8 ms·

Geoffrey Hinton said machine learning would outperform radiologists by now

52 points by Hard_Space 2y ago

napoleoncomplex 2y ago
There is a tremendous share of medicine specialties facing shortages, and fear of AI is not a relevant trend causing it. Even the link explaining shortages in the above article is pretty clear on that. I do agree with the article's author's other premise, radiology was one of those fields that a lot of people (me included) have been expecting to be largely automated, or at least the easy parts, as the author mentions, and that the timelines are moving slower than expected. After all, pigeons perform similarly well to radiologists: https://pmc.ncbi.nlm.nih.gov/articles/PMC4651348/ https://pmc.ncbi.nlm.nih.gov/articles/PMC4651348/ (not really, but it is basically obligatory to post this article in any radiology themed discussion if you have radiology friends). Knowing medicine, even when the tech does become "good enough", it will take another decade or two before it becomes the main way of doing things.
- bachmeier 2y ago
  The reason AI is hyped is because it's easy to get the first 80% or 90% of what you need to be a viable alternative at some task. Extrapolating in a linear fashion, AI will do the last 10-20% in a few months or maybe a couple years. But the low-hanging fruit is easy and fast. It may never be feasible to complete the last few percent. Then it changes from "AI replacement" to "AI assisted". I don't know much about radiology, but I remember before the pandemic one of the big fears was what we'd do with all the unemployed truck drivers.
  - candiddevmike 2y ago
    > Extrapolating in a linear fashion, AI will do the last 10-20% in a few months or maybe a couple years That's a hefty assumption, especially if you're including accuracy.
    - hnthrowaway6543 2y ago
      > That's a hefty assumption, especially if you're including accuracy. That's exactly what the comment is saying. People see AI do 80% of a task and assume development speed will follow a linear trend and the last 20% will get done relatively quickly. The reality is the last 20% is hard-to-impossible. Prime example is self-driving vehicles, which have been 80% done and 5 years away for the past 15 years. (It actually looks further than 5 years away now that we know throwing more training data at the problem doesn't fix it.)
      - ninetyninenine 2y ago
        We are at 95 percent now for self driving. I regularly use Waymo robot cars in sf. No driver. The gap is now just scaling. Take what is 100% complete in one city and do it in another city. Problem was solved… you just missed the boat.
        hnthrowaway6543 2y ago
        Waymo barely works, with 24/7 monitoring by humans in a "fleet response" center[0], in 4 cities in the world. That's only 95% done if you're counting good enough for government work. [0] https://waymo.com/blog/2024/05/fleet-response/ https://waymo.com/blog/2024/05/fleet-response/
        soco 2y ago
        The monitoring might be 24/7 but its reaction time is nothing usable in a life-and-death situation. Or I just cannot imagine a human being notified "I think I'm crashing into something" and able to take over and do anything of significance within that second to avoid the crash (except hitting on the brakes which the car could do just as well). So don't read too much into the response team, it has definitely its use but won't save you from plunging into that sinkhole who just appeared.
    - bdndndndbve 2y ago
      OP is being facetious, the "last 20%" is a common saying implying that you've back-loaded the hard part of a task.
  - pavel_lishin 2y ago
    > Extrapolating in a linear fashion, AI will do the last 10-20% in a few months or maybe a couple years Extrapolating in a linear fashion, in a few years my child will be ten foot tall, weight six hundred pounds, and speak 17 languages. The first 90% is the easy part. It's the other 90% that's hard. People forget that, especially people who don't work in software/technology.
  - edanm 2y ago
    > The reason AI is hyped is because it's easy to get the first 80% or 90% of what you need to be a viable alternative at some task. No, it's because if the promise of certain technologies is reached, it'd be a huge deal. And of course, that promise has been reached for many technologies, and it's indeed been a huge deal. Sometimes less than people imagine, but often more than the naysayers who think it won't have any impact at all.
- infecto 2y ago
  I wouldn’t dismiss the premise so quickly. Other factors certainly play a role, but I imagine that after 2016, anyone considering a career in radiology would have automation as a prominent concern.
  - randomdata 2y ago
    Automation may be a concern. Not because of Hinton, though. There is only so much time in the day. You don't become a leading expert in AI like Hinton has without tuning out the rest of the world, which means a random Average Joe is apt to be in a better position to predict when automation is capable of radiology tasks than Hinton. If an expert in radiology was/is saying it, then perhaps it is worth a listen. But Hinton is just about the last person you are going to listen to on this matter.
- bonoboTP 2y ago
  > There is a tremendous share of medicine specialties facing shortages The supply of doctors is artificially strapped by the doctor cartel/mafia. There are plenty who want to enter but are prevented by artificial limits in the training.
  - mananaysiempre 2y ago
    Medical professionals are highly paid, thus an education in medicine is proportionally expensive. An education in medicine is expensive, thus younger medical professionals need to be highly paid in order to afford their debt. Until the vicious cycle is here is broken (e.g. less accessible student loans? and more easily defaultable is one way to spell less accessible), things are not going to improve. And there’s also the problem that you want your doctors to be highly paid, because it’s a stressful, high-responsibility job with stupidly difficult education.
    - bonoboTP 2y ago
      US doctors are ridiculously overpaid compared to the rest of the developed world, such as the UK or western EU. There's no evidence that this translates to better care at all. It's all due to their regulatory capture. One possible outcome is that healthcare costs continue to balloon and eventually it pops and the mafia gets disbanded and more immigrant doctors will be allowed to practice, driving prices to saner levels.
