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> what do you do when the data from redundant systems disagree? Self driving car systems' rules aren't directly decided by humans. The system is trained to c

> what do you do when the data from redundant systems disagree? Self driving car systems' rules aren't directly decided by humans. The system is trained to come up with its own rules given sensor input. So ideally, you have more different types of input from diverse sources. Machine learning is a lot easier when you have a diverse dataset. > having more data sources means dealing with more edge cases. Yes, and that's a good thing. You want the system to be able to handle the edge case from the article, and a diverse array of sensors can do that. > a single reliable data source (as in the limitations and biases are well known) is better than two less predictable ones. Can you provide an example of a single vehicle sensor that would be preferable to two others? I can't see why this decision would be made, aside from attempts to lower cost. Two sensors provides more reliability than one.

clairity 9y ago
the idea that more is always better is the common misperception that i'm addressing. if you've ever tried to design a system with multiple, sometimes conflicting inputs, complexity is a real cost to think about (not to mention financial cost). two sensors are often in a master-slave relationship to avoid conflicts, and as catbird mentioned, if you need peering redundancy, then three is much better than two (but that's not perfect either), so that there's essentially error correction built in. machine learning is cool, but it's not a panacea. there are plenty of meticulously trained AI's that fail comically in novel situations (like the example in the original article). afaict, we still don't have a lot of knowledge around the boundary conditions of the machine learning systems themselves.
- unityByFreedom 9y ago
  > the idea that more is always better is the common misperception that i'm addressing It's not a misperception in the case of machine learning. The system can easily discard data that isn't useful at a given point in time. When it is determined to be useful, it's great to have it there. I'd suggest trying out some machine learning yourself to get a good understanding of why more data is better. Tutorials for Kaggle's Titanic competition can be a good primer. > if you've ever tried to design a system with multiple, sometimes conflicting inputs, complexity is a real cost to think about (not to mention financial cost). Not when the system is using machine learning, particularly neural networks. All the self driving car systems are using deep convolutional NNs, so the complexity of more data is helpful, not a hindrance. > machine learning is cool, but it's not a panacea. there are plenty of meticulously trained AI's that fail comically in novel situations (like the example in the original article) The failure in the article was due to lack of diverse sensor input, not because the system was built using machine learning. You're right that machine learning isn't a panacea. The limits aren't well defined. Still, in the case of self driving cars, deep learning is state of the art. Nobody is hand-coding rules for how to drive the car. It's all about having as much good data as you can gather.

clairity 9y ago

the idea that more is always better is the common misperception that i'm addressing. if you've ever tried to design a system with multiple, sometimes conflicting inputs, complexity is a real cost to think about (not to mention financial cost). two sensors are often in a master-slave relationship to avoid conflicts, and as catbird mentioned, if you need peering redundancy, then three is much better than two (but that's not perfect either), so that there's essentially error correction built in. machine learning is cool, but it's not a panacea. there are plenty of meticulously trained AI's that fail comically in novel situations (like the example in the original article). afaict, we still don't have a lot of knowledge around the boundary conditions of the machine learning systems themselves.

unityByFreedom 9y ago
> the idea that more is always better is the common misperception that i'm addressing It's not a misperception in the case of machine learning. The system can easily discard data that isn't useful at a given point in time. When it is determined to be useful, it's great to have it there. I'd suggest trying out some machine learning yourself to get a good understanding of why more data is better. Tutorials for Kaggle's Titanic competition can be a good primer. > if you've ever tried to design a system with multiple, sometimes conflicting inputs, complexity is a real cost to think about (not to mention financial cost). Not when the system is using machine learning, particularly neural networks. All the self driving car systems are using deep convolutional NNs, so the complexity of more data is helpful, not a hindrance. > machine learning is cool, but it's not a panacea. there are plenty of meticulously trained AI's that fail comically in novel situations (like the example in the original article) The failure in the article was due to lack of diverse sensor input, not because the system was built using machine learning. You're right that machine learning isn't a panacea. The limits aren't well defined. Still, in the case of self driving cars, deep learning is state of the art. Nobody is hand-coding rules for how to drive the car. It's all about having as much good data as you can gather.