Where's my automatic lawn mower? Where's my robot to unload the dishwasher and put everything away?
We need to find other consumer technology to use AI in before we risk our lives with self driving cars.
Google can't even make GPS mapping work perfectly - it's apparent if you use it for a day. Not to mention that AI vision systems are susceptible to adversarial attacks, which means they can be hacked by external images. This paper just came out yesterday: "Breaking 7/8 of the ICLR 2018 adversarial example defenses" (https://arxiv.org/abs/1802.00420). The situation is dire.
I think they are afraid that drawing a couple of lines with a marker on a street sign might make it invisible, or turn it into another sign. They can't defend against such attacks because the neural net doesn't actually understand the world, it just observes patterns. Understanding requires causal reasoning.
That's like saying a human driver can be blinded with a bright light so the situation is dire.
New world, old rules. You'll still go to jail for manslaughter (or whatever the charge would be).
Adversarial attacks are concerning for any AI model, but autonomous vehicles aren't all AI. Lots of rules and heuristics and pre-knowledge are utilized. In the end if something is uncertain it generally amounts to "slam on the brakes" which is more a ux problem than a safety problem.
Mapping the world is hard because it is complex and evolve way faster than a Google Car army can patrol it for a reasonable cost. More often than not OpenStreetMap which is a community project is more up to date than commercial map, but it’s not precise enough. Beside nobody would bet there life on something that is Wikipedia-like editable by ill-intentioned trolls.
This is precisely why all autonomous car rely heavily on computer vision and why adversial attacks should be carefully considered.
Uncertainty will indeed « slam on brake » but adversial attack don’t aim to generate uncertainty the aim to generate false certitudes.
It's true these maps don't exist for the whole world yet but it's also why all sdc companies are launching in metros. They're building them.
Vision is good at recognition but bad at localization. This is the real problem. In robotics a fundamental problem is SLAM. You should read about it.
I know enough people that drive terribly to see the obvious benefits to autonomous driving. It doesn't need to be perfect to have a net-positive effect on road safety, it just needs to be better than a human on average. And based on the evidence, it seems they're doing a damn good job of achieving that goal.
(I'm referring to devices/products such as the Roomba.)
Cars make sharp turns and get into tight spaces on unmarked, uneven surfaces, with pedestrians walking everywhere around the car.
That's what a self-driving car needs to support. All road rules out of the window, tiny roads you canbarely navigate, and due to a festival everything is redirected around and all your maps are useless, you're told to drive a short bit through a park because there's a parade on the road, the only way to tell where you can and can't drive being a nonstandard sign and a police officer telling you.
This is a real life use case of driving, as given in many European towns. For additional difficulty, do a small Weihnachtsmarkt in a rural place, where you'll have the same, but with so much snow you can barely see anything.
There is a physical challenge arising from the form factor, such that your intelligence won't help. Those robots don't do stairs and tight places very well simply because the hardware is that way.
In driving, the robot has the same controls as the human. It can turn the wheels, brake, apply gas. That basic playing field is level.
It's all about how well you make the AI see and understand, and translate that into turning the wheel, changing forward/reverse, and hitting gas/brakes.
Robovacuums seem to have hit a sweet spot - they do just enough on their own to justify their costs by saving you time and effort. Solving the corner cases you mentioned is possible, but there is not a strong enough economic incentive for that.
That's how we got our century of development. Testing, verifying, refinement. It wasn't by pushing out new stuff to a batch of users and seeing if everything worked out allright.
Other than that, Mrs. Lincoln, how was the play?
Just recently it was discovered a series of air bags manufactured in a small window of a couple of days, would instead of protecting the occupants, would emit high-speed shrapnel into their chest and face killing them. Quite an oversight.
If your airbag fires you’re already in an accident and might die anyway. You’ve already accepted that risk. There are probably better recall examples but I’m not sure it’d disprove the underlying point though.
If the automakers can't catch problems in a simple system like this, just imagine what sort of new and exciting failures we'll see in an inadequately-tested car autopilot! These things need to be very thoroughly tested before we allow lots of them on the road, and that needs to happen before they omit the steering wheel.
[1] http://www.automotive-fleet.com/channel/safety-accident-mana...
[2] https://www.autoblog.com/2015/10/30/fca-recalls-894k-total-v...
Switching from a human driver to software automated driving is a much bigger change in automobiles than has ever come before. It's also one with a much larger reasonable test surface, by orders of magnitude. Systemic risk? How about meltdown and spectre vulnerabilities? The failure mode of most new hardware in cars requires either edge cases or corner cases to result in the worst case scenario of a crash at speed. And yet that's a very common failure mode in autonomous driving. You can't realistically have a car that can autonomously drive itself 90% or even 99% of the time, it must be nearly exactly 100% of the time to be worthwhile and safe.
https://www.husqvarna.com/au/products/robotic-lawn-mowers/au...