Because life is suffering and machines dream of electric sheeps.

It's already this way in most of the world.

Better yet, give us something better to do about the cancer than slash, burn, poison. Something that's less traumatic on the rest of the person, especially in light of the possibility of false positives.

Another big thing to note, we recently had a different but VERY similar headline about finding typhoid early and was able to point it out more accurately than doctors could.

But when they examined the AI to see what it was doing, it turns out that it was weighing the specs of the machine being used to do the scan... An older machine means the area was likely poorer and therefore more likely to have typhoid. The AI wasn't pointing out if someone had Typhoid it was just telling you if they were in a rich area or not.

That's why these systems should never be used as the sole decision makers, but instead work as a tool to help the professionals make better decisions.

Keep the human in the loop!

Breast imaging already relys on a high false positive rate. False positives are way better than false negatives in this case.

good luck reverse-engineering millions if not billions of seemingly random floating point numbers. It's like visualizing a graph in your mind by reading an array of numbers, except in this case the graph has as many dimensions as the neural network has inputs, which is the number of pixels the input image has.

Under no circumstance should we accept a "black box" explanation.

Go learn at least basic principles of neural networks, because this your sentence alone makes me want to slap you.

iirc it recently turned out that the whole black box thing was actually a bullshit excuse to evade liability, at least for certain kinds of model.

IMO, the "black box" thing is basically ML developers hand waiving and saying "it's magic" because they know it will take way too long to explain all the underlying concepts in order to even start to explain how it works.

I have a very crude understanding of the technology. I'm not a developer, I work in IT support. I have several friends that I've spoken to about it, some of whom have made fairly rudimentary machine learning algorithms and neural nets. They understand it, and they've explained a few of the concepts to me, and I'd be lying if I said that none of it went over my head. I've done programming and development, I'm senior in my role, and I have a lifetime of technology experience and education... And it goes over my head. What hope does anyone else have? If you're not a developer or someone ML-focused, yeah, it's basically magic.

I won't try to explain. I couldn't possibly recall enough about what has been said to me, to correctly explain anything at this point.

an image recognition model like this is usually tuned specifically to have a very low false negative (well below human, often) in exchange for a high false positive rate (overly cautious about cancer)!

It's got a decent chunk of good uses. It's just that none of those are going to make anyone a huge ton of money, so they don't have a hype cycle attached. I can't wait until the grifters get out and the hype cycle falls away, so we can actually get back to using it for what it's good at and not shoving it indiscriminately into everything.

Honestly they should go back to calling useful applications ML (that is what it is) since AI is getting such a bad rap.

Yea none of us are going to see the benefits. Tired of seeing articles of scientific advancement that I know will never trickle down to us peasants.

I've been looking at the paper, some things about it:

• the paper and article are from 2021
• the model needs to be able to use optional data from age, family history, etc, but not be reliant on it
• it needs to combine information from multiple views
• it predicts risk for each year in the next 5 years
• it has to produce consistent results with different sensors and diverse patients
• its not the first model to do this, and it is more accurate than previous methods

Well in Turkish, meme beans boob/breast.

They probably meant reiterate