IBM Watson Does Not Answer Questions Like Humans

Posted 19 February 2011 by

Today, 2/14/2011, the first of three Jeopardy! sessions between the top two Jeopardy! champions and IBM Watson will air on national TV. As each question is asked at lot will be taking place and many of us will be wondering just what is going on inside IBM Watson. Just what is going on?

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While IBM Watson’s  entire execution infrastructure has not be published, we do know that each compute element consists of a commercially available IBM POWER 750

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The entire interconnected cluster looks like a set of library shelves.
Many of us will wonder each time a question is asked what is going on in the three seconds given to the contestants. As humans, we can more or less understand being a Jeopardy! contestant. Many people will invariably not know the answer in three second but will retort after the correct response is made — “Oh, I knew that!”. Watson is not doing that, although it has been reported that IBM Watson has a good idea of the types of questions and answers that have been previously asked on Jeopardy! In contrast, Watson’s POWER7 processors are pumping through 15 TB of data (equivalent to about 200 millions pages of text) at a rate 500 GB/s each, concurrently. But first, Watson has to understand the question. It has to determine verbs, nouns, objects and moreover, nuances in the English language not part generally part of the standard English 101 class. Next, Watson must look for the best answer. What might be the basic applications that are used to accomplish this massive test.
It has been reported that Watson runs on Linux, but also DeepQ&A (Watson’s SW application stack) uses Hadoop and UIMA applications. UIMA stands for Unstructured Information Management Architecture, and according to wikipedia, “UIMA is a component software architecture for the development, discovery, composition, and deployment of multi-modal analytics for the analysis of unstructured information and its integration with search technologies developed by IBM. The source code for a reference implementation of this framework has been made available on SourceForge, and later on Apache Software Foundation website.” This is an application that intelligently digests and correlates information that otherwise appears amorphous.
Upon reviewing my previous blog entry, https://www-950.ibm.com/blogs/davidian/entry/what_runs_watson_and_why16?lang=en_us, IBM Watson has 4 TB of storage, but has 16 TB of systems-wide memory. Such an architecture suggests an in-memory databases or at least in-memory data structures. Indeed, Watson uses Apache’s Hadoop framework to facilitate preprocessing the large volume of data in order to create in-memory datasets. To provide effective CPU scheduling, the file system includes location awareness, that is, the physical location of each node, rack & network switch. Hadoop applications can use this information to schedule work on the node where the data is, and, failing that, on the same rack/switch, reducing backbone traffic. The Hadoop file system uses this when replicating data, trying to keep different copies of the data on different racks.
“Watson’s DeepQA UIMA annotators were deployed as mappers in the Hadoop map-reduce framework, which distributed them across processors in the cluster. Hadoop contributes to optimal CPU utilization and also provides convenient tools for deploying, managing, and monitoring the data “analysis process.”For more information see: http://www.ibm.com/common/ssi/cgi-bin/ssialias?infotype=SA&subtype=WH&htmlfid=POW03061USEN&attachment=POW03061USEN.PDF&appname=STGE_PO_PO_USEN_WH
When watching Jeopardy! tonight try to keep in mind that for every question, IBM Watson has to, as a minimum, within 3 seconds:
  • Take the stated question and parse its components
  • Determine relationships between grammatical elements
  • Create items that it must look for or relationships that may expand its search
  • Have Hadoop dispatch work to access information that UIMA has intelligently digested and annotated
  • 2880 POWER7 cores processing through TBs of data looking for the best set of results
  • DeepQA then determining what it considers the best response, and
  • Press a mechanical button as do the human contestants and express the answer in English.

Let the best “man” win!

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