Cognitive AI is increasingly being used to help viewers navigate their entertainment experiences, improving content discovery and recommendations, optimizing user experience (UX), and ultimately helping to keep audiences engaged for longer. Cognitive AI is quickly becoming a viable option for reshaping the entertainment landscape. Leveraging this powerful technology, viewers can discover and enjoy content tailored to their individual tastes with recommendations based on analyzing emotions and moods.
What is Cognitive AI?
Cognitive AI is the use of systems that simulate human thought. Human cognition involves real-time analysis of the real-world environment, context, intent and many other variables that inform a person’s ability to solve problems. Cognitive AI is leading to a revolution in computer science, allowing machines to think and reason just like humans. Using specialized algorithms, computers can now delve deep into complex subjects and make decisions based on contextual understanding – which allows them to mimic human thinking while providing the speed of computation that people simply cannot compete with.
To create this revolution in computer science, culminating technologies such as neural networks, machine learning , deep learning , speech recognitionand object recognition have been merged together into one unified system known as cognitive AI.
It is worth noting that cognitive AI is a quite broad field, for example, chatGPT and DALL-E these generative AI models are part of cognitive AI as they can understand abstract concept and perform creative tasks like understanding human questions and provide relevant answers, also translate text into creative art.
What movie should I watch tonight?
A question we ask ourselves very often, either to a friend whose movie taste we trust — OR by asking content platform we subscribe to. Recommender systems used within all modern streaming platforms, manage to provide us with movies we will probably like. But how do they work?
Recommender systems are information processing methodologies that focus on extracting the best recommendations to users. There are 3 types of recommendation engines:
- Collaborative filtering based: a user is matched with other users based on her/his past behavior and then this matching is used to predict future preferences. Collaborative filtering is based on the assumption that users that had similar preferences in the past will also share similar preferences in the future.
- Content-based. Content-based recommendation engines use the information that stems from discrete characteristics of the movies, such as: directors, genres, locations, and actors or even embeddings which has more rich information than existing metadata.
- Hybrid recommendation engines that combine collaborative and content-based methodologies, aka two-tower neural nets.
However, most of these three methods today rely on human-generated content (either past user preferences or manually labeled content tags) and do not take into account the raw content of the movie itself.
Can AI watch a movie and tell us what the DNA of the film is, like the genre, and mood, and understand the emotions of each scene of the film?
The answer is yes, Vionlabs technology can “watch” and analyze a movie and tell us what emotions the film has, for example, joyful, sad, or scary. It can also tell if the movie’s mood is lighthearted and funny or a tear-jerker or whether the genre is comedy or drama. It can even measure the stress levels of each scene. This is possible by training the AI with datasets with videos and teaching how we humans feel when watching a movie or series.
With a significant focus on storytelling and how stories make us feel, Vionlabs has focused on training AI to understand how movies & series affect human emotions. Every color, object, sound effect, camera movement, and soundtrack in a film is there to affect our emotions. All this gets picked up by the AI and extracts information from the video to enrich and enhance the data available for each title.
WHY DO WE NEED IT?
This type of data and technology enables the film industry to enhance, improve and support the process and algorithms to improve the user experience. Providing underlying data to editorial teams, improving each viewer’s recommendations, or helping the content strategy team understand the audience better and optimize their content library for better engagement.
Having the information on how some content affects the audience emotionally enables the industry to understand the audience on a much deeper level and never again have to assume what type of content the audience wants.
On Valentine’s day, people think that romance movies are a must, but looking at the data, horror movies perform much better during valentines day. Using an AI powered recommendation engine the recommendations become so accurate that the viewer no longer need to look for a title to watch. The titles will find the users instead. This way, entire content libraries can be utilized instead of just having the movies and series with big marketing budgets dominating the services.
To sum up, cognitive AI is important because it helps us understand human emotions. Vionlabs cognitive AI technology is at the forefront of understanding and analyzing human emotions in media. By understanding how users are feeling, we can better tailor content to improve the user experience. Our technology can help you surface the most impactful moments in your video content so that you can keep viewers engaged longer. If you’re interested in learning more about how our technology works or ways that it could be integrated into your platform,, contact us today. We’d be happy to chat with you about how we could help you improve the user experience using our cutting-edge cognitive AI technology.