61 FUTURESHOCK Vionlabs
FEELING EMOTIONAL Vionlabs evaluates content based on the emotional impact it has on the audience
A second approach is collaborative filtering-based recommendation. This is the one where the preferences of multiple users are compared, and results in recommendations of content that other people who have profiles similar to you have liked. In theory, the accuracy of collaborative filtering recommendations gets more refined the more data the system is exposed to. Your recommendations may include something like: “Other people who liked Alien , also liked Blade Runner .” Or “Other people who liked Alien also liked Schindler’s List , or “Other people who liked Alien also liked La La Land ”. Some of these may seem out of the blue, some absurdly obvious, but occasionally there is the opportunity to be exposed to something you might not have thought of, but might really like. Vionlabs suggests a more advanced way of recommending content – emotion- based recommendations. The company has developed a methodology for evaluating content based on the emotional impact the content has on the audience. EMOTIONAL RESPONSE “We’re teaching the computer how people respond to imagery and sound,” explains Vionlabs founder and CEO Arash Pendari. “We taught our computers by listening to and looking at video clips based on the rating system we have built ourselves. We tell them that we think one clip is positive, one clip is negative, one is stressful, one is not stressful, and the machine learning picks up those patterns after a while. That takes all this to the next level.” The method also takes into account the colour palette and the level of movement in each scene, and Vionlabs has produced a fascinating set of motion graphics which give a visual fingerprint of each film. This results in clusters of recommendations becoming more accurate, and also depends on fewer datapoints to produce. “There is so much rich data in the audio too that people don’t think about,” continues Pendari. “When you combine the audio with the visual elements, it’s pretty easy to pick up the actual emotion going on in a scene. There may be a lot of red in a scene, but is that because it’s a horror movie or a Christmas movie? Audio data can determine which it is pretty quickly.”
did. Working that dynamically with data is still very hard with TV operators and the new players in that space.” Danckwardt adds: “I read an interesting piece recently, which said that if you want to quantify human consciousness, you have to use quantum models. The main difference between quantum models and other models is that the answers depend on the questions. With every question you ask, you will change the answer to all the other questions. If we want to take it all the way, we need to think of how the movies we show now will be affecting the movies that are shown much further down the line.” The potential for AI in recommendations could very easily extend to being able to offer content based on the viewer’s current emotional state, which could readily be inferred from data gathered from social media, movement and activity, and purchase data in near real time. “There’s a clear study of anxiety attacks, how they are more likely to occur in the evening,” says Pendari. “So those types of people may not be able to watch dark movies or content later in the day. Recommendations could be made based on what’s best for mental health. “These are the first steps of actually teaching a computer to perceive human emotions, and we’re applying this to the movie and TV industry right now. But I think we do need to be careful when we teach computers human emotion. I like to think there’s a responsibility for us when we work with machine learning to take care in how we drive and how we use it.” RECOMMENDATIONS AND WELL-BEING
RECOMMENDATIONS COULD BE MADE BASED ONWHAT’S BEST FOR MENTAL HEALTH The company has been been working to efficiently analyse the entire dramatic flow of a film, graphing parameters like positive vs. negative emotions, audience stress level and colour (see the analysis of Top Gun above). “Using emotions is better than using keywords for the customer journey too,” says Pendari. “If you’re only using keywords, you get stuck in a bubble. That’s why you’re stuck in a bubble on Facebook and Google and everywhere else. But if you have emotional data, you understand the pulse of each viewer over time. Your pulse may be that in the morning you don’t watch something above certain average speeds, but in the evening your average speed goes up and you start watching action movies.” At present, there is no method for Vionlabs to fine tune the customer journey in real time, so the company has divided up the viewing timeline into four segments – morning, afternoon, evening and night – allowing for different sets of recommendations for each part of the day. But there’s no reason the technology won’t catch up. “Google changes your results as soon as you do one search. The search you do is being affected by the search you just
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