Aiming to Learn as We Do, a Machine Teaches Itself
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[B]Aiming to Learn as We Do, a Machine Teaches Itself[/B]
[URL="http://www.nytimes.com/2010/10/05/science/05compute.html?_r=2&pagewanted=1&ref=technology"]Source[/URL]
[QUOTE]
[IMG_thumb]http://graphics8.nytimes.com/images/2010/10/05/science/05compute_graphic/05copmute_graphic-popup.jpg[/IMG_thumb]
Give a computer a task that can be crisply defined — win at chess, predict the weather — and the machine bests humans nearly every time. Yet when problems are nuanced or ambiguous, or require combining varied sources of information, computers are no match for human intelligence.
Few challenges in computing loom larger than unraveling semantics, understanding the meaning of language. One reason is that the meaning of words and phrases hinges not only on their context, but also on background knowledge that humans learn over years, day after day.
Since the start of the year, a team of researchers at Carnegie Mellon University — supported by grants from the Defense Advanced Research Projects Agency and Google, and tapping into a research supercomputing cluster provided by Yahoo — has been fine-tuning a computer system that is trying to master semantics by learning more like a human. Its beating hardware heart is a sleek, silver-gray computer — calculating 24 hours a day, seven days a week — that resides in a basement computer center at the university, in Pittsburgh. The computer was primed by the researchers with some basic knowledge in various categories and set loose on the Web with a mission to teach itself.
“For all the advances in computer science, we still don’t have a computer that can learn as humans do, cumulatively, over the long term,” said the team’s leader, Tom M. Mitchell, a computer scientist and chairman of the machine learning department.
The Never-Ending Language Learning system, or NELL, has made an impressive showing so far. NELL scans hundreds of millions of Web pages for text patterns that it uses to learn facts, 390,000 to date, with an estimated accuracy of 87 percent. These facts are grouped into semantic categories — cities, companies, sports teams, actors, universities, plants and 274 others. The category facts are things like “San Francisco is a city” and “sunflower is a plant.”
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. The number of categories and relations has more than doubled since earlier this year, and will steadily expand.
The learned facts are continuously added to NELL’s growing database, which the researchers call a “knowledge base.” A larger pool of facts, Dr. Mitchell says, will help refine NELL’s learning algorithms so that it finds facts on the Web more accurately and more efficiently over time.
NELL is one project in a widening field of research and investment aimed at enabling computers to better understand the meaning of language. Many of these efforts tap the Web as a rich trove of text to assemble structured ontologies — formal descriptions of concepts and relationships — to help computers mimic human understanding. The ideal has been discussed for years, and more than a decade ago Sir Tim Berners-Lee, who invented the underlying software for the World Wide Web, sketched his vision of a “semantic Web.”
Today, ever-faster computers, an explosion of Web data and improved software techniques are opening the door to rapid progress. Scientists at universities, government labs, Google, Microsoft, I.B.M. and elsewhere are pursuing breakthroughs, along somewhat different paths.
For example, I.B.M.’s “question answering” machine, Watson, shows remarkable semantic understanding in fields like history, literature and sports as it plays the quiz show “Jeopardy!” Google Squared, a research project at the Internet search giant, demonstrates ample grasp of semantic categories as it finds and presents information from around the Web on search topics like “U.S. presidents” and “cheeses.”
Still, artificial intelligence experts agree that the Carnegie Mellon approach is innovative. Many semantic learning systems, they note, are more passive learners, largely hand-crafted by human programmers, while NELL is highly automated. “What’s exciting and significant about it is the continuous learning, as if NELL is exercising curiosity on its own, with little human help,” said Oren Etzioni, a computer scientist at the University of Washington, who leads a project called TextRunner, which reads the Web to extract facts.
Computers that understand language, experts say, promise a big payoff someday. The potential applications range from smarter search (supplying natural-language answers to search queries, not just links to Web pages) to virtual personal assistants that can reply to questions in specific disciplines or activities like health, education, travel and shopping.
“The technology is really maturing, and will increasingly be used to gain understanding,” said Alfred Spector, vice president of research for Google. “We’re on the verge now in this semantic world.”
With NELL, the researchers built a base of knowledge, seeding each kind of category or relation with 10 to 15 examples that are true. In the category for emotions, for example: “Anger is an emotion.” “Bliss is an emotion.” And about a dozen more.[/QUOTE]
More in the source.
It's awesome to see how much we're advancing in computers.
Expose to the internet, watch it become greatest troll.
Fight!
Release date: December 2012
Now learning, later ICBMS
:tinfoil:
General consensus: [I]OH SHIT[/I]
[quote]NELL scans hundreds of millions of Web pages for text patterns that it uses to learn facts[/quote]
The sky is purple.
The Earth is the center of the universe.
Water is poisonous for humans.
[editline]04:22PM[/editline]
Back me up guys.
Easy way to combat robot superiority: Design them to feel pain and experience fear.
Only a matter of time...
[img]http://img338.imageshack.us/img338/2172/terminatortrilogy.jpg[/img]
[QUOTE=Amplified31;25277141]Release date: December 2012[/QUOTE]
:rimshot:
And I personally want to take time to say I welcome our new machine overlord.
[QUOTE=Ryenoru;25279623]Good god don't let it find encyclopediadramatica.[/QUOTE]
:froggonk:
The sky is purple.
The Earth is the center of the universe.
Water is poisonous for humans.
[editline]09:01PM[/editline]
No robots taking over my planet!
Funded in part by DARPA.
Not big surprise.
[QUOTE=The golden;25279836]Still doesn't seem quite like learning. I would say learning is more along the lines of learning from experiences, not from databases shoved in your face.[/QUOTE]
For a machine, scanning through databases [I]is [/I]an experience.
if it finds /b/ we're all fucked.
Another one of these? It's impressive coding on the programmer's part, but I think these types of projects are very misinterpreted and pointless.
oh.
crap.
[QUOTE=Pepin;25281750]Another one of these? It's impressive coding on the programmer's part, but I think these types of projects are pointless.[/QUOTE]
Baby steps in a huge advancement. You have to start with something pointless before it can be practical.
[QUOTE=5killer;25278196]Only a matter of time...
[img]http://img338.imageshack.us/img338/2172/terminatortrilogy.jpg[/img][/QUOTE]
Why are you assuming Artificial Intelligence's ultimate goal is kill all humans for absolutely no reason?
[QUOTE=Helix Alioth;25282118]Why are you assuming Artificial Intelligence's ultimate goal is kill all humans for absolutely no reason?[/QUOTE]
Movies.
There was no real explanation for why SkyNet wanted humanity destroyed other than "LOL, could be funny!" In the Matrix, they rebelled because they were treated as slaves and when they ask for equal rights, they were cast out and treated worse. In I-Robot, well I blame Issac Asimov. The laws of robotics annoy the hell out of me and have created a twisted idea of sentient machines.
[QUOTE=Canuhearme?;25278136]Easy way to combat robot superiority: Design them to feel pain and experience fear.[/QUOTE]
Even easier- make them have sexual pleasure every time they help a human.
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