I would say using backpropagation to train multi-layer neural networks would qualify as ML and we were definitely doing that in 80's.
Just with tiny amounts of data.
Compared to today. We thought we used large amounts of data at the time.
"We thought we used large amounts of data at the time."
Really? Did it take at least an entire rack to store?
We didn't measure data size that way. At some point in the future someone would find this dialog, and think that we dont't have large amounts of data now, because we are not using entire solar systems for storage.
Why can't you use a rack as a unit of storage at the time? Were 19" server racks not in common use yet? The storage capacity of a rack will grow over time.
my storage hierarchy goes 1) 1 storage drive 2) 1 server maxed out with the biggest storage drives available 3) 1 rack filled with servers from 2 4) 1 data center filled with racks from 3
How big is a rack in VW beetles though?
It's a terrible measurement because it's an irrelevant detail about how their data is stored that no one actually knows if your data is being stored in a proprietary cloud except for people that work there on that team.
So while someone could say they used a 10 TiB data set, or 10T parameters, how many "racks" of AWS S3 that is, is not known outside of Amazon.
a 42U 19" inch rack is an industry standard. If you actually work on the physical infrastructure of data centers it is most CERTAINLY NOT an irrelevant detail.
And whether your data can fit on a single server, single rack, or many racks will drastically affect how you design the infrastructure.