I am not sure how people are designing self-driving cars, but if it were up
to me, it would be very explicitly about maximizing expected utility. A
neural network can be trained to estimate the expected sum of future
rewards, usually with some exponential future discount. Actually, that's
I would argue that if I was an engineer for a hypothetical autonomous car
manufacturer, that it would be critically important to keep a running circular
buffer of all the inputs over time for the car. Sort of like how existing cars
have Dash Cams that continuously record to flash, but only keep
In your hypothetical scenario, if the car can give you as much debugging
information as you suggest (100% tree is there, 95% child is there), you
can actually figure out what's happening. The only other piece of
information you need is the configured utility values for the possible
outcomes.
Say
Unlike humans, who have these pesky things called rights, we can abuse our
computer programs to deduce why they made decisions. I can see a future where
that has to happen. From my experience in trying to best the stock market with
an algorithm I can tell you that you have to be able to explain
