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Using AI in Media


Artificial Intelligence (AI) offers huge promise and opportunities for media companies. It has the capacity to impact everything from research to content curation and the customer experience. It is thought that AI will start influencing all parts of the media value chain helping the creators to become more creative and editors to be more productive. The objective is to take a lot of the effort out of finding relevant content and ensuring it is presented to the reader. This article looks at some of the key ways AI is infiltrating the media sector.

Marketing and Advertising

Machine learning, an application of AI that uses data to create automated and predictive processes, is accelerating the media sector. For example, algorithms can be trained the can extract text, video segments, audio and images from any number of resources and make suggests on how to improve marketing or advertising efficiency. Alibaba Luban is a fantastic example of how AI can virtually reinvent content duration. The platform is capable of generating approximately 8,000 different banner designs in only one second. Imagine how long it would take a marketeer to do that volume of work! Of those designs, people are not able to tell the difference between generated and human work. In a similar vein, IBM trained their IBM Watson technology to design a movie trailer based on classic audio and visual moments that had been tagged and classified. It was able to create a full movie trailer within only 24 hours. This would normally take weeks to produce. Companies investing in this technology have far more time to focus on strategy and insight rather than purely creating their content.


Consumers are demanding personalised experiences. This goes for all industries such as Amazon recommending products, Spotify giving them unique music or Netflix telling them the shows they need to watch. Even social media now displays very tailored content designed for each user and what they are likely to need and want to see. Predicting user behaviour is key to the future modelling of media. As people become ever more connected through electronic devices, the need for fast delivery is ever greater. If a consumer can’t find what they want straight away, with so much competition on the market, they’ll simply go elsewhere. In some industries, businesses might only get 4 seconds to convince somebody to stay on their site. Google mail (Gmail) now has predictive text. As somebody writes an email, it is able to accurately predict what they will want to write next and shows the next sentence without the user having to write. Personalisation often means reducing user effort at the same time.

Search and Classification

Let’s face it, there is a lot of media available online. A few years back, when talking about search, the only real option was to go onto Google or Bing and type in some keywords related to what you were looking for. AI and machine learning techniques have changed all of that. In fact, it is thought that in 2020, the market will be dominated be voice and image searches. Google have evolved their platform in readiness for voice and image searching. Rather than having to type a keyword, users can upload a picture and image recognition technology will search for similar pictures. This uses complex tagging and identification of features. The impact on the media industry will be in the way they prepare content. Tagging images well will be vital to make sure their content get seen. An AI startup called ClarifAI have a computer vision API designed to accelerate the classification of content in movies. If a human were to categorise everything in a movie, it might take hours or even days. The AI platform can do this in real-time. Similar technology will be used by Netflix and Amazon video to tag scenes and objects.


Traditionally, paper and books were the main medium for sharing words and images. Over time, email became a primary channel and then we moved into social media, blogs and vlogs. AI may well be heralding a new era of experiences for visualising content with the booming force of augmented reality (AR) and virtual reality (VR). Whilst initially these technologies sounded like gaming fads or novelties, over the last few years they have started being put to incredible practical use. Machine learning algorithms can now build complex holographic scenes, all through a pair of googles with a lens. Brand new markets will open up in the media sector. As an example, fans can watch sports in a holographic view using VR headsets and get truly immersed in the game and atmosphere. This was massively publicised when Intel were able to launch such a service during the 2018 NFL Super Bowl. Viewing sport from the point of view of an athlete takes experiences to a new level.


One of the reasons that AI in media has only been adopted by a minimal number of pioneers is due to the need for data. In order to be accurate and effective, algorithms require a massive volume of data. Media companies need to have some mechanism to gather data at scale. That can include consumer data, content data and operational data. For example, consider how Netflix operates. To deliver recommendations effectively they needed data on consumer behaviour whilst watching streams. It took them many years to get that right but now they have, Netflix are far and away the leading streaming service. In fact, major shows are developed based on data, House of Cards being a prime example.


AI looks set to be at the forefront of creativity in the media industry over the next decade and beyond. From automating content to scraping media, personalised experiences, AR, VR and enhanced search potential, there is a lot for those working in media to be extremely excited about.

Adoption will be slow until the cost of investment reduces but it is important to start budgeting for the AI revolution.