Correct, when you talk to GPT, it doesn’t learn anything. If you’re having a conversation with it, every time you press “send,” it sends the entire conversation back to GPT, so within a conversation it can be corrected, but remembers nothing from the previous conversation. If a conversation becomes too long, it will also start forgetting stuff (GPT has a limited input length, called the context length). OpenAI does periodically update GPT, but yeah, each update is a finished product. They are very much not “open,” but they probably don’t do a full training between each update. They probably carefully do some sort of “fine-tuning” along with reinforcement-learning-with-human-feedback, and probably some more tricks to massage the model a bit while preventing catastrophic forgetting.

Oh yeah, the latency of signals in the human brain is much, much slower than the latency of semiconductors. Forgot about that. That further muddies the very rough estimates. Also, there are multiple instances of GPTs running, not sure how many. It’s estimated that each instance “only” requires 128 GPUs during inference (responding to chat messages), as opposed to 25k gpus for training. During training, the model needs to process multiple training examples at the same time for various reasons, including to speed up training, so more GPUs are needed. You could also think of it as training multiple instances at the same time, but combining what’s “learned” into a single model/neural network.