A computing system that rivals a human’s ability to answer questions posed in natural language with speed, accuracy and confidence:
IBM Watson
Read more and watch the video from the Engineering blog:
IBM Watson
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Read more and watch the video from the Engineering blog:
Engineering Intelligence: Why IBM’s Jeopardy-Playing Computer Is So Important
sources:
Mashable: Engineering Intelligence: Why IBM’s Jeopardy-Playing Computer Is So Important
IBM Watson: Countdown to Jeopardy!
Language is arguably what makes us most human. Even the smartest and chattiest of the animal kingdom have nothing on our lingual cognition.
In computer science, the Holy Grail has long been to build software that understands — and can interact with — natural human language. But dreams of a real-life Johnny 5 or C-3PO have always been dashed on the great gulf between raw processing power and the architecture of the human mind. Computers are great at crunching large sets of numbers. The mind excels at assumption and nuance.
Enter Watson, an artificial intelligence project from IBM that’s over five years in the making and about to prove itself to the world next week. The supercomputer, named for the technology company’s founder, will be competing with championship-level contestants on the quiz show Jeopardy!. The episodes will air on February 14, 15 and 16, and if recent practice rounds are any indication, Watson is in it to win it.
At first blush, building a computer with vast amounts of knowledge at its disposal seems mundane in our age. Google has already indexed a wide swath of the world’s codified information, and can surface almost anything with a handful of keywords. The difference is that Google doesn’t understand a question like, “What type of weapon is also the name of a Beatles record?” It may yield some information about The Beatles, or perhaps an article that mentions weapons and The Beatles, but it’s not conceptualizing that the weapon and recording in question have the same name: Revolver.
Achieving this is what makes Watson a contender on Jeopardy!, a quiz known for nuance, puns, double entendres and complex language designed to mislead human contestants. Google Search, or any common semantic software, wouldn’t stand a chance against these lingual acrobatics.
What Watson achieves is, quite frankly, mind boggling. And the rig that sustains it is equally so, with hardware consisting of 90 IBM Power 750 Express servers. Each server utilizes a 3.5 GHz POWER7 eight-core processor, with four threads per core. Top that off with 16 terabytes of RAM, and you’ve got a hearty machine that can almost run Call of Duty: Black Ops without lag.
In seriousness, this computational muscle is what drives IBM’s DeepQA software, the real star of the Watson show. Hundreds of algorithms run simultaneously in order to deduce meaning from a clue, check it against hordes of relevant data, and decide which response is most likely to be correct. Watson then determines if it is “confident” enough in the answer to buzz in at all. The entire process takes place in under three seconds.
This feat of answering “open questions,” as computer scientists call them, puts IBM’s last big AI triumph — the chess-playing, Garry Kasparov-beating Deep Blue — into perspective. While chess is a complex game, the number of legal moves available at any time is finite. Not so, with natural language.
To document this historic leap in computer science, IBM allowed one journalist — Stephen Baker — unmatched access inside its labs. Baker’s new book, Final Jeopardy: Man vs. Machine and the Quest to Know Everything, chronicles Watson from the early days of development to its deployment behind the Jeopardy! podium. The e-book is available now, and to avoid spoilers, readers will be able to download the final chapter, which analyzes Watson’s televised match against Ken Jennings and Brad Rutter, the day after the finale airs (February 17).
We had the opportunity to interview Mr. Baker and discuss what makes Watson tick, as well as the project’s ramifications for the future of artificial intelligence.
sources:
Mashable: Engineering Intelligence: Why IBM’s Jeopardy-Playing Computer Is So Important
IBM Watson: Countdown to Jeopardy!
Language is arguably what makes us most human. Even the smartest and chattiest of the animal kingdom have nothing on our lingual cognition.
In computer science, the Holy Grail has long been to build software that understands — and can interact with — natural human language. But dreams of a real-life Johnny 5 or C-3PO have always been dashed on the great gulf between raw processing power and the architecture of the human mind. Computers are great at crunching large sets of numbers. The mind excels at assumption and nuance.
Enter Watson, an artificial intelligence project from IBM that’s over five years in the making and about to prove itself to the world next week. The supercomputer, named for the technology company’s founder, will be competing with championship-level contestants on the quiz show Jeopardy!. The episodes will air on February 14, 15 and 16, and if recent practice rounds are any indication, Watson is in it to win it.
At first blush, building a computer with vast amounts of knowledge at its disposal seems mundane in our age. Google has already indexed a wide swath of the world’s codified information, and can surface almost anything with a handful of keywords. The difference is that Google doesn’t understand a question like, “What type of weapon is also the name of a Beatles record?” It may yield some information about The Beatles, or perhaps an article that mentions weapons and The Beatles, but it’s not conceptualizing that the weapon and recording in question have the same name: Revolver.
Achieving this is what makes Watson a contender on Jeopardy!, a quiz known for nuance, puns, double entendres and complex language designed to mislead human contestants. Google Search, or any common semantic software, wouldn’t stand a chance against these lingual acrobatics.
What Watson achieves is, quite frankly, mind boggling. And the rig that sustains it is equally so, with hardware consisting of 90 IBM Power 750 Express servers. Each server utilizes a 3.5 GHz POWER7 eight-core processor, with four threads per core. Top that off with 16 terabytes of RAM, and you’ve got a hearty machine that can almost run Call of Duty: Black Ops without lag.
In seriousness, this computational muscle is what drives IBM’s DeepQA software, the real star of the Watson show. Hundreds of algorithms run simultaneously in order to deduce meaning from a clue, check it against hordes of relevant data, and decide which response is most likely to be correct. Watson then determines if it is “confident” enough in the answer to buzz in at all. The entire process takes place in under three seconds.
This feat of answering “open questions,” as computer scientists call them, puts IBM’s last big AI triumph — the chess-playing, Garry Kasparov-beating Deep Blue — into perspective. While chess is a complex game, the number of legal moves available at any time is finite. Not so, with natural language.
To document this historic leap in computer science, IBM allowed one journalist — Stephen Baker — unmatched access inside its labs. Baker’s new book, Final Jeopardy: Man vs. Machine and the Quest to Know Everything, chronicles Watson from the early days of development to its deployment behind the Jeopardy! podium. The e-book is available now, and to avoid spoilers, readers will be able to download the final chapter, which analyzes Watson’s televised match against Ken Jennings and Brad Rutter, the day after the finale airs (February 17).
We had the opportunity to interview Mr. Baker and discuss what makes Watson tick, as well as the project’s ramifications for the future of artificial intelligence.
VIDEO: IBM Watson: Countdown to Jeopardy!
- Watson's ability to understand the meaning and context of human language, and rapidly process information to find precise answers to complex questions, holds enormous potential to transform how computers help people accomplish tasks in business and their personal lives. Watson will enable people to rapidly find specific answers to complex questions. The technology could be applied in areas such as health care, for accurately diagnosing patients, to improve online self-service help desks, to provide tourists and citizens with specific information regarding cities, prompt customer support via phone, and much more.
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