Hilarious ChatGPT Code Interpreter Failure!

Table of Contents

1. Introduction
2. GPT's New Code Interpreter Release
3. Disappointment with GPT4's Cap and Errors
4. Troubles with Running Python Code on GPT4
5. Analysis of the Department of Transportation's Award Data
6. Issues with Code Interpreter Not Working Correctly
7. Successful Analysis of Taylor Swift's Song Data
8. Insights from Taylor Swift's Data Analysis
9. Value and Potential of GPT4 in Accounting
10. Conclusion

GPT4's New Code Interpreter Release

GPT4 recently released its new code interpreter, which has been highly anticipated by many users, including myself. In this article, we will explore my experience with using the code interpreter and discuss the disappointments and challenges I encountered along the way. Additionally, we will Delve into the analysis of the Department of Transportation's award data and the successful analysis of Taylor Swift's song data. Throughout the article, we will also touch upon the potential and value of GPT4 in the field of accounting. Let's dive in!

Introduction

The release of GPT4's code interpreter has generated excitement among users, offering the ability to run Python code within the chat interface. However, my initial experiences with the code interpreter were met with disappointment and frustration.

GPT4's Cap Limit and Error Counting

One of the major disappointments with GPT4's code interpreter is the cap of 25 messages every three hours. Even if there is an error in the message generated by GPT4, it still counts towards the message cap. This limitation hampers the ability to fully explore and experiment with the code interpreter. It is important to note that GPT4 is still in beta, and it is expected that improvements will be made to address these issues.

Challenges Running Python Code on GPT4

Using the code interpreter, it is expected that by typing a message, the Python code will be executed and the response will be generated. However, my experience with the code interpreter has been varied. In some instances, the interpreter only types out the Python code without actually running it, providing fake response output. In other instances, it gets stuck in a loop, continuously prompting to view files or repeating actions. These challenges hinder the smooth usage of the code interpreter.

Analysis of the Department of Transportation's Award Data

To explore the capabilities of the code interpreter further, I attempted an analysis of the Department of Transportation's award data. By opening the file with the code interpreter and requesting statistics about it, the expected output should provide insights into the data. Unfortunately, the code interpreter was not functioning correctly, only displaying the code without actually running it. This limitation hampered the analysis process and hindered the generation of Meaningful statistics.

Successful Analysis of Taylor Swift's Song Data

Undeterred by the difficulties faced with the code interpreter, I proceeded to run a separate analysis using the available data on Taylor Swift's song recordings over the years. By creating a moving average of the number of songs recorded per year, I aimed to gain insights into the Patterns and trends in her music career. With some adjustments and refinements, I successfully generated a visualization of the two-year moving average of songs written per year. The visualization showcased the fluctuations and trends in Taylor Swift's song output.

Insights from Taylor Swift's Data Analysis

The analysis of Taylor Swift's song data provided interesting insights. One notable finding was the variation in productivity over the years. Certain years showed higher levels of productivity, while others experienced periods of lower output. The four-year moving average revealed a consistent upward trend in song output, with a significant increase in recent years. However, it is essential to acknowledge the caveats and variations in productivity during specific periods, such as 2017 and 2019.

Value and Potential of GPT4 in Accounting

Despite the challenges faced during the experimentation with the code interpreter, the potential of GPT4 in the field of accounting remains immense. The ability to run Python code within the chat interface opens up possibilities for accountants to perform tasks efficiently and effectively. With further enhancements and refinements, GPT4 has the potential to revolutionize the accounting industry, simplifying complex tasks and providing valuable insights without requiring extensive coding knowledge.

Conclusion

In conclusion, the release of GPT4's code interpreter has brought both excitement and disappointment. While the cap limit and errors pose challenges, there are noteworthy opportunities to leverage the code interpreter for data analysis and automation tasks. The successful analysis of Taylor Swift's song data demonstrates the potential of GPT4 in generating meaningful insights. As GPT4 continues to evolve, it holds promise for transforming the field of accounting and streamlining various processes.