The Three Engineering Problems That Make Industrial AIoT Harder Than It Looks — and More Interesting Than Anything Else
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Most engineers who move from software or ML into industrial AIoT go through the same disorientation. The skills transfer and the fundamentals apply, but the environment has properties that invalidate assumptions so deeply embedded in standard engineering practice that they feel less like assumptions and more like physics. Three of those invalidated assumptions are responsible for most of the interesting engineering work in this space—and for most of the ways that well-built systems fail when…
1Key Takeaways
- Most engineers who move from software or ML into industrial AIoT go through the same disorientation.
- The skills transfer and the fundamentals apply, but the environment has properties that invalidate assumptions so deeply embedded in standard engineering practice that they feel less like assumptions and more like physics.
- Three of those invalidated assumptions are responsible for most of the interesting engineering work in this space—and for most of the ways that well-built systems fail when….
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that most engineers who move from software or ML into industrial AIoT go through the same disorientation.
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