I wanted to combine as many AI technologies as I could think of. Videos offer a powerful opportunity as they offer up both audio, which can be transcribed to text, and analyzed and indexed by natural language processing (NLP) algorithms, and frames, which often contain a multiplicity of objects, and can be analyzed and indexed by computer vision (CV) algorithms. I actually had this idea last March, though I did not know how to implement all the algorithms at that time; since then, I've learned much. On the NLP side, there are algorithms for keyword extraction (graphical model), named entity extraction (nltk chunking), topic modeling (LDA with gensim), and summarization (lexrank). On the CV side, there is a neural network (built on Torch7) trained on the Cifar10 dataset, achieving 57% accuracy (not bad for about an hour of training on a laptop), and a facial recognition system (PCA+SVM, built on scikit-learn). The NLP systems work much better than the CV systems. The data is stored in MongoDB and there's a simple web interface built with Flask.

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