Over the past few years, there has been a great deal of excitement generated by artificial intelligence systems helping to improve the filmmaking process.
Much of the attention has been focused on how AI is helping filmmakers to improve their scripts, choose the most suitable actors, and a range of other tools such as gross movie earnings predictions.
However, there are a number of little-known tools that have been designed to further streamline the filmmaking production process, one of which is AI-assisted scheduling.
In this article, we will take a deeper look to find out how AI is helping to automate the production scheduling of movies.
What is AI Automated Production Scheduling?
Prior to the production stage getting underway, all production companies need to create a schedule that incorporates all the various locations, actors needed, different shots, etc., into one practical timeframe. Given the complexity of the filmmaking process, planning such a schedule is both time-consuming and difficult.
For many lower-budget productions, this represents an enormous drain on the human resources side of things and often pulls many members of the creative team away from their responsibilities.
While large big-budget movies might have the luxury of being able to assign numerous individuals to this task on a full-time basis, the sheer enormity of these large productions, coupled with the huge financial losses that result when planners get it wrong, makes production scheduling a headache for even the most experienced scheduling professional.
Thanks to sophisticated AI systems and big data analytics, it is now possible to automate movie production scheduling. Specially trained AI systems are able to create accurate filming timeframes that maximize the best use of each professional’s time, equipment, locations, and even the weather.
So how does automated production scheduling work?
At the backbone of today’s AI-assisted moviemaking systems is a natural language processing system. What these systems do is allow the AI to be able to ‘understand’ or interpret text, which in the case of movies is the script.
Like all AI systems, they are trained to automatically break down a script into a range of different categories or parts which are all inter-associated. This is done using specially annotated data. Examples include genre, character traits, plot progression, etc.
Once the system has this breakdown or recipe of the film in its entirety, it can then cross-reference its findings with past data. Provided that there is enough data, the AI system is able to accurately extrapolate patterns and offer insights that allow for everything from accurate actor selection to production scheduling.
In the case of production scheduling, once the AI has analyzed the script and ‘understood’ its various components such as when actors are required, shoot locations including times and weather, etc., it can cross-reference this data with all available data pools.
Let’s consider the example of weather.
Our example short film has three exterior shots. One in a morning misty fog, one is a hot cloudless summer’s day, and one in a rainy night. How would the AI be able to create a production timeframe to capture all of these different weather moments?
Well, the system would not simply use weather and geographical data to link the nearest locations in the closest possible timeframe between each one. Instead, the AI system would actually take into a huge number of considerations before creating a plan.
It would consider everything from the production’s budget to the number of other shots required for the rest of the film.
Consider how the budget would influence location and timeframe allocation.
A large-budget production would have the resources to travel large distances to enable the production to be completed as quickly as possible. In this case, an AI system would be able to solve this problem by considering far-away locations in order to reduce time in between shots. Inversely, for a smaller production, the system might devise a plan which reduces travel distances at the expense of a longer shooting schedule or less likely the ideal weather conditions.
This is a blunt example but one that highlights how a comprehensive analysis of the importance of such large ranges of data pools can be tailored by each individual production’s requirements in order to create incredibly accurate production shooting schedules.
While such schedules would take a human weeks, if not months, to create, the beauty of AI systems is that they are designed to handle such enormous pools of data in their stride.
Not only does this mean that they can consider much more data than humans, and therefore be more accurate, but since they can process the data far more quickly too, they can allow movie production planners to actually play around with different criteria to find the best plan.
The ability to quickly add any additional information, i.e., a shoot overrun for a certain scene, or a delay caused by an actor falling sick, and immediately create a modified production schedule in order to factor in this change, will empower filmmakers using this technology with the ability to be highly adaptable, something which can save huge amounts of time and money.
Current AI automated production scheduling systems offer the following tools:
Storyboard and shot-list planning
Automatically generated storyboards and hotlists that are derived from the script which has been uploaded by the user. Visualization tools such as charts and graphs make them clear and easy to interpret.
Perhaps the most important tool for the production’s producers is an automated budget breakdown in which an accurate budget breakdown by scene. This allows producers to get an accurate level of understanding of how to budget each scene including transportation costs from location to location.
This powerful tool allows filmmakers and producers to optimize their shooting schedules around variables such as location, budget, weather, etc. This tool is a powerful aid as it can help productions make changes based on more accurate short-range data such as weather predictions, etc.
An Aid to its Human Operator
While it is easy to think that such systems are going to replace human movie professionals, they are in fact, being designed and developed as tools to aid movie professionals to get it right.
By helping to reduce the time spent doing menial tasks such as trying to interpret data, coupled with being able to provide more accurate insights than a human can achieve, the insights provided by these systems will allow their human operators to make better decisions and so ensure productions cut down on waste that costs both time and money.