion
, is unnecessary.

If I'm throwing a pot on a
pottery wheel and we're 10 years

in the future and we have AI in
our glasses and we can see a

certain shape projected out onto
the throwing wheel so that I

can produce the pot to that
shape, is that creative or is

that just derivative replication
?

I find joy in the creativity.

I find fulfillment in the
creativity and having the words

be my own.

So, personally, I don't use it
for any of my writing.

I had to think for a second
have I used it to have it right

for me in any capacity?

And no, though I do support
others in using it for their own

personal reasons.

Not everybody is like me who
derives joy playing with the

same sentence for 30 minutes
trying to get it just right, and

I appreciate that.

Rebecca Hogue: And how does cost
affect your decision to use

different things?

Jeremy Tuttle: Everything has a
cost.

It's the ROI and the
opportunity cost.

So right now in the
instructional design space there

are a flood of companies coming
in that are touting ai,

supported, ai generated training
in.

In.

Whether it's you put in the,
the prompt, we give you the

material, or we've put in the
prompt and give you the material

.

There are a number of companies
coming out now that do that.

So to differentiate within the
space, is it worth trying to

make a splash doing that same
tactic, or is it a bigger

opportunity to say we don't do
that.

So that's something that niche
academy, my, my company is

weighing is.

Is it worth getting out more,
you know, getting out more

training content through the use
of AI and maybe it being not to

the same standard of quality
that it has been up till now?

Or do we continue our current
pace and make sure that it's

produced thoughtfully by humans
and we advertise that so that

customers can see that this is a
more not necessarily thoughtful

, but a more intentful approach?

Rebecca Hogue: That's a really
good point.

It's like again back into the
future of AI and as a company,

it's like now you're advertising
that we don't use AI and that's

your competitive advantage over
the AI ones.

I've played with a couple of
those AI generative ones and so

far I'm finding everything that
comes out of it is bland.

Yeah, yeah, it's just sort of
like you're spitting out facts

but you're not applying learning
theory.

Jeremy Tuttle: Oh, and so I'm
very glad you mentioned that,

because another aspect of my job
is I've been in conversations

with managers, people who lead
other people within a company

and they're responsible for the
professional development of the

people on their team.

They see generative AI as an
opportunity to release some

responsibility in regards to
that training.

If I don't have to sit through
an hour of training in the

conference room with a slideshow
that I prepared, wouldn't that

be great?

I can just hand this off to my
team and they can go running.

So, ignoring the discussion we
had earlier where you know if

you do that, they're just going
to run down a path quickly and

you're not going to catch them.

Uh, the other aspect of that
these managers who want to just

set them free don't understand
learning outcomes unless they're

instructional designer or
trainer by trade.

So, without thinking about the
learning outcomes or objectives

or competencies whatever term
you use at your organization,

your, your learners, aren't
necessarily going to get the

right information.

Again, going back to the
authority.

So we need to ensure that, for
proper training transfer, that

managers still care about the,
the work that their staff

produces, the effort that they
go through.

You can't have AI be your
fail-safe for quality.

And going back to who was it at
IBM that said you can't, air

computers shouldn't be making
business decisions, because you

can't hold them accountable.

Rebecca Hogue: I hadn't heard
that, but that actually is a

great quote.

Jeremy Tuttle: I'm pulling it up
.

A computer can never be held
accountable for decisions.

Therefore, all computer
decisions are management

decisions.

That's the actual quote, but it
still holds true with training,

in that if you release your
responsibility to something that

can't have accountability, then
you therefore don't have

accountability and you aren't
doing your job.

I'm calling you out, managers
out there who think that you can

release that responsibility and
assume that your staff, your

employees, your team members are
going to be effective and

supported.

Rebecca Hogue: It's a good point
.

We are coming towards the end
of our time, at least for the

podcast portion.

Is there anything else you'd
like to chat about?

You said you had a list.

You had a list.

I'm curious what other
questions I should be asking.

I certainly deviated from the
questions that I had in front of

me because I'm like, okay,
that's not really what we want

to get at.

So again, which is an exact
example of using the AI to

generate the questions but then
using the human to adjust them

to the particular context of the
interview it's like, well,

that's not really the right
question to ask.

Jeremy Tuttle: Absolutely,
Absolutely no.

I think all the points that I
wanted to get across got across

yeah so what's your next big
hope for ai?

Rebecca Hogue: my, my next big
hope yeah, like short term, like

in the next six months.

What are you hoping to get?

