The Amazing Ways
YouTube Uses AI
and Machine Learning

© 2019 Bernard Marr, Bernard Marr & Co. All rights reserved
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IntroductionIntroduction
There are more than 1.9 billion users logged in to YouTube every single month
who watch over a billion hours of video every day. Every minute, creators
upload 300 hours of video to the platform. With this number of users, activity,
and content, it makes sense for YouTube to take advantage of the power of
artificial intelligence (AI) to help operations. Here are a few ways YouTube,
owned by Google, uses artificial intelligence today.
The Amazing Ways YouTube Uses Artificial
Intelligence And Machine Learning

© 2019 Bernard Marr, Bernard Marr & Co. All rights reserved
Automatically Remove
Objectionable Content
In the first quarter of this year, 8.3 million videos were removed from YouTube, and 76%
were automatically identified and flagged by artificial intelligence classifiers. More than
70% of these were identified before there were any views by users. While the algorithms
are not foolproof, they are combing through content much more quickly than if humans
were trying to monitor the platform singlehandedly. In some cases, the algorithm pulled
down newsworthy videos mistakenly seeing them as “violent extremism.” This is just one
of the reasons Google has full-time human specialists employed to work with AI to
address violative content.
In fact, according to Cecile Frot-Coutaz, head of EMEA, YouTube’s “number one priority” is
to protect its users from harmful content. In pursuit of that, the company invested in not
only human specialists but the machine learning technology to support the effort. AI has
contributed greatly to YouTube's ability to quickly identify objectionable content. Before
using artificial intelligence, only 8% of videos containing "violent extremism" (banned on
the platform) were flagged and removed before ten views had occurred; but after machine
learning was used, more than half of the videos removed had fewer than ten views.

© 2019 Bernard Marr, Bernard Marr & Co. All rights reserved
Automatically Remove
Objectionable Content
One of the main drivers for YouTube's diligence in removing objectionable content is the
pressure from brands, agencies, and governments and the backlash that's experienced if
ads appear alongside offensive videos. When ads started appearing next to YouTube
videos supporting racism and terrorism, Havas UK and other brands began pulling their
advertising dollars. In response, YouTube deployed advanced machine learning and
partnered with third-party companies to help provide transparency to advertising
partners.
The company also has a “trashy video classifier” in use which scans YouTube’s homepage
and “watch next” panels. It looks at the feedback from viewers who might report a
misleading title, inappropriate or other objectionable content.

© 2019 Bernard Marr, Bernard Marr & Co. All rights reserved
New Effects on Videos
Move over Snapchat, Google’s artificial intelligence researchers trained a neural network
to be able to swap out backgrounds on videos without the need for specialized
equipment. While it's been possible to do this for decades—think green screens that are
replaced by digital effects—it was a complicated and time-consuming process. The
researchers trained an algorithm with carefully labelled imagery that allowed the
algorithm to learn patterns, and the result is a fast system that can keep up with video.

© 2019 Bernard Marr, Bernard Marr & Co. All rights reserved
“Up Next” Feature
If you have ever used YouTube’s “Up Next” feature, you benefited from the platform’s
artificial intelligence. Since the dataset on YouTube is constantly changing as its users
upload hours of video every minute, the AI required to power its recommendation engine
needed to be different than the recommendation engines of Netflix or Spotify. It had to be
able to handle real-time recommendations while new data is constantly added by users.
The solution they came up with is a two-part system. The first is candidate generation,
where the algorithm assesses the YouTube history of the user. The second part is the
ranking system that assigns a score to each video.
Guillaume Chaslot, a former Google employee and founder of an initiative urging greater
transparency known as AlgoTransparency, explained that the metric used by YouTube’s
algorithm to determine a successful recommendation is watch time. This is good for the
platform and the advertisers, but not so good for the users, he said. This situation could
amplify videos that have outlandish content, and the more people spend time watching it,
the more it gets recommended.

© 2019 Bernard Marr, Bernard Marr & Co. All rights reserved
Training on Depth Prediction
With so much data, YouTube videos provide a fertile training ground for artificial
intelligence algorithms. Google AI researchers used more than 2,000 "mannequin
challenge" videos posted on the platform to create an AI model with the ability to discern
the depth of field in videos. The "mannequin challenge" had groups of people in a video
stand still as if frozen while one person goes through the scene shooting the video.
Ultimately, this skill of depth prediction could help propel the development of augmented
reality experiences.
With the continuing crisis of mass shootings plaguing America, President Trump
requested that social media companies, "develop tools that can detect mass shooters
before they strike." With the assistance of artificial intelligence, YouTube, Twitter, and
Facebook already work to delete terrorist content, but what's new in the President's
request is that they work with the Department of Justice and law enforcement agencies.
There are many questions about how such a partnership would work, if social media
channels could detect actual terrorists before they act and the potential to impact the civil
liberties of innocent people. Whether YouTube and other social media companies could
use artificial intelligence to stop terrorists while not infringing on the rights of others
remains to be seen.

© 2017 Bernard Marr , Bernard Marr & Co. All rights reserved
© 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a
strategic business & technology advisor to governments and companies. He helps
organisations improve their business performance, use data more intelligently, and
understand the implications of new technologies such as artificial intelligence, big data,
blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent
contributor to the World Economic Forum and writes a regular column for Forbes. Every day
Bernard actively engages his 1.5 million social media followers and shares content that
reaches millions of readers.
Visit The
Website
© 2017 Bernard Marr , Bernard Marr & Co. All rights reserved
© 2019 Bernard Marr, Bernard Marr & Co. All rights reserved
Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a
strategic business & technology advisor to governments and companies. He helps
organisations improve their business performance, use data more intelligently, and
understand the implications of new technologies such as artificial intelligence, big data,
blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent
contributor to the World Economic Forum and writes a regular column for Forbes. Every day
Bernard actively engages his 1.5 million social media followers and shares content that
reaches millions of readers.
Visit The
Website

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© 2018 Bernard Marr, Bernard Marr & Co. All rights reserved

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