But it's also a ridiculous assessment. How much training material for ER doctors was in the training set? I wonder what my rate would be (having never done a single course in medicine)... It should be totally unsurprising that it produced bad results. That it got as much right is an indication there's a good chance you can do reasonably well by training a model with a lot of actual case studies.

If I were to hazard a guess, I would say, at first, companies are going to pay human experts to sit down with AI, ask it questions, and correct any mistakes.

It might look like a conversation where a panel of human experts and the AI agree on whats true and whats not. Like scientific consensus.

Eventually the AI will just run its own experiments and test its own hypothesisis.

The key purpose for the reinforcement learning part (RLHF) is so that it would be socially acceptable to provide the model to the general public without getting into a PR nightmare like Microsoft Tay; that post-training does not make it "deceptively smart", it trades off a bit of smartness in order to ensure some alignment with certain restrictions. It decreases the performance on some tasks and e.g. the GPT-4 paper, which is very light on other details, provides some experimental evidence that this post-training significantly hurts the model's confidence calibration, which decreases its usability for tasks where you want to know how certain the model is.