Part of driving well requires a diverse of array of abilities, right? You know what is litter and what is debris because you can make a guess about material properties based on some observations, like looking at something that moves without being touched and is translucent, you probably conclude that its a plastic bag of some sort and not a hazard. Similarly you probably use a wealth of experience to judge that a small piece of tire is not a hazard, but a chunk is a hazard and a whole one is definitely a hazard.
Or, on seeing an anomalous <children's toy> enter the road you can probably guess that a child might follow shortly after.
I'm not suggesting that the problem cannot be solved without AGI, but you can see why some people might think that though, right?
My personal feeling is that we shouldn't be setting the bar at making a car that can handle any situation anywhere way better than any human at any time, but that we should also try to make roads that are more suitable for self driving vehicles. I'd rather we move to driving agents that don't get bored, frustrated, or angry.
I think the engineer's answer to the child entering the roadway would be: The car SHOULD never drive at such a speed that if the child WERE to enter the visible zone that it could swerve+slow enough to not hit it, forget the toy. After that we can move the goal posts and say it's a FAST child on a bike - but then the reasonable solution to that is a human driver may have also hit the biking child. Then, of course, we get into the ethics of fault for the accident.
My agreement with you falls largely under my last paragraph. I'm trying to illustrate a couple examples where driving as a human on roads built for human drivers requires perceptive powers and understanding that are beyond 'merely' driving safely, but also require a sort of holistic understanding of the world. If your goal is to make a better than human substitute driver then I don't think it is a completely unreasonable position to believe you'll need some level of AGI. Of course, as we figure out how to do concrete tasks and incorporate them into a system they'll stop being considered traits that would require general intelligence, but I suppose that is a different discussion.
And your example isn't moving goalposts, its just another legitimate example of a situation thats gotta get figured out. If you think that things like understanding that some kid learning to skateboard nearby could fall a surprisingly far distance and thus you should exercise caution, or being aware of factors that imply fast biking children (say, an adult and a child implies the potential for another fast moving child on the same trajectory), that this sort of situational and contextual awareness is critical for proper driving.. then yeah, that would be a reasonable sounding argument to support "I think self driving cars will require some level of progress in AGI".
Yes, this is very important to keep in mind, thank you. I wonder what sort of things one could do to make roads easier for automation and still serve regular people trying to be outside.
Stacking shelves in a warehouse is one task. Driving is not one task. There are too many corner cases for a modern-day AI system to perform as well as a median driver in, say, 95% of environments and settings in North America and Europe. I think the argument is that such a system might as well be AGI.
The idea that an AI must have the ability to learn how to do anything in order to learn how to drive seems like an extremely pessimistic and misguided goalpost. That is also not how iterative development works.
I think ML is fantastic, and combined with LiDAR, inter-vehicle mesh networking, and geofenced areas where humans take over, we could quickly arrive at mostly automated driving without trying to reinvent the human brain. We should also be more focused on enforcing established legal limits to newly manufactured cars. Just preventing someone from exceeding the speed limit or driving the wrong way would start saving lives immediately. It would also allow traffic flow to be optimized, and eventually prioritize emergency traffic or allow metro areas to be evacuated efficiently for things like natural disasters.
It would be great to see the dawn of AGI, but I don't think it will ever happen with classical computation. GPT-3 spits out nonsense with the input of the largest and easiest to parse portion of reality, and I have not seen any ML approach replicate the abilities of something as simple as bacteria. ML requires constant validation from human operators, so the same is going to hold true for ML powered vehicle navigation.
