Medical sciences in general have terrible gender and racial biases. My basic understanding is that it has got better in the past 10 years or so, but past scientific literature is littered with inaccuracies that we are still going along with. I'm thinking drugs specifically, but I suspect it generalizes.

Yeah, there were also several stories where the AI just detected that all the pictures of the illness had e.g. a ruler in them, whereas the control pictures did not. It's easy to produce impressive results when your methodology sucks. And unfortunately, those results will get reported on before peer reviews are in and before others have attempted to reproduce the results.

The problem is the use of ai in everything as a generic term. Algorithms have been around for awhile and im pretty sure the ai cancer detections are machine learning that are not at all related to LLMs.

That's why too high a level of accuracy in ML is always something that makes me squint... I don't trust it, as an AI researcher and engineer, you have to do the due diligence in understanding your data well before you start training.

well it's also that they used biased data. biased data is garbage data. The problem with these neural networks is the human factor, humans tend to be biased, subconsciously or consciously, hence the data they provide to these networks will often be biased as well. It's like that ML that was designed to judge human faces and it would consistently give non-whites lower scores, because it turned out the input data was mostly full of white faces.

Neural networks work very similarly to human brains, so when somebody points out a problem with a NN, I immediately think about whether a human would do the same thing. A human could also easily fake expertise by looking at pen marks, for example.

And human brains themselves are also usually inscrutable. People generally come to conclusions without much conscious effort first. We call it "intuition", but it's really the brain subconsciously looking at the evidence and coming to a conclusion. Because it's subconscious, even the person who made the conclusion often can't truly explain themselves, and if they're forced to explain, they'll suddenly use their conscious mind with different criteria, but they'll basically always come to the same conclusion as their intuition due to confirmation bias.

But the point is that all of your listed complaints about neural networks are not exclusively problems of neural networks. They are also problems of human brains. And not just rare problems, but common problems.

Only a human who is very deliberate and conscious about their work doesn't fall into that category, but that limits the parts of your brain that you can use. And it also takes a lot longer and a lot of very deliberate training to be able to do that. Intuition is a very important part of our minds, and can be especially useful for very high level performance.

Modern neural networks have their training data manipulated and scrubbed to avoid issues like you brought up. It can be done by hand, for additional assurance, but it is also automatically done by the training software. If your training data is an image, the same image will be used repeatedly. For example, it will be used in its original format. It can be rotated and used. Cropped and used. Manipulated using standard algorithms and used. Or combinations of those things.

Pen marks wouldn't even be an issue today, because images generally start off digital, and those raw digital images can be used. Just like any other medical tool, it wouldn't be used unless it could be trusted. It will be trained and validated like any NN, and then random radiologists aren't just relying on it right after that. It is first used by expert radiologists simulating actual diagnosis who understand the system enough to report problems. There is no technological or practical reason to think that humans will always have better outcomes than even today's AI technology.

Iirc the issue was that the researchers left the manufacturer's logo on the scans.

All of the negative scans were done by the researchers on the same equipment while the positive scans were pulled from various sources. So the AI only learned to identify which scans had the logo.

If a doctor is always going to check it, what's the value of the AI?

I think that's the same point the comic is making, which is why it's called "The four eyes principle," meaning two different people look at it.

I understand the sentiment, but I will maintain that I would choose anything that has the better health outcome.

We absolutely did call it "AI" then. The same applies to chess engines when they were being researched.

Machine Learning is the general field, and I think if we weren't wrapped up in the AI hype we could be training models to do important things like diagnosing disease and not writing shitty code or creating fantasy art work.

chess engines are, and always have been called, AI. computer vision is and always has been AI.

the only reason you might think they’re not is because in the most recent AI winter in which those technologies experienced a boom they avoided terminology like “AI” when requesting funding and advertising their work because people like you who had recently decided that they’re the arbiters of what is and isn’t intelligence.

turing once said if we were to gather the meaning of intelligence from a gallup poll it would be patently absurd, and i agree.

but sure, computer vision and chess engines, the two most prominent use cases for AI and ML technologies - aren’t actual artificial intelligence, because you said so. why? idk. i guess because we can do those things well and the moment we understand something well as a society people start getting offended if you call it intelligence rather than computation. can’t break the “i’m a special and unique snowflake” spell for people, god forbid…