9 ms·

Same thing with making wildly optimistic claims about "obsoleting human radiologists in five years", made more than five years ago by another AI bigwig Geoffrey

Same thing with making wildly optimistic claims about "obsoleting human radiologists in five years", made more than five years ago by another AI bigwig Geoffrey Hinton. They are doubtless brilliant researchers in their field, but they seem to view AI as a sort of cheat code to skip the stage where you actually have to understand the first thing about the problem domain to which it is applied, before getting to the "predictions about where the field is going". Very similar to crypto evangelists boldly proclaiming the world of finance as obsolete. Rumours of you understanding how the financial system works were greatly exaggerated, my dudes.

civilized 4y ago
The tribal thesis in the AI world seems to be that AI workers don't need subject matter expertise, as the AI will figure it out during training. In fact, subject matter expertise can be a negative because it's a distraction from making the AI good enough to figure it out on its own. This assumption has proven to be very fragile, but I don't think the AI bigwigs have accepted that yet. Still flush from the success of things like AlphaZero, where this thesis was more true.
- howlin 4y ago
  There are a lot of problems that arise from lack of domain expertise, but they can be overcome with a multidisciplinary team. The biggest defeating problem for pure AI teams is that they don't understand the domain well enough to know if their data sets are representative. Humans are great at salience assessments, and can ignore tons of the examples and features they witness when using their experience. This affects dataset curation. When a naive ML system trains on this data, it won't appreciate the often implicit curation decisions that were made, and will thus be miscalibrated for the real world. A domain expert can offer a lot of benefits. They could know how to feature engineer in a way that is resilient to these saliency issues. They can immediately recognize when a system is making stupid decisions on out of sample data. And if the ML model allows for introspection, then the domain expert can assess whether the model's representations look sensible. I'm scenarios where datasets actually do accurately resemble the "real world", it is possible for ML to transcend human experts. Linguistics is a pretty good example of this.
  - Avicebron 4y ago
    Unless of course the "domain experts" have fundamental disagreements or have equally limited knowledge of what should constitute what is important to extrapolate data beyond their own scope. E.g. like in comp sci, there might be multiple comparable ways to accomplish n, but which is best to reliably accomplish an unknown or unforseen n+1...depends.
  - kspacewalk2 4y ago
    It makes sense to have a domain expert and an AI expert working together, but I'd offer two important modifications: 1) The AI expert is auxiliary here, and the domain expert is in the driver's seat. How can it be otherwise? You no more put the AI expert in charge than you'd put an electronic health record IT specialist in charge of the hospital's processes. The relationship needs to be outcome-focused, not technology-focused. 2) The end result is most likely to be a productivity tool which augments the abilities/accuracy/speed of human experts rather than replacing them. AGI being not that sciencey of a fiction, we aren't likely to be actually diagnosed by an AI radiologist in our lifetimes, nor will an AI scientist make an important scientific discovery. Ditch the hype and get to work on those productivity tools, because that's all you can do for the foreseeable future. That might seem like a disappointing reduction in ambition, but at least it's reality-based.
  - raincom 4y ago
    > Humans are great at salience assessments, and can ignore tons of the examples and features they witness when using their experience This is called the frame problem in AI.
- aidenn0 4y ago
  An old story: “What are you doing?”, asked Minsky. “I am training a randomly wired neural net to play Tic-Tac-Toe” Sussman replied. “Why is the net wired randomly?”, asked Minsky. “I do not want it to have any preconceptions of how to play”, Sussman said. Minsky then shut his eyes. “Why do you close your eyes?”, Sussman asked his teacher. “So that the room will be empty.” At that moment, Sussman was enlightened.
  - dekhn 4y ago
    Every time I read this I appreciate its sublimity.
  - samvher 4y ago
    Maybe I'm naive - but I don't get it. Closing your eyes doesn't make the room empty. And in the same way not programming preconceptions into the neural net doesn't make the preconceptions go away? I realize explaining a joke or something like this takes away some of the charm (sorry), but would love to get the point :)
    - just_testing 4y ago
      I interpret that as Minsky being... an unpleasurable person, to say the least.
    - usefulcat 4y ago
      I think you’ve exactly described the point.
    - PuppyTailWags 4y ago
      Randomly wiring a neural net doesn't remove preconceptions, it just makes it that you don't know them. Similarly, closing your eyes doesn't make a room empty, it just makes it that you can't see what's there. Minsky is pointing out that Sussman's fundamental assumptions on desiring to remove preconceptions is logically flawed.
