YouTube video streaming now using A.I. that mastered chess and Go

JEREMY KAHN, YouTube video streaming now using A.I. that mastered chess and Go, Fortune, February 11, 2022.

The artificial intelligence algorithm, called MuZero, was developed by YouTube’s London-based sister company within Alphabet, DeepMind, which is dedicated to advanced A.I. research. When applied to YouTube videos, the system has resulted in a 4% reduction on average in the amount of data the video-sharing service needs to stream to users, with no noticeable loss in video quality.

Reinforcement learning

In the case of YouTube’s video compression, Chenjie Gu, one of the DeepMind researchers who worked on the project, said that MuZero often ignored a standard video compression rule of thumb that the bit rate should be maximized for the first frame in a scene and then for a reference frame about 10 frames further into a sequence. MuZero often ignored this, finding that for many video sequences, as long as the bit rate was maximized for one of these two frames, the other did not need much bandwidth, Gu said.

A.I. systems that are trained like MuZero can sometimes fail in surprising ways too. While MuZero works extremely well for complex videos that stump other compression algorithms, it struggles with a simple “slideshow” type of video, Gu said. This is because it doesn’t understand how humans experience video, he said. In a slideshow, what is important to a human viewer are the static images—the “slides”—not the transitions between the slides. But MuZero often allocated more bandwidth to the transition frames because they are more dynamic when compared with the frame sequences before and after, while skimping on the slides themselves, he said. After discovering this flaw, YouTube engineers fixed it, he said, through some hard-coded rules for that kind of video.

 The objective to optimize 

The engineers did this by having MuZero control just one of the compression protocol's metrics, called the quantization parameter, or QP. It determines the bit rate, or number of bits per second of bandwidth, that are allocated for each frame in the video. In general, more complicated scenes require a higher bit rate and more static scenes a lower one in order to maintain an acceptable quality level. To turn this into a gamelike environment, DeepMind converted a series of complicated video quality and bit rate metrics into a single combined score and then had MuZero essentially compete against its own previous attempts to compress the same video. If MuZero beat its previous best combined score, it got a point. If it failed to beat its previous best effort, it scored 0 points.

Julian Schrittwieser et al., MuZero: Mastering Go, chess, shogi and Atari without rules, DeepMind, 23 DEC 2020. (Search MuZero GitHub)