      - tzs 2y ago
        How doctors are licensed in the US compared to western Europe might explain why health care costs are higher in the US, but it does not explain why health care costs are rising so much. That's because health care costs are rising at similar rates in western Europe (and most of the rest of the first world). For example from 2000 to 2018 here's the ratio of per capita health care costs in 2018 to the costs in 2000 for several countries: 2.1 Germany 1.8 France 2.0 Canada 1.7 Italy 2.6 Japan 2.6 UK 2.3 US Here's cost ratios over several decades compared to 1970 costs for the US, the UK, and France: 1980 1990 2000 2010 2020 US 3.2 8.2 13.9 24.1 36.3 UK 3.1 6.3 15.3 27.8 40.5 FR 3.4 7.6 14.9 21.1 28.5 Here's the same data showing the the cost ratio decade to decade instead of from 1970: 1980 1990 2000 2010 2020 US 3.2 2.6 1.7 1.7 1.5 UK 3.1 2.0 2.4 1.8 1.5 FR 3.4 2.2 2.0 1.4 1.4 My data source was https://data.oecd.org/healthres/health-spending.htm https://data.oecd.org/healthres/health-spending.htm but it looks like data.oecd.org reorganized their site so that redirects to https://www.oecd.org/en/data/indicators/health-spending.html https://www.oecd.org/en/data/indicators/health-spending.html which seems to have the data but with a much more limited interface.
- caeril 2y ago
  > even when the tech does become "good enough", it will take another decade or two before it becomes the main way of doing things. What you're advocating for would be a crime against humanity. Every four years, the medical industry kills a million Americans via preventable medical errors, roughly one third of which are misdiagnoses that were obvious in hindsight. If we get to a point at which models are better diagnosticians than humans, even by a small margin, then delaying implementation by even one day will constitute wilful homicide. EVERY SINGLE PERSON standing in the way of implementation will have blood on their hands. From the FDA, to HHS, to the hospital administrators, to the physicians (however such a delay would play out) - every single one of them will be complicit in first-degree murder.
amelius 2y ago
I predict most AI developers will be out of a job soon. Say you are making a robot that puts nuts and bolts together using DL models. In a few years, Google/OpenAI will have solved this and many other physical tasks, and any client that needs nuts and bolts put together will just buy a GenericRobot that can do that. Same for radiology-based diagnostics. Soon the mega companies will have bought enormous amounts of data and they will easily put your small radiology-AI company out of business. Making a tool to do X on a computer? Soon, there will be LLM-based tools that can do __anything__ on a computer and they will just interface with your screen and keyboard directly. Etc. etc. Just about anyone working in AI right now is building a castle in someone else's kingdom.
- hobs 2y ago
  I predict that the current wave of AI will be useful but not nearly as useful as you hope, requiring ever more people to pour their time and energy into the overarching system it creates.
- candiddevmike 2y ago
  > Just about anyone working in AI right now is building a castle in someone else's kingdom. Things are innovating so fast that even if you've convinced yourself that you own the kingdom, it seems like you're one research paper or Nvidia iteration away from having to start over.
- cryptonym 2y ago
  This is not putting people out of a job, this is changing their jobs. As always, workforce will adapt. Just like all previous tech, this is trying to improve efficiency. LLM can't do "anything" on a computer that's a misunderstanding of the tech but they can already interface with screen/keyboard. You overestimate the innovations abilities of big tech companies. They'll buy a lot of companies designing robots that are putting nuts and bolts together, or diagnostics, that's a great opportunity.
  - ben_w 2y ago
    > As always, workforce will adapt I was never a fan of the term "Singularity" for the AI thing. When mathematical singularities pop up in physics, it's usually a sign the physics is missing something. Instead, I like to think of the AI "event horizon", the point in the future — always ahead, yet getting ever closer — beyond which you can no longer predict what happens next. Obviously that will depend on how much attention you pay to the developments in the field (and I've seen software developers surprised by Google Translate having an AR mode a decade after the tech was first demonstrated), but there is an upper limit even for people who obsessively consume all public information about a topic: if you're reading about it when you go to sleep, will you be surprised when you wake up by all the overnight developments? When that happens, no workforce can possibly adapt. How close is that? Dunno, but what I can say is that I cannot tell you what to expect 2 months from now, despite following developments in this field as best I can from the outside, and occasionally implementing one or other of the recent-ish AI models myself.
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bonoboTP 2y ago
Hinton doesn't take social/power aspects into account, he's a naive technical person. Healthcare is an extremely strongly regulated field and its professionals are among those with the highest status in society. Radiology won't be among the first applications to be automated. I see many naive software/ML people trying to enter the medical domain, but they are usually eaten for breakfast by the incumbents. This is not an open market like taxis vs Uber or one entertainment app vs another. If you try to enter the medical domain, you will be deep deep into politics and lobbying and you're going to be defeated. Better deploy it in fields like agriculture and manufacturing that are already in good part automated. Then later in another generation one can try the medical domain.
- jhbadger 2y ago
  >This is not an open market like taxis vs Uber Interesting choice of example. In most places taxis are/weren't an "open market" either but had various laws preventing newcomers from providing taxi rides. In many places what Uber did was illegal. Various locales either banned Uber, changed the laws to allow them, or simply didn't enforce the laws that made them illegal.
  - bonoboTP 2y ago