      - samvher 4y ago
- wpietri 4y ago
  > The tribal thesis in the AI world seems to be that AI workers don't need subject matter expertise Not throwing any stones here, because I've been guilty of the same sort of arrogance in other contexts. But I think the same thing happened a ton during Bubble 1.0 and the software-is-eating-the-world thing. And it's hardly limited to tech: https://xkcd.com/793/ https://xkcd.com/793/ For me, at least, where this came from was ignorance and naivete. Three things cured me. One was getting deeper mastery of particular things, and experiencing a fair bit of frustration when dealing with people who didn't understand those things or respect my expertise. The second was truly recognizing there were plenty of equally smart people who'd spent just as long on other things. And the third was working in close, cross-functional team contexts with those people, where mutual listening and respect were vital to the team doing our best work. So here's hoping that the AI bigwigs learn that one way or another.
- tarsinge 4y ago
  Not only AlphaZero, but hadn't the whole field of computer vision (which LeCun is specialised in) had its major breakthrough with letting the AI figure out the features (i.e. CNNs)?
  - fxtentacle 4y ago
    Yes, but they needed 2 billion images training data to get to the point where the AI usually draws the correct number of limbs... Any radiology AI that needs millions of training sets is useless in practice.
    - Karrot_Kream 4y ago
      > Any radiology AI that needs millions of training sets is useless in practice. Why? I have no doubt that radiology AI might not be that useful (though radiologist friends of mine say AI is making an increasing impact on their field.) But this logic doesn't make sense. So what if an AI needs a million training examples or even a million training sets? Once your topology and weights are set, that net can be copied/used by others and you get a ready-to-go AI. There's an argument to be made that if training scale is what's needed to get to AGI, then maybe AGI is unrealizable, but that's not the same as saying a domain-specific AI is useless because it needs a large training set.
  - civilized 4y ago
    It helps that human scientific expertise in the topic of "recognizing objects" is limited.
arcturus17 4y ago
There is a personal bias I have observed when I have listened to people talk over the last ~5 years, for example: - Startup founders without any domain experience in extremely risky ventures - Crypto bros - Donald Trump I consider myself - and others view me as - a hyperrational person (possibly often to a fault), and I must admit that when I hear an outlandish claim like the ones spewed by the above, I am sometimes left in a strange emotional state... a stupor? Like, I don't quite believe the claim because I'm defensively rational, but I feel a certain dizziness and confusion (thinking to myself, "could this actually be true?") until I come back to my senses. The more outlandish and impassioned the speech, the stronger the effect. It's made me realise we're all built similarly, from the most rational from the most gullible.
- hnfong 4y ago
  I somewhat doubt most people actually have the `"could this actually be true?"` phase. It seems most like... just your rational mind reminding you that whatever you set your priors, if you set it to exactly zero the Bayes' Theorem breaks down?
- burnished 4y ago
  Sounds like cognitive dissonance, I have had similarly humbling experiences.
- hellfish 4y ago
  > Like, I don't quite believe the claim because I'm defensively rational, but I feel a certain dizziness and confusion (thinking to myself, "could this actually be true?") until I come back to my senses. The more outlandish and impassioned the speech, the stronger the effect. I always like to ask myself: "what are they trying to get me to do?". Whether it's a habit, voting pattern, product, etc. Then I ask myself: "is it useful? who does it benefit?" Whether it's true or not, idrc. what's more important (at least to me) is what it does and who it benefits (also in this case to avoid any ethical conundrums, something is useful if it makes $, beneficial to X party if it makes them $)
- exo762 4y ago
  > - Crypto bros I'm in the field. Haven't experienced people who I could have described as bros. Can you point to one example?
twawaaay 4y ago
I think all this work could be useful if only those people understood that the technology is not mature enough to remove people from the loop. For the case of AI analysing x-ray photos, the obvious solution would be a system that can tag photos with information about what AI thinks is going on there. And this information could be passed to the human. This could save a ton of time and help reduce cases where radiologists missed obvious things. My son once broke his arm. I brought him to ER, they made the photo but the two people who looked at the photo said there is nothing wrong with the arm. I asked for the copy of the photo. A week later the swelling did not subside so I took the photo to another doctor and he pointed out an obvious fracture line. There are many ways to deploy automation and I wonder why everybody tries to shoot for removing humans altogether when most of the time this is literally asking for problems.
- teaearlgraycold 4y ago
