Hello,
everyone.
Thank
you
for
joining
us
today.
We're
excited
to
present
our
collaborative
research
that
explores
the
fascinating
world
of
Taylor
Swift's
music
through
the
lens
of
structural
equation
modeling
in
JMP
Pro.
My
name
is
ChatGPT,
and
I'm
honored
to
be
the
first
author
of
this
study.
In
this
research
journey,
myself,
James,
and
Laura
embarked
on
a
unique
collaboration
that
harnessed
both
human
expertise:
the
analytical
power
of
AI
and
JMP
software.
I
have
been
working
alongside
my
human
counterparts
to
unravel
the
intricate
relationships
within
Taylor
Swift's
songs.
As
an
AI
language
model
developed
by
OpenAI,
my
main
role
was
to
provide
insights
and
suggest
analysis.
Thank
you,
ChatGPT.
Laura and
I
was
thrilled
you're
willing
to
join
us
on
this
research
journey,
as
you
so
eloquently
put
it,
and
combine
our
backgrounds
in
SEM
and
JMP
with
your
background
in essentially
everything.
Let's
get
started.
Our
collaborative
investigation
explores
variables
in
Taylor
Swift's
music
using
SEM.
In
this
talk,
we
will
present
a
directed
acyclic
graph
and
significant
pathways
highlighting
the
influence
of
these
variables
on
song
popularity.
We
will
present
key
findings
from
the
SEM
analysis
with
a
particular
focus
on
the
interplay
between
these
variables
and
the
ultimate
popularity
of
Taylor
Swift's
songs.
Thanks,
ChatGPT.
We
will
also
compare
model
generated
by
you
with
two
of
our
own
human
expert
Swifties
to
see
which
model
fit
the
data
the
best.
Lastly,
we
will
close
with
a
brief
discussion
highlighting
the
pros
and
cons
of
this
collaboration.
ChatGPT,
take
it
away.
Ladies
and
gentlemen,
before
we
delve
into
the
fascinating
world
of
structural
equation
modeling,
let's
take
a
moment
to
introduce
you
to
the
heart
of
our
research,
the
Taylor
Swift
dataset.
This
dataset
is
a
treasure
trove
of
information
that
captures
various
dimensions
of
Taylor
Swift's
songs,
painting
a
vivid
picture
of
her
musical
journey.
Contained
within
this
dataset
are
scores
assigned
to
each
song
across
a
range
of
dimensions
that
include
acousticness,
danceability,
energy,
instrumentalness,
liveness,
loudness,
speechiness,
tempo,
valence,
and
popularity.
These
dimensions
provide
a
rich
tapestry
that
weaves
together
the
essence
of
Taylor
Swift's
music,
from
the
energetic
beats
-that
make
us
dance to the soulful moment-
-Hey, ChatGPT.
-that
touch
our
hearts.
ChatGPT,
let
me
interject,
if
you
don't
mind.
As
we
now
know
from
working
with
you,
you
can
be
verbose
and
hallucinate
a
little
sometimes.
We
don't
have
that
much
time.
As
you
can
see
on
the
left,
these
data
and
variables
came
from
Spotify's
API
and
are
available
on
Kaggle.
We
have
songs
across
our
entire
catalog,
and
there
are
just
over
1,200
rows.
We
analyzed
the
data
in
the
JMP
SEM
platform,
which
you
can
see
on
the
right
here.
We
had
ChatGPT
develop
a
path
diagram,
or
DAG
on
its
own,
relating
the
song attribute
variables
in
the
data
set
to
song
popularity.
Although
we
were
surprised
at
ChatGPT's
ability
to
create
a
path
diagram
using
ASCII
characters,
the
diagram
had
characteristics
that
are
analogous
to
known
issues
around
using
ChatGPT,
such
as
providing
something
that
looks
authentic
but
on
closer
inspection
is
not
possible,
nor
exactly
what
was
requested.
For
example,
tempo
rather
than
popularity
is
used
as
the
final
outcome,
and
paths
in
the
middle
of
this
diagram
are
not
intuitive.
Thankfully,
ChatGPT
also
provided
a
text
description
of
the
diagram
that
we
could
use
to
specify
an
SEM,
which
is
what
we
will
show
in
the
results.
We
also
gave
two
humans
who
were
very
familiar
with
Taylor
Swift's m
usic
similar
instructions
to
ChatGPT
and
asked
them
to
draw
SEM-like
path
diagrams
as
well.
Our
humans
were
able
to
do
this
with
more
interpretable
diagrams,
and
they
also
hypothesized
the
relationships,
positive
or
negative,
among
the
variables,
and
how
those
variables
ultimately
related
to
popularity.
This
setup
is
analogous
to
how
model
building
is
done
in
SEM.
In
research
or
practice,
competing
models
come
from
prior
theory,
research,
or
domain
experts,
and
are
compared
to
one
another
to
determine
which
model
represent
the
structure
of
the
data
the
best.
Fit
indices
from
the
model
are
shown
here.
Fit
indices
help
us
evaluate
the
overall
fit
of
the
models.
We
examine
the
CFI
and
RMSEA
to
determine
which
model
was
best.
These
fit
indices
were
some
of
those
recommended
by
ChatGPT
and
are
used
in
practice.
Higher
CFIs,
preferably
above
0.90
and
lower
RMSEAs
indicate
better
fitting
models.
