Blog

We've come a long way from the days when "conversing" meant painting on caves and sending news and information via pigeons to the present, when Facebooking, Snapchatting, or tweeting our thoughts and ideas to the world has become the norm. 

In today's world, which is ruled and influenced by digitalization, technology is a powerful magical tool that gives wings to our pigeons. These pigeons are now on multiple mass media platforms such as TV, newspapers, or news media. 

Artificial intelligence, a technology that mimics human intelligence for use in machines and programming them to think in terms of humans and mimic their behavior, is giving these platforms a whole new shape and identity. 

AI in the media industry enhances the interactivity and interest of visual content. It contributes to delivering data-intensive, personalized, automated content to the audience, enhancing and entertaining their viewing experience. AI applications have recently grown at an unprecedented rate in the media industry. The AI market is expected to reach $312.47 billion by 2027. Businesses must stay current on the latest trends to determine where to incorporate this technology to increase efficiency and compete. 

AI in Media Industry 

Like many other industries, media companies can benefit from AI and machine learning by using the data that these technologies produce and process. A more searchable repository and business process optimization for a personalized news stream for readers and users, market research, and user analytics are all possible use cases. 

However, incorporating AI into media operations enables a new set of services, such as automatic tagging and inferring plots in videos. Furthermore, companies can eliminate manual work and fully leverage the potential of their media repositories and content by utilizing various AI tools such as dataset management, automated training data creation (with the ability to build their own AI models), and indexing of unknown content. 

Applications of AI in the Media Industry 

To say that AI has many applications would be an understatement. Artificial intelligence is being used in every aspect of life and is constantly evolving. In this section, I will provide a brief overview of the use of AI in media. 

Metadata Tagging 

With an infinite amount of content being created every minute, categorizing it and making it easy to find for viewers becomes a difficult task for media company employees. This process necessitates watching videos and identifying scenes, objects, or locations in the video to categorize and tag them. 

Instead, media producers and distributors like CBS Interactive use artificial intelligence tools to annotate videos and analyze each video's content frame by frame, identifying objects and appropriate tags. As a result, any content created by media publishing, broadcasting, and hosting companies is easily accessible. 

Online Advertising with Targeted Audience 

Online advertisements play an essential role in branding and business promotion in the media industry. Furthermore, AI makes online advertising more precise and productive with a targeted audience to increase conversion rates. 

Google Adsense and Adwords are the best examples because they can use the user's history, such as what products they were searching for or browsing on the web or eCommerce sites. And this type of AI-based sensing enables AI to display ads based on the user's preferences. It allows advertisers to target the right audience and get the most out of their advertisements. 

Personalized Chatbots for VA 

AI-powered virtual assistance is becoming increasingly important in the media industry. For example, online music and OTT platforms offer users a convenient customer experience and answer their questions. 

An AI-powered virtual assistance app can also assist media companies in gathering valuable data, such as frequently asked questions, suggestions, and user preferences. Businesses can then use this data to create chatbots or train them to perform better, ultimately improving customer experiences and services. 

Content Personalization 

One reason for the success of top music and video streaming platforms like Netflix and Spotify is that they provide content to people worldwide with varying preferences and tastes. 

These companies use AI and machine learning algorithms to read demographics and user behavior. They then use that data to recommend new music, movies, and television shows to customers, catering to their preferences and providing personalized experiences. 

Subtitle Generation 

Because it is critical to make content accessible to everyone, media companies must provide error-free multilingual subtitles for their audio and video productions. Human translators would need thousands of hours to manually write subtitles for movies and shows in hundreds of languages. 

Because the human translation is prone to errors, media companies are leveraging AI-based technologies such as natural language generation (with the help of text annotation) and natural language processing to overcome these challenges. YouTube publishers, for example, can automatically generate closed captions for their uploaded videos to make them more accessible. 

Controlling the Broadcasting and Detection of Fake Content 

The media industry relies on four types of content to thrive. Aside from common and general subjects, there is obnoxious content broadcasted via outdoor or out-of-home (OOH) media, print media, broadcast media such as radio or television, and the internet. 

The Federal Communications Commission (FCC), for example, requires regulatory authorities to control this type of content. They detect and filter offensive material using AI computer vision. The objectionable content is moderated before the broadcast with the help of an automated content moderation service. 

Extensive Use of VR/AR Technologies 

Virtual reality (VR) and augmented reality (AR) technologies are expected to grow rapidly in the media and entertainment industries. The potential for AR and VR in the entertainment industry is vast, given the widespread demand for gaming apps. These technologically advanced apps assist businesses in developing promotional campaigns for movies and other digital media. 

AI can, however, be used to create interactive content for AR and VR. With a pair of goggles and AI techniques, the entertainment industry can work wonders and make breathtaking scenes. Consumer interest is naturally drawn to creating virtual reality content for cooking shows, reality shows, and live events and programs based on artificial intelligence. Watching television and movies with real emotional impact won't be a pipe dream with all these modern technological advancements; it will undoubtedly become a reality. 

