I can't speak to cloud deep learning, but edge NN's have two practical applications: anomaly detection and object defect detection. (Keyword spotting is complet

I can't speak to cloud deep learning, but edge NN's have two practical applications: anomaly detection and object defect detection. (Keyword spotting is completely stalled. I've been watching Arm-based companies work on keyword spotting for years and they are still 80% accurate in a real setting, which is like talking to an old person with hearing issues.) Anomaly detection (vibration analysis, audio anomalies) are working fairly well. Same goes for assembly lines where defective parts are booted off, and NN's don't improve that much over traditional methods. The hype machine is all the big sexy stuff. GPT, Protein folding and Alpha Go are truly amazing. But I haven't seen a big step-function in deployable NNets (github-ready) in years. Which given the number of people researching this I expected a linear progression. What have we seen since UNet, MobileNet, SSD, Yolo, BERT, ResNet150, Inception ... ? That's worrisome, but it also could be the pendulum swing while people figure out exactly why these NN's work the way they do. I think we need to wait another 20 years, which means lots of hype money will dry up, but I don't think it will vanish. The efficiency of neural nets as a general function approximation might not be super high accuracy, but it is amazing to see such a simple construct perform with such speed.