> like residential culdesacs and parking lots

Literally the two easiest places it could go into

Lower stakes because of the speed, but a lot of special cases. Higher chance of pedestrians and parked cars in the road for both. Parking lots will have a lot of cars moving in atypical ways, and markings aren't as standardized as on roads.

Sure, but at 5mph everything is easier

Sure a computer can beat an expert human at chess, but get back to me when it can beat an expert go player.

Self-driving is not even on the same scale of complexity as a board game. The latter is an inherently digital problem, easy to parse and not played in “real time”

> The latter is an inherently digital problem, easy to parse and not played in “real time”

So people should have been able to correctly predict how easy it would be for a computer to master go, right?

People's inability to accurately assess how easy an inherently easy problem is has no bearing on people's inability to accurately assess how hard an inherently hard problem is.

Said differently, I start from the assumption that some problems are obviously easy, some problems are obviously hard, and every other problem is somewhere in the middle. Self-driving is in the "obviously hard" bucket. Feel free to argue for where you think Go sits in the spectrum, but I would argue no board game is in the "obviously hard" bucket

Hard and easy defined as the ability to solve in a reasonable amount of time with the algorithms and computational models we have today or in the near future -- regardless of ~marginally increasing processing power.

> People's inability to accurately assess how easy an inherently easy problem is has no bearing on people's inability to accurately assess how hard an inherently hard problem is.

Really? It seems to me that if people over-estimated the difficulty of an "easy" problem like mastering go, then they're even more likely to over-estimate the difficulty of a hard problem like self-driving. In fact, the over-estimation could scale up faster than linearly, if estimating two problems of size X is easier than estimating one problem of size 2X.

> with the algorithms and computational models we have today or in the near future

That's the thing. When predictions were being made about the difficulty of mastering go, people didn't have the algorithms and computational models that we have today. Similarly, predictions made today about the progress of self-driving cars may be lacking critical information about the algorithms and computational models that will be available in the near future.

Get back to me when it can go into my kitchen for the first time and make me a cup of coffee.

> customized route

Guess you missed this distinction.

Doing things a bus would not do sounds like it's well beyond "customized route".

If "customized route" counts any drive, then that's not really a limitation any more.