Nanotechnology Now

Our NanoNews Digest Sponsors

Heifer International

Wikipedia Affiliate Button

Home > Press > New computer program aims to teach itself everything about anything

Some of the many variations the new program has learned for three different concepts.
Some of the many variations the new program has learned for three different concepts.

Abstract:
In today's digitally driven world, access to information appears limitless. But when you have something specific in mind that you don't know, like the name of that niche kitchen tool you saw at a friend's house, it can be surprisingly hard to sift through the volume of information online and know how to search for it. Or, the opposite problem can occur - we can look up anything on the Internet, but how can we be sure we are finding everything about the topic without spending hours in front of the computer?

New computer program aims to teach itself everything about anything

Seattle, WA | Posted on June 12th, 2014

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."

The research team will present the project and a related paper this month at the Computer Vision and Pattern Recognition annual conference in Columbus, Ohio.

The program learns which terms are relevant by looking at the content of the images found on the Web and identifying characteristic patterns across them using object recognition algorithms. It's different from online image libraries because it draws upon a rich set of phrases to understand and tag photos by their content and pixel arrangements, not simply by words displayed in captions.

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.

"Major information resources such as dictionaries and encyclopedias are moving toward the direction of showing users visual information because it is easier to comprehend and much faster to browse through concepts. However, they have limited coverage as they are often manually curated. The new program needs no human supervision, and thus can automatically learn the visual knowledge for any concept," said Santosh Divvala, a research scientist at the Allen Institute for Artificial Intelligence and an affiliate scientist at UW in computer science and engineering.

The research team also includes Carlos Guestrin, a UW professor of computer science and engineering. The researchers launched the program in March with only a handful of concepts and have watched it grow since then to tag more than 13 million images with 65,000 different phrases.

Right now, the program is limited in how fast it can learn about a concept because of the computational power it takes to process each query, up to 12 hours for some broad concepts. The researchers are working on increasing the processing speed and capabilities.

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. The team also hopes to offer a smartphone app that can run the program to automatically parse out and categorize photos.

This research was funded by the U.S. Office of Naval Research, the National Science Foundation and the UW.

####

For more information, please click here

Contacts:
Michelle Ma

206-543-2580

Ali Farhadi

206-221-8976

Santosh Divvala

Copyright © University of Washington

If you have a comment, please Contact us.

Issuers of news releases, not 7th Wave, Inc. or Nanotechnology Now, are solely responsible for the accuracy of the content.

Bookmark:
Delicious Digg Newsvine Google Yahoo Reddit Magnoliacom Furl Facebook

Related News Press

News and information

A nano-roundabout for light December 10th, 2016

Keeping electric car design on the right road: A closer look at the life-cycle impacts of lithium-ion batteries and proton exchange membrane fuel cells December 9th, 2016

Further improvement of qubit lifetime for quantum computers: New technique removes quasiparticles from superconducting quantum circuits December 9th, 2016

Scientists track chemical and structural evolution of catalytic nanoparticles in 3-D: Up-close, real-time, chemical-sensitive 3-D imaging offers clues for reducing cost/improving performance of catalysts for fuel-cell-powered vehicles and other applications December 8th, 2016

Software

Companies Now Can Bring Fast and Accurate Nanoparticle Analysis In-House November 11th, 2016

SUN shares its latest achievements during the 3rd Annual Project Meeting November 1st, 2016

Leti to Tackle Tomorrow's Research Strategies with Stanford University’s SystemX Alliance: French R&D Center Is the First Research Institute to Join the Collaboration and Provides Bridges Between Academia and Industry, Leveraging Alliance’s Potential October 4th, 2016

Park Systems Launches Park NX20 300mm Research Atomic Force Microscope with Full 300 mm Semiconductor Wafer Scan - Vastly Improving Productivity August 3rd, 2016

Govt.-Legislation/Regulation/Funding/Policy

Chemical trickery corrals 'hyperactive' metal-oxide cluster December 8th, 2016

Researchers peer into atom-sized tunnels in hunt for better battery: May improve lithium ion for larger devices, like cars December 8th, 2016

Scientists track chemical and structural evolution of catalytic nanoparticles in 3-D: Up-close, real-time, chemical-sensitive 3-D imaging offers clues for reducing cost/improving performance of catalysts for fuel-cell-powered vehicles and other applications December 8th, 2016

Exotic insulator may hold clue to key mystery of modern physics: Johns Hopkins-led research shows material living between classical and quantum worlds December 8th, 2016

Discoveries

A nano-roundabout for light December 10th, 2016

Keeping electric car design on the right road: A closer look at the life-cycle impacts of lithium-ion batteries and proton exchange membrane fuel cells December 9th, 2016

Further improvement of qubit lifetime for quantum computers: New technique removes quasiparticles from superconducting quantum circuits December 9th, 2016

Scientists track chemical and structural evolution of catalytic nanoparticles in 3-D: Up-close, real-time, chemical-sensitive 3-D imaging offers clues for reducing cost/improving performance of catalysts for fuel-cell-powered vehicles and other applications December 8th, 2016

Announcements

A nano-roundabout for light December 10th, 2016

Keeping electric car design on the right road: A closer look at the life-cycle impacts of lithium-ion batteries and proton exchange membrane fuel cells December 9th, 2016

Further improvement of qubit lifetime for quantum computers: New technique removes quasiparticles from superconducting quantum circuits December 9th, 2016

Chemical trickery corrals 'hyperactive' metal-oxide cluster December 8th, 2016

Military

Exotic insulator may hold clue to key mystery of modern physics: Johns Hopkins-led research shows material living between classical and quantum worlds December 8th, 2016

ANU invention to inspire new night-vision specs December 7th, 2016

Infrared instrumentation leader secures exclusive use of Vantablack coating December 5th, 2016

Quantum obstacle course changes material from superconductor to insulator December 1st, 2016

Artificial Intelligence

GLOBALFOUNDRIES Extends FDX™ Roadmap with 12nm FD-SOI Technology: 12FDXTM delivers full-node scaling, ultra-low power, and software-controlled performance on demand September 8th, 2016

Artificial synapse rivals biological ones in energy consumption June 21st, 2016

How insights into human learning can foster smarter artificial intelligence June 15th, 2016

Research showing why hierarchy exists will aid the development of artificial intelligence June 13th, 2016

NanoNews-Digest
The latest news from around the world, FREE




  Premium Products
NanoNews-Custom
Only the news you want to read!
 Learn More
NanoTech-Transfer
University Technology Transfer & Patents
 Learn More
NanoStrategies
Full-service, expert consulting
 Learn More











ASP
Nanotechnology Now Featured Books




NNN

The Hunger Project