  Remove the human and you can charge close to what it costs to employ the human. Aid the human and you’re looking at more like tens to hundreds of dollars per month.
  - melagonster 4y ago
    doctor have so many money can spend to save human life. their time expensive than 100$/month.
- s0rce 4y ago
  I've definitely seen radiologists miss things (as a patient and a researcher) but I've also seen the behavior where if something is labeled or indicated the next person (or in this case the first person) might not give it a true 2nd look and could easily default to agreeing with the AI instead of actually checking things. Overall I think this would be a net benefit as image analysis can help with a lot of these activities, just needs care to not inadvertently remove the humans you still need.
  - deleted 4y ago
    [deleted]
dekhn 4y ago
The funny thing is, of all the areas where ML could help, radiological image classification definitely is the one where ML could shine (and I think it did). HUmans doing radiological image classification are basically a network service now (IE they can do their job on the other side of the world, and their efforts are extremely carefully evaluated using later data such as disease progression).
- kspacewalk2 4y ago
  Image classification could be employed (together with very very good UI) in good productivity tools for radiologists. However, radiologists don't classify images for a living, they make diagnoses, so it's not going to get close to replacing them. I agree that radiology is the most "friendly" discipline to inject ML-based productivity tools into their "pipeline", since they essentially don't even need to be at the hospital, other than interventional radiology.
  - dekhn 4y ago
    Diagnosis is classification. They almost always do work for service rather than dealing with patients on a regular basis.
    - kspacewalk2 4y ago
      >Diagnosis is classification. A radiologist's diagnosis is not image classification, it's reality classification maybe. (That's quite poetic). Watching an educational Youtube video about endangered tiger habitats is not the same thing as segmenting possible embedded pictures of kitties and poachers or whatever, and classifying them as such. There's, like, a lot of additional context.
      - mattkrause 4y ago
        To flesh this out a little more: A (good) radiologist is interpreting the images in light of the patient's history, symptoms, and other tests, with the goal of forming a diagnosis and treatment plan. This is rather different from taking an MxN array of pixels and trying to decide if it contains a tumor. For example, about 10% of people have small gallstones. If an ultrasound incidentally detects some in a healthy, asymptomatic person, nothing happens. The exact same images, coming from a patient with a history of upper-right abdominal pain and jaundice, probably lead to a referral for surgery instead.
EGreg 4y ago
I think HN is skewing heavily against AI and blockchain claims. I pick those to make the point below. For what it's worth, I agree that blockchain itself is a first-generation technology and sucks relative to other things, like giant vacuum tube computers did. However, the concepts it enables (smart contracts) have as much promise as the idea of software programs running on personal computers back when most people wondered why you need them, since they do very little but play pong. When I wrote the following article for CoinDesk in 2020, I didn't want to say "blockchain voting", I wanted to say "voting from your phone". Because there are far better decentralized byzantine-fault-tolerant systems, than blockchains. But that's what they ran with: https://www.coindesk.com/in-defense-of-blockchain-voting https://www.coindesk.com/in-defense-of-blockchain-voting In it, I say: For every technology we use today, there was a time it was laughably inadequate as a replacement for what came before. And that's really, the crux of the issue. It happens slowly, and then all at once. Yes we need to listen to guys like Moxie who are skeptical, but we need to also then go and have a discussion from different perspectives, not just one specific perspective. It has even become fashionable in many liberal circles to be against the type "tech bro" typified by HN, including VCs and Web 2.0 tech bros. So before you downvote, realize that most of you would be on the receiving end of it in other echo chambers, due to this phenomenon of thinking there's only one best narrative. People like Moxie are much more interesting and interested, because they say they' love to be proven wrong. And I am also open to substantive discussion: https://community.intercoin.app/t/web3-moxie-signal-telegram-and-why-decentralization-matters https://community.intercoin.app/t/web3-moxie-signal-telegram... I imagine it's the same with AI claims about traditional fields. Where have we heard that before? "Yes it's cute and impressive but these guys don't really understand what the experts know about chess."
- vaidhy 4y ago
  > For every technology we use today, there was a time it was laughably inadequate as a replacement for what came before. That is just success bias. How many times did people try out perpetual motion machines? The rest of the article tries to make the converse that if something is inadequate today, it will be the replacement in the future. When do you consider something to be not worth spending time on?

