What Is The Challenges Of AI In The Media Industry?
As the media industry joins others by adopting AI to improve user experience, many developers are facing new challenges and higher stakes on the backend.
Various businesses are now implementing AI approaches to improve user experience in their respective fields. With a large majority of people turning to media entertainment while confined to their homes for the larger part of the last two years, AI in media has had one of the most important effects on users.
Subtitling, for example, has enabled viewers to consume information from all over the world by removing linguistic obstacles that have previously prevented them from doing so. As appealing as these new AI solutioning services may appear on the surface, they have raised the stakes for developers, who have had to deal with a new set of issues as a result of dealing with this new technology and its seemingly endless scope.
Understanding the Different Use Cases
As a developer, you must be able to comprehend a particular business use case and determine whether using artificial intelligence to pursue it is the right strategy. In the media sector, application cases such as video subtitling and content recommendation systems are suited for leveraging artificial intelligence’s capabilities. Meanwhile, rather than relying on AI, use cases such as automation for managing studio equipment may already have better-implemented solutions on the market. For some developers, being able to make the appropriate decision at the outset is a struggle.
Challenges Around Data for the Training Model
Obtaining a data set to train the model and build the system’s decision-making capabilities is the first stage in an artificially intelligent model. This is one of the most important factors since it determines the AI system’s decision-making process. In the case of AI media, this information might be related to what viewers watch on a certain streaming site. The developer has a number of obstacles in obtaining a high-quality collection of this training data.
- To begin with, due to ever-changing privacy restrictions around customer data, obtaining data — which in the media sector refers to user data, such as patterns in viewership for a certain streaming platform, etc. — can be difficult. A significant amount of effort goes into being able to gather data that can be used to train the model, especially if AI is being used to construct a new feature. Data scarcity is a big issue as a result of rising limitations imposed by countries in response to reports of firms’ unethical usage of customer data.
- Second, even when this data has been properly extracted, developers must now choose the appropriate data set to submit as an input to the model’s training. Data must be efficiently cleaned by determining which data entities to keep and which to discard as unneeded or infrequent outliers, for example. Because data is at the heart of AI, choosing the right data collection is a critical component of the system, making it an unavoidable difficulty for developers.
Challenges Around Data Storage and Security
Big data in the media industry is an excellent example. Netflix, for example, has over 151 million members and a sizable database, implying that the data utilized by developers to continuously improve the decision-making of AI systems is similarly big. Developers face a two-stage challenge as a result of the rising data utilization. The ability to design an efficient system for organizing and storing this huge amount of data is the first stage. Many developers choose to have data saved in the cloud rather than on-site storage since the introduction of cloud computing. This leads to the second stage, which is to ensure that the data has a data security system in place, as huge data repositories are a prime target for cybercriminals and, if missed, can cause major business disruption.
Challenges Around Integration
Most businesses have an existing system that needs to be replaced or merged with newly developed AI-powered solutions. Not only do developers need to understand how legacy systems work in their organizations, but they also need to work hard to find a way to bridge the gap between the various factors that differ greatly between 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 around outmoded infrastructure in order to maximize the efficiency of existing resources, which is a tough undertaking in and of itself.
Challenges Around Skills and Knowledge
To cope with AI-related development, developers need to have an increasingly competent skill set. Small-scale use cases may still be possible for those with basic knowledge, but real-world projects with large amounts of data, such as those for the media industry, necessitate a honed set of skills and prior domain experience, as well as continuous upskilling to keep up with the latest AI industry additions, methodologies, trends, and so on. The sheer complexity of the computing involved in AI systems necessitates knowledge acquisition in order for developers to be able to deploy solutions across many settings, make them portable, weigh the various frameworks available, select the one best suited to their use case, and so on.
The media business has a lot of room for AI-driven solutions, with new use cases springing up all the time that are then being embraced across all platforms. However, this has posed a number of challenges for developers, who must ensure that they stay current in the market by upskilling themselves to deal with major real-life scenarios such as obtaining, handling, and managing data, as well as extracting patterns and identifying learning trends that can help expand the use of AI across the media industry.