Comment by abj | Hacker News Reader

Comment by abj | Hacker News Reader

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There's value, the tech works, but applying it is surprisingly hard.

I met someone who dedicates their life to using machine learning to replace/aid/automate pathologists 6+ hour days searching for cancer tumors in lungs. They have been at it for 5 years. There is an insane amount of approvals, red tape, knowing the right people, convincing the hospital to use it - all tasks not related to the tech actually working. Building it is the easy part. They were on the way there, working in the research section of a hospital. But they are still more than 5 years away from me being able to walk into a hospital and get my lungs scanned for tumours.

I personally can't wait for these general purpose function approximators to make all our lives better.

It's not that the technology isn't there. It's not that the technology can't be technically applied successfully. It's that convincing systems of people to change is way harder than we might think.

I am a practicing pathologist and I have seen many attempts and publications to use ML in pathology, which all lack in these aspects: 1. ML is trained on simplified sets (preselected ROIs, limited choice of diagnoses), 2. ML is biased by the experts who labeled learning sets, 3. there is no obvious process of learning from failures after initial training, 4. who is responsible in case of ML error with substantial consequences for patient? The first point is especially for the lack of better word.. wishful. In the daily practice we are used to account for "things unexpected" - non-representative biopsies, parasites in tissue where tumor was suspected, foreign body reaction from previous operations, laboratory accidents (such as swapped paraffin blocks of two patients), and so on (the list is much longer). We deal with it. That ML can discern between 5 most common diagnoses is fine, but it is rather narrow problem to solve.

Thanks for the very insightful response, you're right. All those problems are barriers to using the ML practically. So it seems like the technology actually is the problem.

There is a strong chance that the technology to create digital beings will be available before human society is able to integrate and adjust to the current generation of AI.

So what may happen is that the way that AI really gets integrated is by actually replacing human beings who largely die off.

That's a really interesting perspective and a good point. Reminds of "Age of Em". Interested to see what the effects would be of digit beings before integrating current generation of AI would be.

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