Must See TV: IBM Watson Heads for Jeopardy Showdown

By Michael Feldman

February 9, 2011

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.

The DeepQA programmers have also been refining the algorithms themselves over the past several years. In 2007, Watson could only answer a small fraction of Jeopardy clues with reasonable confidence and even at that, was only correct 47 percent of the time. When forced to answer the majority of the clues, like a grand champion would, it could only answer 15 percent correctly. By IBM’s own admission, Watson was playing “terrible.” The highest performing Jeopardy grand champions, like Jennings and Rutter, typically buzz in on 70 to 80 percent of the entries and give the correct answer 85 to 95 percent of time.

By 2010 Watson started playing at that level. Ferrucci says that while the system can’t buzz in on every question, it can now answer the vast majority of them in competitive time. “We can compete with grand champions in terms of precision, in terms of confidence, and in terms of speed,” he says.

In dozens of practice rounds against former Jeopardy champs, the computer was beating the humans with a 65 percent win rate. Watson also prevailed in a 15-question round against Jennings and Rutter in early January of this year. See the performance below.

None of this is a guarantee that Watson will prevail next week. But even if the machine just makes a decent showing, IBM will have pulled off quite possibly the best product placement in television history. Open domain question answering is not only one of the Holy Grails of artificial intelligence but has enormous potential for commercial applications. In areas as disparate as healthcare, tech support, business intelligence, security and finance, this type of platform could change those businesses irrevocably. John Kelly, senior vice president and director of IBM Research, boasts, “We’re going to revolutionize industries at a level that has never been done before.”

In the case of healthcare, it’s not a huge leap to imagine “expert” question answering systems helping doctors with medical diagnosis. A differential diagnosis is not much different from what Watson does when it analyzes a Jeopardy clue. Before it replaces Dr. House, though, the machine will have to prove itself in the game show arena.

If Jennings and Rutter defeat the supercomputer this time around, IBM will almost certainly ask for a rematch, as it did when Deep Blue initially lost its first chess match with Kasparov in 1996. The engineers will keep stroking the code and retraining the computer until Watson is truly unbeatable. Eventually the machine will prevail.

—–

For a broader discussion on this topic between the author and InterSect360 Research CEO Addison Snell, download this week’s HPCwire Soundbite podcast.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

RSC Reports 500Tflops, Hot Water Cooled System Deployed at JINR

April 18, 2018

RSC, developer of supercomputers and advanced HPC systems based in Russia, today reported deployment of “the world's first 100% ‘hot water’ liquid cooled supercomputer” at Joint Institute for Nuclear Research (JI Read more…

By Staff

New Device Spots Quantum Particle ‘Fingerprint’

April 18, 2018

Majorana particles have been observed by university researchers employing a device consisting of layers of magnetic insulators on a superconducting material. The advance opens the door to controlling the elusive particle Read more…

By George Leopold

Cray Rolls Out AMD-Based CS500; More to Follow?

April 18, 2018

Cray was the latest OEM to bring AMD back into the fold with introduction today of a CS500 option based on AMD’s Epyc processor line. The move follows Cray’s introduction of an ARM-based system (XC-50) last November. Read more…

By John Russell

HPE Extreme Performance Solutions

Hybrid HPC is Speeding Time to Insight and Revolutionizing Medicine

High performance computing (HPC) is a key driver of success in many verticals today, and health and life science industries are extensively leveraging these capabilities. Read more…

Hennessy & Patterson: A New Golden Age for Computer Architecture

April 17, 2018

On Monday June 4, 2018, 2017 A.M. Turing Award Winners John L. Hennessy and David A. Patterson will deliver the Turing Lecture at the 45th International Symposium on Computer Architecture (ISCA) in Los Angeles. The Read more…

By Staff

Cray Rolls Out AMD-Based CS500; More to Follow?

April 18, 2018

Cray was the latest OEM to bring AMD back into the fold with introduction today of a CS500 option based on AMD’s Epyc processor line. The move follows Cray’ Read more…

By John Russell

IBM: Software Ecosystem for OpenPOWER is Ready for Prime Time

April 16, 2018

With key pieces of the IBM/OpenPOWER versus Intel/x86 gambit settling into place – e.g., the arrival of Power9 chips and Power9-based systems, hyperscaler sup Read more…

By John Russell

US Plans $1.8 Billion Spend on DOE Exascale Supercomputing

April 11, 2018

On Monday, the United States Department of Energy announced its intention to procure up to three exascale supercomputers at a cost of up to $1.8 billion with th Read more…

By Tiffany Trader

Cloud-Readiness and Looking Beyond Application Scaling

April 11, 2018

There are two aspects to consider when determining if an application is suitable for running in the cloud. The first, which we will discuss here under the title Read more…

By Chris Downing

Transitioning from Big Data to Discovery: Data Management as a Keystone Analytics Strategy

April 9, 2018

The past 10-15 years has seen a stark rise in the density, size, and diversity of scientific data being generated in every scientific discipline in the world. Key among the sciences has been the explosion of laboratory technologies that generate large amounts of data in life-sciences and healthcare research. Large amounts of data are now being stored in very large storage name spaces, with little to no organization and a general unease about how to approach analyzing it. Read more…

By Ari Berman, BioTeam, Inc.

