Disney is an entertainment powerhouse – it captured 61% of movie industry earnings in 2016 . In 2017, the studio entertainment division earned 2.3 billion dollars . This is a small part of the company’s earnings, but it is the lifeblood as the characters and stories create the basis for merchandise, theme parks and interactive experiences .
However, Disney is not immune to changing consumer preferences pressuring the profitability of the industry (more demand for streaming, lower willingness to buy tickets etc.) . To remain competitive, it is crucial to pick the right content and produce it quickly, at low cost without sacrificing quality. Thus, Disney is leveraging the power of machine learning to stay ahead of consumer preferences and automate the animation process.
In the short term, Disney is working on further automating the animation process using machine learning. Getting the lighting of animations right is a difficult process, which when sped up produces visual imperfections known as “noise” . In collaboration with Pixar and the University of California Santa Barbara, Disney has improved the previously time-intensive and partly manual “denoising” by using a “Convolutional Neural Network” . This deep-learning model relies on previous Disney movies as guides to improve the image quality of new productions . Thus, Disney can save time and money in producing its future movies and leverage past productions.
In the medium term, Disney is moving towards leveraging machine learning to guide content choices. Disney has been capturing the second-by-second facial reactions of its audiences . Using an algorithm, factorized variational autoencoders (FVAEs), millions of data-points can be interpreted to signal which moments in a movie elicited which audience response . This technique could be used in assessing different versions of movies before releases and gives Disney a vast knowledge base to how content decisions affect viewers . While using test-audiences is not new, this innovation allows for massively more data to be analyzed and does not rely on subjective customer feedback gathered through surveys or interviews . This FVAE technology also has a dual application – its large calculation power can be used to analyze how scenes behave in nature and translate that into automation . For instance, it can capture how different trees on a hill react to wind and automate the translation of this image into animation .
The most ambitious use of machine learning is Disney’s attempt to judge potential content through neural networks . This would mean not only making small adaptations based on audience preferences, but deciding to produce stories identified by AI. For now, the research has focused on predicting the popularity of “Quora” posts as an indication of the attractiveness of a narrative, but Disney has high hopes . The end outcome might be to have scripts or movie ideas evaluated and altered by machine learning to speed up the production process.
Disney is clearly pioneering using machine learning to facilitate the animation process. Complex tasks are being rendered less labor intensive and thus cheaper . Disney has purposefully founded The Walk Disney Studios LAB, together with HP Enterprises, Cisco and Accenture Interactive, to explore how technological innovations can improve story-telling . It is unclear if these changes will result in enough cost and time savings to allow Disney to better adapt to industry pressures (e.g. producing more content for streaming services).
The bigger challenge will be instituting the right guardrails for using machine learning to dictate content decisions. It will be crucial for Disney to monitor biases and errors machine learning-based content choices can create:
- FVAEs are already being questioned. For instance, they might misread the natural human tendency to mirror an expression seen on-screen (e.g. smiling because we see a smiling baby on screen) as a genuine positive reaction to the image as opposed to an automatic response .
- By not ensuring a diverse enough audience in the FVAE screenings, certain customer segment preferences could be excluded . This is especially relevant for a global company as cultural differences will affect reactions to content.
- Supplying the neural networks with historically-successful narratives might not produce the forward-thinking Disney has tried to become known for, like diverse characters such as in Moana or The Princess and the Frog .
Along with improving the content-generating technology, Disney should ensure content-choices based on machine learning will be evaluated critically and adjusted by human judgement as necessary .
- Disney is investing heavily into novel technologies – do they intend to use them only in the entertainment space or is there room for them to branch into other applications? (e.g. using FVAEs to improve care for patients unable to speak )
- What is the long-term relationship between human imagination and machine learning in determining content to produce?
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