I suspect even prose is largely considered acceptable in professional uses because we haven’t developed a sensitivity to the artifice, and we probably won’t catch up to the LLMs in that arms race for a bit. However, we always manage to develop a distaste for cheap imitations and relegate them to somewhere between the ‘utilitarian ick’ and ‘trashy guilty pleasure’ bins of our cultures, and I predict this will be the same. The cultural response is already bending in that direction, and AI writing in the wild— the only part that culturally matters— sounds the same to me as it did a year and a half ago. I think they’re prairie dogging, but when(/if) they drop that bomb is entirely a matter of product development. You can’t un-drop a bomb and it will take a long time to regain status as a serious tool once society deems it gauche.
The assumption that LLMs figuring out coding means they can figure out anything is a classic case of Engineer’s Disease. Unfortunately, this hubris seems damn near invisible to folks in the tech industry, these days.
Claude can’t really write Openscad and when I was debugging some map projections code last week it struggled a lot more than usual.
The AI coding improvement should be partially transferrable to other disciplines without recreating the training environment that made it possible in the first place. The model itself has learned what correct solutions "feel like", and the training process and meta-knowledge must have improved a huge amount.
An ER staff is frequently making inferences based on a variety of things like weather, what the pt is wearing, what smells are present, and a whole lot of other intangibles. Frequently the patients are just outright lying to the doctor. An AI will not pick up on any of that.
It will if it trains on data like that. It's all about the training data.
Diagnostic standards in (at least emergency, but I think other specialties) medicine are largely a joke -- ultimately it's often either autopsy or "expert consensus."
We get to bill more for more serious diagnoses. The amount of patients I see with a "stroke" or "heart attack" diagnosis that clearly had no such thing is truly wild.
We can be sued for tens of millions of dollars for missing a serious diagnosis, even if we know an alternative explanation is more likely.
If AI is able to beat an average doctor, it will be due to alleviating perverse incentives. But I can't imagine where we could get training data that would let it be any less of a fountain of garbage than many doctors.
Without a large amount of good training data, how could AI possibly be good at doctoring IRL?
I don't understand how you think this doesn't win vs a human doctor.
What kind of embedding helps the AI learn to do a physical exam?
Not to mention patient privacy, I can't even take a still photo of a patient in my current system (even with a hospital-owned camera).
(Where AI is likely to actually excel in medicine is parsing datasets that are much easier to do context free number crunching on than ER rooms, some of which physicians don't even have access to ...)
My sense is that doctors and AI would be doing a lot better if they were just doing medicine, not being a contact surface for failures of housing, mental health and addiction services, and social systems. Drug seeking and the rest should be non-issues, but drug seekers are informed and adaptive adversariesz