Guess I missed the start, which was Yesterday....
Next week the IBM supercomputer known as "Watson" will take on two of the most accomplished Jeopardy players of all time, Ken Jennings and Brad Rutter, in a three-game match starting on February 14. If Watson manages to best the humans, it will represent the most important advance in machine intelligence since IBM's "Deep Blue" beat chess grandmaster Garry Kasparov in 1997. But this time around, the company also plans to make a business case for the technology. Trivial pursuit this is not.
And impressive technology it is. On the hardware side, Watson is comprised of 90 Power 750 servers, 16 TB of memory and 4 TB of disk storage, all housed in a relatively compact ten racks. The 750 is IBM's elite Power7-based server targeted for high-end enterprise analytics. (The Power 755 is geared toward high performance technical computing and differs only marginally in CPU speed, memory capacity, and storage options.) Although the enterprise version can be ordered with 1 to 4 sockets of 6-core or 8-core Power7 chips, Watson is maxed out with the 4-socket, 8-core configuration using the top bin 3.55 GHz processors.
The 360 Power7 chips that make up Watson's brain represent IBM's best and brightest processor technology. Each Power7 is capable of over 500 GB/second of aggregate bandwidth, making it particularly adept at manipulating data at high speeds. FLOPS-wise, a 3.55 GHz Power7 delivers 218 Linpack gigaflops. For comparison, the POWER2 SC processor, which was the chip that powered cyber-chessmaster Deep Blue, managed a paltry 0.48 gigaflops, with the whole machine delivering a mere 11.4 Linpack gigaflops.
But FLOPS are not the real story here. Watson's question-answering software presumably makes little use of floating-point number crunching. To deal with the game scenario, the system had to be endowed with a rather advanced version of natural language processing. But according to David Ferrucci, principal investigator for the project, it goes far beyond language smarts. The software system, called DeepQA, also incorporates machine learning, knowledge representation, and deep analytics.
Even so, the whole application rests on first understanding the Jeopardy clues, which, because they employ colloquialisms and often obscure references, can be challenging even for humans. That's why this is such a good test case for natural language processing. Ferrucci says the ability to understand language is destined to become a very important aspect of computers. "It has to be that way," he says. "We just cant imagine a future without it."
But it's the analysis component that we associate with real "intelligence." The approach here reflects the open domain nature of the problem. According to Ferrucci, it wouldn't have made sense to simply construct a database corresponding to possible Jeopardy clues. Such a model would have supported only a small fraction of the possible topics available to Jeopardy. Rather their approach was to use "as is" information sources -- encyclopedias, dictionaries, thesauri, plays, books, etc. -- and make the correlations dynamically.
The trick of course is to do all the processing in real-time. Contestants, at least the successful ones, need to provide an answer in just a few seconds. When the software was run on a lone 2.6 GHz CPU, it took around 2 hours to process a typical Jeopardy clue -- not a very practical implementation. But when they parallelized the algorithms across the 2,880-core Watson, they were able to cut the processing time from a couple of hours to between 2 and 6 seconds.
Even at that, Watson doesn't just spit out the answers. It forms hypotheses based on the evidence it finds and scores them at various confidence levels. Watson is programmed not to buzz in until it reaches a confidence of at least 50 percent, although this parameter can be self-adjusted depending on the game situation.
To accomplish all this, DeepQA employs an ensemble of algorithms -- about a million lines of code --- to gather and score the evidence. These include temporal reasoning algorithms to correlate times with events, statistical paraphrasing algorithms to evaluate semantic context, and geospatial reasoning to correlate locations.
It can also dynamically form associations, both in training and at game time, to connect disparate ideas. For example it can learn that inventors can patent information or that officials can submit resignations. Watson also shifts the weight it assigns to different algorithms based on which ones are delivering the more accurate correlations. This aspect of machine learning allows Watson to get "smarter" the more it plays the game.
Next week the IBM supercomputer known as "Watson" will take on two of the most accomplished Jeopardy players of all time, Ken Jennings and Brad Rutter, in a three-game match starting on February 14. If Watson manages to best the humans, it will represent the most important advance in machine intelligence since IBM's "Deep Blue" beat chess grandmaster Garry Kasparov in 1997. But this time around, the company also plans to make a business case for the technology. Trivial pursuit this is not.
The 360 Power7 chips that make up Watson's brain represent IBM's best and brightest processor technology. Each Power7 is capable of over 500 GB/second of aggregate bandwidth, making it particularly adept at manipulating data at high speeds. FLOPS-wise, a 3.55 GHz Power7 delivers 218 Linpack gigaflops. For comparison, the POWER2 SC processor, which was the chip that powered cyber-chessmaster Deep Blue, managed a paltry 0.48 gigaflops, with the whole machine delivering a mere 11.4 Linpack gigaflops.
But FLOPS are not the real story here. Watson's question-answering software presumably makes little use of floating-point number crunching. To deal with the game scenario, the system had to be endowed with a rather advanced version of natural language processing. But according to David Ferrucci, principal investigator for the project, it goes far beyond language smarts. The software system, called DeepQA, also incorporates machine learning, knowledge representation, and deep analytics.
Even so, the whole application rests on first understanding the Jeopardy clues, which, because they employ colloquialisms and often obscure references, can be challenging even for humans. That's why this is such a good test case for natural language processing. Ferrucci says the ability to understand language is destined to become a very important aspect of computers. "It has to be that way," he says. "We just cant imagine a future without it."
But it's the analysis component that we associate with real "intelligence." The approach here reflects the open domain nature of the problem. According to Ferrucci, it wouldn't have made sense to simply construct a database corresponding to possible Jeopardy clues. Such a model would have supported only a small fraction of the possible topics available to Jeopardy. Rather their approach was to use "as is" information sources -- encyclopedias, dictionaries, thesauri, plays, books, etc. -- and make the correlations dynamically.
The trick of course is to do all the processing in real-time. Contestants, at least the successful ones, need to provide an answer in just a few seconds. When the software was run on a lone 2.6 GHz CPU, it took around 2 hours to process a typical Jeopardy clue -- not a very practical implementation. But when they parallelized the algorithms across the 2,880-core Watson, they were able to cut the processing time from a couple of hours to between 2 and 6 seconds.
Even at that, Watson doesn't just spit out the answers. It forms hypotheses based on the evidence it finds and scores them at various confidence levels. Watson is programmed not to buzz in until it reaches a confidence of at least 50 percent, although this parameter can be self-adjusted depending on the game situation.
To accomplish all this, DeepQA employs an ensemble of algorithms -- about a million lines of code --- to gather and score the evidence. These include temporal reasoning algorithms to correlate times with events, statistical paraphrasing algorithms to evaluate semantic context, and geospatial reasoning to correlate locations.
It can also dynamically form associations, both in training and at game time, to connect disparate ideas. For example it can learn that inventors can patent information or that officials can submit resignations. Watson also shifts the weight it assigns to different algorithms based on which ones are delivering the more accurate correlations. This aspect of machine learning allows Watson to get "smarter" the more it plays the game.