Jeremy Tuttle: I can't remember
what the status is in the EU on

legislation regarding the
training material for large

language models and other
generative software, but I hope

to have stronger legislation
around it stronger legislation

around it.

I'm friends with many artists
and a lot of the artists that I

talk with are strongly concerned
about software just coming and

gobbling up their style, their
work, so that people can pay

pennies to get artwork in their
style.

And are we in an age where art
is no longer valuable?

I hope not, but if that isn't
taken care of, it will be the

case where anybody who wants any
sort of artwork, they just go

generate it themselves.

Rebecca Hogue: Sort of changes?

What art is?

Jeremy Tuttle: Yeah, it would no
longer be a human endeavor, and

a large part of art is making
human connection through a

medium of some sort.

So if it's no longer about
human connection, then those

participating or engaging with
non-human media it.

It doesn't feel the same, at
least to me and so how does

legislation help that?

it prevents certain art from
being generated, that it

shouldn't only be from the
artists themselves.

So there's a long list of
artists that was posted, trying

to remember when and by whom.

But Dolly was trained on a list
of hundreds of artists and in

that list of artists there are
people who adamantly affirm that

they did not give consent for
their art to be put into the

system to be part of the
training material.

And so people can go into dolly
and say make me this thing in

this person's style.

And had that not been the case,
had that dolly not been trained

on that style, it wouldn't have
been able to spit out that kind

of art.

The only way to have received
that kind of art would be to go

to the artist themselves, so it
has effectively removed money

from their pockets.

There's an argument to be made
that art is free, but also no,

but it is.

Rebecca Hogue: I understand that
part of it, but the as long as

it's generating a true remix and
not reproducing the exact same

thing.

Jeremy Tuttle: At what
percentage?

Right, if you talk to the music
industry, you say I'm going to

remix your song and I only remix
it 5%.

The music industry is going to
be no, I'm slamming you with a

DMCA, take it down, because it
was that.

Rebecca Hogue: 5% does not
qualify as new and innovative or

whatever the copyright,
legality or legal terms are yeah

, it's, and I find it
interesting that the same didn't

happen for the text-based
generative AI, but it does for

the visual-based AI, which I
find that fascinating.

You can say write me a poem in
this style and it will, and.

And there hasn't been that same
pushback that we get in the

minute you go visual, suddenly
there is just much more pushback

from the artists on.

You know, wait a second.

I didn't give my permission for
that and that's actually one of

the big things I teach when
people are creating web pages

and eBooks and whatever it's
like.

Yeah, is that Creative Commons?

Do you have a license to use
that?

Jeremy Tuttle: Absolutely.

Rebecca Hogue: Is that valid?

Is there enough Creative
Commons, Rebex, CCO as opposed

to CC BY out there?

That would allow the dallys of
the world to get their databases

so that they can be generative.

Jeremy Tuttle: Absolutely.

Visual generators shouldn't or
can't use art produced by

artists, just that those artists
should be able to consent into

being part of that training pool
and they should be compensated

as part of that.

Had this list of hundreds of
artists been compensated, I

don't think there would be
nearly the uproar as it

currently is in this space, and
I think that's what the

legislation in the EU is trying
to and I'm not an expert in that

space.

I've only read it once, so I'm
just going off of what I vaguely

remember, so I could be
completely wrong, but they're

trying to set up those ethical
standards in regards to how the

system is trained so that the
people who are affected by its

training have either recourse or
ability to also profit from

yeah, that that actually brings
up an interesting ethical

question on the use of some of
the visual stuff.

Rebecca Hogue: If I'm using it
to create something, am I

participating in this unethical
behavior because I'm using it to

create something?

Jeremy Tuttle: Yeah, and an
interesting parallel is back in

the early 2000s, using napster,
but it was downloading music for

free and an ethical problem.

In the modern day we we can
think about and go, yeah, that

kind of feels like stealing, but
at the time Napster was a giant

company and millions upon
millions of people were

downloading music for free.

Did they?

Were they moral and ethical
monsters that in that moment I

don't think they felt that way.

Rebecca Hogue: No, yeah, it's
kind of.

Yeah, that's an interesting
parallel example.

Okay, any any last thoughts
before we close up.

Jeremy Tuttle: No, thank you for
having me.

It's always fun to come on here
and chat with you, hear your

insights, and I'll gladly come
back anytime.

Rebecca Hogue: Well, thank you
very much.

I really appreciate your
willingness to come on and chat

with us about what is this AI
beast, especially now that it's

been around for a little while
it's not quite so new anymore,

and so that's quite interesting.