Science-fiction is littered with tales of cybernetic revolt, such as the legendary "Colossus and Guardian" team from the books of Dennis Feltham Jones, or SKYNET from the Terminator series of films, where super-computers, originally built to augment Man’s own power, seize that power for themselves and attempt to wipe out their creators.

The super-computers are typically given global access to all information, including nuclear warhead control systems, which they then use to systematically obliterate the world.

Of course, we would be far too sensible to do anything similar in reality, wouldn’t we?

Erm…well, it appears not. In today’s digitally driven world, computers already have limitless access to information, but now computer scientists from the University of Washington and the Allen Institute for Artificial Intelligence in Seattle have created the first fully automated computer program that teaches everything there is to know about any visual concept. Called Learning Everything about Anything, or LEVAN, the program searches millions of books and images on the Web to learn all possible variations of a concept, then displays the results to users as a comprehensive, browsable list of images, helping them explore and understand topics quickly in great detail.

"It is all about discovering associations between textual and visual data," said Ali Farhadi, a UW assistant professor of computer science and engineering. "The program learns to tightly couple rich sets of phrases with pixels in images. This means that it can recognize instances of specific concepts when it sees them."


Users can browse the existing library of roughly 175 concepts. Existing concepts range from "airline" to "window," and include "beautiful," "breakfast," "shiny," "cancer," "innovation," "skateboarding," "robot," and the researchers’ first-ever input, "horse."

If the concept you’re looking for doesn’t exist, you can submit any search term and the program will automatically begin generating an exhaustive list of subcategory images that relate to that concept. For example, a search for "dog" brings up the obvious collection of subcategories: Photos of "Chihuahua dog," "black dog," "swimming dog," "scruffy dog," "greyhound dog." But also "dog nose," "dog bowl," "sad dog," "ugliest dog," "hot dog" and even "down dog," as in the yoga pose.

The technique works by searching the text from millions of books written in English and available on Google Books, scouring for every occurrence of the concept in the entire digital library. Then, an algorithm filters out words that aren’t visual. For example, with the concept "horse," the algorithm would keep phrases such as "jumping horse," "eating horse" and "barrel horse," but would exclude non-visual phrases such as "my horse" and "last horse."
Once it has learned which phrases are relevant, the program does an image search on the Web, looking for uniformity in appearance among the photos retrieved. When the program is trained to find relevant images of, say, "jumping horse," it then recognizes all images associated with this phrase.


The research team also includes Carlos Guestrin, a UW professor of computer science and engineering, and the project was funded by the U.S. Office of Naval Research, the National Science Foundation and the UW.

Thankfully for the future of Mankind, the program is currently limited in how fast it can evolve because of the computational power it takes to process each query, up to 12 hours for some broad concepts. However, researchers are working on increasing the processing speed and capabilities (of course they are!), though the budding "Colossus" does not appear to be doing too badly so far: it was launched in March with only a handful of concepts and has since grown to tag more than 13 million images with 65,000 different phrases.

That’s not at all worrying.

Obviously there could be huge benefits for this type of technology – the team wants the open-source program to be both an educational tool as well as an information bank for researchers in the computer vision community, and also hopes to offer a smartphone app that can run the program to automatically parse out and categorize photos. Yet with just a few cyber leaps, could this evolve into something that will eventually end up knowing far too much about its creators?

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