    Yes, even Uber was pushed out in many places with lawfare and jobs reasons. Taxi drivers do have some moderately strong lobby in many places. So just think about taking on doctors... The discussion was never really about which service is better. It was about whose toes they were stepping on. In healthcare you can't take the "asking for forgiveness is easier than permission" route. That's a quick way to jail. In taxiing, it's just some fines that fit the budget. My overall point isn't to argue whether these disruptions are socially beneficial. I'm trying to point at the more neutral observation that who you're taking on and how much power they have will be crucial to the success of a disruptor. It's not just about "product quality" based on some objective dispassionate politics-free scientific benchmarks.
  - eszed 2y ago
    In context, I think GP meant "open" from a sociopolitical point of view. The "taxi-industrial complex" had minimal political power, so Uber was repeatedly able to steamroll the incumbents - exactly as you describe in your final sentence. Medical interests have immense sociopolitical power, so the same playbook won't work. The differences in your positions are merely semantic.
richrichie 2y ago
He is by no means god father of AI.
- jhbadger 2y ago
  It's fair to call him the "godfather of deep learning", and many people seem to confuse the two, ignoring that AI research has been going on since the 1950s.
righthand 2y ago
I worked for a start up that was contracted by a German doctor. We built some software for automating identifying lung lesions: a viewer app for each xray, scientific trials software, and an LLM to process data from the trials software. The result was pretty cool and probably would have moved it forward. We even held a few trials to gather data from many doctors in China. Then the doctor didn’t pay his bills and the project was shut down. This wouldn’t have put radiologists out of work but would have made the process to diagnose much quicker maybe more affordable.
vessenes 2y ago
We are in the self-referential whirlpool of AI complaints at this point; interesting. Soon AI will be provably Turing complete at preemptively ruining particular ways of life. Upshot of the article: we don't have enough radiologists because Geoffrey Hinton said in 2011 they wouldn't be needed. Hinton have had an impact on people choosing radiology, I don't really know. But, in the US, demographers and old-guard types in the medical profession made a REALLY bad call much more than ten years ago, and cut the number of med school students they were calling for, and we all are living with the consequences. If you're going to complain about Hinton you should at least normalize for the lack of doctors in other fields that he didn't call out as well. Boomers are consuming a LOTT of medical, and in the US we have knock-on effects from the opioid crisis. It was not a good idea to cut the physician recruitment and enrollment when they did, whether they did for principled reasons (We only need so many and this way we'll have the best), or for ... less principled ones (A legion of cheap young doctors competing for our jobs doesn't sound that great to the physician's board at this research hospital).
chx 2y ago
A chatbot just killed a teen. While I don't think AI will be be banned wholesale from, say, interstate commerce despite it really should until the industry self regulates the moment it starts killing people in medicine there it will be banned. And it's obvious it'll kill people. For eg in radiology it'll add its bullshit to images which is just not there. And so forth. It's just a question of how and when , not if. No dystopian novel has predicted how fucking mundane it'll be when AI begins to kill people.
- rafram 2y ago
  > A chatbot just killed a teen. Assuming you’re referring to this story, that’s a stretch: https://www.nytimes.com/2024/10/23/technology/characterai-lawsuit-teen-suicide.html https://www.nytimes.com/2024/10/23/technology/characterai-la... “His stepfather’s .45 caliber handgun” being unlocked and available to him seems like the proximate cause.
  - jvanderbot 2y ago
    Gun's don't kill people, people do /s (But I think the gun _helps_). Have you heard the one about suicide in the UK reducing when they switched to non-toxic cooking gas? https://pmc.ncbi.nlm.nih.gov/articles/PMC478945/pdf/brjprevsmed00022-0018.pdf https://pmc.ncbi.nlm.nih.gov/articles/PMC478945/pdf/brjprevs... Not hard to propose that difficulty of killing / dying does decrease likelihood of killing / dying. Lock up your guns.
    - ben_w 2y ago
      Same with the UK limiting how many paracetamol pills people could buy in one go, even though it's obviously possible to just go to the same shop several times in a row (or all the adjacent shops on the high street selling them) and keep buying more packets: https://www.ncbi.nlm.nih.gov/books/NBK374099/#:~:text=We%20have%20shown%20that%20the,coroner%20and%20deaths%20receiving%20a https://www.ncbi.nlm.nih.gov/books/NBK374099/#:~:text=We%20h... That said, stuff like this definitely makes me think there's a lot of people on the margin who would be affected by what a chatbot says. Even a much older model like ELIZA.
bee_rider 2y ago
It looks like there are 3 possible outcomes: AI will do absolutely nothing, AI will enhance the productivity of radiologists, or AI will render their skills completely obsolete. In case 1, learning radiology is a fine idea. In case 2, it becomes a little tricky, I guess if you are one of the most skilled radiologists you’ll do quite well for yourself (do the work of 10 and I bet you can take the pay of 2). For case 3, it becomes a bad career choice. Although, I dunno, it seems like an odd thing to complain about. I mean, the nature of humans is that we make tools—we’re going to automate if it is possible. Rather I think the problem is that we’ve decided that if somebody makes the wrong bet about how their field will go, they should live in misery and deprivation.
- hilux 2y ago
  Scenario 2 has already arrived - it's just not fully rolled out.
- gtirloni 2y ago
  Scenario 2 is the most likely by far. It's just the continuation of a trend in radiology. Clinics already employ many more "technicians" with basic training and have a "proper" radiologist double checking and signing their work. If you're a technician (or a radiologist NOT supervising technicians), you're in hot water.
hggigg 2y ago
