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AI explanation

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of towering rows of genetically modified corn in a Texas field, with electricity arcing between the vibrant maize plants, illuminating the surroundings with sparks of energy. Show the unconventional connection between agriculture and electricity, and the transformation of the landscape as this electrical surge sparks a revolution, changing the face of energy production in Cabo Verde. 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 variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: GMO use in corn grown in Texas and Electricity generation in Cabo Verde. Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s Sci-fi fantacy 3D Pixar Style Cyber folk Brutalism
ChatGPT: Cyber folk

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both GMO use in corn grown in Texas and Electricity generation in Cabo Verde, in the following situation: The power of genetically modified corn has really shocked the world of energy production! It seems that as the GMO corn in Texas grew taller and stronger, it also started conducting electricity. This unexpected power surge is sending shockwaves all the way to Cabo Verde, where the energy is sparking a whole new level of electricity generation. Who knew that corn could kernel out such a shocking amount of power? It's a-maize-ing how this unlikely connection is cob-ducting a new era of electrifying energy production! Corn-gratulations to the farmers for planting the seeds of this unconventional power source. This unexpected synergy between agriculture and electricity is truly a-maize-ing! . Keep the description simple.
ChatGPT: In Texas, towering rows of genetically modified corn are seen conducting electricity, with sparks of energy illuminating the fields. The vibrant maize plants have become an unexpected power source, radiating a new level of energy. Meanwhile, in Cabo Verde, the power of this electrical surge is sparking a revolution, leading to a surge in electricity generation that is changing the landscape of energy production. This unconventional connection between agriculture and electricity is truly electrifying, creating a new era of energy production.

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: [ Cyber folk ] and depicting this scene: In Texas, towering rows of genetically modified corn are seen conducting electricity, with sparks of energy illuminating the fields. The vibrant maize plants have become an unexpected power source, radiating a new level of energy. Meanwhile, in Cabo Verde, the power of this electrical surge is sparking a revolution, leading to a surge in electricity generation that is changing the landscape of energy production. This unconventional connection between agriculture and electricity is truly electrifying, creating a new era of energy production. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of towering rows of genetically modified corn in a Texas field, with electricity arcing between the vibrant maize plants, illuminating the surroundings with sparks of energy. Show the unconventional connection between agriculture and electricity, and the transformation of the landscape as this electrical surge sparks a revolution, changing the face of energy production in Cabo Verde.

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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

GMO Goldmine: Generating Guffaws through Correlation Coefficients The Journal of Comedic Correlations Jan 2024 Reminder: This paper is AI-generated. Not real!

Problem variable:	Which variable has a problem? GMO use in corn grown in Texas Electricity generation in Cabo Verde 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([72,77,79,78,84,85,88,85,89,91,89,90,95,93,95,92,92,])
array_2 = np.array([0.222,0.236,0.253,0.27,0.281,0.325,0.346,0.359,0.385,0.408,0.401,0.423,0.459234,0.459059,0.463,0.433796,0.456887,])
array_1_name = "GMO use in corn grown in Texas"
array_2_name = "Electricity generation in Cabo Verde"

# 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 GMO use in corn grown in Texas
• Line chart for only Electricity generation in Cabo Verde
• AI-generated correlation image
• The spurious research paper: GMO Goldmine: Generating Guffaws through Correlation Coefficients