> 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

> 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.