The approach they show in the end - it's still machine learning though? Exploring a space and finding parameters to optimize for a loss function (speed around the track), just not deep learning with neural nets.
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?
But machine learning has predated neural networks by hundreds of years. The core mathematical basis of all machine learning coursework linear regression and decision trees. Other models like SVMs, Bayesian models, nearest neighbor indexes, TFIDF text search, naive Bayes classifier, etc., are basically like machine learning 101, and they have many different properties regarding interpretability depending on the problem to solve.
I know this is super pedantic, but it's important to remember the roots of things, and that even things which appear new have precursors that are much older than a lot of people realise.
But perhaps using a cost function or a loss function is enough to call it machine learning. A machine just used an algorithm to learn another algorithm after all.
I do agree some optimization algorithms are rooted in other fields. I wasn’t trying to say that machine learning is the only historic field from which optimization methods were developed. I just wanted to point out it is a major historic field where some highly respected search and optimization procedures were first created, since people often overlook how old machine learning is and the vast set of modeling procedures apart from neural networks that make up the core of machine learning.
Instead it seems you falsely believe you are writing with some sarcasm that endows rhetorical flair to undercut my comment. It’s very rude and juvenile in addition to being wholly ineffective.