Feature
The entertainment industry continues to tap into the labor-saving power of artificial intelligence (AI). AIs sort through massive consumer preference data to create instant recommendations on what to watch and which concepts production companies would be wise to greenlight into production. Entertainment pros use generative AI to help them illustrate, imagine and create their ideas. They use AI to create animations, deepfakes of actors to fill out scenes and populate video games with animated characters. Here, we look at several case studies of AI being applied in entertainment.
1. Peridot
Virtual pets have been charming children and adults since the ‘90s. Whether you have a Tamagotchi, Neopet or spend your weekends hunting Pokémon, you are likely familiar with these cute companions. In the augmented reality (AR) game Peridot, the developer, Niantic, used Meta’s Llama generative AI model to imbue its virtual creatures, called Dots in the game, with actions and behaviors that make engaging with them more realistic, according to the company.
The AI can quickly generate actions and reactions for each Dot. And these reactions change and evolve as the Dot learns and grows. This sort of randomness, unpredictability and change would have once required massive programming time. Even then, the game and the Dots’ actions would have included the range of actions the developers created. Generative AI can generate reactions and behaviors on its own, vastly increasing the pets’ variety of actions.
“We see this as an early example of how game designers and producers can use generative AI to make characters in games more lifelike and interactive,” the Peridot team says.
Results
- Ensured developers didn’t have to manually program every reaction by a virtual pet
- Created unique and surprising virtual pet reactions
- Enabled developers to use a vast library of unused animations that were difficult to use
2. FOX
Media companies have long used survey tools to gather information about what consumers watch and want to watch. Historically, survey systems can be cumbersome, inaccurate and slow. AI is rapidly changing survey systems by gathering data and allowing Fox to use it instantly, according to a case study.
FOX implemented Amazon’s AI-driven tools — such as Sagemaker, Personalize, Bedrock and Titan — to pull insights from the data it collects from consumers to better inform viewers, advertisers and broadcasters. FOX used AI to generate dynamic recommendations to consumers, offer deep levels of information to broadcasters on live programming that inform the programming as it unfolds, create sports highlights on the fly and offer opportunities for advertisers to place ads directly into programming that best suits the product they’re selling.
“For the first time ever, large language models have allowed us to take data and go straight from observation to a product that we can act on,” says Lindsay Silver, SVP of data and commercial Technology, FOX.
Results
- Automatically compressed games into most important highlights at any moment for in-game recaps
- More targeted ads in moments that best align with advertiser messaging
- Provided sportscasters in-game insights about players and referees
3. Disney
Since its first cartoons, Disney has been a technology-forward company. In the six decades since the first Mickey Mouse, the company amassed a vast store of content. And it long insisted that all of its content be available to the creatives who are working on animations and other programming. Accessing that warehouse of content, though, has become an increasingly impossible challenge, according to a case study.
Disney worked with AWS to automatically add metadata to each piece of content to make it more accessible. The AI learned from humans how to tag everything from “Bambi” to “Gray’s Anatomy.” The team worked to build a model that can accurately tag everything from animated characters to movements and behaviors. The tagging was intended to allow an illustrator to call up a specific character pose or a writer to quickly ask, for instance, how often a particular show-specific action has happened in the series in order to avoid redundancy.
“We have new characters in TV shows, football players changing teams, new weapons for superheroes, new shows,” says Miquel Farré, the Disney team’s technical lead, and AWS says “all of it requires a heap of fresh metadata.”
Results
- Enabled granular content tagging that wasn’t otherwise plausible
- Tagged content could be quickly accessed through a search interface
- Model applied to various types of content, such as sports, for content recommendations
4. GRAMMY Awards
The GRAMMY Awards, hosted by the Recording Academy, are a large production — the culmination of a year’s worth of work. Thousands of people attend the event in person. Many more tune in through digital channels. And in those digital channels, viewers are looking for rich content to fuel their interest in their favorite artists. But nearly 1,000 artists are nominated for GRAMMY Awards, and the event is dynamic.
The Recording Academy’s editorial team wanted to showcase each winner for the fans who view the event online. But they cannot create and publish stories about hundreds of artists in real-time. To deliver that content dynamically, the Recording Academy tapped IBM’s expertise in AI.
“This is the digital age, and digital experiences define your brand, whether you’re a retailer, a bank or the Recording Academy,” says Panos A. Panay, president, the Recording Academy. Panay says IBM has been “essential in helping us take our digital experience to the next level.”
IBM used generative AI and the Recording Academy’s data to produce content that is consistent with the brand’s standards and voice and fast enough to deliver it during the show.
“It’s awesome,” says Katie Stockman, director of business intelligence, the Recording Academy. “IBM built a generative AI content engine in a matter of weeks. Our team is still very much in the loop, reviewing and editing the content. But the quality of the output has been great. And it is really helping us reinvent how work gets done.”
Results
- Over 200 artists featured with instant, AI-generated stories
- Simplified and accelerated publishing process with AI Content Builder dashboard
- Pushed content directly to website and social channels as event unfolded
5. NBA
The goal of the NBA stats team is to engage basketball fans with exciting stats about players, the game and more. To do this better and faster, the team turned to Microsoft Azure for an AI that would allow them to process data about a player's live body movements, speed, dunk height, number of passes, dribbles and even injury risk and turn the data into statistics for fans, according to a case study.
With 30 NBA teams, more than 500 NBA players, each team playing 82 games a season and a computer vision-based player tracking system that gathers millions of data points for every game, the team collected an enormous amount of data.
To analyze the data, the stats team pulled the data into Azure Kubernetes Service (AKS), which managed the AI models while ensuring scalability and reliability. From there, it was piped into a real-time 3D model that allowed the stats team to analyze it visually.
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“From the height of a dunk to who’s more effective in the paint, we can pinpoint 29 distinct points on a player’s body. We see how a player moves. We see which way they’re facing on specific plays,” says Caroline McKee, senior software engineer of machine learning at NBA. “That kind of detailed data is extremely valuable to the teams and for fans.”
Results
- Delivered real-time statistical overlays to viewer screens
- Generated highly specific data for fans, such as release-point height and a shot’s distance from basket
- Allowed coaches to look for and identify player patterns, strengths and weaknesses to improve performance
