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> It terrifies me to think of relying on image recognition software to correctly determine (... snip) Do you not drive using only your eyes? If you are not te

> It terrifies me to think of relying on image recognition software to correctly determine (... snip) Do you not drive using only your eyes? If you are not terrified of the sensors, then software? Turing's central belief was that the human brain was 'just' a computer. Regards doing things like embedding reflectors in roads and other ways to simplify lane holding, completely agree. But we can't forego the cameras etc that deal with situations that don't contain reflectors.

PakG1 9y ago
If the brain is just a computer, its machine learning algorithms for image recognition at a busy intersection are believably currently more accurate than any image processing software run on any computer that Tesla puts in its cars.
- kaeluka 9y ago
  There are ways in which current machine learning is better than your brain -- tracking many objects at the same time is one of them. Sure, it's worse in many other ways.
  - lttlrck 9y ago
    The ability to track multiple objects doesn't necessarily mean it's any good at integrating a solution. My camera can track multiple faces and smiles - that doesn't mean it can take better photos.
TeMPOraL 9y ago
The thing is, human "machine vision" has graceful failure modes. You don't switch from seeing cars to seeing nothing to seeing an elephant just because the input got a little noisy. The same cannot be said about current ML demonstrations - because they operate on just vision. Humans continuously reconcile visual input with their model of the world and with other inputs, to the point of overriding visual data if needed. I suppose you could make ML avoid sharp changes in recognition, but I don't trust the current neural-network models to do so reliably.
- aidos 9y ago
  I'm not sure the teslas are going to be trained to see elephants either!
  - asdfgadsfgasfdg 9y ago
    They will need to be if they are to be able to recognise all the potential real world phenomena they might encounter!
  - joeblau 9y ago
    You don't necessarily have to train it to see elephants, but Neural Networks fail in pretty interesting ways. This that are very obvious to you and I have the ability to confuse a CNN[1]. [1] - http://www.evolvingai.org/fooling http://www.evolvingai.org/fooling
    - ReverseCold 9y ago
      Those aren't too bad (out of context of the Tesla article), I can see how the ai might misinterpret those images. The bagel one, for example, is pretty on point.
      - TeMPOraL 9y ago
        Those examples show that ML algorithms "see" things much differently from us. It also shows they're very vulnerable to nonsense data. How can I be reasonably sure that some random play of shadows and lights, that would never fool a human, will cause a failure mode in a neural network in a vision-only self-driving car?
        falcolas 9y ago
        Or worse - paint jobs created specifically to muck with image recognition. Or when the graffiti becomes intentionally malicious (thinking of a variation of Wiley E. Coyote's tricks with paint and solid walls)?
Cybiote 9y ago
We don't drive with just our eyes. We fuse vision, sound, touch and proprioception. We self-locate easily. Most important though, is we do sophisticated visual, theory of mind and physics based inference. If you take the example of Chess, humans and machines play very different styles. Computers replace predictive understanding with excellent memory and fast search. Similarly for cars, we want to replace the lack of a powerful predictive model with some compensatory mechanism, such as sophisticated sensors. The problem is that, even with faster reaction times, better sensors and fuller awareness, these cars' compensatory abilities against a lack of powerful predictive models are still far from sufficient. At this point in time, a car with more sensors and failsafes to augment the human against collision is safer than a self-driving car that depends on a lack of lapses of attention. That depends on proper behavior in the customer. Even dividing tasks, with the human controlling the steering (but allowing the car to takeover when highly certain of danger) and the car controlling acceleration would be safer. This set up acknowledges the human propensity to mind wander when attention is not engaged. Until self-driving cars no longer require human rescue in difficult situations, one can't rightly say that self-driving software are better drivers than humans.
eddyg 9y ago
Is the "computer vision" on Tesla good enough to ignore this false "lane marker" and use the actual yellow lane marker, not the "fake" one caused by the sun/shadow/pavement interaction? http://i.imgur.com/fk3OvEW.jpg http://i.imgur.com/fk3OvEW.jpg This situation generated constant lane departure warnings for the driver.
- rayiner 9y ago
  Here in the southeast, tons of roads have leftover sets of lane markings from when lanes were shifted over. Or no lane markings because of a lag between relaxing and marking lanes.
- arcticfox 9y ago
  Not sure but that's pretty confusing to me too tbh without close inspection. The car in front seems to be treating it as a fog line too.
- Matthias247 9y ago
  I can't speak for Tesla. But I have a newest generation VW with lane assist. And lots of road construction sites in my area, where the normal white lanes markers are "overridden" with some yellow lane markers, which are narrower and often also take different pathes. Once I'm inside a yellow lane and drive straight it halfway works and the lane assist keeps that lane. However on the first position where the lanes depart (e.g. normal white lane goes straight, but yellow lane turns left to change over to other side of the road) the car has not really an idea what to do and mostly follows the wrong lane --> Without manual intervention a crash in these conditions would be guaranteed.
DennisP 9y ago
The human visual cortex is far more powerful than the computers we can put in cars.
intended 9y ago
I really want to see one of these put on the road in India and see how it works. That's the real litmus test for me.
takeda 9y ago
We are far away from hard AI (computer actually can think) some people start to believe we might not be able to reach it. What we currently succeeding is developing soft AI (solving specific problems) the problem is that such AI might do fine when things are clearly labeled, but might not know what to do in unexpected situations. With car stopping might often be correct, but in other situations actually not stopping is best etc. Another thing is that your eyes can last you 100+ years, technology is not that durable and breaks more often, the objectives in cameras get dirty etc. The cameras typically aren't as good as human eye, for example there are reports where Tesla wanted to jam into a truck in front only because its back in the camera couldn't be differentiated from the sky. Yes, relying on cameras only is perfectly fine when you play PS4 game, but when life is on the line you want extra safety checks. Kind of like cars have brakes on every wheel and then also a handbrake. How every car has minimum of 3 stop lights (how often you see that one or more lights are bad in other cars?) Your belief that we have hard AI (and this is kind of indirectly created by companies like Tesla, Google, Amazon, IBM etc) gives you conclusion that just two cameras should be enough in reality we did not made much progress in that area and instead just concentrated on specialized AI that is capable solving specialized problems, that kind of approach absolutely works better with more sensors.