That is because Mr Hinton is full of shit. He constantly overstates the progress to bolster his position on associated risks. And now someone gave him a bloody Nobel so he'll be even more intolerable. What is behind the curtain is becoming obvious and while there are some gains in some specific areas, the ability for this technology as it stands today to change society is mostly limited to pretending to be a solution for usual shitty human behaviour of cost cutting and reducing workforce. For example IBM laying off people under the guise of AI when actually it was a standard cost cutting exercise with some marketing smeared over the top while the management told the remaining people to pick up the slack. A McKinsey special! And generating content which is to be consumed by people who can't tell the difference between humans and muck. A fine example is from the mathematics side we are constantly promised huge gains in LLMs but we can't replace a flunked undergrad with anything yet. And this is because it is the wrong tool for the fucking job. Which is the problem in one simple statement: it's mostly the wrong tool for most things. Still I enjoyed the investment ride! I could model that one with my brain fine.
- jamal-kumar 2y ago
  I think my favorite thing is how people are out there reconsidering their programming careers and doing shit like quitting when you have a bunch of less than quality developers lazily deploying code they pulled out of the ass of an LLM which does no bounds checking, no input validation, none of that basic as can be security shit at all, they just end up deploying all that code to production systems facing the internet and then nobody notices because either the competent developers left for greener pastures or got laid off. I keep on telling people this is going to take like 5 or 10 years to untangle considering how hard it is in a lot of cases to fix code put into tons of stuff like embedded devices mounted up on a light pole or whatever that was considered good enough for a release, but I bet it'll be even more of a pain than that. These are after all security holes in devices that in many cases peoples day or life going well depends on. What it has done so far for me is put copywriters out of a job. I still find it mostly useful for writing drivel that gets used as page filler or product descriptions for my ecommerce side jobs. Lately the image recognition capabilities lets me generate the kind of stuff I'd never write, which is instagram posts with tons of emojis and other things I'd never have the gumption to do myself but increases engagement. I actually used to use markov chain generator for this going back to 2016 though so the big difference here is that at least it can form more coherent sentences.
nopinsight 2y ago
I wonder what experts think about this specialized model: "Harrison.rad.1 excels in the same radiology exams taken by human radiologists, as well as in benchmarks against other foundational models. The Fellowship of the Royal College of Radiologists (FRCR) 2B Rapids exam is considered one of the leading and toughest certifications for radiologists. Only 40-59% of human radiologists pass on their first attempt. Radiologists who re-attempt the exam within a year of passing score an average of 50.88 out of 60 (84.8%). Harrison.rad.1 scored 51.4 out of 60 (85.67%)." https://harrison.ai/harrison-rad-1/ https://harrison.ai/harrison-rad-1/
odyssey7 2y ago
Why automate the analysis of an MRI when the bottleneck is that they only have two MRI machines in the whole city, for some reason?
Kalanos 2y ago
My cousin is an established radiologist. Since I am well-versed in AI, we talked about this at length. He says that the automated predictions help free up radiologists to focus on the edge cases and more challenging disease types.
oldpersonintx 2y ago
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stego-tech 2y ago
Not a bad piece. I quite liked how they manage to find the likely middle ground between the doomers on both sides while still twisting the knife in the typical “workforce will adapt” chuds who think they’re above the fracas. It’s not about whether the present AI bubble is a giant sham that’ll implode like a black hole, or if it’s the real deal and AGI is days away. It’s always been about framing the consequences of either outcome in a society where 99.9% of humans must work to survive, and the implications of a giant economic collapse on those least-able to weather its impact in a time of record wealth and income inequality. It doesn’t matter which side is right, because millions of people will lose their jobs as a result and thousands will die. That is what some of us are trying to raise the alarm about, and in a post-2008 collapse world, young people are absolutely going to do what it takes to survive rather than what’s best for society - which is going to have knock-on effects like the author described (labor shortages in critical fields) for decades to come. In essence, if one paper from one guy managed to depress enrollment in a critical specialty for medicine, and he was flat-out wrong, then imagine the effects the Boosters and Doomers are having on other fields in the present cycle. If I were a betting dinosaur, I suspect there will be a similar lack of highly-skilled programmers and knowledge workers due to the “copilot tools will replace programmers” hype of present tools, and then we’ll have these same divorced-from-society hypesters making the same predictions again, ignorant of the consequences of their prior actions. Which is all a lot of flowery language just to say, “maybe we need to stop evaluating these things solely as ways to remove humans from labor or increase profit margins, and instead consider the broader effects of our decisions before acting on them.”
naveen99 2y ago
He updated the timeline to 10-15 years in 2023. That is like an eternity. Even artists, lawyers, programmers are scared about being replaced now in that time frame. We might have humanoid robots in 15 years.
- bonoboTP 2y ago
  I wanted to say don't be unrealistic, but then I realized 15 years ago was 2009 and it was basically the stone age. In computer vision, the state-of-the-art was HOG+SVM, SIFT features and bag-of-words. Pixel-level tasks were extremely brittle, best you could do was something like GrabCut etc. Neural nets were still in their uncool phase. In NLP you were happy if you could do reliable part of speech tagging. 2039 is really hard to foresee.
betaby 2y ago
Radiologists are cartel in Canada/USA making ~400-600k/year. Those jobs are protected and will continue to be human-staffed for foreseeable future regardless of AI efficiency.

