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.
