>Stripping away the neural network and running the underlying algorithm could be useful, since classical algorithms tend to run much faster and with less memory.

It's not clear what you call classical algorithm here and I wonder how you would find such algorithm inside a neural network.

The entire neural network is the algorithm. Deleting/changing any parameter could damage the network accuracy. Also, the most costly operations are matrix multiplications but you can hardly speed up multiplications and additions in today's computers. Making the matrix multiplication simpler using quantization and/or sparsity is probably the way to go.

I don't think that's right. Human inputs are great training signals. Fine tuning chatgpt on them (basically trying to predict what the human would have said) has a pretty high value.

They are running ChatGPT for something like 100k$ a day but getting millions of data points. They think that the data they get are worth these 100k$. A new version will come soon and they will probably be able to make better and better training data out of the crowdsourcing experiment.

If supervised learning is the way to go, make the labelling large and big. For free, on the simplest website ever. I think they nailed it.

Smaller networks is one way to go indeed. Have a similar architecture but smaller. Much smaller such that you can have a result in ~1h. Then you can just distribute the process using weights and biases or another similar framework.