52 points by Hard_Space 2y ago

There is a tremendous share of medicine specialties facing shortages, and fear of AI is not a relevant trend causing it. Even the link explaining shortages in the above article is pretty clear on that. I do agree with the article's author's other premise, radiology was one of those fields that a lot of people (me included) have been expecting to be largely automated, or at least the easy parts, as the author mentions, and that the timelines are moving slower than expected. After all, pigeons perform similarly well to radiologists: https://pmc.ncbi.nlm.nih.gov/articles/PMC4651348/ https://pmc.ncbi.nlm.nih.gov/articles/PMC4651348/ (not really, but it is basically obligatory to post this article in any radiology themed discussion if you have radiology friends). Knowing medicine, even when the tech does become "good enough", it will take another decade or two before it becomes the main way of doing things.

bachmeier 2y ago
The reason AI is hyped is because it's easy to get the first 80% or 90% of what you need to be a viable alternative at some task. Extrapolating in a linear fashion, AI will do the last 10-20% in a few months or maybe a couple years. But the low-hanging fruit is easy and fast. It may never be feasible to complete the last few percent. Then it changes from "AI replacement" to "AI assisted". I don't know much about radiology, but I remember before the pandemic one of the big fears was what we'd do with all the unemployed truck drivers.
- candiddevmike 2y ago
  > Extrapolating in a linear fashion, AI will do the last 10-20% in a few months or maybe a couple years That's a hefty assumption, especially if you're including accuracy.
  - hnthrowaway6543 2y ago
    > That's a hefty assumption, especially if you're including accuracy. That's exactly what the comment is saying. People see AI do 80% of a task and assume development speed will follow a linear trend and the last 20% will get done relatively quickly. The reality is the last 20% is hard-to-impossible. Prime example is self-driving vehicles, which have been 80% done and 5 years away for the past 15 years. (It actually looks further than 5 years away now that we know throwing more training data at the problem doesn't fix it.)
    - ninetyninenine 2y ago
      We are at 95 percent now for self driving. I regularly use Waymo robot cars in sf. No driver. The gap is now just scaling. Take what is 100% complete in one city and do it in another city. Problem was solved… you just missed the boat.
      - hnthrowaway6543 2y ago
        Waymo barely works, with 24/7 monitoring by humans in a "fleet response" center[0], in 4 cities in the world. That's only 95% done if you're counting good enough for government work. [0] https://waymo.com/blog/2024/05/fleet-response/ https://waymo.com/blog/2024/05/fleet-response/
        soco 2y ago
        The monitoring might be 24/7 but its reaction time is nothing usable in a life-and-death situation. Or I just cannot imagine a human being notified "I think I'm crashing into something" and able to take over and do anything of significance within that second to avoid the crash (except hitting on the brakes which the car could do just as well). So don't read too much into the response team, it has definitely its use but won't save you from plunging into that sinkhole who just appeared.
  - bdndndndbve 2y ago
    OP is being facetious, the "last 20%" is a common saying implying that you've back-loaded the hard part of a task.
- pavel_lishin 2y ago
  > Extrapolating in a linear fashion, AI will do the last 10-20% in a few months or maybe a couple years Extrapolating in a linear fashion, in a few years my child will be ten foot tall, weight six hundred pounds, and speak 17 languages. The first 90% is the easy part. It's the other 90% that's hard. People forget that, especially people who don't work in software/technology.
- edanm 2y ago
  > The reason AI is hyped is because it's easy to get the first 80% or 90% of what you need to be a viable alternative at some task. No, it's because if the promise of certain technologies is reached, it'd be a huge deal. And of course, that promise has been reached for many technologies, and it's indeed been a huge deal. Sometimes less than people imagine, but often more than the naysayers who think it won't have any impact at all.
infecto 2y ago
I wouldn’t dismiss the premise so quickly. Other factors certainly play a role, but I imagine that after 2016, anyone considering a career in radiology would have automation as a prominent concern.
- randomdata 2y ago
  Automation may be a concern. Not because of Hinton, though. There is only so much time in the day. You don't become a leading expert in AI like Hinton has without tuning out the rest of the world, which means a random Average Joe is apt to be in a better position to predict when automation is capable of radiology tasks than Hinton. If an expert in radiology was/is saying it, then perhaps it is worth a listen. But Hinton is just about the last person you are going to listen to on this matter.
bonoboTP 2y ago
> There is a tremendous share of medicine specialties facing shortages The supply of doctors is artificially strapped by the doctor cartel/mafia. There are plenty who want to enter but are prevented by artificial limits in the training.
- mananaysiempre 2y ago
  Medical professionals are highly paid, thus an education in medicine is proportionally expensive. An education in medicine is expensive, thus younger medical professionals need to be highly paid in order to afford their debt. Until the vicious cycle is here is broken (e.g. less accessible student loans? and more easily defaultable is one way to spell less accessible), things are not going to improve. And there’s also the problem that you want your doctors to be highly paid, because it’s a stressful, high-responsibility job with stupidly difficult education.
  - bonoboTP 2y ago
    US doctors are ridiculously overpaid compared to the rest of the developed world, such as the UK or western EU. There's no evidence that this translates to better care at all. It's all due to their regulatory capture. One possible outcome is that healthcare costs continue to balloon and eventually it pops and the mafia gets disbanded and more immigrant doctors will be allowed to practice, driving prices to saner levels.
    - tzs 2y ago
      How doctors are licensed in the US compared to western Europe might explain why health care costs are higher in the US, but it does not explain why health care costs are rising so much. That's because health care costs are rising at similar rates in western Europe (and most of the rest of the first world). For example from 2000 to 2018 here's the ratio of per capita health care costs in 2018 to the costs in 2000 for several countries: 2.1 Germany 1.8 France 2.0 Canada 1.7 Italy 2.6 Japan 2.6 UK 2.3 US Here's cost ratios over several decades compared to 1970 costs for the US, the UK, and France: 1980 1990 2000 2010 2020 US 3.2 8.2 13.9 24.1 36.3 UK 3.1 6.3 15.3 27.8 40.5 FR 3.4 7.6 14.9 21.1 28.5 Here's the same data showing the the cost ratio decade to decade instead of from 1970: 1980 1990 2000 2010 2020 US 3.2 2.6 1.7 1.7 1.5 UK 3.1 2.0 2.4 1.8 1.5 FR 3.4 2.2 2.0 1.4 1.4 My data source was https://data.oecd.org/healthres/health-spending.htm https://data.oecd.org/healthres/health-spending.htm but it looks like data.oecd.org reorganized their site so that redirects to https://www.oecd.org/en/data/indicators/health-spending.html https://www.oecd.org/en/data/indicators/health-spending.html which seems to have the data but with a much more limited interface.
caeril 2y ago
> even when the tech does become "good enough", it will take another decade or two before it becomes the main way of doing things. What you're advocating for would be a crime against humanity. Every four years, the medical industry kills a million Americans via preventable medical errors, roughly one third of which are misdiagnoses that were obvious in hindsight. If we get to a point at which models are better diagnosticians than humans, even by a small margin, then delaying implementation by even one day will constitute wilful homicide. EVERY SINGLE PERSON standing in the way of implementation will have blood on their hands. From the FDA, to HHS, to the hospital administrators, to the physicians (however such a delay would play out) - every single one of them will be complicit in first-degree murder.

