8 ms·

A few months ago there were articles going around about how Samsung galaxy phones were upscaling images of the Moon using AI [0]. Essentially, the model was art

by eig 2y ago

A few months ago there were articles going around about how Samsung galaxy phones were upscaling images of the Moon using AI [0]. Essentially, the model was artificially adding landmarks and details based on its training set when the real image quality was too poor to make out details. Needless to say, AI upscaling as described in this article would be a nightmare for radiologists. 90% of radiology is confirming the absence of disease when image quality is high, and asking for complementary studies when image quality is low. With AI enhanced images that look "normal", how can the radiologist ever say "I can confirm there is no brain bleed" when the computer might be incorrectly adding "normal" details when compensating for poor image quality? [0] - https://news.ycombinator.com/item?id=35136167 https://news.ycombinator.com/item?id=35136167

wslh 2y ago
The interesting, indeed concerning thing, is that problem is not only applied to medical machines and mobile phones but to zillions of daily used wearable devices such as smart watches, brain eeg (e.g. Muse), and others without adverting users that what they see (e.g. HRV) couldn't be interpreted easily by a computer program. Not saying that we humans are always better but saying that we are believing in number and conclusions from apps created as-is.
atoav 2y ago
This is one aspect about machine learning models I keep discussing with non-technical passengers of the AI-hype-train: They are (in their current form) unsitable for applications where correctness is absolutely critical.
- cactusfrog 2y ago
  This is not true, but it is a major challenge. See https://www.pathai.com/ https://www.pathai.com/
  - atoav 2y ago
    That is why I said "in its current form".
- bufferoverflow 2y ago
  As long as AI makes things better on average, it's useful. It doesn't have to be 100% correct.
  - atoav 2y ago
    So if an AI fantasized your face into the extrapolated pixels of the evidence for a documented murder case you would be happy with the conviction, because on average it might be somewhat correct? I don't wanna hurt anybodies feelings by stating that AI isn't a magical wand that makes everything better — but every technology has use cases at which it excels (e.g. pattern recognition) and use cases for which it is fundamentally unsuitable. If you try to screw on a nut using a hammer, that doesn't mean hammers suck, it means the user has a wrong idea what a hammer is capable of. The point is: Don't be that person if you can avoid it.
  - pavel_lishin 2y ago
    There are applications - such as finding out whether you have a tumor or not - when "improving on average while ignoring outliers" is not acceptable.
- teaearlgraycold 2y ago
  I don’t know enough to make absolute statements here, but deep learning models can beat out human experts at discerning between signal and noise. Using that to guess at data and then hand it off to humans gives you the worst of both worlds. Two error probabilities multiplied together. But to simply render a verdict on whether a condition exists I’d trust a proven algorithm.
  - atoav 2y ago
    Yes, pattern recognition is one of the applications ML shines at. Now the question was about using ML to extrapolate between sparse pixels and how much humans can rely on the added detail. The goal would be to find a way to make ML extrapolate only pixels that really describe actual really present features and never imagining detail that wasn't there in the first place. Now I am no expert at the matter, but what I know of deep learning models they are really good at the latter as they basically make statistic guesses on what would be plausible. Getting a plausible guess on what looks like a convincing answer works really well for answering a question. But the problem at hand is more like predicting the words someone said based on the first and last word in a sentence. Imagine a criminal case where the evidence is fragmented like that: I am pretty sure a LLM could give a convincing prediction here, but I am not sure how much you could rely on that prediction being reflective of what was actually said. I certainly wouldn't feel comfortable with a conviction the result of that prediction even if it was reflective of the ground truth in 90% of times.
  - coffeebeqn 2y ago
    There are a lot of models that are simply good at that without hallucinating nonsense. LLMs are a specific thing with their own tradeoffs and goals. If you have a ML model that says how much does this microscope photo look like an anomaly in this persons blood on a scale from 0-100 it can certainly do better than a human.
BobbyTables2 2y ago
The Samsung phone wasn’t a technological advancement, it was sheer fraud. A camera is supposed to take pictures of what it sees. Imagine going to a restaurant, ordering French onion soup, and getting a bowl of brown food coloring in water.
- afn 2y ago
  Good gravy! https://www.youtube.com/watch?v=G4wh-Pbxgok https://www.youtube.com/watch?v=G4wh-Pbxgok
- farseer 2y ago
  Now that you mention it. I recently picked up a bottle of Red Vinegar with large pictures of red grapes on it. Naturally I assumed this was grape vinegar. How shocking it was to discover that this Chinese company was selling acetic acid mixed with food colors.
- jajko 2y ago
  I wouldn't go that far to call it as a fraud, unless you call literally every phone-with-camera manufacturer these days a fraud. Then I agree as my trusty old nikon fullframe always catches only whats there, including noise and instability that modern phones handle easily. People were commenting on that thread how apple phone ie mirrored only bunny within bigger picture of a bunny in the grass (thats rather hilarious 'bug'), and we all know how apple consistently removes all moles and wrinkles, changes completely skin tone and overall tonality like every single picture looks like its taken in the golden sunset hour. Ie that nasty samsung is much more truthful when it comes to this, including latest flagships. That's outright lying too, IMHO much worse - moon is tidally locked so showing exactly same side with same features for millions of years, so they were adding details that are there, just impossible to see on non-stabilized tiny plastic lens&sensor combo in the night. Making somebody 20 years younger, much prettier and changing their overall look on most important feature we humans have, doing it by default without any real option to turn it off, does a lot of long term body-perception damage in young folks.
- YetAnotherNick 2y ago
  > A camera is supposed to take pictures of what it sees. You wouldn't like the picture of what it sees. The lens is just not big enough. Even the pro raw and other features that phone introduced apply processing.
nullc 2y ago
The state of the art MRI stuff uses "compressed sensing" -- essentially image completion in some domain or another. Presumably, carefully designed to not hallucinate details or one would hope. There isn't necessarily a particularly neutral choice here: the MRI scan isn't in the pixel domain, artifacts are going to be 'weird' looking-- e.g. edges that move during the scan ringing across the whole image.
- CooCooCaCha 2y ago
  Compressed sensing is far more mathematically rigorous.
  - nullc 2y ago
    I don't think we know what's in the black box here. It could be an equivalent relatively unopinionated regularizer ("the pixel domain will be locally smooth, to the extent it has edges they're spatially contiguous") or it could be "just look up the most similar image from a library and present that instead" or anywhere in between. :)
    - CooCooCaCha 2y ago
      They specifically said they use deep learning which implies a sizeable neural network.
      - phkahler 2y ago
        But they're using it to eliminate stray or environmental EMI from the RF signals. That might not create fake stuff at the voxel level. Depends on the specifics.
andbberger 2y ago
the future is already here, GE has put this into production. if you have remove the onerous constraint of being correct you can make some really crispy images! 9/10 radiologists. that was literally what the FDA approval process was, surveyed a bunch of radiologists to see what they preferred. no adults in the room. heads should roll etc
- wholinator2 2y ago
  Do you have a link or anything? I'm highly interested but unable to find more on this
  - andbberger 2y ago
    https://www.gehealthcare.com/products/magnetic-resonance-imaging/air-recon-dl https://www.gehealthcare.com/products/magnetic-resonance-ima... https://www.gehealthcare.com/about/newsroom/press-releases/ge-healthcares-air-recon-dl-receives-fda-clearance-of-3d-and-motion-insensitive#_ftn1 https://www.gehealthcare.com/about/newsroom/press-releases/g... https://www.accessdata.fda.gov/cdrh_docs/pdf21/K213717.pdf https://www.accessdata.fda.gov/cdrh_docs/pdf21/K213717.pdf
m463 2y ago
They've already made this mistake. There was one model that detected skin cancer because there was always a ruler in the images.
- mbirth 2y ago
  So we’re not that much further than the neural networks urban legend from the early 90s. https://gwern.net/tank https://gwern.net/tank