civilized 4y ago

The tribal thesis in the AI world seems to be that AI workers don't need subject matter expertise, as the AI will figure it out during training. In fact, subject matter expertise can be a negative because it's a distraction from making the AI good enough to figure it out on its own. This assumption has proven to be very fragile, but I don't think the AI bigwigs have accepted that yet. Still flush from the success of things like AlphaZero, where this thesis was more true.

howlin 4y ago
There are a lot of problems that arise from lack of domain expertise, but they can be overcome with a multidisciplinary team. The biggest defeating problem for pure AI teams is that they don't understand the domain well enough to know if their data sets are representative. Humans are great at salience assessments, and can ignore tons of the examples and features they witness when using their experience. This affects dataset curation. When a naive ML system trains on this data, it won't appreciate the often implicit curation decisions that were made, and will thus be miscalibrated for the real world. A domain expert can offer a lot of benefits. They could know how to feature engineer in a way that is resilient to these saliency issues. They can immediately recognize when a system is making stupid decisions on out of sample data. And if the ML model allows for introspection, then the domain expert can assess whether the model's representations look sensible. I'm scenarios where datasets actually do accurately resemble the "real world", it is possible for ML to transcend human experts. Linguistics is a pretty good example of this.
- Avicebron 4y ago
  Unless of course the "domain experts" have fundamental disagreements or have equally limited knowledge of what should constitute what is important to extrapolate data beyond their own scope. E.g. like in comp sci, there might be multiple comparable ways to accomplish n, but which is best to reliably accomplish an unknown or unforseen n+1...depends.
- kspacewalk2 4y ago
  It makes sense to have a domain expert and an AI expert working together, but I'd offer two important modifications: 1) The AI expert is auxiliary here, and the domain expert is in the driver's seat. How can it be otherwise? You no more put the AI expert in charge than you'd put an electronic health record IT specialist in charge of the hospital's processes. The relationship needs to be outcome-focused, not technology-focused. 2) The end result is most likely to be a productivity tool which augments the abilities/accuracy/speed of human experts rather than replacing them. AGI being not that sciencey of a fiction, we aren't likely to be actually diagnosed by an AI radiologist in our lifetimes, nor will an AI scientist make an important scientific discovery. Ditch the hype and get to work on those productivity tools, because that's all you can do for the foreseeable future. That might seem like a disappointing reduction in ambition, but at least it's reality-based.
- raincom 4y ago
  > Humans are great at salience assessments, and can ignore tons of the examples and features they witness when using their experience This is called the frame problem in AI.
aidenn0 4y ago
An old story: “What are you doing?”, asked Minsky. “I am training a randomly wired neural net to play Tic-Tac-Toe” Sussman replied. “Why is the net wired randomly?”, asked Minsky. “I do not want it to have any preconceptions of how to play”, Sussman said. Minsky then shut his eyes. “Why do you close your eyes?”, Sussman asked his teacher. “So that the room will be empty.” At that moment, Sussman was enlightened.
- dekhn 4y ago
  Every time I read this I appreciate its sublimity.
- samvher 4y ago
  Maybe I'm naive - but I don't get it. Closing your eyes doesn't make the room empty. And in the same way not programming preconceptions into the neural net doesn't make the preconceptions go away? I realize explaining a joke or something like this takes away some of the charm (sorry), but would love to get the point :)
  - just_testing 4y ago
    I interpret that as Minsky being... an unpleasurable person, to say the least.
  - usefulcat 4y ago
    I think you’ve exactly described the point.
  - PuppyTailWags 4y ago
    Randomly wiring a neural net doesn't remove preconceptions, it just makes it that you don't know them. Similarly, closing your eyes doesn't make a room empty, it just makes it that you can't see what's there. Minsky is pointing out that Sussman's fundamental assumptions on desiring to remove preconceptions is logically flawed.
    - samvher 4y ago
wpietri 4y ago
> The tribal thesis in the AI world seems to be that AI workers don't need subject matter expertise Not throwing any stones here, because I've been guilty of the same sort of arrogance in other contexts. But I think the same thing happened a ton during Bubble 1.0 and the software-is-eating-the-world thing. And it's hardly limited to tech: https://xkcd.com/793/ https://xkcd.com/793/ For me, at least, where this came from was ignorance and naivete. Three things cured me. One was getting deeper mastery of particular things, and experiencing a fair bit of frustration when dealing with people who didn't understand those things or respect my expertise. The second was truly recognizing there were plenty of equally smart people who'd spent just as long on other things. And the third was working in close, cross-functional team contexts with those people, where mutual listening and respect were vital to the team doing our best work. So here's hoping that the AI bigwigs learn that one way or another.
tarsinge 4y ago
Not only AlphaZero, but hadn't the whole field of computer vision (which LeCun is specialised in) had its major breakthrough with letting the AI figure out the features (i.e. CNNs)?
- fxtentacle 4y ago
  Yes, but they needed 2 billion images training data to get to the point where the AI usually draws the correct number of limbs... Any radiology AI that needs millions of training sets is useless in practice.
  - Karrot_Kream 4y ago
    > Any radiology AI that needs millions of training sets is useless in practice. Why? I have no doubt that radiology AI might not be that useful (though radiologist friends of mine say AI is making an increasing impact on their field.) But this logic doesn't make sense. So what if an AI needs a million training examples or even a million training sets? Once your topology and weights are set, that net can be copied/used by others and you get a ready-to-go AI. There's an argument to be made that if training scale is what's needed to get to AGI, then maybe AGI is unrealizable, but that's not the same as saying a domain-specific AI is useless because it needs a large training set.
- civilized 4y ago
  It helps that human scientific expertise in the topic of "recognizing objects" is limited.