Why Should Media Companies Apply AI? 

Tools with built-in artificial intelligence have the potential to revolutionize the way media companies operate. AI tools can perform simple editorial tasks. One prominent example is the creation of standardized match reports in amateur soccer. 

However, more research is required before AI can produce high-quality reports and stories. The use of AI to support editorial work, particularly in the online environment, has become more mature. In this case, artificial intelligence can relieve newsrooms of repetitive tasks, freeing up space for original journalistic work. Such tools can assist editors in dealing with the new demands of the digital world. It improves the efficiency of editorial workflows, and output can be automatically enhanced and post-processed. 

Publishers, media outlets, and especially online media editorial offices have an essential asset for using AI: they have large amounts of (digitized) information that can be used to train AI, i.e., to design their own AI models, thanks to their publications. The benefit of deploying AI is that subsequent keywording of content is no longer required. Instead, the AI examines existing texts and categorizes them according to topics and keywords. This is true for both articles and web content. 

Challenges of AI in the Media Industry 

As the media industry joins others in embracing AI to improve user experience, many backend developers face new challenges and higher stakes. 

Challenges Around Data for the Training Model 

The first step in developing an artificially intelligent model is to collect data to train the model and develop the system's decision-making capability. This is one of the most important aspects because it determines the AI system's decision-making process. In the case of AI media, this data could be related to what users watch on a streaming platform. The developer faces several challenges in obtaining a high-quality dataset of this training data. 

• Firstly, obtaining data — which in the media industry refers to user data, such as patterns in viewership for a specific streaming platform, etc. — can be difficult due to constantly changing privacy laws governing customer data. Data scarcity is a significant issue due to increased restrictions imposed by countries in response to news of companies' unethical use of customer data. 
• Second, even after this data has been successfully extracted, developers must now determine which data set to send as input to the model's training. Data must be cleaned efficiently by deciding which data entities to keep and which to discard as unnecessary or rare outliers, and so on. 

Challenges Around Data Storage and Security 

The media industry's data is an excellent example of big data. Netflix, for example, has approximately 151 million subscribers and a correspondingly large database, implying that the data used by developers to improve AI systems' decision-making continuously is also quite large. This high data usage creates a two-stage challenge for developers. 

• The first stage is to develop an effective system for managing and storing this massive data. With the advent of cloud computing, many developers prefer to store data in the cloud rather than on-site. 
• This brings us to the second stage, ensuring that the data has a data security mechanism in place because large data stores are a hot target for cyber crooks and, if ignored, can cause havoc for the business. 

Challenges Around Integration 

Most organizations have a legacy system in place that must be replaced or integrated with newly developed AI-powered solutions. This means that developers need to do more than just comprehend how the legacy systems used by their organizations' function; they also need to put a great deal of effort into sorting out how to bridge the gap between the various aspects that significantly differentiate the used legacy and AI solutions (for example, the computational speed, efficiency, etc., while also ensuring a logically and practically viable flow of the entire process is maintained). 

Furthermore, many developers must work around or with outdated infrastructure to achieve the best possible efficiency using those existing resources, which is a difficult task. 

Challenges Around Skills and Knowledge 

To deal with AI-related development, developers must have an increasingly competent skill set. Small-scale use cases may still be achievable with basic knowledge. Still, real-life projects with highly expansive data, similar to those for the media industry, necessitate a chiseled set of skills with prior experience in the domain. They necessitate developers continuously upskill to be aware of and in touch with the latest additions to the AI industry, methodologies, trends, and so on. 

The sheer complexity of the computing involved in AI systems necessitates developers acquiring knowledge to deploy those solutions across multiple environments, make them portable, weigh the various frameworks available, select the best suited to their use case, and so on. 

Future of AI in the Media Industry 

The world is expected to spend $118.6 billion on AI by 2025, with the media and entertainment (M&E) industry spending another $1,860.9 million (up from $329 million in 2019). It is not enough to say that AI is gaining traction in the media industry; instead, AI integration in media workflows is becoming mainstream. 

Thanks to AI, media companies are now more effective at comprehending their audiences, anticipating their choices before making suitable recommendations, etc. AI is a game-changer in this sector and is already upending conventional marketing tactics, automating media processes, and ultimately generating revenue. 

Conclusion 

I'd like to conclude that while Artificial Intelligence possesses enormous power and capacity, as well as the promise of evolution and growth in the media and its various sectors, its very power may also prove to be its most lethal weapon. Thus, for AI to be a positive advancement, it must be effectively channeled and accurately used. It goes without saying that AI is revolutionizing the media and entertainment industry. It undoubtedly has a significant impact on raising the effectiveness of the processes involved in the entertainment industry. 

AI technology is a beneficial and promising way for media companies to remain competitive in 2022 and beyond. As discussed in this article, using AI to develop entertainment apps will reshape the field's landscape.