Ibm watson - who what why

IBM Systems and Technology
An IBM White Paper
February 2011
Watson – A System
Designed for Answers
The future of workload optimized systems design

2 Watson – A System Designed for Answers
Executive summary
Over the last century, IBM has achieved numerous scientiﬁc
breakthroughs through its commitment to research and its tradi-
tion of Grand Challenges. These Grand Challenges—such as
Deep Blue®, which was designed to rival world chess champion
Gary Kasparov—work to push science in ways that weren’t
thought possible before. Watson is the latest IBM Research
Grand Challenge, designed to further the science of natural
language processing through advances in question and answer
technology.
Watson is a workload optimized system based on IBM DeepQA
architecture running on a cluster of IBM® POWER7®
processor-based servers. After four years of intense research and
development by a team of IBM researchers, Watson competed
on Jeopardy! in February 2011, performing at the level of human
experts in terms of precision, conﬁdence and speed against two
of the best-known and most successful Jeopardy! Champions,
Ken Jennings and Brad Rutter. This white paper explains
Watson’s workload optimized system design, how it’s emblematic
of the future of systems design, and why this represents a new
computing paradigm.
Jeopardy! The IBM challenge
In 1997, Deep Blue, the computer chess-playing system devel-
oped by IBM Research, captured worldwide attention by com-
peting successfully against world chess champion Gary Kasparov.
It was the culmination of a grand challenge to advance the sci-
ence of computing in a way that created great popular interest.
Today, with companies increasingly capturing critical business
information in natural language documentation, there is growing
interest in workload optimized systems that deeply analyze the
content of natural language questions to answer those questions
with precision. Advances in question answering (QA) technology
will increasingly help support professionals in critical and timely
decision making in areas such as health care, business intelli-
gence, knowledge discovery, enterprise knowledge management,
and customer support.
With QA in mind, IBM settled on a challenge to build a com-
puter system called “Watson” (after Thomas J. Watson, the
founder of IBM), which could compete at the human champion
level in real time on the American TV quiz show Jeopardy! The
program, which has been broadcast in the United States for
more than 25 years, pits three human contestants against one
another to answer rich natural language questions over a broad

IBM Systems and Technology 3
range of topics, with penalties for wrong answers. In this three-
person competition, confidence, precision and answering speed
are of critical importance, as contestants usually come up with
their answers in the few seconds it takes for the host to read a
clue. To compete in this game at human-champion levels, a
computer system would need to answer roughly 70 percent of
the questions asked with greater than 80 percent precision in
three seconds or less.
Watson represents an impressive leap forward in systems design
and analytics. It runs IBM’s DeepQA technology, a new kind of
analytics capability that can perform thousands of simultaneous
tasks in seconds to provide precise answers to questions.
Powered by IBM POWER7 processor technology, Watson is an
example of the complex analytics workloads that are becoming
increasingly common and critical to business success and
competitiveness in today’s data-intensive environment.
Watson competed against two of the most well-known and suc-
cessful Jeopardy! champions—Ken Jennings and Brad Rutter—in
a two-match contest aired over three consecutive nights begin-
ning on February 14, 2011.
IBM DeepQA
DeepQA is a massively parallel probabilistic evidence-based
architecture. For the Jeopardy! Challenge, more than 100 differ-
ent techniques are used to analyze natural language, identify
sources, find and generate hypotheses, find and score evidence,
and merge and rank hypotheses. Far more important than any
particular technique is the way all these techniques are combined
in DeepQA such that overlapping approaches can bring their
strengths to bear and contribute to improvements in accuracy,
confidence, or speed.

