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Surprising Facts About the Use of AI in Netflix Originals

Surprising Facts About the Use of AI in Netflix Originals
The Siliconreview
17 November, 2021

Netflix's decisions seem to be perfectly calculated. No wonder it has a huge number of subscribers. As of 2021, Netflix has approximately 209 million subscribers worldwide, With the U.S and Canada taking up roughly 74 million subscribers.

That is why it's able to come up with hundreds of originals. The key behind this success is no other than AI, and it is the core of their business.

With AI and data, Netflix knows what viewers are watching, what we pause or skip, etc. Indeed, that’s why it's considered the most popular based video streaming service worldwide.

Thinking of Medici, Squid Game, and Maid?

How do you think Netflix knows which genre best fits your tastes?

The use of AI in Netflix is based on predicting success that narrows down to getting connections that determine the audience size. This would not even match with the Nielsen ratings; Netflix is much higher.

Don’t forget it is not all machines; there is a bit of human touch that helps promote the brand experience. Netflix has not ignored the fact that humans also can discover some of the subtle elements to maintain perfect functions; this has greatly helped Netflix to strike a balance.

Whenever we think of AI, we think of algorithms and big data. And this is correct. Netflix can provide content in an infinite set with many variations. The algorithm understands based on user behaviour and then suggests the Netflix films and shows best suited to the viewer. The understanding of consumers is becoming more and more granular, and the use of data has produced more new and better content.

Surprising facts on how Netflix Originals uses AI

Netflix has made its content extremely successful. This can be based on four main concepts: getting more content that tags content to get a nuanced view of consumers. It helps to predict which best genres the customers will be interested in.

The second one is about recommendations. For example, Netflix recommends a certain movie or show as a way of improving engagements.AI has made it possible to get connections that help to define the audience's magnitude.

The third one is the generation of thumbnails. The starting point of any generation of thumbnails is annotating images from an existing movie or show. With the use of AI, Netflix can rank the images obtained to identify the thumbnails that seem to have the highest likelihood of being clicked by the consumers. The calculations are based on other viewers who viewed similar content and clicked. For instance, a user who likes Marvel movies is likely to click thumbnails with certain characters.

The last one is streaming optimization. Any user experience has to be based on how effectively they can view their movies or shows in the best quality.  Adaptation of AI technology in improving video encoding helps to improve the quality but also greatly assists in reducing the data needed to stream, which also helps avoid slow connections.

Even if a consumer has the best recommendations, this would still not be enough; the service has to produce visuals that meet high-quality standards that the audience expects to see. It is all about upscaling, filters, brightness adjustments, great effects, and animations that can be made possible through video enhancement.

AI can be used in image enhancement to give the viewers the best experience. AI tools would ensure that the enhancement level matches the video’s need.

Did you know that Netflix data warehouse can store about 10 petabytes of information, with no manifestation or signs of slowing down? All that you view is stored and distributed from a personalized network. And all this work can be attributed to AI.

How would it be possible to learn what movies you like, what device you are using, when you pause, and when you rewatch the movie without a top-notch architecture?

AI sucks up data

Netflix’s system is algorithm-based. The major success comes from machine learning. Netflix realized that when data is used effectively, it can transform.

Determining which films the audience likes is best derived by looking at what they watch most. More programs are recommended as you spend time on your movies or shows, and the algorithm learns the data and collects it.

To make recommendations, Netflix has to collect lots of data from the devices where the visualization is made, content being viewed, and much more. Typically, this data collected serves to feedback the Netflix recommendation algorithm. That is why you get similar content to your former interactions every time you check a certain movie or a show.

The data derived helps Netflix achieve the insight and create personalized experiences.

The human touch

Netflix is about integrating AI and humans. While AI has made Netflix stand out, it has not ignored the fact that it needs to utilize human intervention to develop a better consumer experience. Most of the popular ANI systems involve human intervention.

Open-source AI

Have you ever wondered how Netflix uses open source to disclose your favourites? Its content delivery network (CDN) is all about open source. 

Netflix can use various technologies on open-source algorithms or open-source data sets. This way, it has achieved its full potential by generating new solutions compared to when it could be closed. 

It seems Netflix will keep on booming. And with the great partnership with Disney Plus and Marvel, you can expect them to remain on top. And with just one click, you can enjoy what you love.