The article says: > They found that it’s 99% accurate in letter recognition from the MNIST dataset, 85.7% accurate on image classification from the CIFAR-10 da

The article says: > They found that it’s 99% accurate in letter recognition from the MNIST dataset, 85.7% accurate on image classification from the CIFAR-10 dataset, 84.7% accurate on Google speech command recognition and showed a 70% reduction in image-reconstruction error on a Bayesian image recovery task. These are all small scale models that run on very low power, on edge devices. Not gonna load GPT-3 on it soon. It's for wake word detection and security cameras.