AI explanation

^{Show image generation prompt} Model: dalle-3
Prompt: Certainly! Prompt: Generate an image of a futuristic scene in Columbus, Indiana on a clear and sunny day. Show a contrast of blue skies against the improved air quality, with people happily hanging their arms out of car windows to feel the refreshing breeze. Illustrate the trouble brewing as the wind picks up, causing seat belts to tangle and twist, leading to unintended tango-like interactions between drivers and their seat belts. Some drivers appear to be more aggressive, making sudden stops and starts, putting strain on their seat belts. Capture dramatic moments where drivers fling themselves into their cars, putting undue pressure on their seat belts, while hinting at a surge in automotive recalls due to seat belt issues caused by the unexpected consequences of improved air quality. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.

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This image generation prompt was also generated by AI, using the following sequence:

Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the situation described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here is a situation: The increase in good air quality in Columbus, Indiana caused automotive recalls for seat belt issues to also increase because, as the air quality improved, people were more likely to roll down their windows and hang their arms out, feeling the breeze. Unfortunately, this led to a surge in reports of seat belt malfunctions, as the belts became tangled in the wind and tangled up the drivers, unintentionally preforming a type of seat belt tango. Additionally, the crisp, clean air made drivers feel more alive and bold, leading to increased aggression in their driving style, which meant more sudden stops and starts, putting additional stress on the seat belts and causing them to malfunction. And last but not least, with the fresh air, drivers were more likely to unleash their inner divas and dramatically fling themselves into their cars, causing undue strain on the seat belts and triggering the recalls.. Given that situation, which of the following image descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand and the tone of the explanation. Please respond with only one of the following potential descriptions: Cutsy art illustration Hyper-realistic Brutalism Cyberpunk Futuristic
ChatGPT: Futuristic

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated and the explanation described. Do not reference graphs or charts; describe physical objects.
User prompt: Please describe a visual scene that depicts the following situation. Keep the description simple. The increase in good air quality in Columbus, Indiana caused automotive recalls for seat belt issues to also increase because, as the air quality improved, people were more likely to roll down their windows and hang their arms out, feeling the breeze. Unfortunately, this led to a surge in reports of seat belt malfunctions, as the belts became tangled in the wind and tangled up the drivers, unintentionally preforming a type of seat belt tango. Additionally, the crisp, clean air made drivers feel more alive and bold, leading to increased aggression in their driving style, which meant more sudden stops and starts, putting additional stress on the seat belts and causing them to malfunction. And last but not least, with the fresh air, drivers were more likely to unleash their inner divas and dramatically fling themselves into their cars, causing undue strain on the seat belts and triggering the recalls.
ChatGPT: Imagine a sunny, clear day in Columbus, Indiana. The blue skies are a stark contrast to the usual pollution, as the air quality has noticeably improved. In this picture, you see car windows rolled down, and people happily hanging their arms out to feel the refreshing breeze. However, you also notice some trouble brewing. As the wind picks up, seat belts get tangled and twisted, causing various drivers to unintentionally tango with their own seat belts. Drivers suddenly become more aggressive, with sudden stops and starts, adding strain to their seat belts. You even notice a few dramatic moments as some drivers fling themselves into their cars, putting undue pressure on their seat belts. These scenes depict the unexpected consequences of the improved air quality, leading to a surge in automotive recalls for seat belt issues.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ Futuristic ] and depicting this scene: Imagine a sunny, clear day in Columbus, Indiana. The blue skies are a stark contrast to the usual pollution, as the air quality has noticeably improved. In this picture, you see car windows rolled down, and people happily hanging their arms out to feel the refreshing breeze. However, you also notice some trouble brewing. As the wind picks up, seat belts get tangled and twisted, causing various drivers to unintentionally tango with their own seat belts. Drivers suddenly become more aggressive, with sudden stops and starts, adding strain to their seat belts. You even notice a few dramatic moments as some drivers fling themselves into their cars, putting undue pressure on their seat belts. These scenes depict the unexpected consequences of the improved air quality, leading to a surge in automotive recalls for seat belt issues. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Certainly! Prompt: Generate an image of a futuristic scene in Columbus, Indiana on a clear and sunny day. Show a contrast of blue skies against the improved air quality, with people happily hanging their arms out of car windows to feel the refreshing breeze. Illustrate the trouble brewing as the wind picks up, causing seat belts to tangle and twist, leading to unintended tango-like interactions between drivers and their seat belts. Some drivers appear to be more aggressive, making sudden stops and starts, putting strain on their seat belts. Capture dramatic moments where drivers fling themselves into their cars, putting undue pressure on their seat belts, while hinting at a surge in automotive recalls due to seat belt issues caused by the unexpected consequences of improved air quality.

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*Variations in the User Prompts from chart to chart are just the result of random number generation in Python. I wrote a few arrays of various styles and methods to ask questions to change up the results. Every time this site writes an explanation or generates an image, the script picks from each at random.

I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.

AI academic paper

In Columbus Clean Air and Seatbelt Repair: A Connection That's Rare The Journal of Quirky Connections in Public Health Jan 2024

Problem variable:	Which variable has a problem? Air quality in Columbus, Indiana Automotive recalls for issues with the Seat Belts Neither / both
Issue:	What is the issue? The data is incorrect The chart isn't displaying properly There is a typo The source link is incorrect A calculation is wrong Something else
Additional details: Optional
Confirm you are a human:

Data details

Why this works

1. Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
2. Lack of causal connection: There is probably^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
   no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
3. Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple^Note Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
   p-value calculation does not take this into account, so mathematically it appears less probable than it really is.

Try it yourself

# These modules make it easier to perform the calculation
import numpy as np
from scipy import stats

# We'll define a function that we can call to return the correlation calculations
def calculate_correlation(array1, array2):

    # Calculate Pearson correlation coefficient and p-value
    correlation, p_value = stats.pearsonr(array1, array2)

    # Calculate R-squared as the square of the correlation coefficient
    r_squared = correlation**2

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([0.763547,0.625,0,0,0,0,0,0,0,0,0,0.946565,0.95045,0,0,0,0,0,0,0,0,0,0,0,0.817757,0.722527,0.665753,0.781421,0.862637,0.794521,0.818681,0.882514,0.832418,0.868493,])
array_2 = np.array([21,25,21,15,15,11,30,24,24,13,10,42,38,24,19,38,7,11,14,11,4,15,10,10,24,38,27,26,38,24,21,42,46,31,])
array_1_name = "Air quality in Columbus, Indiana"
array_2_name = "Automotive recalls for issues with the Seat Belts"

# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)

# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)

Reuseable content

• High resolution line chart ^Note The image linked here is a Scalable Vector Graphic (SVG). It is the highest resolution that is possible to achieve. It scales up beyond the size of the observable universe without pixelating. You do not need to email me asking if I have a higher resolution image. I do not. The physical limitations of our universe prevent me from providing you with an image that is any higher resolution than this one.
  
  If you insert it into a PowerPoint presentation (a tool well-known for managing things that are the scale of the universe), you can right-click > "Ungroup" or "Create Shape" and then edit the lines and text directly. You can also change the colors this way.
  
  Alternatively you can use a tool like Inkscape.
  
• High resolution line chart, optimized for mobile
• Alternative high resolution line chart
• Scatterplot
• Portable line chart (png)
• Portable line chart (png), optimized for mobile
• Line chart for only Air quality in Columbus, Indiana
• Line chart for only Automotive recalls for issues with the Seat Belts
• The spurious research paper: In Columbus Clean Air and Seatbelt Repair: A Connection That's Rare