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Comment by londons_explore | Hacker News Reader

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londons_explore·

Speech recognition algorithms today require lots of data, lots of training computation, and a decent design.

Decent designs are in published papers all over the place, so thats a solved issue.

Lots of compute requires lots of $$$, which isn't opensource-friendly.

Lots of data also isn't really opensource friendly.

Sadly this is a niche that the opensource business model doesn't really fit.

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People would probably say the same about wikipedia 20 years ago. People said similar things about gnu, gcc and linux 30 years ago.

GNU is heavily skewed to developer tools and infrastructure, and gcc is no counterexample. There are so many classes of software where this does not work. Pretty much anything for heavily regulated industries is not well served by FLOSS. There are few open source insurance software or medical records systems (the few that exist are highly niche and/or limited), EDA/CAD is not well served by FLOSS (I’ve toyed with FreeCAD, but even hobbyists gravitate to Fusion). Outside of developer tooling and infrastructure: commercial, generally closed source, closed development software is king.

* besides the hard part of standing up an EMR is not installing a prepackaged software.

> Lots of compute requires lots of $$$, which isn't opensource-friendly.

Not really, look up BOINC.

There's more involved in than just raw CPU cycles. It's not something that is easily adapted to BOINC, but trying to offload things to BOINC to free up clusters better suited to training models might make sense.

Sounds like a viable model for certain universities, though.

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