This is wrong, stupid and extremely confusing.

As someone who uses this in their day job, I have no problem using loose terminology to describe a well known procedure. Most of the time when I am referring to the act of optimization, it doesn’t matter exactly what method I am using, and I can use backprop as a stand in. If I’m talking about the technical details of my work, I will state the specific optimization strategy. Everyone on my team does similar things, and no one is confused or misled. Use rigorous language when necessary, and use colloquial language when appropriate.

> Use rigorous language when necessary, and use colloquial language when appropriate.

Do you think a peer-reviewd publication is formal enough to warrant precise language? These publications are not only read by specialists in the field. I use automatic differentiation in my daily work, but I'm not familiar with machine learning. Thus I am very confused when "backpropagation" is used to mean an optimization algorithm.

EDIT: It is as if physicists used the term "special relativity" to talk about "quantum mechanics" because, after all, quantum mechanics happens in Lorentzian spacetime. Now for specialists of quantum physics it may make sense, since they are using "special relativity" to distinguish it from fancier quantum theories that combine field theory with GR. But for normal people it would be certainly misleading. Using "backpropagation" to include optimization has the same feeling.

QM doesn't happen in lorentzian spacetime, QFT does though

yes, sorry, I meant quantum field theory

Don't worry about it, it was close enough to get your point across.

"Gradient descent" is more accurate and equally "loose".

Could you provide a definition correct, smart and sorta helpful?