Comment by chubot | Hacker News Reader

Comment by chubot | Hacker News Reader

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I guess it's worth reminding people that in 2016, Geoff Hinton said some pretty arrogant things that turned out to be totally wrong:

Let me start by saying a few things that seem obvious. I think if you work as a radiologist, you're like the coyote that’s already over the edge of the cliff but hasn’t yet looked down

It’s just completely obvious that within five years deep learning is going to do better than radiologists.… It might be 10 years, but we’ve got plenty of radiologists already.”

https://www.youtube.com/watch?v=2HMPRXstSvQ

This article has some good perspective:

https://newrepublic.com/article/187203/ai-radiology-geoffrey...

His words were consequential. The late 2010s were filled with articles that professed the end of radiology; I know at least a few people who chose alternative careers because of these predictions.

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According to US News, radiology is the 7th best paying job in 2025, and the demand is rising:

https://money.usnews.com/careers/best-jobs/rankings/best-pay...

https://radiologybusiness.com/topics/healthcare-management/h...

I asked AI about radiologists in 2025, and it came up with this article:

https://medicushcs.com/resources/the-radiologist-shortage-ad...

The Radiologist Shortage: Rising Demand, Limited Supply, Strategic Response

(Ironically, this article feels spammy to me -- AI is probably being too credulous about what's written on the web!)

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I read Cade Metz's book about Hinton and the tech transfer from universities to big tech ... I can respect him for persisting in his line of research for 20-30 years, while others saying he was barking up the wrong tree

But maybe this late life vindication led to a chip on his shoulder

The way he phrased this is remarkably confident and arrogant, and not like the behavior of respected scientist (now with a Nobel Prize) ... It's almost like Twitter-speak that made its way into real life, and he's obviously not from the generation that grew up with Twitter

Yeah, even forgot about that... I suppose that the same kind of confidence made him stick with neural nets for so long too, despite mainstream AI thinking it's a dead end. But that's the thing in academia, bold claims get encouraged, as ideas still get you the credit, even if they prove useful decades later and not in the way you imagined.

I wouldn't be too hard on Hinton. Researchers in image processing, geophysics and medicine have been saying the same thing since at least the early 1980's. There was always something coming that was just over the next hill that would take the human out of the loop. That special something always evaporated with time. I suppose it did keep funding coming in.

The bottom line is that predicting the future is hard. I'm always skeptical of people who pretend they can.

Of course, because you have different people all predicting a different future, some of them are bound to get it right. That doesn't mean the same person will be right again.

I think the key insight is that AI is (undoubtedly going to be) better at analysis and diagnosis than radiologists, but isn't yet widely deployed because:

1. The medical world doesn't accept new technologies easily. Humans get a much higher pass on bad performance than technology and especially than new technology. Things need to be extensively tested and certified, so adoption is slow.

2. AI is legally very different than a radiologist. The liability structure is completely different, which matters a lot in an environment that deals with life or death decisions.

3. Image analysis is not language analysis and generation. This specific machine learning part is not the bit of machine learning that has advanced enormously in the past two years. General knowledge of the world doesn't help that much when the task is to look at pixels and determine whether it's cancer or not. Now this can be improved by integrating the image analysis with all the other possibly relevant information (case history etc.) and diagnosing the case via that route.

Well maybe, but none of that implies that there will be fewer radiologists will be employed, or that people studying radiology now are fools.

The overwhelming likely thing is that radiologist jobs will change, just like programming jobs will change.

e.g. see my comment on: Did Google and Stack Overflow "replace" programmers?

https://news.ycombinator.com/item?id=43013363

That is, I do not think programmers will be "replaced". The job will just be different; people will come to rely on LLMs for their jobs, like they rely on search engines.

Likewise, you can probably hire fewer doctors now because Google appeared in ~2000, but nobody talked about them being "replaced". There is NOT less demand for doctors.

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It also reminds me of the prediction around self-driving cars, which is 13+ years ago at this point:

https://news.ycombinator.com/item?id=45149270

I believe Hacker News mostly fell for the hype in ~2012-2016. And even though the predictions turned out to be comically wrong, many people are still attached to them

https://en.wikipedia.org/wiki/List_of_predictions_for_autono...

i.e. I don't think Hinton will be proven "right" with ANY amount of time. The whole framing is just off.

It's not humans xor AI, it's humans + AI. And the world is not static

The world is indeed not static. That it hasn't happened yet doesn't mean it won't.

Predictions about self driving were off, but far from "comically wrong". Waymo's operations are proof of that.

And to conclude things based on the state of the replacement of programmers after only 2-3 years of ChatGPT being a thing is folly.

The reality is that AI has far fewer limitations and legacy cruft than humans to deal with. Don't get me wrong, I like humans, but our performance is very close to the peak of what it could ever be. That of AI not so much. Remember that AI has been evolving for less than 100 years and it is already where it is today. That took us/biology orders of magnitude more time.

The only real question is how fast it will replace (which) human labor.

To be fair we haven't hit 2026 yet so his prediction might still turn out to be somewhat accurate. But yeah, probably not.

the guy invented AI who cares if he is a couple years wrong with a prediction that will come to fruition, jeez. I would call the geezer confident not arrogant. Who is this? Some burned post doc? No offence but you are randomly laying into the guy as if this is Oprah

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