IBM Expands Quantum Computing Network

April 5, 2018

IBM is positioning itself as a first mover in establishing the era of commercial quantum computing. The company believes in order for quantum to work, taming qu Read more…

By Tiffany Trader

FY18 Budget & CORAL-2 – Exascale USA Continues to Move Ahead

April 2, 2018

It was not pretty. However, despite some twists and turns, the federal government’s Fiscal Year 2018 (FY18) budget is complete and ended with some very positi Read more…

By Alex R. Larzelere

Nvidia Ups Hardware Game with 16-GPU DGX-2 Server and 18-Port NVSwitch

March 27, 2018

Nvidia unveiled a raft of new products from its annual technology conference in San Jose today, and despite not offering up a new chip architecture, there were still a few surprises in store for HPC hardware aficionados. Read more…

By Tiffany Trader

Inventor Claims to Have Solved Floating Point Error Problem

January 17, 2018

"The decades-old floating point error problem has been solved," proclaims a press release from inventor Alan Jorgensen. The computer scientist has filed for and Read more…

By Tiffany Trader

Researchers Measure Impact of ‘Meltdown’ and ‘Spectre’ Patches on HPC Workloads

January 17, 2018

Computer scientists from the Center for Computational Research, State University of New York (SUNY), University at Buffalo have examined the effect of Meltdown Read more…

By Tiffany Trader

Russian Nuclear Engineers Caught Cryptomining on Lab Supercomputer

February 12, 2018

Nuclear scientists working at the All-Russian Research Institute of Experimental Physics (RFNC-VNIIEF) have been arrested for using lab supercomputing resources to mine crypto-currency, according to a report in Russia’s Interfax News Agency. Read more…

By Tiffany Trader

How the Cloud Is Falling Short for HPC

March 15, 2018

The last couple of years have seen cloud computing gradually build some legitimacy within the HPC world, but still the HPC industry lies far behind enterprise I Read more…

By Chris Downing

Chip Flaws ‘Meltdown’ and ‘Spectre’ Loom Large

January 4, 2018

The HPC and wider tech community have been abuzz this week over the discovery of critical design flaws that impact virtually all contemporary microprocessors. T Read more…

By Tiffany Trader

Fast Forward: Five HPC Predictions for 2018

December 21, 2017

What’s on your list of high (and low) lights for 2017? Volta 100’s arrival on the heels of the P100? Appearance, albeit late in the year, of IBM’s Power9? Read more…

By John Russell

How Meltdown and Spectre Patches Will Affect HPC Workloads

January 10, 2018

There have been claims that the fixes for the Meltdown and Spectre security vulnerabilities, named the KPTI (aka KAISER) patches, are going to affect applicatio Read more…

By Rosemary Francis

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Leading Solution Providers

Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate Read more…

By Rob Farber

Lenovo Unveils Warm Water Cooled ThinkSystem SD650 in Rampup to LRZ Install

February 22, 2018

This week Lenovo took the wraps off the ThinkSystem SD650 high-density server with third-generation direct water cooling technology developed in tandem with par Read more…

By Tiffany Trader

AI Cloud Competition Heats Up: Google’s TPUs, Amazon Building AI Chip

February 12, 2018

Competition in the white hot AI (and public cloud) market pits Google against Amazon this week, with Google offering AI hardware on its cloud platform intended Read more…

By Doug Black

HPC and AI – Two Communities Same Future

January 25, 2018

According to Al Gara (Intel Fellow, Data Center Group), high performance computing and artificial intelligence will increasingly intertwine as we transition to Read more…

By Rob Farber

New Blueprint for Converging HPC, Big Data

January 18, 2018

After five annual workshops on Big Data and Extreme-Scale Computing (BDEC), a group of international HPC heavyweights including Jack Dongarra (University of Te Read more…

By John Russell

US Plans $1.8 Billion Spend on DOE Exascale Supercomputing

April 11, 2018

On Monday, the United States Department of Energy announced its intention to procure up to three exascale supercomputers at a cost of up to $1.8 billion with th Read more…

By Tiffany Trader

Momentum Builds for US Exascale

January 9, 2018

2018 looks to be a great year for the U.S. exascale program. The last several months of 2017 revealed a number of important developments that help put the U.S. Read more…

By Alex R. Larzelere

Google Chases Quantum Supremacy with 72-Qubit Processor

March 7, 2018

Google pulled ahead of the pack this week in the race toward "quantum supremacy," with the introduction of a new 72-qubit quantum processor called Bristlecone. Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This