amelius 2y ago

I predict most AI developers will be out of a job soon. Say you are making a robot that puts nuts and bolts together using DL models. In a few years, Google/OpenAI will have solved this and many other physical tasks, and any client that needs nuts and bolts put together will just buy a GenericRobot that can do that. Same for radiology-based diagnostics. Soon the mega companies will have bought enormous amounts of data and they will easily put your small radiology-AI company out of business. Making a tool to do X on a computer? Soon, there will be LLM-based tools that can do __anything__ on a computer and they will just interface with your screen and keyboard directly. Etc. etc. Just about anyone working in AI right now is building a castle in someone else's kingdom.

hobs 2y ago
I predict that the current wave of AI will be useful but not nearly as useful as you hope, requiring ever more people to pour their time and energy into the overarching system it creates.
candiddevmike 2y ago
> Just about anyone working in AI right now is building a castle in someone else's kingdom. Things are innovating so fast that even if you've convinced yourself that you own the kingdom, it seems like you're one research paper or Nvidia iteration away from having to start over.
cryptonym 2y ago
This is not putting people out of a job, this is changing their jobs. As always, workforce will adapt. Just like all previous tech, this is trying to improve efficiency. LLM can't do "anything" on a computer that's a misunderstanding of the tech but they can already interface with screen/keyboard. You overestimate the innovations abilities of big tech companies. They'll buy a lot of companies designing robots that are putting nuts and bolts together, or diagnostics, that's a great opportunity.
- ben_w 2y ago
  > As always, workforce will adapt I was never a fan of the term "Singularity" for the AI thing. When mathematical singularities pop up in physics, it's usually a sign the physics is missing something. Instead, I like to think of the AI "event horizon", the point in the future — always ahead, yet getting ever closer — beyond which you can no longer predict what happens next. Obviously that will depend on how much attention you pay to the developments in the field (and I've seen software developers surprised by Google Translate having an AR mode a decade after the tech was first demonstrated), but there is an upper limit even for people who obsessively consume all public information about a topic: if you're reading about it when you go to sleep, will you be surprised when you wake up by all the overnight developments? When that happens, no workforce can possibly adapt. How close is that? Dunno, but what I can say is that I cannot tell you what to expect 2 months from now, despite following developments in this field as best I can from the outside, and occasionally implementing one or other of the recent-ish AI models myself.

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bonoboTP 2y ago

Hinton doesn't take social/power aspects into account, he's a naive technical person. Healthcare is an extremely strongly regulated field and its professionals are among those with the highest status in society. Radiology won't be among the first applications to be automated. I see many naive software/ML people trying to enter the medical domain, but they are usually eaten for breakfast by the incumbents. This is not an open market like taxis vs Uber or one entertainment app vs another. If you try to enter the medical domain, you will be deep deep into politics and lobbying and you're going to be defeated. Better deploy it in fields like agriculture and manufacturing that are already in good part automated. Then later in another generation one can try the medical domain.

