this post was submitted on 25 Aug 2024
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The most successful ML in-house projects I've seen took at least 3 times as long than initially projected to become usable, and the results were underwhelming.
You have to keep in mind that most of the corporate ML undertakings are fundamentally flawed because they don't use ML specialists. They use eager beavers who are enthusiastic about ML and entirely self-taught and will move on in 1 year and want to have "AI" on their resume when they leave.
Meanwhile, any software architect worth their salt will diplomatically avoid to give you any clear estimate for anything having to do with ML – because it's basically a black box full of hopes and dreams. They'll happily give you estimates and build infrastructure around the box but refuse to touch the actual thing with a ten foot pole.
There aren't enough AI specialists. More are being created by picking up these projects.
The problem is that AI is too hyped and people are trying to solve things it probably can't solve. The projects I have seen work are basically fancy data ingress/parsing/summarisation apps. That's where the current AI tech can really shine.