> If you don’t have the second how can you trust the first? Without the dataset to test on your human experts will deliver you slop and be confident about it.

> If you don’t have the second how can you trust the first? Without the dataset to test on your human experts will deliver you slop and be confident about it. One of the key things is that if you don't understand how things work, your test dataset needs to be the world. A classical system can be analyzed, and you can pick a test dataset which maximally stresses it. You can also engineer environments where you know it will work, and 9 times out of 10, part of the use of classical machine vision in safety-critical systems is to understand the environments it works in, and to only use it in such environments. Examples: - Placing the trackball sensor inside of the mouse (or the analogue for a larger machine) allows the lighting and everything else to be 100% controlled - If it's not 100% controlled, in an industrial environment, you can still have well-understood boundaries. You test beyond those bounds, and you understand that it works there, and by interpolation, it's robust within the bounds. You can also analyze things like error margin since you know if an edge detection is near the threshold or has a lot of leeway around it. One of the differences with neural networks is that you don't understand the failure modes, so it's hard to know the axes to test on. Some innocuous change in the background might throw it completely. You don't have really meaningful, robust measures of confidence, so you don't know if some minor change somewhere won't throw things. That means your test set needs to be many orders of magnitude bigger. For nitpickers: You can do sensitivity analysis, look at how strongly things activate, or a dozen other things, but the keywords there were "robust" and "meaningful."