jhbadger 2y ago
>This is not an open market like taxis vs Uber Interesting choice of example. In most places taxis are/weren't an "open market" either but had various laws preventing newcomers from providing taxi rides. In many places what Uber did was illegal. Various locales either banned Uber, changed the laws to allow them, or simply didn't enforce the laws that made them illegal.
- bonoboTP 2y ago
  Yes, even Uber was pushed out in many places with lawfare and jobs reasons. Taxi drivers do have some moderately strong lobby in many places. So just think about taking on doctors... The discussion was never really about which service is better. It was about whose toes they were stepping on. In healthcare you can't take the "asking for forgiveness is easier than permission" route. That's a quick way to jail. In taxiing, it's just some fines that fit the budget. My overall point isn't to argue whether these disruptions are socially beneficial. I'm trying to point at the more neutral observation that who you're taking on and how much power they have will be crucial to the success of a disruptor. It's not just about "product quality" based on some objective dispassionate politics-free scientific benchmarks.
- eszed 2y ago
  In context, I think GP meant "open" from a sociopolitical point of view. The "taxi-industrial complex" had minimal political power, so Uber was repeatedly able to steamroll the incumbents - exactly as you describe in your final sentence. Medical interests have immense sociopolitical power, so the same playbook won't work. The differences in your positions are merely semantic.

richrichie 2y ago

He is by no means god father of AI.

jhbadger 2y ago
It's fair to call him the "godfather of deep learning", and many people seem to confuse the two, ignoring that AI research has been going on since the 1950s.

righthand 2y ago

I worked for a start up that was contracted by a German doctor. We built some software for automating identifying lung lesions: a viewer app for each xray, scientific trials software, and an LLM to process data from the trials software. The result was pretty cool and probably would have moved it forward. We even held a few trials to gather data from many doctors in China. Then the doctor didn’t pay his bills and the project was shut down. This wouldn’t have put radiologists out of work but would have made the process to diagnose much quicker maybe more affordable.

vessenes 2y ago

We are in the self-referential whirlpool of AI complaints at this point; interesting. Soon AI will be provably Turing complete at preemptively ruining particular ways of life. Upshot of the article: we don't have enough radiologists because Geoffrey Hinton said in 2011 they wouldn't be needed. Hinton have had an impact on people choosing radiology, I don't really know. But, in the US, demographers and old-guard types in the medical profession made a REALLY bad call much more than ten years ago, and cut the number of med school students they were calling for, and we all are living with the consequences. If you're going to complain about Hinton you should at least normalize for the lack of doctors in other fields that he didn't call out as well. Boomers are consuming a LOTT of medical, and in the US we have knock-on effects from the opioid crisis. It was not a good idea to cut the physician recruitment and enrollment when they did, whether they did for principled reasons (We only need so many and this way we'll have the best), or for ... less principled ones (A legion of cheap young doctors competing for our jobs doesn't sound that great to the physician's board at this research hospital).

chx 2y ago

A chatbot just killed a teen. While I don't think AI will be be banned wholesale from, say, interstate commerce despite it really should until the industry self regulates the moment it starts killing people in medicine there it will be banned. And it's obvious it'll kill people. For eg in radiology it'll add its bullshit to images which is just not there. And so forth. It's just a question of how and when , not if. No dystopian novel has predicted how fucking mundane it'll be when AI begins to kill people.

rafram 2y ago
> A chatbot just killed a teen. Assuming you’re referring to this story, that’s a stretch: https://www.nytimes.com/2024/10/23/technology/characterai-lawsuit-teen-suicide.html https://www.nytimes.com/2024/10/23/technology/characterai-la... “His stepfather’s .45 caliber handgun” being unlocked and available to him seems like the proximate cause.
- jvanderbot 2y ago
  Gun's don't kill people, people do /s (But I think the gun _helps_). Have you heard the one about suicide in the UK reducing when they switched to non-toxic cooking gas? https://pmc.ncbi.nlm.nih.gov/articles/PMC478945/pdf/brjprevsmed00022-0018.pdf https://pmc.ncbi.nlm.nih.gov/articles/PMC478945/pdf/brjprevs... Not hard to propose that difficulty of killing / dying does decrease likelihood of killing / dying. Lock up your guns.
  - ben_w 2y ago
    Same with the UK limiting how many paracetamol pills people could buy in one go, even though it's obviously possible to just go to the same shop several times in a row (or all the adjacent shops on the high street selling them) and keep buying more packets: https://www.ncbi.nlm.nih.gov/books/NBK374099/#:~:text=We%20have%20shown%20that%20the,coroner%20and%20deaths%20receiving%20a https://www.ncbi.nlm.nih.gov/books/NBK374099/#:~:text=We%20h... That said, stuff like this definitely makes me think there's a lot of people on the margin who would be affected by what a chatbot says. Even a much older model like ELIZA.

bee_rider 2y ago

It looks like there are 3 possible outcomes: AI will do absolutely nothing, AI will enhance the productivity of radiologists, or AI will render their skills completely obsolete. In case 1, learning radiology is a fine idea. In case 2, it becomes a little tricky, I guess if you are one of the most skilled radiologists you’ll do quite well for yourself (do the work of 10 and I bet you can take the pay of 2). For case 3, it becomes a bad career choice. Although, I dunno, it seems like an odd thing to complain about. I mean, the nature of humans is that we make tools—we’re going to automate if it is possible. Rather I think the problem is that we’ve decided that if somebody makes the wrong bet about how their field will go, they should live in misery and deprivation.

hilux 2y ago
Scenario 2 has already arrived - it's just not fully rolled out.
gtirloni 2y ago
Scenario 2 is the most likely by far. It's just the continuation of a trend in radiology. Clinics already employ many more "technicians" with basic training and have a "proper" radiologist double checking and signing their work. If you're a technician (or a radiologist NOT supervising technicians), you're in hot water.