by eig 2y ago

wslh 2y ago

The interesting, indeed concerning thing, is that problem is not only applied to medical machines and mobile phones but to zillions of daily used wearable devices such as smart watches, brain eeg (e.g. Muse), and others without adverting users that what they see (e.g. HRV) couldn't be interpreted easily by a computer program. Not saying that we humans are always better but saying that we are believing in number and conclusions from apps created as-is.

atoav 2y ago

This is one aspect about machine learning models I keep discussing with non-technical passengers of the AI-hype-train: They are (in their current form) unsitable for applications where correctness is absolutely critical.

cactusfrog 2y ago
This is not true, but it is a major challenge. See https://www.pathai.com/ https://www.pathai.com/
- atoav 2y ago
  That is why I said "in its current form".
bufferoverflow 2y ago
As long as AI makes things better on average, it's useful. It doesn't have to be 100% correct.
- atoav 2y ago
  So if an AI fantasized your face into the extrapolated pixels of the evidence for a documented murder case you would be happy with the conviction, because on average it might be somewhat correct? I don't wanna hurt anybodies feelings by stating that AI isn't a magical wand that makes everything better — but every technology has use cases at which it excels (e.g. pattern recognition) and use cases for which it is fundamentally unsuitable. If you try to screw on a nut using a hammer, that doesn't mean hammers suck, it means the user has a wrong idea what a hammer is capable of. The point is: Don't be that person if you can avoid it.
- pavel_lishin 2y ago
  There are applications - such as finding out whether you have a tumor or not - when "improving on average while ignoring outliers" is not acceptable.
teaearlgraycold 2y ago
I don’t know enough to make absolute statements here, but deep learning models can beat out human experts at discerning between signal and noise. Using that to guess at data and then hand it off to humans gives you the worst of both worlds. Two error probabilities multiplied together. But to simply render a verdict on whether a condition exists I’d trust a proven algorithm.
- atoav 2y ago
  Yes, pattern recognition is one of the applications ML shines at. Now the question was about using ML to extrapolate between sparse pixels and how much humans can rely on the added detail. The goal would be to find a way to make ML extrapolate only pixels that really describe actual really present features and never imagining detail that wasn't there in the first place. Now I am no expert at the matter, but what I know of deep learning models they are really good at the latter as they basically make statistic guesses on what would be plausible. Getting a plausible guess on what looks like a convincing answer works really well for answering a question. But the problem at hand is more like predicting the words someone said based on the first and last word in a sentence. Imagine a criminal case where the evidence is fragmented like that: I am pretty sure a LLM could give a convincing prediction here, but I am not sure how much you could rely on that prediction being reflective of what was actually said. I certainly wouldn't feel comfortable with a conviction the result of that prediction even if it was reflective of the ground truth in 90% of times.
- coffeebeqn 2y ago
  There are a lot of models that are simply good at that without hallucinating nonsense. LLMs are a specific thing with their own tradeoffs and goals. If you have a ML model that says how much does this microscope photo look like an anomaly in this persons blood on a scale from 0-100 it can certainly do better than a human.

