The big difference between this and the other approach mentioned in the article is the model is a simple one that's easy to understand instead of a many layered neural network which is rather opaque.
I think the article may be better titled "You might not need neural networks."
Since a linear model is essentially a single layer neural network with linear activation, we can't even say that. The athor was using a neural network without realising it :)
Doing automatically controlled systems was still possible before computers just that it required a bit more creative use of analog components.
I think that it's important to note here that this was the set of problems that computer science researchers didn't know how to solve.
In the early days, they mostly ended up re-inventing statistical methods.
And to be fair, a neural net is just a bunch of linear models joined by a non-linearity. In that case, it's essentially stacked logistic regression, which was invented before computers.
Nobody did this before computers though.
> . In that case, it's essentially stacked logistic regression, which was invented before computers.
It isn't "basically logistic regression", it is just a technique which uses logistic regressions. The full technique is ML. If you remove the ML parts it is basically just logistic regression left though.
I think this would probably be a more profitable discussion if you could define Machine Learning for me.
How does your evidence support your claim?