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This paper discusses how some artificial intelligence (AI) researchers and search experts are using AI methods to try to improve the accuracy of video search results. One example is a University of Oxford project in which researchers use statistical machine learning, specifically computer vision methods for face detection and facial feature localization, to provide automatic annotation of video with information about all the content of the video. Another example is the video search engine from Blinkx that objectively analyzes video content using speech recognition and matches the spoken words to context gleaned from a massive database. Finally, researchers at Dartmouth University are working on a technology that shows whether images or video clips have been doctored. This technique uses support vector machines to differentiate computer-generated images from photographic images. The paper goes on to discuss computer Go programs. Go is an ancient Asian board game which has become a challenge for AI researchers around the world. Go is resistant to Deep Blue´s brute-force search of the game tree; the number of possible moves is too large. This inspires researchers to develop hybrid methods combining different methods and algorithms