arcturus17 4y ago

There is a personal bias I have observed when I have listened to people talk over the last ~5 years, for example: - Startup founders without any domain experience in extremely risky ventures - Crypto bros - Donald Trump I consider myself - and others view me as - a hyperrational person (possibly often to a fault), and I must admit that when I hear an outlandish claim like the ones spewed by the above, I am sometimes left in a strange emotional state... a stupor? Like, I don't quite believe the claim because I'm defensively rational, but I feel a certain dizziness and confusion (thinking to myself, "could this actually be true?") until I come back to my senses. The more outlandish and impassioned the speech, the stronger the effect. It's made me realise we're all built similarly, from the most rational from the most gullible.

hnfong 4y ago
I somewhat doubt most people actually have the `"could this actually be true?"` phase. It seems most like... just your rational mind reminding you that whatever you set your priors, if you set it to exactly zero the Bayes' Theorem breaks down?
burnished 4y ago
Sounds like cognitive dissonance, I have had similarly humbling experiences.
hellfish 4y ago
> Like, I don't quite believe the claim because I'm defensively rational, but I feel a certain dizziness and confusion (thinking to myself, "could this actually be true?") until I come back to my senses. The more outlandish and impassioned the speech, the stronger the effect. I always like to ask myself: "what are they trying to get me to do?". Whether it's a habit, voting pattern, product, etc. Then I ask myself: "is it useful? who does it benefit?" Whether it's true or not, idrc. what's more important (at least to me) is what it does and who it benefits (also in this case to avoid any ethical conundrums, something is useful if it makes $, beneficial to X party if it makes them $)
exo762 4y ago
> - Crypto bros I'm in the field. Haven't experienced people who I could have described as bros. Can you point to one example?

twawaaay 4y ago

I think all this work could be useful if only those people understood that the technology is not mature enough to remove people from the loop. For the case of AI analysing x-ray photos, the obvious solution would be a system that can tag photos with information about what AI thinks is going on there. And this information could be passed to the human. This could save a ton of time and help reduce cases where radiologists missed obvious things. My son once broke his arm. I brought him to ER, they made the photo but the two people who looked at the photo said there is nothing wrong with the arm. I asked for the copy of the photo. A week later the swelling did not subside so I took the photo to another doctor and he pointed out an obvious fracture line. There are many ways to deploy automation and I wonder why everybody tries to shoot for removing humans altogether when most of the time this is literally asking for problems.

