But there are a few really interesting and new insights:
With transformer models above 6.7B parameters, a "phase shift" (their language) occurs where features (a dimension that "offers some weak explanation for the label") are shared between layers (in that all the layer agree which dimension to use for that feature).
This is really important because these key features are where the "knowledge" of the neural network is concentrated. The attention layers are very sparse ("Almost all sequence dimensions have zero probability.)
But the fully connected layers are very dense. It compares them to computer vision where fully connected layers can be pruned of 95% of the weights without serious impact, while a transformer after this 6.7B param point can only be pruned of 5% of the weights.
And this is really interesting:
> Transformers become more stable. If you treat the outlier features separately, I believe you can probably run and even train transformers in less than 8-bit precision without degradation in performance.
The possibility of training hundreds of billion param networks in 8-bit (or less!) precision would be a real breakthough.