The application of machines to tasks once performed by human beings or, increasingly, to tasks that would otherwise be impossible. Although the term mechanization is often used to refer to the simple replacement of human labour by machines, automation generally implies the integration of machines into a self-governing system. Automation has revolutionized those areas in which it has been introduced, and there is scarcely an aspect of modern life that has been unaffected by it. — Britannica

The term “automation” was first used by Ford employee D.S. Harder in 1946
Automation isn’t changing the world, it has been changing the world
Machine learning is simply accelerating the progress at a compounding rate
In short, the machines are teaching themselves to automate faster
The more computing power we throw at it, the faster it works

Also according to Britannica:Machine learning discipline concerned with the implementation of computer software that can learn autonomously.Expert systems and data mining programs are the most common applications for improving algorithms through the use of machine learning. Among the most common approaches are the use of artificial neural networks (weighted decision paths) and genetic algorithms (symbols “bred” and culled by algorithms to produce successively fitter programs).History
Automation is not a new concept
Cotton gin
Steam engine
Industrial revolution
Computers
Quantum computing and nanotechnology

Bill Gates is often attributed with a quote:I will always choose a lazy person to do a difficult job because a lazy person will find an easy way to do it.

As a Bill Gates fan, I’ve never been able to substantiate the attribution, but it seems to be similar to a quote from a 1920 article in “Popular Science Monthly”

Bill probably didn’t say that, but it doesn’t make it a bad quote. Humans have always looked for easier ways to do hard jobs. Which brings us to, CGP Grey’s 2014 YouTube film, Human’s Need Not Apply.Humans Need Not Apply

In the film by CGP Grey he brilliantly and succinctly outlines how humans have allows innovated an easier (or lazier) way of doing things — and modern automation, machine learning and artificial intelligence is no different. We’re trying to unlock “easy mode” for complex problems.

At its core, there is absolutely nothing inherently wrong about mankind’s evolution into machine reliance, but at the foundation of it all is there is the serious threat to our society as we know it — and that is economics. As machines can do more jobs, that will mean fewer jobs for people.(0:46–1:03) Some people have been specialized to be programmers … whose job it is to build mechanical minds

Like their “dumb” mechanical forefathers, “Mechanical Minds” are making “Mechanical Brains” in less demand.(1:50–2:16) General purpose is a big deal. Think computers … when cheap-ish personal computers appeared they quickly became vital to everything

General purpose is a big deal, as machines can do more than one job investments in them will grow exponentially(3:13–31) We think of technological change as the fancy new expensive stuff, but the real change comes from last decade stuff becoming cheaper and faster…

Again, as product improvements iterate faster over time, costs drop exponentially lowering the barrier for entry more and more.

He then goes on to discuss horses, and the fact that their population peaked in 1915, just and quickly went into a free fall due to the proliferation of the automobile. There is a correlation between horses and humans in this regard, because like horses who were “workers” for centuries before the automobile, they became obsolete not that long afterward… and the important part is, they didn’t find “new jobs” when it happened. “Mechanical Minds” will eventually send humans the way of the horse, if we do not prepare.(4:26–4:59) As mechanical horses pushed horses out of the economy, mechanical minds will do the same to humans… so now does the car show us the shape of