(the math is based on # of US miles driven in 2017 divided by the number of car-caused pedestrian fatalities in the same year)
So cherrypicking the smallest number seems very disingenuous to me.
So I'm going to kill a pedestrian every 100 years? Assuming I drive normally for ~50 years, 500k miles, I have a 50% chance to kill a pedestrian?
I can understand a person being distracted, reacting slowly, not seeing me, etc. I can also mitigate this risk. Crossing the street I can make eye contact with a person and be really confident that they aren’t going to run me over.
Walking in front of a robot? I have no internal model for the kind of mistakes they make. It seems equally likely in any circumstance that they’ll make the mistake of running me down, and there is nothing i can do to avoid it.
Examples:
Elevators where the doors take extremely long to close on their own.
Elevators where the doors close too soon after opening.
Elevators where the limb sensors are not very responsive.
Cars that take a couple of tries to start in cold weather.
Automatic doors that are a little bit slow to open.
Etc.
Maybe a single 'driver' agent in a free-moving ground vehicle in close proximity to vulnerable actors and objects is not the right model for scalable, efficient, safe personal transport?
737 Max fiasco notwithstanding - it's worth mentioning that MCAS had nothing to do with flying the plane, only making it feel more like the 737NG to its human pilots
Not entirely true, the original issue is that the engines are bigger and the center of gravity is moved forward ( because the 737's are too low), thus the planes tend to stall. MCAS is there to correct the stall so that they feel more like 737NGs, but the issue is forcing bigger engines on an obsolete design.
My understanding is none, or nearly none. VNAV follows GPS waypoints with well described, manually designed behaviors for things like climb rate, bank angle, etc.
Autoland isn't machine learning either, and has such a large list of requirements to be allowed that it's basically like designing a self driving computer for a train.
