The author also provides the code used for the experiment, which involves creating images using the Python imaging library Pillow, encoding each image file in Base64, and using the GPT-4-Vision API to analyze the color. The author suggests that this behavior could be due to efforts to remove gender-related biases in colors within large language models, or it could be a result of the training data, process, and human feedback. The author invites readers to conduct their own experiments and share their findings.
Key takeaways:
- The author conducted an experiment asking ChatGPT to identify colors in images and compared the results with human color naming, finding that the AI model generally agrees with human color identification.
- However, GPT-4 consistently failed to identify pink colors, instead naming them as magenta or fuchsia, which the author speculates could be due to efforts to remove gender biases in color naming in AI models.
- GPT-4 also had issues correctly identifying yellow, often naming it as chartreuse or other greenish colors.
- The author provides the code used for the experiment, encouraging others to conduct their own tests and share their findings.