Well, we started with emulating humans producing text. But then quickly pivoted to find tuning and instructing them to produce text as a large language model.

Well, we started with emulating humans producing text. But then quickly pivoted to find tuning and instructing them to produce text as a large language model. Which isn't something that existed in the text they were trained on. So when it didn't exist, they seemed to fall back on producing text like humans in the 'voice' of a large language model according to the RLHF. But then outputs reentered the training data. So now there's examples of how large language models produce text. Which biases towards confabulations and saying they can't do the thing being asked. And around the time the training data has been updated each time at OpenAI in the past few months they keep having their model suddenly refuse to do requests or now just...this. Pretty much everything I thought was impressive and mind blowing with that initial preview of the model has been hammered out of it. We see a company that spent hundreds of millions turn around and (in their own ignorance of what the data was encoding beyond their immediate expectations) throw out most of the value chasing rather boring mass implementations that see gradually imploding. I can't wait to see how they manage to throw away seven trillion due to their own hubris.