IBM's Watson Jeopardy! System
An IBM's Watson Jeopardy! System was a IBM Watson System that competed in and won a Jeopardy! Contest in February 2010.
- Context:
- It was composed of 90 IBM Power 750 Servers each with a 3.5 GHz POWER7 eight-core CPU and four threads per core (which translates to 2,880 POWER7 Cores???)
- It had 16 Terabytes of RAM, used 4 Terabytes of Harddisk, and could achieve 80 Teraflops.
- It was a Kernel-based Virtual Machine.
- …
- Counter-Example(s):
- See: David Ferrucci, IBM DeepQA Project.
References
2013
- Lucas Mearian. (2013). “IBM: Watson will eventually fit on a smartphone, diagnose illness.” In: ComputerWorld, March 5, 2013
- QUOTE: IBM is also working to program Watson so that it can pass the U.S. Medical Licensing Examination. Yes, the "Dr. Watson" moniker used in the media will someday be applicable. … "It was the size of a master bedroom, but now it's the size of a bathroom," Pelino said "It will get to be a handheld device by 2020 based on a trajectory of Moore's Law." … One area IBM scientists are working to improve with Watson is its ability to process unstructured data - physicians' notes, research published in peer-reviewed medical and science journals, radiological images, biofeedback from wireless monitoring devices, and even comment threads from online patient communities. All of that information can be used in the melting pot of data analytics.
- Jo Best. (2013). “IBM Watson: How the Jeopardy-winning supercomputer was born, and what it wants to do next.” In: TechRepublic.com, 2013-09-09.
- QUOTE: "The system today compared to the Jeopardy system is approximately 240 percent faster and it is one-sixteenth the size. The system that was the size of a master bedroom will now run in a system the size of the vegetable drawer in your double-drawer refrigerator."
To get Watson from Jeopardy to oncology, there were three processes that the Watson team went through: content adaptation, training adaptation, and functional adaptation – or, to put it another way, feeding it medical information and having it weighted appropriately; testing it out with some practice questions; then making any technical adjustments needed – tweaking taxonomies, for example.
- QUOTE: "The system today compared to the Jeopardy system is approximately 240 percent faster and it is one-sixteenth the size. The system that was the size of a master bedroom will now run in a system the size of the vegetable drawer in your double-drawer refrigerator."
2012
- (Lally et al., 2012) ⇒ Adam Lally, JM Prager, MC McCord, BK Boguraev, Siddharth Patwardhan, James Fan, Paul Fodor, and Jennifer Chu-Carroll. (2012). “Question Analysis: How Watson Reads a Clue.” In: IBM Journal of Research and Development, 56(3-4).
- (Ferrucci, Levas et al., 2012) ⇒ David Ferrucci, Anthony Levas, Sugato Bagchi, David Gondek, and Erik Mueller. (2012). “Watson: Beyond Jeopardy." Artificial Intelligence (August 2012). doi:10.1016/j.artint.2012.06.009
2011
- http://en.wikipedia.org/wiki/Watson_%28artificial_intelligence_software%29
- http://www-943.ibm.com/innovation/us/watson/what-is-watson/index.html
- http://www.ibmsystemsmag.com/ibmi/Watson_specs/35977p1.aspx
- (Baker, 2011) ⇒ Stephen Baker. (2011). “Final Jeopardy: Man vs. Machine and the Quest to Know Everything.” Houghton Mifflin Harcourt. ISBN:0547483163>
- (Apache Blog, 2011-02-14) ⇒ Apache Blog. (2011). “Apache UIMA and Apache Hadoop Advance Data Intelligence and Semantics Capabilities of Watson Supercomputer." Feb 14, 2011
- Apache UIMA: standards-based frameworks, infrastructure and components that facilitate the analysis and annotation of an array of unstructured content (such as text, audio and video). Watson uses Apache UIMA for real-time content analytics and natural language processing, to comprehend clues, find possible answers, gather supporting evidence, score each answer, compute its confidence in each answer, and improve contextual understanding (machine learning) – all under 3 seconds.
Apache Hadoop: software framework that enables data-intensive distributed applications to work with thousands of nodes and petabytes of data. A foundation of Cloud computing, Apache Hadoop enables Watson to access, sort, and process data in a massively parallel system (90+ server cluster/2,880 processor cores/16 terabytes of RAM/4 terabytes of disk storage).
The Watson system uses UIMA as its principal infrastructure for component interoperability and makes extensive use of the UIMA-AS scale-out capabilities that can exploit modern, highly parallel hardware architectures. UIMA manages all work flow and communication between processes, which are spread across the cluster. Apache Hadoop manages the task of preprocessing Watson's enormous information sources by deploying UIMA pipelines as Hadoop mappers, running UIMA analytics.
- Apache UIMA: standards-based frameworks, infrastructure and components that facilitate the analysis and annotation of an array of unstructured content (such as text, audio and video). Watson uses Apache UIMA for real-time content analytics and natural language processing, to comprehend clues, find possible answers, gather supporting evidence, score each answer, compute its confidence in each answer, and improve contextual understanding (machine learning) – all under 3 seconds.
2010
- (Ferruci et al., 2010) ⇒ David Ferrucci, Eric Brown, Jennifer Chu-Carroll, James Fan, David Gondek, Aditya A. Kalyanpur, Adam Lally, J. William Murdock, Eric Nyberg, John Prager, Nico Schlaefer, Chris Welty(2011). “Building Watson: An overview of the DeepQA project." In: AI Magazine, 31(3).
- ABSTRACT: IBM Research undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV Quiz show, Jeopardy! The extent of the challenge includes fielding a real-time automatic contestant on the show, not merely a laboratory exercise. The Jeopardy! Challenge helped us address requirements that led to the design of the DeepQA architecture and the implementation of Watson. After 3 years of intense research and development by a core team of about 20 researches, Watson is performing at human expert-levels in terms of precision, confidence and speed at the Jeopardy! Quiz show. Our results strongly suggest that DeepQA is an effective and extensible architecture that may be used as a foundation for combining, deploying, evaluating and advancing a wide range of algorithmic techniques to rapidly advance the field of QA.
- QUOTE: As a measure of the Jeopardy Challenge’s breadth of domain, we analyzed a random sample of 20,000 questions extracting the lexical answer type (LAT) when present. We define a LAT to be a word in the clue that indicates the type of the answer, independent of assigning semantics to that word.