ToyADMOS-dataset

> However, places where it isn't stagnating are things like vibration and anomaly detection. This is a case where https://github.com/YumaKoizumi/ToyADMOS-dataset really shines because it adds something that didn't exist before, and it doesn't have to be 100% perfect: anything is better than nothing. This is a link to a dataset, unless I'm missing something it's not about anomaly detection. I looked into this area a few years ago and always try to keep my eye open for breakthroughs... care to share any other links?

This article is dead-on, but I think it is missing a fairly large segment of where ML is actually working well: anomaly detection and industrial defect detection. While I agree that everyone was shocked, myself included, when we saw how well SSD and YOLO worked, the last mile problem is stagnating. What I mean is: 7 years ago I wrote an image pipeline for a company using traditional AI methods. It was extremely challenging. When we saw SSDMobileNet do the same job 10x faster with a fraction of the code, our jaws dropped. Which is why the dev ship turned on a dime: there's something big in there. The industry is stagnated for exactly the reasons brought up: we don't know how to squeeze out the last mile problem because NNs are EFFING HARD and research is very math heavy: e.g., it cannot be hacked by a Zuck-type into a half-assed product ov