PakG1 9y ago

If the brain is just a computer, its machine learning algorithms for image recognition at a busy intersection are believably currently more accurate than any image processing software run on any computer that Tesla puts in its cars.

kaeluka 9y ago
There are ways in which current machine learning is better than your brain -- tracking many objects at the same time is one of them. Sure, it's worse in many other ways.
- lttlrck 9y ago
  The ability to track multiple objects doesn't necessarily mean it's any good at integrating a solution. My camera can track multiple faces and smiles - that doesn't mean it can take better photos.

TeMPOraL 9y ago

The thing is, human "machine vision" has graceful failure modes. You don't switch from seeing cars to seeing nothing to seeing an elephant just because the input got a little noisy. The same cannot be said about current ML demonstrations - because they operate on just vision. Humans continuously reconcile visual input with their model of the world and with other inputs, to the point of overriding visual data if needed. I suppose you could make ML avoid sharp changes in recognition, but I don't trust the current neural-network models to do so reliably.

aidos 9y ago
I'm not sure the teslas are going to be trained to see elephants either!
- asdfgadsfgasfdg 9y ago
  They will need to be if they are to be able to recognise all the potential real world phenomena they might encounter!
- joeblau 9y ago
  You don't necessarily have to train it to see elephants, but Neural Networks fail in pretty interesting ways. This that are very obvious to you and I have the ability to confuse a CNN[1]. [1] - http://www.evolvingai.org/fooling http://www.evolvingai.org/fooling
  - ReverseCold 9y ago
    Those aren't too bad (out of context of the Tesla article), I can see how the ai might misinterpret those images. The bagel one, for example, is pretty on point.
    - TeMPOraL 9y ago
      Those examples show that ML algorithms "see" things much differently from us. It also shows they're very vulnerable to nonsense data. How can I be reasonably sure that some random play of shadows and lights, that would never fool a human, will cause a failure mode in a neural network in a vision-only self-driving car?
      - falcolas 9y ago
        Or worse - paint jobs created specifically to muck with image recognition. Or when the graffiti becomes intentionally malicious (thinking of a variation of Wiley E. Coyote's tricks with paint and solid walls)?

