Into the Future
New ways of using data are regularly being invented and used. Surveys and categorization of age ranges that are used in data are becoming more finely sharpened. “That blunt instrument is fast giving way to computers that can render us in fine detail by picking up the trails of digital breadcrumbs we leave online and building them into predictive models of what we like and don’t like.” (Big Data and Hollywood, n.d.). The methods allow for a much more exact prediction model as to who will want to see which movie and how to get that movie to appeal to an even wider audience. “Studios can use these real-time opinion assessments to do all kinds of tweaking after a movie has been made; targeting specific demographics in marketing campaigns, tailoring trailers so that they appeal to the kinds of people who will be drawn to a particular movie, pushing distribution to geographic areas where the target audience lives.” (Big Data and Hollywood, n.d.). implementing these into the film industry will allow a film company to take a hold of their box-office success to an extent. This will allow those companies to obtain a much wider audience for their films. And in doing so, they will be able to increase their box-office success in ways that were previously not available to them.
Another potentially game changing system comes from Cinelytic. They have been working on an AI system that aids film production companies in new ways. “It licenses historical data about movie performances over the years, then cross-references it with information about films’ themes and key talent, using machine learning to tease out hidden patterns in the data.” (Vincent, 2019). This essentially creates an AI producer for companies. Analyzing all aspects of the data allows for it to look for those trends that work and when they may work and when to shift the focus to try to gain attention from a wider audience.
A problem also arises with the use of AI in film. For example, say that it was to be used to gather audience feedback before the film was released. It could then make recommendations that can be used to make the film more in line with what the audience is expecting it to be. For example, before the film Snakes on a Plane was released, it began to garner attention and so the studio decided to have reshoots for parts of the film so that they could incorporate feedback from the audience (Simon & Schroeder, 2019). And upon doing so, when it came time for the theatrical release of Snakes on a Plane, it fell short of what it was predicted to make at the box office. Taking this into consideration, while this is speculatory, if the AI is to take into account what this audience is interested in seeing based on their feedback, what is there to stop the method from being the same in terms of failure when relating this method to the one used for Snakes on a Plane?
