A.I.: The New Hitmaker

Artificial intelligence has been a presence in Hollywood for many years, first as a subject of dystopian science fiction plots (“2001: A Space Odyssey,” “The Terminator”) and later as a CGI tool used in making many of the same type of films. Now, it’s moving behind the scenes entirely.

The Hollywood Reporter reported this week that Warner Bros. has signed a deal with L.A.-based Cinelytic to use its A.I.-powered project management system to provide predictive analytics on proposed film projects at the greenlight stage.

Cinelytic’s system can assess the value of star in any territory using data from multiple sources, and make projections of a movie’s likely earnings from theaters and ancillary distribution channels.

With the deal, Warner becomes the first major studio to sign up Cinelytic, following its earlier deals with Ingenious Media and Productivity Media.

Cinelytic is not the only developer shopping A.I.-powered predictive analytics to the studios, however. Belgium-based ScriptBook has trained an A.I. algorithm to be able to analyze a script alone –without stars, director or other creatives attached, — to generate box-office projections based on a range of story and character elements.

Although no studio has publicly signed on to use ScriptBook’s system, three of seven it has been presented to have expressed interest and are currently in discussions with the company, ScriptBook founder Nadira Azermai tells me.

Nor is Hollywood the only corner of the entertainment business where predictive analytics is finding traction. The music industry is also showing growing interest in the predictive powers of A.I.

Silicon Valley-based Chartmetric , for example, says its A.I.-powered A&R tool can shortlist which of the nearly 2 million artists it tracks will have a big career breakthrough within the next week. Pandora-owned Next Big Sound says it can predict which of nearly 1 million emerging artists it tracks will hit the Billboard 200 for the first time within the next year. Secret Chord Laboratories‘ dopr tool combines data from 33 years of Billboard charts with insights from neuroscience research to predict how a particular audience will respond to a song.

Big data

The use of analytics is not new, of course, particularly in Hollywood. The studios have long had green eye-shade types poring over past box-office, home video and ratings data to try to assess the likelihood of success for a new project. Even the determinedly non-empirical record industry will consult chart position and airplay to decide how much promotion to put behind an act.

The difference today is the immense amount of data, generated by streaming, that is available to analyze, and the computing power to sift through it all.

“The system can calculate in seconds what used to take days to assess by a human.”

Cinelytic founder Tobias Queisser

Neural networks and machine-learning algorithms make it possible for A.I. systems to analyze petabytes of data and discern patterns within it that elude humans.

Moreover, unlike humans, machine-learning algorithms get better at their job as they are fed more data to process. ScriptBook began training its algorithm with a library of about 30,000 produced scripts. But just as the natural language processing algorithms that power Siri and Alexa get better at recognizing human speech the more of it they hear, ScriptBook’s algorithm will be able to produce more refined and precise projections over time as it analyzes more scripts.

The limits of human processing historically left plenty of room for critical creative decisions still to be made by “ear,” or “gut” or “feel.” Data could inform but not supersede human intuition.

But with A.I.’s vastly superior processing power, and the mathematical improvement in its powers of discernment over time, that gap is starting to close, raising difficult questions about how and where to draw the line between man and machine.

A.I. developers, their eye on human purchasing managers, are careful not to put to fine a point on the question.

“Artificial intelligence sounds scary. But right now, an AI cannot make any creative decisions,” Cinelytic founder Tobias Queisser told The Reporter. “What it is good at is crunching numbers and breaking down huge data sets and showing patterns that would not be visible to humans. But for creative decision-making, you still need experience and gut instinct.”

Still, with potentially millions of dollars at stake, some mission creep is inevitable. ScriptBook, for instance, a tool it calls Deep Story, which Azermai describes as “the next generation writers room.”

By entering a few parameters (genre, character names, source material, etc.) Deep Story’s algorithm can generate an initial script, which human writers can then use as a starting point to produce a shooting script.

Similar technology is already in use in newsrooms, where A.I. systems sift through documents and real-time data feeds to suggest story ledes and angles to editors and reporters.

Even creative writers have begun experimenting with A.I.-powered story generation.

All of which is adding greater urgency to the questions being asked by the U.S. Patent & Trademark Office, the World Intellectual Property Organization, and other intellectual property agencies about how, whether and to whom copyright protection should be assigned or apportioned for works produced, initiated or assisted by algorithms.

I am currently preparing a larger report on the evolving role and implications of A.I. technology in the media and creative industries that will be published later this year by Penske Business Media’s Variety Intelligence Platform. Stay tuned.