YouTube is the home of the modern world. It holds stories, memories, and activities of millions of people around the globe. Views on youtube is also their motivation and source of entertainment. YouTube is an escape and a way to get into the real world. YouTube has over a billion users who log in daily to view and stream more than one billion hours of video daily. This creates an endless amount of chaos and activity daily.

With many users, uploaded content, and engaging activities, Artificial Intelligence is a powerful tool for YouTube to streamline its activities and aid in its efforts to improve its platform. It has relied more heavily on Artificial Intelligence since the emergence of COVID-19. However, YouTube staff are now restricted to their homes for safety reasons.

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YouTube uses Artificial Intelligence.

Here are some examples of how YouTube uses artificial intelligence today:

Fake news: How to deal

YouTube and other social media platforms such as Facebook and Twitter have been trying to combat fake news and misinformation in recent years. Unlike other social media platforms flag fake content, YouTube has adopted artificial intelligence to stop such offensive content.

YouTube turned to AI during the initial stages of the COVID19 pandemic to get rid of approximately 11 million videos. According to the latest Community Guidelines Enforcement Report by YouTube, this is the most videos it has been able to thwart within a quarter. However, this report was released in the second quarter of 2020. The efforts of AI moderators helped to flag around 10.8 million videos from the platform’s 11.4 million video deletions in the second quarter. YouTube’s policies have enabled automated systems to remove any harmful content.

Test AI-generated video chapters

YouTube has just begun testing machine learning, allowing users to add chapters to their videos instantly.

The platform made the trial on YouTube’s test features and experiment page made available.

“We want people to be able to navigate video chapters easily, so we’re experimenting with adding video chapters automatically (so creators don’t have to add timestamps manually). However, Machine learning will be used to recognize text and automatically generate video chapters. This is a test with a limited number of videos.

This system can be used to add video chapters to your videos. It was launched by the platform in 2020.

Creators can now divide videos into sections by using the feature. However, viewers can then jump directly to the section they want to see.

The platform claims that enabling chapters allows viewers to stream more video and increases their chances of returning. The creators of videos are currently required to add timestamps to the descriptions manually. Automated generation of chapters would greatly reduce their time and effort.

Unfit content is automatically blocked.

YouTube’s determination to tackle inappropriate and disgusting content has been fueled by the pressure and disapproval faced by the government, brands, and agencies. Advertisements appearing alongside offensive content were met with backlash. This was when advertisements were shown alongside videos propagating racism and terrorism. Havas UK and other brands pulled their advertising budgets. YouTube responded by using advanced machine learning and collaborating with third-party organizations to improve transparency for its advertising partners.

While the platform’s algorithms may not always be perfect or accurate, they go through content faster than humans can. This is why Google employs full-time human specialists to work alongside AI to address violative content.

YouTube also has a “trashy” video classifier that scans YouTube’s homepage and “watch next” panels.

Video effects: New effects

Although switching backgrounds on videos have been possible for many years, it was slow and complicated. Google’s AI researchers developed a neural network to swap backgrounds between videos without needing special equipment. The algorithm was trained with carefully labeled imagery that allows it to absorb patterns. This results in a fast system that can keep up with the video.

“Up Next” feature

YouTube’s “Up Next” feature is one example of artificial intelligence on the platform. YouTube’s data constantly changes, with users uploading new videos every minute. The platform’s AI had to be distinct from recommendation engines on platforms like Spotify or Netflix. This was because the platform needed to provide real-time suggestions and allow users to add new data.

The platform came up with a solution that is a two-part solution. The first part of this system would be the candidate generation. This is where the algorithm analyzes the history and activity of each user. The ranking system is the second. This system assigns a score for every video.

YouTube’s recommendation system is based on the watch history and the Ranking system.

YouTube’s recommendation system focuses on specific areas.

The algorithm significantly impacts the user’s YouTube homepage, trending videos, notifications, and subscriptions. click here

Guillaume Chaslot (a former Google employee now the founder of AlgoTransparency) stated that YouTube’s algorithm is based primarily on the amount of time viewed. This metric, he said, is beneficial to the platform and its advertisers but not for its users. It could help popularize videos with disapproving content and increase their recommendation.

Researchers use this method of working to target a specific issue. They call it “implicit bias.” This bias refers to how recommendations can impact users’ behavior. This can lead to the system’s adverse effects over time and steer users away from the content they want to stream.

Training in-depth prediction

YouTube videos provide a rich training ground for AI algorithms thanks to their wealth of data. Google AI researchers adopted more than 2,000 videos from “mannequin challenges” to develop an AI model that can measure the depth of field in videos.

YouTube recently announced that it would adopt an advanced AI to prevent children from streaming videos for mature audiences.

The platform has asked its creators to flag videos with age restrictions. However, it adopted the algorithm to flag videos in extreme cases. The platform now plans to use a similar machine learning algorithm to help focus on the most suitable videos for specific age groups.

The platform currently has a children’s application for the under-13 age group. However, the platform’s flagged content includes age gates that allow users to block extremist content. The platform introduced machine learning technology in 2017 to eliminate such content. The platform plans to use a similar technology to determine videos suitable for mature audiences.


These are just a few ways YouTube has used Artificial Intelligence to streamline its processes and tasks. The platform’s development and influence have been made possible by artificial intelligence.

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