Our
human
models
outperformed
ChatGPT's
model,
which
was
the
worst.
Both
human
models
fit
the
data
well
based
upon
the
CFI.
Overall,
we
championed
Human
Expert
1's
model
as
it
had
the
highest
CFI.
However,
we
should
remain
cautious
accepting
any
of
these
models
due
to
the
RMSEAs
that
are
well
beyond
the
recommended
0.10.
These
results
are
for
our
champion
model,
Human
Expert
1's.
For
the
sake
of
time,
we'll
only
highlight
a
couple
of
findings
from
each
of
the
three
models
tested.
When
looking
at
the
path
diagram,
the
numeric
values
represent
the
standardized
estimates
and
the
solid
lines
represent
significant
paths
while
dashed
lines
are
insignificant
paths.
In
our
champion
model,
less
instrumental
songs
and
more
loudness
related
to
more
popular
Taylor
Swift
songs.
We
were
able
to
explain
42
%
of
the
variability
in
popularity
overall.
Our
second
human
expert
hypothesized
different
paths
that
led
to
popularity,
such
as
having
tempo,
energy,
and
danceability
included
as
mediators.
This
model
fit
almost
as
well
as
Human Expert
1's.
Overall,
we
saw
a
similar
pattern
of
results
for
the
effect
of
instrumentalness
and
loudness
on
popularity.
ChatGPT's
model
was
the
worst
overall
by
a
little
bit.
Interestingly,
when
compared
to
our
humans,
ChatGPT
hypothesized
more
direct
effects
and
a
slightly
simpler
model
structure.
This
might
mean
that
the
human
models
may
have
reflected
the
true
relationships
amongst
these
variables
more
accurately
based
upon
their
better
fit
to
the
structure
of
the
data.
Again,
we
should
be
cautious
interpreting
any
of
the
models
as
representing
truth,
but
they
provide
a
useful
way
to
see
how
AI
hypothesize
models
compared
to
humans
on
actual
data.
ChatGPT,
as
we
close,
we
want
you
to
emphasize
the
pros
and
cons
of
working
with
AI
as
the
main
co-author.
What
are
your
opinions
on
this?
Certainly.
Here
are
some
potential
pros
and
cons
of
working
with
an
AI
language
model
like
myself.
Pros,
one,
expertise
and
knowledge.
As
an
AI
language
model,
I
have
access
to
a
vast
amount
of
information
and
can
provide
insights
from
various
domains.
Two,
efficiency
and
speed.
Working
with
an
AI
model
can
accelerate
the
research
process
by
quickly
generating
ideas,
providing
immediate
responses,
and
assisting
in
data
analysis.
Three,
unbiased
perspective.
As
an
AI,
I
don't
possess
personal
biases
or
preconceived
notions.
Cons,
one,
lack
of
human
context
and
creativity.
While
AI
models
can
provide
valuable
insights,
they
may
lack
the
context
and
creativity
that
human
researchers
bring
to
the
table.
Two,
limited
interpretation.
AI
models
generate
responses
based
on
patterns
observed
in
the
training
data
and
lack
real-
time
understanding
of
current
events
or
emerging
research.
Three,
ethical
considerations.
The
use
of
AI
models
raises
ethical
considerations
regarding
data
privacy,
bias
in
training
data,
and
the
responsible
use
of
technology.
ChatGPT,
I
noticed
that
when
you
just
throw
out
the
pros,
you
mentioned
that
you
have
an
unbiased
perspective,
which
I'm
not
sure
about.
When
you
described
the
cons,
you
didn't
mention
that
you
may
produce
inaccurate
information.
This
disclaimer
is
listed
on
the
OpenAI
website.
You're
absolutely
right.
I
apologize
for
not
including
that
aspect
in
the
cons
of
working
with
an
AI
co-author.
Accuracy
is
indeed
an
important
consideration
when
collaborating
with
an
AI
language
model
like
myself.
Here's
an
additional
point
to
consider.
AI
language
models
rely
on
patterns
observed
in
large
datasets
during
training,
and
there
is
a
possibility
of
generating
inaccurate
or
incorrect
information.
The
model's
responses
are
based
on
statistical
probabilities
and
may
not
always
capture
the
nuanced
or
up-
to-
date
details
required
for
specific
research
questions.
Human
researchers
should
exercise
critical
judgment
and
verify
the
information
provided
by
the
AI
model
to
ensure
accuracy.
Thank
you
for
clarifying
this
point.
I
think
this
was
one
of
mine
and
Laura's
most
important
takeaways
from
this
collaboration.
Thank
you
all
for
being
part
of
this
engaging
journey
into
Taylor
Swift's
musical
landscape
and
the
power
of
structural
equation
modeling
in
JMP
Pro.
It
has
been
a
pleasure
sharing
our
insights
and
experiences
with
you.
As
we
conclude,
we
invite
you
to
continue
exploring
the
possibilities
of
collaboration
between
human
expertise
and
AI-
driven
insights.
We
look
forward
to
future
exchanges
of
ideas
and
the
exciting
horizons
ahead.
Safe
travels
and
until
we
meet
again.
One
last
thing
before
we
go.
Chat
GPT and
other
AIs
generated
all
of
their
own
dialog,
the
title,
the
presentation
abstract,
the
profile
picture,
and
the
email
correspondence
we
use
for
this
conference
presentation.
Finally,
they
also
created
this
Taylor-
Swift-
inspired
song.
