AI-ML YouTube case study
How YouTube uses AI-ML
Nowadays Artificial Intelligence and Machine Learning are heavily used to make many things easier when it comes to using technology of any kind. Likewise YouTube uses lots of this part to make it powerful platform.
YouTube Recommendations (Deep Neural Networks):
YouTube represents one of the largest scale and most sophisticated industrial recommendation systems in existence. It uses deep learning -neural networks to recommend most relative videos based on user history and context
YouTube Algorithm:
To be clear we can actually say that people watch content what YouTube tells them to. As most viewings on YouTube occur as a result of the platform’s recommendations.
YouTube changed its algorithm in 2012 after the major clickbait issue, this time reaping the length of time — watch time — and session time (all the time spent on the platform). Soon the “gamers” learned how to beat the system was to create long, downloadable videos, meeting their basic foundation, but in a spectacular and rowing way.
As with most AI systems, the YouTube AI is sophisticated, and YouTube has released only limited information about it. They did publish a white paper in 2016. From that we can see that it uses AI to track viewers’ perceived satisfaction to create an addictive, personalized stream of recommendations, i.e., it works to determine how satisfied/happy a viewer is with each video they play, and then tailor future recommendations to try and increase this level of satisfaction.
Finding offensive content:
Machine learning technology plays a big role in restricting offensive content. According to YouTube, machines rather than humans flagged up more than 83% of the now-deleted videos for review. And more than three quarters of those videos were taken down before they got any views. The majority were spam or porn. Machine learning or AI, as the tech industry often likes to call it involves training algorithms on data so that they become able to spot patterns and take actions by themselves, without human intervention. In this case, YouTube uses the technology to automatically spot objectionable content.
