Are AI labs pelicanmaxxing? (dylancastillo.co)

Article summary

The article investigates whether AI labs are optimizing their models to perform well on a specific benchmark, known as the 'pelican on a bicycle' test, which involves generating an SVG of a pelican riding a bicycle. The author conducted an experiment, generating 1,008 SVGs across seven models and 48 prompts, and found no evidence of 'pelicanmaxxing'. The results suggest that AI labs are not specifically optimizing for this benchmark, but may be improving their overall SVG generation capabilities. The experiment's findings are based on a quantitative analysis of the generated images, using a fixed-effects regression model to account for the inherent difficulty of each prompt.

Main themes

• AI model optimization
• Benchmarking
• SVG generation
• Model generalization
• Overfitting
• Evaluation metrics

What commenters say

• The 'pelican on a bicycle' benchmark may no longer be a useful evaluation metric due to the large number of pelican images in pretraining data.
• Some commenters argue that the benchmark is still useful, but its value lies in its ability to proxy for non-public tests, rather than its absolute performance.
• Others suggest that the lack of evidence for 'pelicanmaxxing' does not necessarily mean that AI labs are not optimizing for the benchmark, as they may be using more sophisticated methods to improve their performance.
• There is a concern that overfitting to benchmarks can lead to decreased overall fitness and decreased correlation with real-world performance, as described by Goodhart's law.
• Some commenters propose alternative benchmarks, such as generating SVGs of other objects or scenes, to evaluate AI models' capabilities.
• The discussion highlights the challenge of creating effective evaluation metrics for AI models, as they can be gamed or optimized for, and the need for more robust and generalizable benchmarks.