The “it” in AI models is the dataset. – Non_Interactive – Software & ML

🌈 Abstract

The article discusses the author's observations about the similarities in the behavior of generative models they have trained at OpenAI, regardless of the model architecture, hyperparameters, or optimizer choices. The key insight is that the model behavior is primarily determined by the dataset it is trained on, rather than the specific model configuration.

🙋 Q&A

[01] Similarities in Generative Model Behavior

1. What has the author observed about the similarities in the behavior of generative models they have trained?

• The author has observed that, when trained on the same dataset for long enough, pretty much every model with enough weights and training time converges to the same point.
• Sufficiently large diffusion conv-unets produce the same images as ViT generators, and AR sampling produces the same images as diffusion.
• This implies that model behavior is not determined by architecture, hyperparameters, or optimizer choices, but rather by the dataset the model is trained on.

2. What does this observation suggest about the nature of these generative models?

• The models are truly approximating their datasets to an incredible degree, learning not only what it means to be a dog or a cat, but also the interstitial frequencies between distributions that don't matter, like what photos humans are likely to take or words humans commonly write down.

3. What does this mean when referring to models like "Lambda", "ChatGPT", "Bard", or "Claude"?

• When referring to these models, it's not the model weights that you are referring to, but rather the dataset that the model was trained on.