Couple of years ago, there's been an article posted here: How to Recognize AI Snake Oil. https://readhacker.news/c/4cSun https://readhacker.news/c/4cSun It look

Couple of years ago, there's been an article posted here: How to Recognize AI Snake Oil. https://readhacker.news/c/4cSun https://readhacker.news/c/4cSun It looks like this was the case with Zillow. Most of commenters here insist ML and data science have nothing to do with this failure, and put all blame on the management. I don't by it's not failure of ML. In the last several years I consistently see Tech leads oversell the possibilities of ML and theorizing that ML could solve many problems with deep learning. The result is always modest. Error margins for real world metrics that involve location data are around 20-30%, which is somewhat better than analysts predictions in Excel, but not enough to rely on them. I saw very competent ML engineers try to improve a model, take a huge amount of satellite imagery, tracking data, etc., train their neural networks for couple of months, and only improve error margin by 1 percent point! (e.g. 25% -> 24%)