In today’s Academic Minute, Dr. Tom Mitchell of Carnegie Mellon University introduces us to NELL, a language learning computer.
Tom Mitchell is a professor of computer science and Chair of the Machine Learning Department at Carnegie Mellon University in Pittsburgh, Pennsylvania. His current research projects are focused on determining how the human brain represents word meaning and creating a computer that learns independently by reading the Internet. He earned his Ph.D. at Stanford University.
Dr. Tom Mitchell – Language Learning Computer
Our Never-Ending Language Learner (we call it NELL, for short) is a computer program that is learning, 24 hours a day, to read the web. Each day NELL has two tasks. First, it must collect more factual beliefs by *reading* text it finds on the web -- beliefs like “Obama is president of the US,” or “T-shirts are often worn with blue jeans.” Second, each day NELL must *learn* to read better than it could the day before, so that it can extract more facts, more accurately tomorrow.
NELL has been running nonstop like this for over three years, and the result so far is a collection of 70 million beliefs NELL has read, which it holds with different confidences. And, NELL is learning to read better – it is a better reader today than last month, and much better than last year.
Beyond learning to read, NELL is also now learning to *reason* -- to draw its own conclusions from the facts it has read. How? By analyzing the 70 million beliefs it has read, NELL can discover regularities that enable it to infer new beliefs. For example, NELL discovered that usually “IF a person plays on some team, AND that team plays baseball, THEN that person also plays the sport baseball.” NELL uses this self-discovered common-sense rule – and tens of thousands of others – to reason its way to new beliefs that it has not yet read.
You can follow NELL’s progress, and give it some guidance as it tries to learn, by visiting its website.