21–30 of 66 posts
- 2007: Street View released
- 2007: DARPA Urban Challenge
- After 2007: Google hires several of the teams that won. This is where they got most of their expertise in self driving cars.
- Also note that the Urban Challenge winners didn't rely on "lots of data" but instead "lots of sensors"
Not really a fan of this type of random speculation.
Decent thought experiment but the whole "wolf in sheep's clothing" part is just link bait title or a really bad analogy. A more appropriate analogy would be "a head fake" as the late Randy Pausch so aptly put it.
Probably just a really bad analogy. They also used "vicious cycle of driving and learning" where "virtuous circle" (which I take to mean a system with positive feedback) would be more appropriate.
Decent thought experiment but the whole "wolf in sheep's clothing" part is just link bait title or a really bad analogy. A more appropriate analogy would be "a head fake" as the late Randy Pausch so aptly put it.
Probably just a really bad analogy. They also used "vicious cycle of driving and learning" where "virtuous circle" (which I take to mean a system with positive feedback) would be more appropriate.
Lots of data won't do any good with imperfect algorithms. Just look at all the nonsense generated by Google Translate. It doesn't even know grammar.
Look at Norvig (Google's head of research, and AI-demigod) at al's paper "The Unreasonable Effectiveness of Data."
Lots of data won't do any good with imperfect algorithms. Just look at all the nonsense generated by Google Translate. It doesn't even know grammar.
Actually, Google Translate was an enormous leap forward in machine translation quality. It won a number of awards for its astonishingly good performance. And its design premise is that you can use really simple algorithms if you have crazy amounts of training data (a then controversial approach called 'statistical machine translation' - as opposed to rule/grammar based). Look at Norvig (Google's head of research, and…
But it still produces a great deal of nonsense.
My only semi-informed opinion (hunch) is that the Google/Norvig brand of statistical approach to AI is a 80% solution to a lot of things, but that last 20% is going to be killer to get.
Right now this approach to AI is a great boost for humans, who can finish off that last 20% themselves, but I have doubts about the autonomous versions...
I am curious to see what happens with the self-driving car in real world use. And if translate ever gets much better than it is today. Or if, for that matter, Google search gets much better than it is today.
Street view cars all work during the daytime. There is no nighttime street imagery on streetview, and anecdotally, I've never seen a streetview car at night.
Having self-driving cars during daylight hours only is still an ambitious goal, though.
If you have ever used street view to catch a glimpse of your destination at an unknown address or to look for key landmarks at important intersections, you'll notice right away that the Street View data is the last thing you would want powering an autonomous car. Many of the mapped roads only show the view from one lane, and if the road is more than one lane wide, you're missing data on the positions of the other lan…