![]() ![]() “A video is a series of still frames and each still frame is like a step in the game,” he said. Murdoch said that his team had an intuition that video compression could be converted into a kind of gamelike environment to which MuZero could then be applied. In this way, MuZero was able to master chess, Go, the Japanese strategy game Shogi, and a host of classic Atari video games. MuZero learns entirely by playing games against itself and gradually discovering the rules of the game and effective tactics and strategies. system could learn to play at superhuman level almost any game in which players have complete information about the status of the game and that it could do so starting from zero knowledge about how to play, including not knowing the rules. After discovering this flaw, YouTube engineers fixed it, he said, through some hard-coded rules for that kind of video.ĭeepMind originally created MuZero in 2019 to show that an A.I. 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. In a slideshow, what is important to a human viewer are the static images-the “slides”-not the transitions between the slides. ![]() This is because it doesn’t understand how humans experience video, he said. While MuZero works extremely well for complex videos that stump other compression algorithms, it struggles with a simple “slideshow” type of video, Gu said. systems that are trained like MuZero can sometimes fail in surprising ways too. 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. 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. This can make it difficult, at first, for humans to trust these A.I. is not given any past examples of effective strategies, and therefore must learn entirely through its own experience.Īlgorithms trained in this way have the advantage of being able to figure out completely novel tactics that can surpass what humans have been able to do-but they often do so in ways that strike human experts as counterintuitive or alien. ![]() training, the software is given an objective to try to achieve, and it is given feedback on whether its decisions are getting it closer to achieving that objective. algorithm that learns completely by trial and error, a method called reinforcement learning. ![]() algorithms it had developed, representing an increase of more than $760 million on the prior year’s figure. In 2020, the last year for which figures are available, DeepMind said it was paid more than $1.1 billion from other Alphabet companies for use of A.I. Colin Murdoch, DeepMind’s chief business officer, who leads the company’s commercial collaborations, declined to say how much YouTube paid DeepMind for adapting MuZero for YouTube’s video compression. DeepMind is paid royalty fees by Google for the use of its technology. DeepMind, which has said it will not work on military applications of A.I., did not participate in that project. Air Force said it had used an open-source version of the software to control the radar systems on a modified U2 spy plane during a simulated strike on an enemy airbase. This is the first full-scale business application for MuZero, although the algorithm has been used in other real-world contexts. system specifically works to improve on an open-source video compression method called VP9 that is widely used by YouTube, although some of its content is compressed using other protocols. The system is now in active use across most, but not all, of the videos on YouTube, Zhernov said. ![]()
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