4 ms·
From the article it seems to me, to be a backup solution only. Kinda a replacement for tape, with the difference that it is faster writing. What got my attentio
by V__ 3y ago
From the article it seems to me, to be a backup solution only. Kinda a replacement for tape, with the difference that it is faster writing. What got my attention:
> The images are then sent to be processed and decoded, which leans on machine learning model to convert analog signals to digital data.
Looking at the paper [1] (section 3.2.):
> Machine learning models are able to better learn and account for any noise properties inherent in the end-to-end write and read processes including: inter-symbol interference between adjacent voxels in the glass, scattered light from neighbouring layers during readout, variability between optical components, and more. By contrast, traditional signal processing techinques require extensive understanding of all these characteristics and careful (often hand-crafted) processing to remove their effects.
It feels weird to me, that they target "sensitive industries including finance, scientific research and healthcare", but decoding/reading the data is not really deterministic.
[1] https://www.microsoft.com/en-us/research/uploads/prod/2023/09/ProjectSilica-SOSP23.pdf https://www.microsoft.com/en-us/research/uploads/prod/2023/0...
- great_psy 3y agoI don’t think the reading method is a substitute for checksums and so on. You would still read the exact same data you write, even if part of the process is not deterministic.
- fmobus 3y agoThat doesn't sound good in terms of future-proofing this technology. If I want to read stuff 200 years from now, the knowledge necessary for that process will be buried deep inside some opaque ML blob.
- wildzzz 3y agoWhen they say machine learning, do they really just mean error correcting codes and convolutional encoding? Is this just a case of using buzzwords or is the ML just doing RS/Viterbi?
- great_psy 3y agoNot really… You have a signal that has noise, and the goal is to get eliminate the noise. In 200 years the goal will be the same, and hopefully we will have a better algorithm of eliminating the noise.