In 2016, Geoffrey Hinton – computer scientist and Turing Award winner – declared that ‘people should stop training radiologists now’.
If we had followed every AI evengelist sugestion the world would have collapsed.
If we had followed every AI evengelist sugestion the world would have collapsed.
But he said it in the context of a Q&A session that happened to be recorded. Unless you're a skilled politician who can give answers without actually saying anything, you're going to say silly things once in a while in unscripted settings.
Besides that, I'd hardly call Geoffrey Hinton an AI evangelist. He's more on the AI doomer side of the fence.
With interventional radiologists and radio-oncologists it's different but were talking about radiologists here...
By the way, even if I sound dismissive I have great respect for the skills required by your profession. Reading an IRM is really hard when you have the radiologist report in hand and to my untrained eyes it's impossible without it!
And since you talk to patients frequently, I have an even greater respect of you as a radiologist.
I also recently had surgery and the surgeon consulted with the radiologist that read my MRI before operating.
Or maybe it's related to socialized Healthcare because in the article there is a breakdown of the time spent by a radiologists in Vancouver and talking to patients isn't part of it.
At the time? I would say he was a AI evangelist.
When I do write something up, it is usually very finalized at that time; the process of getting to that point is not recorded.
The models maybe need more naturalistic data and more data from working things out.
Scale is not always about trougput. You can be constrained by many things, in this case, data.
People can't tell what they'll eat next sunday but they'll predict AGI and singualrity in 25 years. It's comfy because 25 years seems like a lot of time, it isn't.
https://en.wikipedia.org/wiki/List_of_predictions_for_autono...
> I'd wager in 25 years we'd get there so I think his opinion still has a large percentage of being correct.
What percent, and which maths and facts let you calculate it ? The only percent you can be sure about is that it's 100% wishful thinking
> It's comfy because 25 years seems like a lot of time, it isn't.
I don't know how old you are but life 25 years ago from a tech perspective was *very* different.
That doesn’t mean that you can’t predict anything with high certainty. You just don’t know whether the status quo will be disturbed. And when you need a status quo disturbance for your prediction, you’re in pure luck category. When your prediction requires lack of status quo changes, then your prediction is safer. And of course sorter the term the better. When ChatGPT came out, Cursor and Claude Code could be predicted, I predicted them. Because no changes in status quo was required and it was a short term prediction. But if there would have been a new breakthrough, then those wouldn’t have been created. When they predicted fully self driving cars, or less people checking X-rays, you needed a status quo change: legal first, but in case of general, fully self driving cars, even technical breakthroughs. Good luck with that.
You cannot predict whether you'll be alive tomorrow, you can be 99.99999% sure, but never 100%. The point is that based on certain information/data you can form a personal opinion, and someone can form a different one on the same data. My opinion is that AI is going to keep booming due to the enormous amount of private and government cash being injected into it with both pacifistic and militaristic application of said AI resulting in it being capable of successful interpretation of radiology imaging in 20 years.
I could see the assumption that one radiologist supervises a group of automated radiology machines (like a worker in an automated factory). Maybe assume that they'd be delegated to an auditing role. But that they'd go completely extinct? There's no evidence of, even historically, a service being consumed that has zero human intervention.
Maybe don't?
I mean if you change the data to fit your argument you will always make it look correct.
Lets assume we stop in 2016 like he said, where do we get the 1000 radiologist the US needs a year?
The training lasts 5 years, 2021 - 5 = 2016 If they stopped accepting people into the radiologist program but let people already in to finish, then you would stop having new radiologist in 2021.
So 5 + 5 + [0,2] is [10,12] years of training.
That sentence and what you wrote are not 100% the same.