> This system would probably have to be far better than 99% to be helpful. This article does talk about this specifically: > “One drawback is that we had a li

> This system would probably have to be far better than 99% to be helpful. This article does talk about this specifically: > “One drawback is that we had a limited data sample for this demonstration, but we could improve the neural network in the future if a large number of these tags were deployed in a supply chain, giving us a lot more data samples,” Lee says. I.e. they're well aware that 99% would need to be improved, but it sounds like they've managed to hit that level with a handful of individually created samples and a tiny project to train a model. This is just a prototype really, and 99% accuracy when testing a prototype doesn't seem unreasonable, it's not like they're planning to ship this tomorrow. It's a demonstration that recognizing these patterns automatically is actually possible in the first place, and in a serious roll-out scenario it would _probably_ be possible to train a model to do very significantly better.