Unless you design the network to specifically answer "how similar is this?" and pair it with a training set and the output of another executing neural net, a "confidence meter" isn't possible.

This is one of the trickier bits to wrap one's mind around with neural networks. The systems we train for specialized tasks have no concept of 'confidence' or being wrong. There is no meta-awareness that judges how well a task is being performed in real-time outside of the training environment.

Humans don't suffer from this issue (as much) due to the mind bogglingly large and complex neural nets we have to deal with the world. Every time you set yourself to practicing a task, you are putting a previously trained neural network through further training and evolution. You can recognize when you are doing it because the process of doing so takes conscious effort. You are not 'just doing <the task>', you are 'doing <the task>, and comparing your performance against an ideation or metric of how you WANT <the task> to be performed'. This process is basically your pre-frontal cortex and occipital lobe for instance, tuning your hippocampus, sensory and motor cortex to perfect the perfect tennis swing.

When we train visual neural networks, we're talking levels of intelligence supplied by the occipital lobe , hippocampus, alone. Imagine every time you hear about a neural net that a human was lobotomized until they could perform ONLY that task with any reliability.

Kinda changes the comfort level with letting one of these take the wheel doesn't it?

Neural nets are REALLY neat. Don't get me wrong. I love them. Unfortunately, the real world is also INCREDIBLY hard to safely operate in. Many little things that humans 'just do' are the results of our own brains 'hacking' together older neural nets with a higher level one.