4 Watson – A System Designed for Answers
DeepQA is an architecture with an accompanying methodology,
but it is not specific to the Jeopardy! Challenge. IBM has begun
adapting it to different business applications and additional
exploratory challenge problems including medicine, enterprise
search and gaming.
The overarching principles in DeepQA are:
1. Massive parallelism: Exploit massive parallelism in the con-
sideration of multiple interpretations and hypotheses.
2. Many experts: Facilitate the integration, application and con-
textual evaluation of a wide range of loosely coupled proba-
bilistic question and content analytics.
3. Pervasive confidence estimation: No single component
commits to an answer; all components produce features and
associated confidences, scoring different question and content
interpretations. An underlying confidence processing substrate
learns how to stack and combine the scores.
4. Integrate shallow and deep knowledge: Balance the use of
strict semantics and shallow semantics, leveraging many
loosely formed ontologies.
Speed and scale-out
DeepQA is developed using Apache UIMA, a framework
implementation of the Unstructured Information Management
Architecture. UIMA was designed to support interoperability
and scale-out of text and multimodal analysis applications. All of
the components in DeepQA are implemented as UIMA annota-
tors. These are components that analyze text and produce anno-
tations or assertions about the text. Over time Watson has
evolved so that the system now has hundred of components.
UIMA facilitated rapid component integration, testing and
evaluation.
Early implementations of Watson ran on a single processor,
which required two hours to answer a single question. The
DeepQA computation is embarrassing parallel, however, and so
it can be divided into a number of independent parts, each of
which can be executed by a separate processor. UIMA-AS, part
of Apache UIMA, enables the scale-out of UIMA applications
using asynchronous messaging. Watson uses UIMA-AS to scale
out across 2,880 POWER7 cores in a cluster of 90 IBM Power®
750 servers. UIMA_AS manages all of the inter-process commu-
nication using the open JMS standard. The UIMA-AS deploy-
ment on POWER7 enabled Watson to deliver answers in one to
six seconds.
Watson has roughly 200 million pages of natural language
content (equivalent to reading 1 million books). Watson uses the
Apache Hadoop framework to facilitate preprocessing the large
volume of data in order to create in-memory datasets used at
run-time. Watson’s DeepQA UIMA annotators were deployed
as mappers in the Hadoop map-reduce framework, which dis-
tributed them across processors in the cluster. Hadoop con-
tributes to optimal CPU utilization and also provides convenient
tools for deploying, managing, and monitoring the data
analysis process.
Harnessing POWER7
Watson harnesses the massive parallel processing performance
of its POWER7 processors to execute its thousands of
DeepQA tasks simultaneously on individual processor cores.
Each of Watson’s 90 clustered IBM Power 750 servers features
32 POWER7 cores running at 3.55 GHz. Running the Linux®
operating system, the servers are housed in 10 racks along with
associated I/O nodes and communications hubs. The system has
a combined total of 16 Terabytes of memory and can operate at
over 80 Teraflops (trillions of operations per second).
With its innovative, eight-core processor design, POWER7 is
ideally suited for massively parallel processing of Watson’s
analytics algorithms. POWER7 also features 500 gigabytes of
on-chip communications bandwidth, contributing to exceptional
efficiency of both memory and processor utilization. And since
each server packs 32 high performance POWER7 cores with up
to 512 GB of memory, the Power 750 makes an ideal platform
for Watson’s processor and memory-hungry Java processes.

5IBM Systems and Technology
Designing Watson on commercially available Power 750 servers
was a deliberate choice to ensure more rapid adoption of opti-
mized systems in industries such as healthcare and financial serv-
ices. That goal was a fundamental difference between Watson
and Deep Blue, which was a highly customized supercomputer.
Deep Blue was based on an earlier generation of Power proces-
sor technology, featuring a 30 node RS/6000 SP system, with
each node containing a single 120 MHz POWER2 processor.
But in addition to the regular POWER2 processors, Deep Blue’s
performance was enhanced with 480 special purpose chess
processor chips.
The same Power 750 server used by Watson is already
deployed today by thousands of organizations in optimized
systems that provide for both complex analytics and transaction
processing. Rice University in Houston, Texas, for example, uses
IBM Power 750 systems to accelerate the understanding of the
molecular basis of cancer through the application of genome
analysis technologies. POWER7 systems have given Rice more
flexibility and efficiency, enabling them to pursue a broader
range of research challenges on a single system than was possible
before. And GHY International, a customs brokerage firm in
Canada, migrated to a new Power 750 running Power AIX®,
Power i and Power Linux to better support their clients’
increased engagement in international trading. With
PowerVM™ virtualization, GHY is now able to deploy new
capabilities in as little as five minutes to support their clients’
changing needs.
A system designed for answers
After four years of intense research and development by a team
of IBM researchers, Watson has demonstrated its ability to
compete on Jeopardy! against champion players, performing at
human-expert levels in terms of precision, confidence and speed.
The project has advanced the fields of unstructured data
analytics, natural language processing, and the design of work-
load optimized systems. Beyond Jeopardy!, the technology
behind Watson can be adapted to solve business and societal
problems—for example, diagnosing disease, handling online
technical support questions, and parsing vast tracts of legal
documents—and to drive progress across industries.
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 can help people accomplish tasks in
business and their personal lives.

Please Recycle
For more information
To learn more about Watson, POWER7 and workload opti-
mized systems, please contact your IBM marketing representa-
tive or IBM Business Partner, or visit the following websites:
● ibm.com/systems/power/advantages/watson
● ibm.com/systems/power © Copyright IBM Corporation 2011
IBM Systems and Technology Group
Route 100
Somers, NY 10589
Produced in the United States of America
February 2011
All Rights Reserved
IBM, the IBM logo, ibm.com, Power, POWER7 and DEEP BLUE are
trademarks of International Business Machines Corporation in the
United States, other countries or both. If these and other IBM trademarked
terms are marked on their ﬁrst occurrence in this information with a
trademark symbol (® or ™), these symbols indicate U.S. registered or
common law trademarks owned by IBM at the time this information was
published. Such trademarks may also be registered or common law
trademarks in other countries. A current list of IBM trademarks is
available on the web at “Copyright and trademark information” at
ibm.com/legal/copytrade.shtml
Other company, product or service names may be trademarks or service
marks of others.
POW03061-USEN-00

Ibm watson - who what why

More Related Content

What's hot (20)

Viewers also liked (20)

Similar to Ibm watson - who what why (20)

More from Rick Bouter (20)

Recently uploaded (20)

Ibm watson - who what why