Cybiote 9y ago

We don't drive with just our eyes. We fuse vision, sound, touch and proprioception. We self-locate easily. Most important though, is we do sophisticated visual, theory of mind and physics based inference. If you take the example of Chess, humans and machines play very different styles. Computers replace predictive understanding with excellent memory and fast search. Similarly for cars, we want to replace the lack of a powerful predictive model with some compensatory mechanism, such as sophisticated sensors. The problem is that, even with faster reaction times, better sensors and fuller awareness, these cars' compensatory abilities against a lack of powerful predictive models are still far from sufficient. At this point in time, a car with more sensors and failsafes to augment the human against collision is safer than a self-driving car that depends on a lack of lapses of attention. That depends on proper behavior in the customer. Even dividing tasks, with the human controlling the steering (but allowing the car to takeover when highly certain of danger) and the car controlling acceleration would be safer. This set up acknowledges the human propensity to mind wander when attention is not engaged. Until self-driving cars no longer require human rescue in difficult situations, one can't rightly say that self-driving software are better drivers than humans.

eddyg 9y ago

Is the "computer vision" on Tesla good enough to ignore this false "lane marker" and use the actual yellow lane marker, not the "fake" one caused by the sun/shadow/pavement interaction? http://i.imgur.com/fk3OvEW.jpg http://i.imgur.com/fk3OvEW.jpg This situation generated constant lane departure warnings for the driver.

rayiner 9y ago
Here in the southeast, tons of roads have leftover sets of lane markings from when lanes were shifted over. Or no lane markings because of a lag between relaxing and marking lanes.
arcticfox 9y ago
Not sure but that's pretty confusing to me too tbh without close inspection. The car in front seems to be treating it as a fog line too.
Matthias247 9y ago
I can't speak for Tesla. But I have a newest generation VW with lane assist. And lots of road construction sites in my area, where the normal white lanes markers are "overridden" with some yellow lane markers, which are narrower and often also take different pathes. Once I'm inside a yellow lane and drive straight it halfway works and the lane assist keeps that lane. However on the first position where the lanes depart (e.g. normal white lane goes straight, but yellow lane turns left to change over to other side of the road) the car has not really an idea what to do and mostly follows the wrong lane --> Without manual intervention a crash in these conditions would be guaranteed.

DennisP 9y ago

The human visual cortex is far more powerful than the computers we can put in cars.

intended 9y ago

I really want to see one of these put on the road in India and see how it works. That's the real litmus test for me.

takeda 9y ago

We are far away from hard AI (computer actually can think) some people start to believe we might not be able to reach it. What we currently succeeding is developing soft AI (solving specific problems) the problem is that such AI might do fine when things are clearly labeled, but might not know what to do in unexpected situations. With car stopping might often be correct, but in other situations actually not stopping is best etc. Another thing is that your eyes can last you 100+ years, technology is not that durable and breaks more often, the objectives in cameras get dirty etc. The cameras typically aren't as good as human eye, for example there are reports where Tesla wanted to jam into a truck in front only because its back in the camera couldn't be differentiated from the sky. Yes, relying on cameras only is perfectly fine when you play PS4 game, but when life is on the line you want extra safety checks. Kind of like cars have brakes on every wheel and then also a handbrake. How every car has minimum of 3 stop lights (how often you see that one or more lights are bad in other cars?) Your belief that we have hard AI (and this is kind of indirectly created by companies like Tesla, Google, Amazon, IBM etc) gives you conclusion that just two cameras should be enough in reality we did not made much progress in that area and instead just concentrated on specialized AI that is capable solving specialized problems, that kind of approach absolutely works better with more sensors.