Challenges of AI in the Media Industry and How Developers Are Facing It
Different industries today are adopting AI techniques to improve user experience in their own domains. AI in media has one of the most significant impacts on users with a high percentage of people resorting to media entertainment while being confined to their homes for the better part of the last two years. Subtitling, for example, has opened doors to the consumption of content from all over the globe, and has broken barriers of language that long restrained content consumption for users. As alluring and attractive as these new AI solutioning services may seem, on the backend, it has increased stakes for developers who have had to deal with a whole new list of challenges while dealing with this new technology and its limitless scope.
Understanding the Different Use Cases
As a developer, it is important to be able to understand a given business use case and decide whether pursuing the same with the help of artificial intelligence is the best approach. In the media industry, use cases like video subtitling and recommendation systems for content are appropriate to use cases that can leverage the abilities of artificial intelligence. Meanwhile, use cases like automation for managing equipment required in a studio may already have better-implemented solutions in the market that can be utilized rather than using AI. Being able to make the right call at the very beginning is a challenge for some developers.
Challenges Around Data for the Training Model
The preliminary step to any artificially intelligent model is to obtain a data set to train the model and develop the system’s decision-making capability. This is one of the most critical aspects, as it determines the basis of the decision-making process of the AI system. With respect to AI media, this data may pertain to what users view on a particular streaming platform. Obtaining a high-quality dataset of this training data comes with a set of challenges for the developer.
- Firstly, being able to obtain data — which in the media industry pertains to user data, such as patterns in viewership for a certain streaming platform, etc. — can be arduous due to ever-changing privacy laws around customer data. Especially if AI needs to be applied towards building a new feature, a considerable amount of effort goes into being able to find data that can be used for training the model. Data scarcity is a major issue due to increased restrictions by countries because of news of the unethical use of customer data by companies.
- Secondly, even after this data is successfully extracted, the next challenge for developers is to be able to determine the right data set to send as an input to the training of the model. Data needs to be cleaned efficiently by deciding which data entities to retain and which to let go as unnecessary or rare outliers, etc. Data is the core of AI, hence determining the appropriate data set is a key element to the entire system, making it an undeniable challenge for developers.
Challenges Around Data Storage and Security
Data in the media industry is a good example of big data. Netflix, for example, has about 151 million subscribers and a proportionately large database, which also means that the data used by developers for continuously improving the decision-making of the AI systems is also significantly large. This high usage of data leads to a two-stage challenge for developers. The first stage is being able to create an efficient system of managing and storing this massive amount of data. With the advent of cloud computing, many developers prefer having data stored in the cloud rather than on-location storage. This leads us to the second stage, which is to ensure that the data has a data security mechanism in place, as such large data stores are a hot target for cyber crooks and, if overlooked, may cause havoc for the business.
Challenges Around Integration
For most organizations, a legacy system is in place, which needs to either be replaced or integrated with newly developed AI-powered solutions. Not only does this mean that developers need to understand how their organizations’ legacy systems work, but they also need to expend effort in finding a way to bridge the gap between different factors that vary greatly between the legacy and AI solutions being used (for example, the computational speed, efficiency, etc., while also ensuring a logically and practically viable flow of the entire process is maintained). In addition, many developers also need to work around or work with outdated infrastructure and achieve the best efficiency possible using those existing resources, which in itself is a difficult task.
Challenges Around Skills and Knowledge
For developers, an increasingly competent skill set is required to be able to deal with AI-related development. Small-scale use cases may still be achievable by those with basic knowledge, but real-life projects with highly expansive data, similar to the ones for the media industry, require a chiseled set of skills with prior experience in the domain, and they require developers to continuously upskill to be aware of and in touch with latest additions to the AI industry, methodologies, trends, etc. The sheer complexity of the computing involved in AI systems calls for developers to acquire knowledge to be able to deploy those solutions across multiple environments, to make them portable, to be able to weigh the different frameworks that are available, to choose the one most suitable for their use case, and so on.
Conclusion
The media industry has tremendous scope for AI-driven solutions with new use cases popping up every so often that are then being increasingly adopted across all media platforms. However, this has also posed a lot of challenges for developers who need to ensure that they upskill themselves to remain relevant in the market and to deal with major real-life scenarios of obtaining, handling, and managing data, and being able to extract patterns and identify learning trends that can help expand the use of AI across the media industry.
