4 ms·

"it moves at the speed a car moves at, it's in the road, it's overtaking on the right hand lane" is video classification, which is not solved. In fact, at least

"it moves at the speed a car moves at, it's in the road, it's overtaking on the right hand lane" is video classification, which is not solved. In fact, at least how you described it (you could probably change the problem statement to avoid this), this would involve an ML model that must learn a model of physics - also unsolved. You've just specified a manually hardcoded set of decision rules. This is not machine learning, and is incredibly brittle.

georgespencer 10y ago
I think we're talking across one another. I had thought that in your original post you were agnostic about the methodology for identifying a car, but were remarking that, in a world where it's possible to do it using whatever form of classification, it would be possible to 'stump' any reliable model by modifying the appearance of a car. I'm observing that any model for classification almost certainly would not rely on aesthetics. > You've just specified a manually hardcoded set of decision rules. This is not machine learning, and is incredibly brittle. I'm pointing this out to illustrate that the technology already deployed to solve this problem does not get confused by aesthetics.
- argonaut 10y ago
  I was talking about deep learning. The comment I was replying to was making the specific problem seem as if it were easy. Certainly there may one day be a classification technique that does what you say will do. But you may as well have said there will one day be a perfect classification technique that will just perfectly output steering angles, end thread. What use is there in conjecturing about perfect unknown classification techniques? Not to mention that there is no guarantee such a perfect method would not rely on aesthetics. Even if the train set has more than just aesthetics (e.g. video of cars in motion) maybe this perfect classifier would just cheat and rely on aesthetics, you don't know. So I'm pointing out the methodology you suggested is not currently feasible, or is currently widely considered by the community to be the wrong practical approach. Because theoretical solutions will not solve self driving cars.

georgespencer 10y ago

I think we're talking across one another. I had thought that in your original post you were agnostic about the methodology for identifying a car, but were remarking that, in a world where it's possible to do it using whatever form of classification, it would be possible to 'stump' any reliable model by modifying the appearance of a car. I'm observing that any model for classification almost certainly would not rely on aesthetics. > You've just specified a manually hardcoded set of decision rules. This is not machine learning, and is incredibly brittle. I'm pointing this out to illustrate that the technology already deployed to solve this problem does not get confused by aesthetics.

argonaut 10y ago
I was talking about deep learning. The comment I was replying to was making the specific problem seem as if it were easy. Certainly there may one day be a classification technique that does what you say will do. But you may as well have said there will one day be a perfect classification technique that will just perfectly output steering angles, end thread. What use is there in conjecturing about perfect unknown classification techniques? Not to mention that there is no guarantee such a perfect method would not rely on aesthetics. Even if the train set has more than just aesthetics (e.g. video of cars in motion) maybe this perfect classifier would just cheat and rely on aesthetics, you don't know. So I'm pointing out the methodology you suggested is not currently feasible, or is currently widely considered by the community to be the wrong practical approach. Because theoretical solutions will not solve self driving cars.