[rexreed]

So you're saying we have sufficient trust in the same sort of NN technology that confuses 8's and 0's in OCR text will be used to impute image data which might or might not exist? Sure, NN's are great at "filling in the gaps" and colorizing pictures based on what might be assumed, but when accuracy matters, does this approach truly work?

EDIT: I just want to point out that the original subject title of the post on HN was "A low-cost and shielding-free ultra-low-field brain MRI scanner Using AI" ... and the Using AI part of the post title was subsequently removed.

[tshaddox]

Do you have any data on the reliability of OCR systems used in production? I don’t have any such data, but given that the USPS was using OCR to sort mail over 50 years ago I would be surprised if these systems aren’t incredibly accurate.

[rexreed]

From: https://research.aimultiple.com/ocr-technology/

"There are still no OCR tools that work at human level in most applications"

and also from my personal experience working with this technology every day. There are many more mistakes in OCR even with printed material than might be expected.

There is a major problem with Xerox Scanners and the 8's and 0's issue I reference.

[isoprophlex]

See other comments. The nn is used to clear up electromagnetic inference as there's no shielding cage. It's not anything lik a superresolution approach on the processed voxel data.

[rexreed]

Ok good clarification as the title of the post seems to imply much more than just fixing interference. As always, the article subject is the hook that gets you in and then you realize it's not as might have been expected.