My current work involves ground-truthing sensor and classifier hits from various automotive OEMs. Take, for example, reading old-fashioned speed limit signs. In

My current work involves ground-truthing sensor and classifier hits from various automotive OEMs. Take, for example, reading old-fashioned speed limit signs. In principle, doing this correctly and reliably in context is a relatively simple machine learning problem, particularly when restricted to a single country. I have yet to work with an automotive OEM that doesn't consistently mis-classify some speed limit signs in some common cases that are completely unambiguous. It leads one to wonder about the quality of classifier design and testing such that these end up in production vehicles. The cases that cause a classifier to fail are different across automotive OEMs too, it isn't intrinsic; other automotive OEMs will handle that specific case just fine. On the other hand, when it works it often works surprisingly well. The good/bad news is that most of the problems I see are ultimately caused by poor model design and testing. These are all surmountable with better quality AI engineering. There is more AI to self-driving cars than the classifiers that pull features out of their environmental sensors. However, the driving AIs are making decisions based on the output of those classifiers -- garbage in, garbage out. These are the kinds of situations that will produce relentless incremental improvements over time, and there is still a lot of low-hanging fruit to improve on.