teaearlgraycold 4y ago
Remove the human and you can charge close to what it costs to employ the human. Aid the human and you’re looking at more like tens to hundreds of dollars per month.
- melagonster 4y ago
  doctor have so many money can spend to save human life. their time expensive than 100$/month.
s0rce 4y ago
I've definitely seen radiologists miss things (as a patient and a researcher) but I've also seen the behavior where if something is labeled or indicated the next person (or in this case the first person) might not give it a true 2nd look and could easily default to agreeing with the AI instead of actually checking things. Overall I think this would be a net benefit as image analysis can help with a lot of these activities, just needs care to not inadvertently remove the humans you still need.
- deleted 4y ago
  [deleted]

dekhn 4y ago

The funny thing is, of all the areas where ML could help, radiological image classification definitely is the one where ML could shine (and I think it did). HUmans doing radiological image classification are basically a network service now (IE they can do their job on the other side of the world, and their efforts are extremely carefully evaluated using later data such as disease progression).

kspacewalk2 4y ago
Image classification could be employed (together with very very good UI) in good productivity tools for radiologists. However, radiologists don't classify images for a living, they make diagnoses, so it's not going to get close to replacing them. I agree that radiology is the most "friendly" discipline to inject ML-based productivity tools into their "pipeline", since they essentially don't even need to be at the hospital, other than interventional radiology.
- dekhn 4y ago
  Diagnosis is classification. They almost always do work for service rather than dealing with patients on a regular basis.
  - kspacewalk2 4y ago
    >Diagnosis is classification. A radiologist's diagnosis is not image classification, it's reality classification maybe. (That's quite poetic). Watching an educational Youtube video about endangered tiger habitats is not the same thing as segmenting possible embedded pictures of kitties and poachers or whatever, and classifying them as such. There's, like, a lot of additional context.
    - mattkrause 4y ago
      To flesh this out a little more: A (good) radiologist is interpreting the images in light of the patient's history, symptoms, and other tests, with the goal of forming a diagnosis and treatment plan. This is rather different from taking an MxN array of pixels and trying to decide if it contains a tumor. For example, about 10% of people have small gallstones. If an ultrasound incidentally detects some in a healthy, asymptomatic person, nothing happens. The exact same images, coming from a patient with a history of upper-right abdominal pain and jaundice, probably lead to a referral for surgery instead.

EGreg 4y ago

I think HN is skewing heavily against AI and blockchain claims. I pick those to make the point below. For what it's worth, I agree that blockchain itself is a first-generation technology and sucks relative to other things, like giant vacuum tube computers did. However, the concepts it enables (smart contracts) have as much promise as the idea of software programs running on personal computers back when most people wondered why you need them, since they do very little but play pong. When I wrote the following article for CoinDesk in 2020, I didn't want to say "blockchain voting", I wanted to say "voting from your phone". Because there are far better decentralized byzantine-fault-tolerant systems, than blockchains. But that's what they ran with: https://www.coindesk.com/in-defense-of-blockchain-voting https://www.coindesk.com/in-defense-of-blockchain-voting In it, I say: For every technology we use today, there was a time it was laughably inadequate as a replacement for what came before. And that's really, the crux of the issue. It happens slowly, and then all at once. Yes we need to listen to guys like Moxie who are skeptical, but we need to also then go and have a discussion from different perspectives, not just one specific perspective. It has even become fashionable in many liberal circles to be against the type "tech bro" typified by HN, including VCs and Web 2.0 tech bros. So before you downvote, realize that most of you would be on the receiving end of it in other echo chambers, due to this phenomenon of thinking there's only one best narrative. People like Moxie are much more interesting and interested, because they say they' love to be proven wrong. And I am also open to substantive discussion: https://community.intercoin.app/t/web3-moxie-signal-telegram-and-why-decentralization-matters https://community.intercoin.app/t/web3-moxie-signal-telegram... I imagine it's the same with AI claims about traditional fields. Where have we heard that before? "Yes it's cute and impressive but these guys don't really understand what the experts know about chess."

vaidhy 4y ago
> For every technology we use today, there was a time it was laughably inadequate as a replacement for what came before. That is just success bias. How many times did people try out perpetual motion machines? The rest of the article tries to make the converse that if something is inadequate today, it will be the replacement in the future. When do you consider something to be not worth spending time on?