Fascinating and Powerful Pattern Recognition

This week, a practical application of ML/AI is introduced- Pattern Recognition. Recently, Convolutional Neural Networks (ConvNet), a Deep Learning algorithm, is the most popular way to achieve pattern recognition. In the blog of Karpathy, he also used ConvNet to decide what is a good selfie. According to the classification system in Dougherty’s Pattern Recognition and Classification, Karpathy firstly found a mass database with 500 million images with the hashtag of selfie. Then, for pre-processing, he used another ConvNet to label 200 million images that contain at least one face. He input the standards of a good selfie (can we say the standard he put in is a hyper-parameter?) to extract features/kernels. It is worth noting that, in the experiment, the standard he took, which I was skeptical about before I read the article, is fair-ranking with certain weights for the audience, likers, and followers (it would be potentially useful for influencers on social media; and since time may be a critical influence factor, can we also add it into the standard?).  After those steps, he got a sufficient dataset to train his ConvNet model with Caffe, a deep learning framework. The model then processed the dataset in its hidden layers to give the classification results.

This experiment demonstrates the word from Crash Course #35- “abstraction is the key to building complex systems, and the same is true in computer vision.” The abstraction in the experiment is complicated since there are too many features in a photo to consider, and that’s really fascinating. Since, according to Dougherty, document recognition is also a part of pattern recognition, I am wondering how the algorithm could tell the difference in the translation process between characters in Japanese and characters in Chinese without their codes (for example, “学生” means “student” in both Chinese and Japanese)? I know the pattern recognition algorithm is context-sensitive, but does it mean the translating algorithm need to train with both Chinese and Japanese dataset?