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Convergence of Al, digital platforms, and popular culture

Al and digital platforms go hand in hand, as digital platforms are the major users of Al in the realms of media and popular culture. Several traditional media and cultural areas like music, film, and newspaper have gradually utilized Al for cultural production. However, the use of Al in the digital platform sector has been larger and much faster than in these traditional media, which means that the convergence of Al and digital platforms, not only technologically but also commercially, has become very common and powerful. Media convergence is mainly about the integration of old media and new media as well as digital technologies and popular culture as Jenkins (2006) explains. However, popular culture now can be produced and circulated by Al and digital platforms, sometimes together, at other times separately, and the boundary of old media and new media is not clear; therefore, we may say that the convergence of Al and digital platforms can be made possible as part of the broader notion of media convergence or as a new form of media convergence.

Taking Netflix as an example, convergence has been one of the most important practices for Netflix, as it has grown based on the convergence of new media and popular culture since the late 1990s when broadband, the internet, and smartphones consecutively helped the integration of popular culture and these new digital technologies. Broadband services became accessible in tandem with other digital technologies. The rise of cable channels expanded the number of “available programming choices and promoted the idea that content should be tailored to niche audiences. The proliferation of smartphones and wireless connections shifted expectations about accessibility and convenience, popularizing presumptions that culture circulates best on an on-demand basis” (McDonald and Smith-Rowsey, 2018, 2).

Netflix later utilized algorithms converged with Al to create one of the most powerful recommendation systems in the media and cultural industries, while YouTube, Facebook, and Twitter have used Al to develop their business models to attract customers. Netflix and Facebook represent two different business models (see Chapter 6). As one of the most noticeable cases in the field of popular culture, Netflix develops Al-based algorithmic recommendations as a new business model that other OTT service platforms follow. As Plummer (2017) points out, “Netflix uses machine learning and algorithms to help break users’ preconceived notions and find shows like movies and television dramas” that they might not have originally selected.

Netflix’s recommendation engine—a set of algorithms that connect people to content they want—is “the most well-known element of the Netflix Al system” (Frank et al., 2018). In fact, Netflix itself improves over time. As people use it more, it learns about people’s tastes and serves up the best content available in a highly personalized way. The core of the Netflix system is a remarkable piece of software design and engineering, which is nearly invisible (Frank et al., 2018). Machine-learning algorithms are excellent at predicting whether a person engages with a video clip or not. As Tercek (2019) points out, Netflix is at the forefront of applied Al in every stage of video delivery. However, Al also helps to govern the quality of service to the subscribers. Netflix uses Al to monitor bandwidth in the network and optimize a particular household’s video and audio streams based on available bandwidth and network congestion.

Netflix even uses Al to monitor whether subscribers share their passwords. Al can also aid monetization of video by improving the environment for advertising. Today several firms compete to offer systems powered by Al for brand safety, efficient targeting and more completed views.

(Tercek, 2019)

With the rapid growth in the number of its global subscribers, Netflix has changed its algorithms that recommend what users should watch next since 2016. Instead of suggesting recommendations based on regional models,

Netflix started to recommend movies a customer may enjoy and looks to other users with similar tastes around the world regardless of where they live (Brownell, 2016). For Netflix, algorithmic recommendations are used to auto-curate selections of content geared around individual users’ data profiles. Every video selection that appears on TV or mobile gadgets is the result of intricate calculations based on user-submitted data, collaborative filtering, and manual coding of content for all conceivable metadata points (Lobato, 2019). As a Western-based OTT platform, Netflix has increased its capital gains through algorithmic recommendations. Netflix is working as a mediator: the platform strategically groups people based on their common interests in order to maximize its profits. Netflix offers space for consuming entertainment products (Srnicek, 2016), not as a simple intermediary, but as a crucial mediator.

The major problem here is that people as the subscribers of Netflix lose cultural diversity and, therefore, cultural democracy. Since the personalization engine algorithms utilized by Netflix don’t display different ideas and tastes, subscribers mostly see similar cultural content. In other words, the recommendation system “carries the risk of the extinction of diversity” (Fili-beli, 2019,99). As digital platform users, when people use Netflix, Facebook, and Twitter, they get easily biased cultural content and information based on their preferences, but intensified by these platforms’ recommendation systems. People who have far-right ideology may never see cultural content, including news, on leftwing politics or vice versa (Filibeli, 2019). When they start to enjoy some particular genres of movies, they have no opportunities or less opportunities than other subscribers in enjoying movies portraying different genres and themes. As diversity has been one of the most significant characteristics in cultural democracy, this business norm certainly hurts not only cultural pluralism but also cultural democracy. Furthermore, what Netflix implements is the intensification of established hierarchies of cultural authority and power (Hadley and Belfiore, 2018) that must be challenged. Netflix equipped with Al and algorithms has strengthened the digital platform’s power in people’s cultural activities and, therefore, continues to harm cultural democracy.

Arguably, Netflix is one of the most significant examples of a digital platform, as it not only circulates cultural content but also produces cultural products, and therefore, Netflix backed by cutting-edge Al and algorithms controls the entire cultural chain, from production to distribution. Characterized by unequal technological and cultural flows, the state of platform development implies a technological and relevant socio-economic domination of U.S.-based or U.S.-origin firms that have tremendously influenced the majority of people and countries. Unlike other fields like culture and hardware, in which the method for sustaining unequal power relations among countries is mainly the exportation of goods and related services, the means of dominating foreign countries in digital platforms are commercial values

Al, digital platforms, and popular culture 29 that are embedded in platforms, which are significant for the expansion of power and capital accumulation (Jin, 2015).

Meanwhile, Facebook as one of the largest social media platforms has used deep learning as deep learning algorithms become more sophisticated, and they can increasingly be applied to more data that people share, from simple text to pictures to videos (Marr, 2016). Deep learning is used to gain value and help Facebook achieve its goals of providing greater convenience to users. “Facebook utilizes deep neural networks—the foundation stones of deep learning—to decide which advertisements to show to which users. This has always been the cornerstone of its business,” but by tasking machines themselves to find out as much as they can about the users, and to cluster the users together in the most insightful ways when serving the users ads, “it hopes to maintain a competitive edge against other high-tech competitors” such as YouTube and Twitter who fight for supremacy of the social media market (Marr, 2016).

Facebook as a digital platform has increased its influence in the social media market and now uses Al to attract users. As The Intercept (Biddle, 2018) reported, Facebook’s new advertising service expands the ways in which it sells corporations’ access to Facebook’s users and their lives.

Instead of merely offering advertisers the ability to target people based on demographics and consumer preferences, Facebook instead offers the ability to target them based on how they mill behave, what they mill buy, and what they mill think. These capabilities are the fruits of a self-improving, artificial intelligence-powered prediction engine, first unveiled by Facebook in 2016 and dubbed “FBLearner Flow.”

(Biddle, 2018)

Of course, as will be fully discussed in Chapter 7, the same social media company utilizes Al to control fake news. Facebook has increased its use of Al, both for good and bad, and therefore, the convergence of Al and Facebook has been practically real.

As such, Al-equipped Netflix and Facebook have substantially influenced local cultural industry firms, cultural creators, and customers, while greatly appropriating the global media and cultural markets. This means that the convergence of Al and Facebook and, in general, social media and Netflix and other OTT platforms plays a crucial role for digital platforms to intensify the disparity between Al-equipped platform owners and platform users who provide data to platforms in the early 21st century.

 
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