Back in ~2004, when I was looking for a job for the industrial placement year of my degree, one of the options was a nut packing factory using computer vision systems. I was intrigued, but went for the job processing satellite images instead.
Even well before that, ML is very closely related to statistics, so early practical applications would have been as simple as gathering data points on widget production and doing the kinds of analyses that are now backed into free spreadsheet software.
From what I remember reading at the time, most of industrial vision applications 20 years ago had very little to do with neural networks. Or even with ML in general, relying on bespoke feature detectors.
I know the history and the fist USPS applications. My point is in any random industrial vision setup on the verge of millennium you'd unlikely found any sort of NN.
I'm not sure why are you arguing with me, my point was what was the historical reality at the time. Machine vision used feature detectors, cellphones had keyboards, teenagers wore low cut jeans.
My ‘argument’ (not sure we’re really in disagreement, actually) is that the actual usage/use cases were so limited back then, and the economic impact because of it was so much smaller, that they’re really not equivalent even if they might technically be called the same thing.
CNN’s literally changed the game.
It’s like talking about cell phones, so we attempt to compare [https://en.m.wikipedia.org/wiki/Motorola_DynaTAC] and a modern flagship Apple or Samsung smart phone. Technically they can both make mobile phone calls, yes.
And it isn’t really wrong to compare them.
Technically, if we limit the specific scope of what we’re comparing and squint a lot, and avoid the context, they’re equivalent.
But they aren’t really the same type of thing either, eh?
In fact, if we take a broader view, the Motorola DynaTAC was a portable radio connected to the phone system, not what we’d call a phone.
Even well before that, ML is very closely related to statistics, so early practical applications would have been as simple as gathering data points on widget production and doing the kinds of analyses that are now backed into free spreadsheet software.