[tomkat0789]

+1 to this from someone who learned the math behind ML in a PhD and was looking forward to being a gatekeeper :)

My favorite academic paper ever [0] was a comparison against a bunch of dimensionality reduction algorithms and 100 year old PCA was tough to beat!

Glad I was able to pivot my career out of AI and ML. My PhD wasn't at Stanford, MIT, et al so I couldn't find any jobs doing the "actual research" - if they existed at all outside academia.

EDIT to add another funny "frustration" paper more directly related to ML [1]. I consider DR is more of a data analysis thing.

[0]: van der Maaten, et al. Dimensionality Reduction: A Comparative Review https://members.loria.fr/moberger/Enseignement/AVR/Exposes/T...

[1]: Dacrema, et al. Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches https://arxiv.org/pdf/1907.06902.pdf

[platz]

What is you pivot to?

[tomkat0789]

Regular old engineering - modeling, controls, signal processing. My background in AI and ML helped me develop some great transferable skills (technical programming, mainly Python) and added some very attractive buzzwords to my resume! 4 years out of graduate school and I don't regret studying AI/ML. It was fun and made me more ambitious about my research and career than something more traditional would have.

[mycall]

> modeling, controls, signal processing

I wish AI/ML was added to Pure Data [0] or Max [1]. This would require all your skills if you could help out :)

[0] https://puredata.info/ [1] https://cycling74.com/products/max

[neolog]

Great links, thanks.