Frames of preference
A new Ai-driven search engine can “read” film stills, picking up on emotion, colour, mood and style. Could it help with your vague movie memories or should you be afraid, very afraid?
A new Ai-driven search engine can “read” film stills, picking up on emotion, colour, mood and style. Should you be afraid, be very afraid?
For movie buffs, life just got easier (and more complicated). It used to be that if you were trying to track down a move by title, release year or a member of the cast and crew, you’d head to the Internet Movie Database (IMDB). To measure Cara Delevingne’s privilege, you’d look up her illustrious family tree on Wikipedia. To track a film poster, you’d look through the million uploads on Cinematerial. Rotten Tomatoes collates reviews. The Complete Index to World Film lives up to its title. There’s even a dedicated Internet Movie Firearms Database.
But what if all you had was a hazy memory of a scene, a half-remembered shot? You know it had a puppeteer. Perhaps putting on an inappropriate street show. A nun may have been involved. With keywords this vague, Google is of little use.
Enter Flim.ai. The algorithm-driven searchable archive is just under a year old, and is already the world’s largest database of film-related images. Flim’s algorithm “reads” through more than 3 lakh images from movies, documentaries, anime, advertising and music videos.
The bot can detect colour palette, genre (from the film’s metadata) and aspect ratio. It can also precisely identify dog-walkers, ham sandwiches, red-lipped Asian women, animated metropolises and other visual cues. It’s pattern identification, facial recognition, data mining and pop-culture cross-analysis all rolled into one.
For those vague memories, it’s perfect. Flim.ai immediately connected our keywords to a minor scene from the 1999 indie film Being John Malkovich (it did feature a nun, but as one of the puppets).
The site is the work of Dan Perez, 35, and Victor de Casteja, 33, who met as video photography students in Paris in 2009. They’ve since produced music videos and ads, and worked in fashion and art. “I created Flim because I am mad about movies,” says Perez. “As a student, I watched a lot of films and spent a lot of time screenshotting them for inspiration for my own videos.”
Some 40,000 of those shots ended up as an early experiment. Perez put them online, allowing visitors to share their screenshots too. He realised then, that there was a market for what he calls “iconographic searching”.
Still in its beta phase — it’s supported by French incubators Paris&co and Belle de Mai — the site is already a hit with creative folks. “It’s a handy tool for when you’re looking for something specific,” says Sara da Costa, a Mumbai-based ad-film director. Clients rarely share the same pop-culture references as creative people. They struggle to visualise, say, the shower of rose petals from American Beauty or the marigold fantasy from Monsoon Wedding. “So having a handy visual reference is useful,” she says.
Where Flim.ai flounders is where AI everywhere is floundering. Machines can’t yet make connections that mimic the human experience. When da Costa used Flim to look for green rooms, the AI showed her greenwalled rooms rather than, say, a backstage scene from Birdman or Black Swan. “Ultimately, my memory and that of my assistant director are more reliable,” she says.
Prepare for a glut of anime shots when you search with certain keywords (“noodles”, “alone”). There also aren’t enough stills from Indian films, apart from a handful of contemporary hits such as Devdas and Kuch Kuch Hota Hai. “It’s a drop in the ocean of Indian movies,” Perez admits. But there are plans to collaborate with distributors and add world cinema to the site. “The aesthetic in Indian movies is very strong, a visual treasure that we lack on Flim.”
For anyone in the visual arts who’s struggled to build a moodboard, the idea of a bot doing it sounds both liberating and limiting. Interior design assistant Prakrit Kumar tried using the site for ideas for a commission he’s working on — the bedroom of a teenage girl. “I saw all these American stereotypes – muted pink, a photo wall, fairy lights – all things I’d avoid as a designer,” he says. “In a creative field, a machine can only go so far, the human must ultimately take the standard idea a step forward.”