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It's not the cameras that worry me, it's the whole issue that AI is not remotely intelligent. Image recognition is step 1, and NNs are pretty good at it, but ca

It's not the cameras that worry me, it's the whole issue that AI is not remotely intelligent. Image recognition is step 1, and NNs are pretty good at it, but can still be fooled. However taking those images, properly interpreting them with no error, keeping track of temporary occluded objects, unusual circumstances, and then making decisions is not something I trust NNs to do, as there just is not enough training data, despite the valiant efforts to gather it. One difference between true intelligence and what we have in NNs today is, if you showed an image or two of an object to a person they could identify that object in other orientations and lighting conditions pretty well. To do the same with NNs requires thousands of examples and counter examples to train. This is why with driving how could you have enough examples of strange issues to ensure the car responds safely? The only advantage I see today for cars driving is they don't get bored or distracted or drunk, but they certainly are not intelligent.

md2020 5y ago
> if you showed an image or two of an object to a person they could identify that object in other orientations and lighting conditions pretty well. To do the same with NNs requires thousands of examples and counter examples to train. There’s work from 2019 on gauge equivariant NNs that looks to solve this, there’s a paper and explainer video from Qualcomm Research about it. Also, Tesla presumably does have thousands of examples for most driving scenarios. There’s also lots of work in the ML community on generalization—that’s arguably what all the research is about in some way or another. I don’t intend to be flippant, but I would bet that these are issues that the researchers at Tesla are well aware of.
- tails4e 5y ago
  I agree, if an amateur like me can articulate an issue I've no doubt the experts are well aware of it. However it does not change the fact that there is no real intelligence, it's fancy pattern matching that works when given enough examples, and so I'd question what happens for edge cases is has not 'seen' before.

md2020 5y ago

> if you showed an image or two of an object to a person they could identify that object in other orientations and lighting conditions pretty well. To do the same with NNs requires thousands of examples and counter examples to train. There’s work from 2019 on gauge equivariant NNs that looks to solve this, there’s a paper and explainer video from Qualcomm Research about it. Also, Tesla presumably does have thousands of examples for most driving scenarios. There’s also lots of work in the ML community on generalization—that’s arguably what all the research is about in some way or another. I don’t intend to be flippant, but I would bet that these are issues that the researchers at Tesla are well aware of.

tails4e 5y ago
I agree, if an amateur like me can articulate an issue I've no doubt the experts are well aware of it. However it does not change the fact that there is no real intelligence, it's fancy pattern matching that works when given enough examples, and so I'd question what happens for edge cases is has not 'seen' before.