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From the confines of a basement laboratory in Pittsburgh, a powerful machine named NELL is slowly but surely getting smarter by reading internet-based content 24/7. The process is automated, so NELL can “read” at an astonishing pace. Over the course of millions and millions of documents, it is fine-tuning an incredible ability to detect and understand semantic relationships between people, places and things, an ability that was once thought to be unique to humans. With this final skill in place, the door could be open for machines to overthrow and possibly eliminate their long-time adversaries.

What are people doing to stop NELL’s rise? Nothing; in fact, they’re encouraging it!

Researchers at Carnegie Mellon University created the computer earlier this year after receiving several prestigious grants to explore this area of computer intelligence.

beamily The Never-Ending Language Learning System Project and the Semantic Web

As a recent NYTimes article explains, computers have traditionally failed to comprehend anything that is ambiguous or context-dependent. When a task involves human language, which is rich in synonyms, subtleties and shades of meaning, a machine is next to useless despite all of its powerful capabilities in other contexts.

But that all might be starting to change, as the NELL project aims to prove. The much-hyped “Semantic Web” is the next generation of technology in which data found on the internet will be linked together in meaningful ways based on categories, revealing relationships to machines that scan the text. NELL and other machines learn facts like “Austria is a country” and “Arnold Schwarzenegger is an actor.”

To illustrate another class of knowledge that NELL picks up, the article gives an NFL example, providing hope for millions that a computer could automatically win their Fantasy Football League for them in a few years:

NELL also learns facts that are relations between members of two categories. For example, Peyton Manning is a football player (category). The Indianapolis Colts is a football team (category). By scanning text patterns, NELL can infer with a high probability that Peyton Manning plays for the Indianapolis Colts — even if it has never read that Mr. Manning plays for the Colts. “Plays for” is a relation, and there are 280 kinds of relations (edit: between these two categories.) (article)

Those that are interested in creating this kind of technology or study how it works should consider an online Information Technology degree from SJU Online.

Just how intelligent the machine will become and the implications for future system design remain to be seen, but researchers are hoping that NELL doesn’t develop an addiction to mindless YouTube videos, Facebook or online casino gambling.

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