391–400 of 440 posts
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> How do humans handle edge cases anyways? 30,000 deaths per year suggest that humans fail at these edge cases all the time. Maybe ate their best a human outperforms a machine but as any person who drives if other drivers are at their best.
Divide by millions. Statistically quite low failure.
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"There's a ball in the middle of the road, is a kid going to pop up from behind a parked car and run towards it?"
"There's no ball in the middle of the road, a kid pops up from behind a car, you have 0.1 seconds to apply the brakes before you hit the kid." It's just as easy to come up with any sort of contrived situation for which a machine would outperform a human.
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Humans suck at these classification tasks things too, though, if you set your expectations high. Is it a cat or a bag, or a weird shadow? Doesn't matter, I ran it over before I could figure it out and react. Or maybe it turned out to be a boulder? Now I'm one of 35,000 traffic fatalities this year in this country. Computer vision has been improving rapidly in the last 10 years, I think it's too soon to rule out the v…
Expectations for CNNs with Tesla's tech is currently "Does not run into firetrucks". And they're not exactly succeeding right now. https://www.wired.com/story/tesla-autopilot-why-crash-radar/ Humans are way, way better than current CNNs on this field. We can talk about cats, shadows, and boulders when CNN-based methods stops crashing into concrete barriers, parked fire-trucks, and 18-wheelers making a left turn. I do…
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Agree. It's a fun game to think up edge cases that would challenge a self-driving car. On average these cars will far outperform human drivers. I see human drivers blow through stop signs every day completely unaware that they did so.
Didn't Uber car did exactly that? Perfect robot would outperform average human. Sure. Average robot to average human? That's much more interesting...
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> How do humans handle edge cases anyways? A minimum 14 years of "human learning".
Or, judging by traffic's constant deaths ... Their don't . Anyone who's seen a Waymo car drive around the valley knows they're much safer than human drivers. They're annoyingly good, because for instance they just never miss a bike and are careful around them, and if you can't tell why it's a bit annoying. But I've always come away from such incidents with a feeling of "I should have seen that". I've yet to see them…
I don't think anyone is debating that in perfect conditions the computer cars can sometimes out perform the worst drivers.
If your concern is terrible drivers killing themselves and others, what you should be arguing for is much stricter standards for obtaining a driver's license. Probably removing 'full coverage' insurance and only allowing liability would go a long way as well. Can't keep wrecking your vehicle if nobody's buying you a new one.
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I see this fallacious argument made as a counterpoint time and time again and it is getting tiresome. If you look at all the relative statistics, both IIHS and insurance, the drivers who practice safe driving techniques and do not drive distracted have something crazy like a 3-4 fold lower accident rate. Moreover, those drivers who have had an accident are much more likely to have another one. Have you ever met someo…
>the drivers who practice safe driving techniques and do not drive distracted have something crazy like a 3-4 fold lower That's quite the qualifier. Problem is I see at least 3 people with their eyes on their phone during every 30min commute to work ...so... >When the "worst" AI's that are responsible for some amount of crashes, who is going to pay the price? Whose license is going to get deducted? Whose insurance is…
You are either young, or extremely naive (or both) if you don't think these are going to be challenging problems.
> so we should stop trying?
Not sure why you thought that this begged that question. What I am saying is we should stop making arguments that compare driverless car habits, problems, and safety to the current habits, problems, and safety of human drivers. They are generally bad arguments. They won't bear any fruit. Statistics aren't people. Trying to sell a generation on driverless cars on some unproven projection that "they will be X times safer!" will fall on deaf ears to drivers who have made it a lifetime of safe driving.
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Other edge cases include: Amish buggies, rotaries in Boston, Syracuse's upside down traffic light (green is on top), etc. The possibilities are endless; how can you test them all? Humans are really good about adapting to novel situations on the fly, not so much computers.
One approach would be to record 100-to-1000 trials of how human drivers navigate each particular edge case in vehicles instrumented for autonomous driving. Sadly, no one seems to have figured out a way to economically motivate human drivers to do this.
- human drivers in demanding situations
- sensor and compute package planned for autonomous driving
From there it is mostly a question of
1. getting permissions to collect the data
2. finding some efficient way to store -> sift for interesting situations -> magic happens here -> lots of regression testing -> self driving car -> profit!
I would think?
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The "neural network" terminology is really cringeworthy all things considered. ("Deep learning" is much better.)
I dunno. I think "auto-optimization" best describes the process. Even calling it "learning" is kinda overselling itself. There really are only two camps of "Learning": auto-optimization (against a trained dataset), and auto-categorization (aka self-training). Its auto-optimization: the algorithm self-corrects itself to try and look more like the training-set's "ground truth". Or auto-categorization, as the algorithm…
Have you looked into Hubert Dreyfus yet?
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"There's no ball in the middle of the road, a kid pops up from behind a car, you have 0.1 seconds to apply the brakes before you hit the kid." It's just as easy to come up with any sort of contrived situation for which a machine would outperform a human.
Except us humans drive in the middle of the street in residential areas often below the posted speed limit just for this reason. And if we live on this street, we can better anticipate the conditions.
Why?
- They aren't as easily distracted as humans,
- you can program them to stick to the rules even if slowing down results in problems for the passengers
- you can program them to care about any neighborhood, not just the ones we live in