Could Algorithms Be Better at Picking the Next Big Blockbuster Than Studio Execs? (wired.com)

In a world where artificial intelligence is no longer just a Spielberg-Kubrick collaboration, could algorithms be better at picking the next big blockbuster than studio execs? From a report: "Filmmakers are getting closer to understanding what moviegoers go to theaters to see thanks to neural networks fed off of data from previous box office hits," says Landon Starr, the head of data science at Clearlink, which uses machine learning to help companies understand consumer behavior. "Although this technology isn't spot-on quite yet, AI-powered predictions are likely stronger than the human calculations used in the past." And they're advancing quickly.

Vault, an Israeli startup founded in 2015, is developing a neural-network algorithm based on 30 years of box office data, nearly 400,000 story features found in scripts, and data like film budgets and audience demographics to estimate a movie's opening weekend. The company is only a couple years in, but founder David Stiff recently said that roughly 75 percent of Vault's predictions "come 'pretty close'" to films' actual opening grosses.

Scriptbook takes a similar approach, using its own AI platform to predict a movie's success based on the screenplay only. The Antwerp startup's AI analyzed 62 movies from 2015 and 2016, and claims it was able to successfully predict the box office failure or success of 52 of them, judging 30 movies correctly as profitable and 22 movies correctly as not profitable.

Comments

Don't doubt algorithms could improve the median.

[hey!]

Every person in charge of acquiring new material for a big media company is always on the lookout for the same thing, only different. That's because the financial backers have two, largely mutually exclusive goals: guaranteed audiences and a runaway hit. In economic terms they're looking for an investment with higher than normal returns for its risk.

That's why pop culture is so clogged with retreads. It's only a matter of time before we see Star Trek: With Tits.

Now I happen to know more about publishing than movie making, so I'll focus on that for a moment. New authors submit their manuscripts on spec to agents and publisher acquisition editors. These agents and editors are usually pretty sharp, but that makes their time valuable. So someone like an intern has to wade through the "slush pile". It's a horrible job because 99.9% of the slushpile is pure rubbish.

What the slushpile reader does for hours on end is skim the first page, and toss, skim the first page, and toss. The first page is about ten lines of text in standard manuscript format. But if an algorithm could make the first cut, it would be able to examine entire manuscripts for the desired combination of (a) resemblance to past hits and (b) differences from recently published books, winnowing hundreds or thousands of manuscripts down to a couple dozen candidates fit for human eyes.

The exact same process could be used for movie or TV spec scripts. American broadcast TV shows often have a problem ginning up enough story ideas to fill an entire season, but accepting spec scripts means someone has to deal with the slushpile. So there's usually a couple of writers-don't-have-any-ideas episodes each season. If you could process a couple of thousand spec scripts and pick a dozen candidates that fit the show, you might find an idea you could use.