I agree snow is a lot harder (I don't think it's decades, though). But the cases moritonal brought up mostly don't require that.

The mere fact that it won't panic on black ice is already a huge advantage. And it will have very detailed feedback to and from the traction control systems.

What's the basis for your imagining? Whenever I read some strong "computer > human" statement like that, I can't help but think it's substantially based on science fiction (which can make technology work fantastically well because it's not real, e.g. like Six Million Dollar Man vs actual prosthetic limbs).

Also, have you heard about the time I was driving on the highway in winter weather, and my adaptive cruise control would stop working every few dozen miles because I had to get out and scrape ice off the sensor?

That doesn't really matter, because I was responding to the the sci-fi sentiment that "of course the computer must be better because computer." It doesn't matter if the humans are doing a poor job if the computer is still worse.

Especially because the claim is only about snowy situations. Even in a reply about snow, someone that thinks computers are automatically better would normally just say that directly.

Parktronics routinely get covered by snow/slush/ice when driving.

When that happens I hit a button to turn on the defroster that then gets rid of the snow and ice.

Could they use a similar system?

Can I patent that?

Literally the two easiest places it could go into

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

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.

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.

Guess you missed this distinction.

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