Artificial Intelligence and media: the DAM revolution

More and more developers are looking to design new ways to integrate AI into DAM solutions. But what exactly is AI? And how can it be integrated into Digital Asset Management solutions?

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Artificial Intelligence and Media Analysis

Artificial Intelligence aims to make a program perform tasks that require human reasoning. In the development of Artificial Intelligence or more precisely a neural network, “Machine Learning” refers to the learning of new situations and knowledge by a program, under human supervision. The purpose of artificial intelligence is to imitate real intelligence with phases of understanding, learning and then applying the new knowledge acquired. AI can be assigned to various tasks: translation or text analysis, mathematical problem solving, diagnosis (e. g. medical or computer), image or video analysis…

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Many uses are already in place

The most advanced DAM solutions nowadays include artificial intelligence in their capabilities, highlighting many key applications. With the functions of natural language recognition, computer vision, OCR, sound indexing, AI can identify words written in a text or spoken in a video. AI can even “understand” the meaning of a text or conversation. Understanding natural language allows the creation of chat boxes on corporate websites and to converse “as” a human could. It is also possible to automatically generate video subtitles in several languages. A human proofreading will of course be necessary, but AI already saves a considerable amount of time.

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By recognizing objects, animals, situations, landscapes, emotions and even products, AI can automatically identify and add related keywords or categories of elements present in an image or video to enrich the metadata and asset classification. By means of a specific learning mechanism (Machine Learning), users themselves create their own sets of keywords or categories, teach AI and improve the indexing capacity of the DAM media library.

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Machine Learning also has other uses. At Netflix, AI saves $1 billion each year to maintain user engagement and reduce unsubscription rates. At Amazon, AI “learns” to make even more optimized recommendations to you. It is also at the heart of their most recent strategies. In a Digital Asset Management (DAM) solution, the use of Artificial Intelligence goes beyond enriching assets and can be used to make recommendations to users as well as in analysis for data prediction purposes.

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AI can deploy facial recognition to automatically classify assets by individuals. Are you looking for a picture with a particular model? The AI can search it for you. It recognizes individuals in a video and even identifies the emotions expressed by the person’s face. This function is crucial in the search and moderation of user-generated content. By automatically detecting faces expressing joy or laughter, it’s easier and quicker to retrieve the media that will best highlight your products. In the future, artificial intelligence will even be able to detect different levels of language, from slang to the most formal level.

Deep learning: the future of AI

With the development in access to AI technologies, the possibilities offered by Deep Learning have been added to those of Machine Learning. It is no longer only a question of training a computer, but also of AI’s autonomous learning capacity. Thanks to a series of iterative processes, it is possible for Artificial Intelligence to find the most “logical” solution to a given problem by itself. With Deep Learning, AI learns by itself using one or more algorithms – the “machine” functions almost like a real brain by discovering and analyzing new concepts. Thanks to deep learning, Google succeeded in creating AlphaGo, which beat the world champion in the game, Go. In 2012, another Google artificial intelligence succeeded in discovering the concept of “chat” by itself, without human intervention.

More recently, it’s been the “deep dream” program that has caused more people to talk about Deep Learning. This program allows you to view the Deep Learning process “live” by letting AI interpret shapes and patterns in an image. In a DAM solution, such an approach could lead AI to learn to classify and recognize any new media without human intervention.

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