They only difference with Google or Facebook is that they’re big enough to absorb the losses.
This isn’t to say that ML is a dead end, but instead to point out thatjust because they are investing a lot doesn’t make it good.
ML is unlikely to be one of those places, but appealing to the efficiency of large, bureaucratic companies is a poor argument.
Many businesses are seeing real, measurable impacts from NN based software that would be impossible without it.
[1] agree with your comment, not much real business use for it, but I wanted to work in it to be sure
Citation needed. Decision trees are still state of the art.
How do you do anything vision related with decision trees? Or anything beyond n-grams with text?
But here's some citations as requested:
https://casetext.com/blog/game-changing-ai-litigators/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6512995/
https://possibility.teledyneimaging.com/advances-in-ai-for-i...
https://www.lifewhisperer.com/
etc
(I'm pointing at these particular fields because I personally have worked on NNs in applications in these fields, but there are plenty more)
aside from fraud detection, autonomous vehicles, language translation, facial recognition, voice preproduction and market insights of course
(Business probably just wants Bayesian inference instead, but that's too hard, let's go hardware shopping instead.)