How You Tube uses Artificial Intelligence And Machine Learning
ARTIFICIAL INTELLIGENCE:
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.
Artificial intelligence is based on the principle that human intelligence can be defined in a way that a machine can easily mimic it and execute tasks, from the most simple to those that are even more complex. The goals of artificial intelligence include learning, reasoning, and perception.
AI is continuously evolving to benefit many different industries. Machines are wired using a cross-disciplinary approach based in mathematics, computer science, linguistics, psychology, and more.
MACHINE LEARNING:
Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.
How You Tube uses AI and ML:
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, 300 hours of videos are uploaded 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.
Automatically remove objectionable content:
In the first quarter of 2019, 8.3 million videos were removed from YouTube, and 76% were automatically identified and flagged by AI 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. Here i am going to explain how YouTube, owned by Google, uses AI.
In the first quarter of 2020, 49.9% of the videos that were removed from youtube, had 0 views, 29.4% of videos had only 1–10 views and 22.7% of the videos had more than 10 views. AI is the one who made this possible. Among the removed videos, 1021380 videos belonged to United States, 826661 videos were from India, 484536 videos from Brazil and many other countries are there.
The above Bar Graph is showing number of removed videos country wise
YouTube’s top 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” 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.
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. Google is working with third-party companies to make sure YouTube content is safe for brands while also deploying advanced machine learning to better identify content that might be deemed offensive to viewers and advertisers.
YouTube has been a target of criticism in the past for not taking necessary steps to stop this trash videos trend on its platform. Google has installed an AI (Artificial Intelligence) software that examines tons of videos on its own and blocks videos from the home page of website and home screen of the app, which looks troubling for the platform. According to people working with the project, this Artificial Intelligence software is known as “trashy video classifier”. This system plays an essential role in attracting and holding the visitors on the homepage of YouTube. Despite being so significant, the company hasn’t reported this trashy video classifier before. The AI examines the feedback from users who report videos that are with a misleading title, misleading thumbnail and inappropriate videos.
New effects on videos:
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. The researchers trained an algorithm with carefully labeled imagery that allowed the algorithm to learn patterns, and the result is a fast system that can keep up with video.
How does the YouTube algorithm work in 2020?
According to YouTube, the algorithm is basically a “real-time feedback loop that tailors videos to each viewer’s different interests.” It decides which videos will get suggested to individual users.
The algorithm’s goals are twofold: find the right video for each viewer, and get viewers to keep watching. Therefore, the algorithm is watching user behavior as closely as it watches video performance.
The two most important places the algorithm impacts are search results and recommendation streams.
How the YouTube algorithm influences search results:
Unsurprisingly, the videos you get when you search “carnivorous house plants” will be different from the videos I get when I search “carnivorous house plants.” Search results are based on factors like:
- Your video’s metadata (title, description, keywords) and how well those match the user’s query
- Your video’s engagement (likes, comments, watch time)
How the YouTube algorithm influences recommended videos:
The recommendation stream is a two-fold process for the algorithm.
First, it ranks videos by assigning them a score based on performance analytics data. (Scroll down for our list of all known factors.)
Second, it matches videos to people based on their watch history, and what similar people have watched.
The idea is not to identify “good” videos, but to match viewers with videos that they want to watch. The end goal is that they spend as much time as possible on the platform (and therefore see as many ads as possible.)
For the record, there are three other places the algorithm makes a big impact:
- Your YouTube homepage
- Trending videos
- Your subscriptions
- Your notifications