4 ms·
> "The car will almost never be on the left side of the road, and the cameras will never flip angles, so training on flipped data forces the network to overgene
by function_seven 8y ago
> "The car will almost never be on the left side of the road, and the cameras will never flip angles, so training on flipped data forces the network to overgeneralize to situations it will never see." What could possibly go wrong?
That quote jumped off the page for me as well. This sounds like the start of a
new blog post: Falsehoods Programmers Believe About Driving
You can throw a few more in there.
- "Pedestrians will use crosswalks"
- "Pedestrians will be walking"
- "Traffic signals will always illuminate one of the three lamps"
- "Traffic signals have three lamps"
- "Lane markings are either white or yellow"
- "Lane markings exist"
- "There are lanes"
Your other comment is right on I think. Relying on DNNs to get near-100%
coverage on possible scenarios is a fool's errand. There's a very long tail of
possible circumstances on the road that no amount of training data is going to
cover. When you're trying to classify your photo library, this is okay. When
making real-time decisions on live input, it's not going to be okay. A more
structured and scrutible system is needed.
Until there's a breakthrough in our ability to understand what a NN "thinks", I
don't think we should place too much trust in them.
Or, I guess, we can wait and see if the imperfect NNs are doing better than
humans in the long run. From what I read, that may be true today only in ideal
circumstances (sunny, dry, maintained roads, etc.)
- Piskvorrr 8y agoAnd wait, is this a car? Is this a cyclist? Is this a pedestrian? recognizing...recognizing...recogni-CRUNCH We've already been down this road with Uber; apparently that's not an issue. https://www.regit.cars/car-news/how-to-pass-horses-on-the-road_45703 https://www.regit.cars/car-news/how-to-pass-horses-on-the-ro...
- mikeash 8y agoStructured, scrutible systems perform really badly on this task and I don’t think anyone knows how to do much better. The current state of the art is an extremely unstructured and inscrutable system with no design behind it whatsoever, and that system gets a lot of people killed. The moment anything can do better than that, we should push it hard even if it has stupid failures.
- Piskvorrr 8y agoWith the obvious assumption that we do have something better. So far...nope.
- mikeash 8y agoWith the assumption that we will, not that we currently do. That’s not a given, but it seems likely. The bar to clear is not high.
- Piskvorrr 8y agoAnd what was an assumption, again becomes a circular axiom by the end of the line: "[it will be easy to do, because] the bar to clear is not high, [because it's easy]" Do you have any data to support that? (Or more precisely, are we talking about the 80/20 Pareto bar? ("it is okay, as long as it kills fewer people per million miles, on average"))
- mikeash 8y agoIt’s not circular. I’m referring to specific objections people raise, like misclassifying objects or failing to react or whatever. The current state of the art often spends several seconds at a time with its cameras pointed at a screen instead of at the road.
- function_seven 8y agoI appreciate you continuing to refer to sacks of meat as "the current state of the art". It does drive the point home that we don't need to outrun the bear, just the other camper :)
- Piskvorrr 8y agoI suppose that's a part of the issue: the SDV camp has been overenthusiastic in flying their "Mission Accomplished" banners, and hitting (pun not intended) yet another unexpected problem right afterwards. In other words, we're not at that point yet - in fact, we might not even have the complete toolset to measure this. The field is in flux - alchemic approaches "what if we try something unrelated" might work for unrelated reasons etc.; practical applications that don't spontaneously combust are still some way out there.