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Cake day: August 25th, 2023

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  • bluefishcanteen@sh.itjust.workstoScience Memes@mander.xyzBreast Cancer
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    5 months ago

    This is a great use of tech. With that said I find that the lines are blurred between “AI” and Machine Learning.

    Real Question: Other than the specific tuning of the recognition model, how is this really different from something like Facebook automatically tagging images of you and your friends? Instead of saying "Here’s a picture of Billy (maybe) " it’s saying, “Here’s a picture of some precancerous masses (maybe)”.

    That tech has been around for a while (at least 15 years). I remember Picasa doing something similar as a desktop program on Windows.




  • I run calibre off my desktop. You can enable the Calibre content server and it can serve up your books for download (or provide a web reader).

    If you have an Android device, you can use something like Moon Reader (or any other reading app that supports epub or Pdf) to download content from the Calibre content server.

    With respect to covers and metadata, Calibre can tag and fill in this info as well - out of the box it will scrape information from Amazon.



  • Technically no. The tolerances should be more or less the same (generally 90%-110% label claim for the active ingredient) . Manufacturers aim for 100% and generally hit that target (or get very close to it).

    The bioavailability could be different though - if you are doing a bioequivalence trial for generic VS brand, the generic would have to be between 80% - 120%. This difference is generally a result of the starches, fillers, and other stuff that may be in a generic formulation.

    Same net effect as your comment (wider tolerances), but there is a bit more nuance.