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Joined 2 years ago
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Cake day: June 15th, 2023

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  • No they are not “a tool like any other”. I do not understand how you could see going from drawing on a piece of paper to drawing much the same way on a screen as equivalent as to an auto complete function operated by typing words on one or two prompt boxes and adjusting a bunch of knobs.

    I don’t do this personally but I know of wildlife photographers who use AI to basically help visualize what type of photo they’re trying to take (so effectively using it to help with planning) and then go out and try and capture that photo. It’s very much a tool in that case.


  • Unfortunately proprietary professional software suites are still usually better than their FOSS counterparts. For instance Altium Designer vs KiCAD for ECAD, and Solidworks vs FreeCAD. That’s not to say the open source tools are bad. I use them myself all the time. But the proprietary tools usually are more robust (for instance, it is fairly easy to break models in FreeCAD if you aren’t careful) and have better workflows for creating really complex designs.

    I’ll also add that Lightroom is still better than Darktable and RawTherapee for me. Both of the open source options are still good, but Lightroom has better denoising in my experience. It also is better at supporting new cameras and lenses compared to the open source options.

    With time I’m sure the open source solutions will improve and catch up to the proprietary ones. KiCAD and FreeCAD are already good enough for my needs, but that may not have been true if I were working on very complex projects.




  • Exactly, the assumption (known as the inductive hypothesis) is completely fine by itself and doesn’t represent circular reasoning. The issue in the “proof” actually arises from the logic coming after this, in which they assume that they can form two different overlapping sets by removing a different horse from the total set of horses, which fails if n=1 (as then they each have a single, distinct horse).


  • The main benefit I think is massive scalability. For instance, DOE scientists at Argonne National Laboratory are working on training a language model for scientific uses. This isn’t something you can do on even 10s of GPUs for a few hours, like is common for jobs run in university clusters and similar. They’re doing this by scaling up to use a large portion of ALCF Aurora, which is an Exascale supercomputer.

    Basically, for certain problems you either need both the ability to run jobs on lots of hardware and the ability to run them for long (but not too long to limit other labs’ work) periods of time. Big clusters like Aurora are helpful for that.