BobbyTables2 2y ago

The Samsung phone wasn’t a technological advancement, it was sheer fraud. A camera is supposed to take pictures of what it sees. Imagine going to a restaurant, ordering French onion soup, and getting a bowl of brown food coloring in water.

afn 2y ago
Good gravy! https://www.youtube.com/watch?v=G4wh-Pbxgok https://www.youtube.com/watch?v=G4wh-Pbxgok
farseer 2y ago
Now that you mention it. I recently picked up a bottle of Red Vinegar with large pictures of red grapes on it. Naturally I assumed this was grape vinegar. How shocking it was to discover that this Chinese company was selling acetic acid mixed with food colors.
jajko 2y ago
I wouldn't go that far to call it as a fraud, unless you call literally every phone-with-camera manufacturer these days a fraud. Then I agree as my trusty old nikon fullframe always catches only whats there, including noise and instability that modern phones handle easily. People were commenting on that thread how apple phone ie mirrored only bunny within bigger picture of a bunny in the grass (thats rather hilarious 'bug'), and we all know how apple consistently removes all moles and wrinkles, changes completely skin tone and overall tonality like every single picture looks like its taken in the golden sunset hour. Ie that nasty samsung is much more truthful when it comes to this, including latest flagships. That's outright lying too, IMHO much worse - moon is tidally locked so showing exactly same side with same features for millions of years, so they were adding details that are there, just impossible to see on non-stabilized tiny plastic lens&sensor combo in the night. Making somebody 20 years younger, much prettier and changing their overall look on most important feature we humans have, doing it by default without any real option to turn it off, does a lot of long term body-perception damage in young folks.
YetAnotherNick 2y ago
> A camera is supposed to take pictures of what it sees. You wouldn't like the picture of what it sees. The lens is just not big enough. Even the pro raw and other features that phone introduced apply processing.

nullc 2y ago

The state of the art MRI stuff uses "compressed sensing" -- essentially image completion in some domain or another. Presumably, carefully designed to not hallucinate details or one would hope. There isn't necessarily a particularly neutral choice here: the MRI scan isn't in the pixel domain, artifacts are going to be 'weird' looking-- e.g. edges that move during the scan ringing across the whole image.

CooCooCaCha 2y ago
Compressed sensing is far more mathematically rigorous.
- nullc 2y ago
  I don't think we know what's in the black box here. It could be an equivalent relatively unopinionated regularizer ("the pixel domain will be locally smooth, to the extent it has edges they're spatially contiguous") or it could be "just look up the most similar image from a library and present that instead" or anywhere in between. :)
  - CooCooCaCha 2y ago
    They specifically said they use deep learning which implies a sizeable neural network.
    - phkahler 2y ago
      But they're using it to eliminate stray or environmental EMI from the RF signals. That might not create fake stuff at the voxel level. Depends on the specifics.

andbberger 2y ago

the future is already here, GE has put this into production. if you have remove the onerous constraint of being correct you can make some really crispy images! 9/10 radiologists. that was literally what the FDA approval process was, surveyed a bunch of radiologists to see what they preferred. no adults in the room. heads should roll etc

wholinator2 2y ago
Do you have a link or anything? I'm highly interested but unable to find more on this
- andbberger 2y ago
  https://www.gehealthcare.com/products/magnetic-resonance-imaging/air-recon-dl https://www.gehealthcare.com/products/magnetic-resonance-ima... https://www.gehealthcare.com/about/newsroom/press-releases/ge-healthcares-air-recon-dl-receives-fda-clearance-of-3d-and-motion-insensitive#_ftn1 https://www.gehealthcare.com/about/newsroom/press-releases/g... https://www.accessdata.fda.gov/cdrh_docs/pdf21/K213717.pdf https://www.accessdata.fda.gov/cdrh_docs/pdf21/K213717.pdf

m463 2y ago

They've already made this mistake. There was one model that detected skin cancer because there was always a ruler in the images.

mbirth 2y ago
So we’re not that much further than the neural networks urban legend from the early 90s. https://gwern.net/tank https://gwern.net/tank