So, I was reading one of my favorite tech blogs, known as Gizmodo, today, and I came across this article that I thought was extremely amuzing, considering what the story was about.
Archive for the ‘Around the Net’ Category
At issue: well, pretty much everything. MobileMe, the App Store, antenna design, wireless email, location based services. As always, this will end either with a dismissal or one corporate monolith paying another corporate monolith a whole lot of money. At some point, though, we should get all these guys in group therapy, right?
Motorola Mobility Sues Apple for Patent Infringement
LIBERTYVILLE, Ill., Oct. 6 /PRNewswire-FirstCall/ — Motorola, Inc. (NYSE: MOT) today announced that its subsidiary, Motorola Mobility, Inc., has filed a complaint with the U.S. International Trade Commission (ITC) alleging that Apple’s iPhone, iPad, iTouch and certain Mac computers infringe Motorola patents. Motorola Mobility also filed patent infringement complaints against Apple (Nasdaq: AAPL) in the Northern District of Illinois and the Southern District of Florida.
Overall, Motorola Mobility’s three complaints include 18 patents, which relate to early-stage innovations developed by Motorola in key technology areas found on many of Apple’s core products and associated services, including MobileMe and the App Store. The Motorola patents include wireless communication technologies, such as WCDMA (3G), GPRS, 802.11 and antenna design, and key smartphone technologies including wireless email, proximity sensing, software application management, location-based services and multi-device synchronization.
Motorola Mobility has requested that the ITC commence an investigation into Apple’s use of Motorola’s patents and, among other things, issue an Exclusion Order barring Apple’s importation of infringing products, prohibiting further sales of infringing products that have already been imported, and halting the marketing, advertising, demonstration and warehousing of inventory for distribution and use of such imported products in the United States. In the District Court actions, Motorola Mobility has requested that Apple cease using Motorola’s patented technology and provide compensation for Apple’s past infringement.
Kirk Dailey, corporate vice president of intellectual property at Motorola Mobility, said, “Motorola has innovated and patented throughout every cycle of the telecommunications industry evolution, from Motorola’s invention of the cell phone to its development of premier smartphone products. We have extensively licensed our industry-leading intellectual property portfolio, consisting of tens of thousands of patents in the U.S. and worldwide. After Apple’s late entry into the telecommunications market, we engaged in lengthy negotiations, but Apple has refused to take a license. We had no choice but to file these complaints to halt Apple’s continued infringement. Motorola will continue to take all necessary steps to protect its R&D and intellectual property, which are critical to the company’s business.”
In a basement at Carnegie Mellon University, a computer is reading the web. It’s been doing so for nearly nine months, teaching itself the complexities and nuances of the English language. And the smarter it gets, the faster it learns.
That computer is NELL, the Never-Ending Language Learning system, and it’s the star of a project involving researchers from Carnegie Mellon, supercomputers from Yahoo!, and grants from Google and DARPA. The project’s aim is an elusive but important one: to design a machine that can figure out the subtleties of language all on its own. As Tom Mitchell, chairman of the school’s machine learning department explains, “we still don’t have a computer that can learn as humans do, cumulatively, over the long term.” NELL would be the first that does so.
The system trawls hundreds of millions of web pages, collecting facts and sorting them into one of 280 categories, classifications like cities, plants, or actors. It has learned nearly 400,000 such facts to date, with 87% accuracy. NELL also currently knows some 280 relations, pieces of language that connect two facts together. NELL probably knows that James Franco (actor) lives in (relation) New York City (city). And the more NELL learns, the faster and more efficient it gets at teaching itself:
Its tools include programs that extract and classify text phrases from the Web, programs that look for patterns and correlations, and programs that learn rules. For example, when the computer system reads the phrase “Pikes Peak,” it studies the structure – two words, each beginning with a capital letter, and the last word is Peak. That structure alone might make it probable that Pikes Peak is a mountain. But NELL also reads in several ways. It will mine for text phrases that surround Pikes Peak and similar noun phrases repeatedly. For example, “I climbed XXX.”
NELL, Dr. Mitchell explains, is designed to be able to grapple with words in different contexts, by deploying a hierarchy of rules to resolve ambiguity. This kind of nuanced judgment tends to flummox computers. “But as it turns out, a system like this works much better if you force it to learn many things, hundreds at once,” he said.
There are some instances in which an autodidactic computer can get off track. At one point, NELL sorted “internet cookies” into its “baked goods” category. That resulted in associated terms like “computer files” being labeled as baked goods, too. In these cases, Dr. Mitchell and his team correct the error and put NELL back on course. But he points out that no human learns completely on his own, either.
So what’s it all for? Well, a computer that understood language like a human could answer search queries not with links but with real answers. Personal computers could be operated simply by telling them what to do. And computerized assistants could understand requests like “go get me a sub sandwich” and not waste their time looking for a sandwich shop that was located in a submarine. Too bad! That’d be funny.