The imagenet competition is a great concrete example. This is a massive dataset of images and the competition is to identify correctly the object in the image (truck, person, etc). The results the past few years look something like this: https://lh4.googleusercontent.com/0FLG_HLy-dHk5kPFIocPMHn5jc...
Prior to 2012, this was done using traditional computer vision algorithms with hand picked features. The accuracy was often around 30-40%, until the first deep learning model beat the 2nd place competitor by near half the error. Today, almost every single competitor is deep learning based and accuracy is equal to human classifiers.
Since deep learning has so many applications, it's hard to generalize what the "edge" is against all other approaches. In my opinion, other approaches often require handpicking features (where a ANN learns them) and struggle to recognize patterns in many areas (where a ANN does).
Thanks! This is exactly the kind of comparison I'm looking for (other fields are still welcome).
If I look at the link you provide, I see an improvement which is great. However, the improvement seems to be somewhat linear year-on-year. In the original post, the first sentence is: "Some think the excitement around Artificial Intelligence is overhyped. They might be right. But if they’re wrong, we’re on the precipice of something really big. We can’t afford to ignore what might be the biggest technological leap since the Internet." Am I right in assuming that there is the expectation of AI/ML to have "hockey stick" growth in the (not-so distant) future? If so, what kind of methods could possibly make this real? If anyone could give me some papers, an essay or some words to search for this would help me a lot. Essentially, I am intrigued why there is this expectation of hockey stick growth. If my premise is wrong, please enlighten me too.
It's a family of new techniques that has had great success doing things that are usually very hard or tedious to do with computers. These techniques seem more similar to how a human would do things than most other ways of programming things. This is the reason for a lot of the interest and hype.
Prior to 2012, this was done using traditional computer vision algorithms with hand picked features. The accuracy was often around 30-40%, until the first deep learning model beat the 2nd place competitor by near half the error. Today, almost every single competitor is deep learning based and accuracy is equal to human classifiers.
Since deep learning has so many applications, it's hard to generalize what the "edge" is against all other approaches. In my opinion, other approaches often require handpicking features (where a ANN learns them) and struggle to recognize patterns in many areas (where a ANN does).