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

The author shares their experience of training numerous generative models at OpenAI for almost a year. They observed that regardless of the model configurations and hyperparameters, all models approximate their datasets to an incredible degree. This means that models learn not only the obvious patterns but also the subtle frequencies between distributions, such as common human behaviors. The author found that given enough time and weights, every model trained on the same dataset converges to the same point, regardless of the architecture, hyperparameters, or optimizer choices.

The author concludes that the behavior of a model is determined solely by the dataset, not by the model's architecture or other factors. All other elements serve to efficiently deliver compute to approximate that dataset. Therefore, when referring to models like "Lambda", "ChatGPT", "Bard", or "Claude", it's not the model weights that are being referred to, but the dataset.

Key takeaways:

The author has observed that all generative models, regardless of their configurations and hyperparameters, tend to approximate their datasets to an incredible degree.
Given enough training time and weights, different models trained on the same dataset tend to converge to the same point.
The behavior of a model is not determined by its architecture, hyperparameters, or optimizer choices, but by the dataset it is trained on.
When referring to models like “Lambda”, “ChatGPT”, “Bard”, or “Claude”, it's not the model weights that are being referred to, but the dataset they are trained on.

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

Key takeaways:

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