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I agree. To their defense, he had a few hundred papers published before joining, according to PubMed, and was a leader in his field: https://pubmed.ncbi.nlm.nih

I agree. To their defense, he had a few hundred papers published before joining, according to PubMed, and was a leader in his field: https://pubmed.ncbi.nlm.nih.gov/?term=Masliah%20E&filter=dates.1000%2F1%2F1-2015%2F12%2F30 https://pubmed.ncbi.nlm.nih.gov/?term=Masliah%20E&filter=dat... My concern is that with AI getting better and easier to use (e.g., in Photoshop) fraud will be extremely hard to detect. We might need to re-think how research is done and results verified.

fluidcruft 2y ago
Maybe we need instruments that sign results cryptographically and use a blockchain mechanism to establish provenance. We should have cameras that can establish that published images have not been modified (or at least provide raw and adjusted pairs--digital radiology has the concept of a "presentation state" that I think could work). In theory at least research should be auditable to a lab notebook. The problem with photos and such is you can't tell if it was modified before it was pasted to the page and large datasets just can't be put in to paper. And electronic notebooks I've used tend to be even more annoying than paper (too rigidly formatted and not adaptive to workflow optimization but it's difficult to explain). Anyway those sorts of things that establish provenance should also protect against deep learning. You may be able to create fake data, but can you deep fake data that was signed by Nikon device #XYZ with cryptographicly confirmed hashes published to a blockchain 3 years ago at the time the data was generated?