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Wrong. They can do a second take. You should just code it in.
by doombolt 8y ago
Wrong. They can do a second take. You should just code it in.
- majos 8y agoIs this sarcasm? The point of the second take is that humans often know they're confused and will go back to think about the image and remedy the confusion, whereas having a neural network just look at the image again isn't going to do anything (plus, existing architectures don't seem to have any capacity to say "I'm confused" anyway).
- antpls 8y agoParent is not sarcastic. Automated recognition systems are systems, which means they are made of several components (sensors, databases, hardwares, softwares) working together. Neural networks are one of those sub-components and no one ever claimed that neural networks are 100% accurate and sufficient to build automated decision systems. The problem described in the article is taken into account when building systems (like autonomous cars) using neural networks.
- sorokod 8y agoThe problem described in the article is taken into account when building systems (like autonomous cars) using neural networks. Where can I find more information on this?
- antpls 8y agoThis is a vast engineering field studied before the existence of neural networks. Keywords are "control theory", "sensor fusion", "automated decision under uncertainty" that you can look on Google, Wikipedia and arXiv. Also "Simultaneous localization and mapping" which is a good example of using uncertain data points from different sensors to build a representation of the reality. In those systems, a neural network is just another sensor providing augmented information.
- sorokod 8y agoThank you, I will have a look. Just out of curiosity, what do you mean by 'uncertain' in this context?
- Qworg 8y agoErrors in precision and accuracy.
- sorokod 8y agoHmm... there is no doubt about the sensor input, the elephant is there. The issue is with precision and accuracy of the model itself. Edit: I need to rephrase, the interesting case for me ( and the one the article is describing) is when the model fails with accurate input data due to assumptions that are intrinsic to the model itself.
- Qworg 8y agoThere are doubts in both, frankly.
- unstuckdev 8y agoOptical sensors don't have anywhere near the dynamic range of a retina. I think poor low light performance has been implicated in at least one self-driving crash. Eyes can at least render objects and shapes in extreme low light even if the brain doesn't have enough information to identify them. The tiny sensors in a camera compact enough to be practical still have a long way to go.