I waded through half of it hoping a coherent point would emerge before heading over to hn comments to confirm my suspicions.It’s a mash up of a couple of different pop opinions on the state of ML without any real insight.
at least one of them is true. The effort spending "cleaning data" which I think by her description she means "connecting pipes" is underestimated. I work for a company that deploys an ML model that works on a "lowest common denominator" between multiple different downstream SAASes, and this week I have been struggling with a field that should be an integer, but is a string in one SAAS provider, (and there are entries that are not parsable as integer). I can't simply convert it to a string because the ML model is not expecting it.
Yes. "AI" (read Deep Learning/LogReg/SVM models) do indeed perform better given more data. I can vouch for this myself. And there was also a paper regarding this.