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A Few Reasonable Words

A commonplace book

Tag: movies

Posted on April 15, 2016

“That Movie with the Sliding Doors and Time Travel … You Know…Maybe in Polish”

Book store staff, librarians, and movie store clerks (back when there still were movie stores!) all have stories of perplexing requests from patrons, generally when there is a very specific title in mind, but the question is a complete puzzle, with a title described in such vague or idiosyncratic ways as to require mind-reading more than reference desk skills.

A few tidbits from Library Thing,

” I need that book that’s called Shakespeare, but it’s spelled with a “Ch” and the author starts with M…”

Fortunately, I was in my groove that day, and it only took me a few seconds to figure out that the patron wanted the book Chesapeake, written by James Michener.

••••••••

My friend, as a young lass, once ran up to a librarian, very excited, and yelled out, “Do you have ‘The Cat Who Shat?'”

••••••••

I worked in a record store all through my (many) undergraduate years and we would get many crazy folks into the store on a regular basis. The overall most common silly request was; ” Can you help me find this song? I don’t know what it’s called or who sings it, but it’s about love.”

Aren’t they all?

As a classical music person, I’ve encountered my share of these mysteries: Somebody raving about hearing “Faust’s Requiem.” (I figured it was some outrageous East German world premiere, but it was just the Fauré one.).

When I was responsible for an information service on opera (which is a fairly nutty thing to start with), I did reliably got calls about “that opera where she dies at the end.”  I checked my impulse to say, “if she doesn’t die at the end, it’s  probably just Rodgers and Hammerstein.”

hunt Library Stacks.sm
The automated, as in robot served, stacks at NC State’s Hunt Library. A lot of metadata within and without. Maybe someday we’ll be able to tell a robot, “I want that blue book, about a whale named Ahab…”

But on reflection, all these misfires disclose that the brain is an interesting thing, and though no doubt we may all share much common neural architecture, we certainly don’t seem to keep track of and remember things or their associations in the same way. (In my case, I would charitably call this “neural metadata diversity of recall” but I suppose it could just be that I’m ‘getting on’ as the Brits would have it.) There is after all, a specific target for all these descriptions, it’s just that the getting there is fraught.

For myself, I do okay remembering  music facts (I started early in this domain, and, since I listen to and play music every day and write about it almost as much, it remains relatively fresh), but boy do I get foggy about movie details. And am notorious for groping to remember titles of films I have seen or want to see. “The one about the farmer who drives to someplace in the upper Midwest to collect his life insurance pay out starring the guy who was in The Great Gatsby.”

“Train doors close and open on two different paths for the story.”

“Brad Pitt turns into a baby. Unbearably long “

Turns out, there’s an AI-powered web site that makes this a snap. “What Is My Movie?” assesses deep content using technology from cognitive science to suss out those elusive links in your query and unravel the mystery of which movie you were looking for on Netflix.

From their website:

Whatismymovie.com is a descriptive movie search engine that was originally created as a showcase for Arctic15, Helsinki in 5/2015. Its purpose is to present a new way of searching video content, using movies and TV as the chosen approach. Descriptive movie search is based on our research on what is called “Deep Content”. Deep Content is everything we can see and hear in a video, but cannot mechanically analyze – until now. Deep Content includes transcripts, audio, visual patterns and basically any form of data feed that describes the video content itself. After analyzing the deeper levels of the video, we automatically convert it into advanced metadata. This metadata is then processed by the beating heart of our engine: a cognitive machine learning system that understands natural language queries and matches it with our metadata.

It’s not foolproof. It got Sliding Doors and the (completely intolerable) Benjamin Button in the examples above, but didn’t realize I was thinking about Nebraska for the first. (Although it did suggest Borat!).

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