The reverse mode has famously been re-discovered (or re-applied) many times, for example as backpropagation in ML, and as AAD in finance (to compute "Greeks", ie partial derivatives of the value of a product wrt many inputs).

A few resources here:

An overview, with a bias towards finance: https://informaconnect.com/a-brief-introduction-to-automatic...

On the history: Andreas Griewank, Who Invented the Reverse Mode of Differentiation? https://ftp.gwdg.de/pub/misc/EMIS/journals/DMJDMV/vol-ismp/5...

On the history of back propagation: https://en.wikipedia.org/wiki/Backpropagation#History

The article that introduced it to finance: Michael Giles and Paul Glasserman, Smoking adjoints: fast Monte Carlo Greeks https://www0.gsb.columbia.edu/faculty/pglasserman/Other/Risk...

Survey of the application in finance: Cristian Homescu, Adjoints and Automatic (Algorithmic) Differentiation in Computational Finance https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1828503

It was in one of the fast.ai courses, I think where Jeremy did back propagation using Excel

https://www.fast.ai/

Could be that someone else here remember the exact video

Hope you don't mind me plugging my blog post, that covers chain rule -> autodiff -> training of nn. https://sidsite.com/posts/autodiff/

Absolutely not. Thank you for sharing.