CSAIL’s machine-learning system enables smoother streaming that can better adapt to different network conditions. We’ve all experienced two hugely frustrating things on YouTube: our video either suddenly gets pixelated, or it stops entirely to rebuffer. Both happen because of special algorithms that break videos into small chunks that load as you go. If your internet is slow, YouTube might make the next few seconds of video lower resolution to make sure you can still watch uninterrupted — hence, the pixelation. If you try to skip ahead to a part of the video that hasn’t loaded yet, your video has to stall in order to buffer that part. YouTube uses these adaptive bitrate (ABR) algorithms to try to give users a more consistent viewing experience. They also save bandwidth: People usually don’t watch videos all the way through, and so, with literally 1 billion hours of video streamed every day, it would be a big waste of resources to buffer thousands of long videos for all users at all times. While ABR algorithms have generally gotten the job done, viewer expectations for streaming video keep inflating, and often aren’t met when sites like Netflix and YouTube have to make imperfect trade-offs between things like the quality of the video versus how often it has to rebuffer. “Studies show that users abandon video sessions if the quality is too low, leading to major losses in ad revenue for content providers,” says MIT Professor Mohammad Alizadeh. “Sites constantly have to be looking for new ways to innovate.” Along those lines, Alizadeh and his team at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed “Pensieve,” an artificial intelligence (AI) system that uses machine learning to pick different algorithms depending on network conditions. In doing so, it has been shown to deliver a higher-quality streaming experience with less rebuffering than existing systems.”

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