>I would add "AI" produces results that are hard to understand and work with. Sorry but no, this rhetoric is often used as an easy escape of responsibility but

>I would add "AI" produces results that are hard to understand and work with. Sorry but no, this rhetoric is often used as an easy escape of responsibility but I don't buy it. Sure, if you're talking about the neural nets used in Google translate or YouTube recommendation system, or any big tech company domain of problem that is based on vast amount of data with extreme size of state space and variability - fine. But we are not talking about interpretability of the result but the reliability of it, and in robotics domain you rarely have to tackle that size of state space, but rather a highly noisy one. Let's take your example of low level control of bipedal robot. At whatever stage of control/manipulation you're talking about, you always have to consider that the input will have some noise, and how to handle it. This is the reliability; you know how to(or how much) of the input noise you can handle, and how much you will leave (or amplify) to the rest of the system. Based on this you also know the limit of the system, so you can also get an indication of failure. You can make AI/Neural Network to consider the noise and also give an indication of failure, but you have to think about that from the data gathering step! Most control algorithms have this: "what input noise can I handle" question answered; The same requirement should be set to the data. I assume you thought about Reinforcement Learning of sorts when you thought about control, but what most people don't realize is that those algorithms works only because you can use high precision sensor that reduce these noise level (combined with sensor fusion) to negligible level. But now you're relying on high precision, extremely expensive sensors. They almost always fail when you get out of the lab and have to suddenly consider cost. (and at this stage traditional control algorithm creeps back) Let's take a real example. On my original comment I wrote "work in lab at 1pm" and I weren't sarcastic. Apparently not only did the AI solution (in Computer Vision) I were given only work in the lab, but at the exact lightning condition under high noon at the lab, with all the ceiling lights on all the time. We are now not talking about interpretability of the AI, but the reliability; This is easy to identify beforehand. You can also mitigate this (the lightning noise) by including it in the model (data). This should have been glaringly obvious if the person actually knew his toolset rather than just retraining a existing model using tensorflow magic.