hggigg 2y ago

That is because Mr Hinton is full of shit. He constantly overstates the progress to bolster his position on associated risks. And now someone gave him a bloody Nobel so he'll be even more intolerable. What is behind the curtain is becoming obvious and while there are some gains in some specific areas, the ability for this technology as it stands today to change society is mostly limited to pretending to be a solution for usual shitty human behaviour of cost cutting and reducing workforce. For example IBM laying off people under the guise of AI when actually it was a standard cost cutting exercise with some marketing smeared over the top while the management told the remaining people to pick up the slack. A McKinsey special! And generating content which is to be consumed by people who can't tell the difference between humans and muck. A fine example is from the mathematics side we are constantly promised huge gains in LLMs but we can't replace a flunked undergrad with anything yet. And this is because it is the wrong tool for the fucking job. Which is the problem in one simple statement: it's mostly the wrong tool for most things. Still I enjoyed the investment ride! I could model that one with my brain fine.

jamal-kumar 2y ago
I think my favorite thing is how people are out there reconsidering their programming careers and doing shit like quitting when you have a bunch of less than quality developers lazily deploying code they pulled out of the ass of an LLM which does no bounds checking, no input validation, none of that basic as can be security shit at all, they just end up deploying all that code to production systems facing the internet and then nobody notices because either the competent developers left for greener pastures or got laid off. I keep on telling people this is going to take like 5 or 10 years to untangle considering how hard it is in a lot of cases to fix code put into tons of stuff like embedded devices mounted up on a light pole or whatever that was considered good enough for a release, but I bet it'll be even more of a pain than that. These are after all security holes in devices that in many cases peoples day or life going well depends on. What it has done so far for me is put copywriters out of a job. I still find it mostly useful for writing drivel that gets used as page filler or product descriptions for my ecommerce side jobs. Lately the image recognition capabilities lets me generate the kind of stuff I'd never write, which is instagram posts with tons of emojis and other things I'd never have the gumption to do myself but increases engagement. I actually used to use markov chain generator for this going back to 2016 though so the big difference here is that at least it can form more coherent sentences.

nopinsight 2y ago

I wonder what experts think about this specialized model: "Harrison.rad.1 excels in the same radiology exams taken by human radiologists, as well as in benchmarks against other foundational models. The Fellowship of the Royal College of Radiologists (FRCR) 2B Rapids exam is considered one of the leading and toughest certifications for radiologists. Only 40-59% of human radiologists pass on their first attempt. Radiologists who re-attempt the exam within a year of passing score an average of 50.88 out of 60 (84.8%). Harrison.rad.1 scored 51.4 out of 60 (85.67%)." https://harrison.ai/harrison-rad-1/ https://harrison.ai/harrison-rad-1/

odyssey7 2y ago

Why automate the analysis of an MRI when the bottleneck is that they only have two MRI machines in the whole city, for some reason?

Kalanos 2y ago

My cousin is an established radiologist. Since I am well-versed in AI, we talked about this at length. He says that the automated predictions help free up radiologists to focus on the edge cases and more challenging disease types.

oldpersonintx 2y ago

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stego-tech 2y ago

Not a bad piece. I quite liked how they manage to find the likely middle ground between the doomers on both sides while still twisting the knife in the typical “workforce will adapt” chuds who think they’re above the fracas. It’s not about whether the present AI bubble is a giant sham that’ll implode like a black hole, or if it’s the real deal and AGI is days away. It’s always been about framing the consequences of either outcome in a society where 99.9% of humans must work to survive, and the implications of a giant economic collapse on those least-able to weather its impact in a time of record wealth and income inequality. It doesn’t matter which side is right, because millions of people will lose their jobs as a result and thousands will die. That is what some of us are trying to raise the alarm about, and in a post-2008 collapse world, young people are absolutely going to do what it takes to survive rather than what’s best for society - which is going to have knock-on effects like the author described (labor shortages in critical fields) for decades to come. In essence, if one paper from one guy managed to depress enrollment in a critical specialty for medicine, and he was flat-out wrong, then imagine the effects the Boosters and Doomers are having on other fields in the present cycle. If I were a betting dinosaur, I suspect there will be a similar lack of highly-skilled programmers and knowledge workers due to the “copilot tools will replace programmers” hype of present tools, and then we’ll have these same divorced-from-society hypesters making the same predictions again, ignorant of the consequences of their prior actions. Which is all a lot of flowery language just to say, “maybe we need to stop evaluating these things solely as ways to remove humans from labor or increase profit margins, and instead consider the broader effects of our decisions before acting on them.”

naveen99 2y ago

He updated the timeline to 10-15 years in 2023. That is like an eternity. Even artists, lawyers, programmers are scared about being replaced now in that time frame. We might have humanoid robots in 15 years.

bonoboTP 2y ago
I wanted to say don't be unrealistic, but then I realized 15 years ago was 2009 and it was basically the stone age. In computer vision, the state-of-the-art was HOG+SVM, SIFT features and bag-of-words. Pixel-level tasks were extremely brittle, best you could do was something like GrabCut etc. Neural nets were still in their uncool phase. In NLP you were happy if you could do reliable part of speech tagging. 2039 is really hard to foresee.

betaby 2y ago

Radiologists are cartel in Canada/USA making ~400-600k/year. Those jobs are protected and will continue to be human-staffed for foreseeable future regardless of AI efficiency.