WATSON is a question answering computer system capable of answering
questions posed in natural language , developed in
IBM 's DeepQA
project by a research team led by principal investigator David
Ferrucci . Watson was named after IBM's first CEO, industrialist
Thomas J. Watson . The computer system was specifically developed to
answer questions on the quiz show
Jeopardy! and, in 2011, the Watson
computer system competed on
Jeopardy! against former winners Brad
Ken Jennings winning the first place prize of $1
Watson had access to 200 million pages of structured and unstructured
content consuming four terabytes of disk storage including the full
text of , but was not connected to the
Internet during the
game. For each clue, Watson's three most probable responses were
displayed on the television screen. Watson consistently outperformed
its human opponents on the game's signaling device, but had trouble in
a few categories, notably those having short clues containing only a
In February 2013,
IBM announced that Watson software system's first
commercial application would be for utilization management decisions
in lung cancer treatment at
Memorial Sloan Kettering Cancer Center ,
New York City
New York City , in conjunction with health insurance company WellPoint
IBM Watson's former business chief, Manoj Saxena, says that 90% of
nurses in the field who use Watson now follow its guidance.
* 1 Description
* 1.1 Software
* 1.2 Hardware
* 1.3 Data
* 2 Operation
* 2.1 Comparison with human players
* 3 History
* 3.1 Development
* 3.2.1 Preparation
* 3.2.2 Practice match
* 3.2.3 First match
* 3.2.4 Second match
* 3.2.5 Final outcome
* 3.2.6 Philosophy
* 3.2.7 Match against members of the United States Congress
* 4 Current and future applications
* 4.1 Healthcare
IBM Watson Group
* 4.3 Chatterbot
* 4.4 Building Codes
* 4.5 Teaching Assistant
* 4.7 Tax Preparation
* 5 See also
* 6 References
* 7 Further reading
* 8 External links
* 8.1 J! Archive
* 8.2 Videos
The high-level architecture of IBM's DeepQA used in Watson
Watson is a question answering (QA) computing system that
to apply advanced natural language processing , information retrieval
, knowledge representation , automated reasoning , and machine
learning technologies to the field of open domain question answering .
The key difference between QA technology and document search is that
document search takes a keyword query and returns a list of documents,
ranked in order of relevance to the query (often based on popularity
and page ranking), while QA technology takes a question expressed in
natural language, seeks to understand it in much greater detail, and
returns a precise answer to the question.
According to IBM, "more than 100 different techniques are used to
analyze natural language, identify sources, find and generate
hypotheses, find and score evidence, and merge and rank hypotheses."
Watson uses IBM's DeepQA software and the
Apache UIMA (Unstructured
Information Management Architecture) framework. The system was written
in various languages, including Java ,
C++ , and
Prolog , and runs on
SUSE Linux Enterprise Server
SUSE Linux Enterprise Server 11 operating system using the Apache
Hadoop framework to provide distributed computing.
The system is workload-optimized, integrating massively parallel
POWER7 processors and built on IBM's DeepQA technology, which it uses
to generate hypotheses, gather massive evidence, and analyze data.
Watson employs a cluster of ninety
IBM Power 750 servers, each of
which uses a 3.5 GHz
POWER7 eight-core processor, with four threads
per core. In total, the system has 2,880
POWER7 processor threads and
16 terabytes of RAM.
According to John Rennie , Watson can process 500 gigabytes, the
equivalent of a million books, per second. IBM's master inventor and
senior consultant, Tony Pearson, estimated Watson's hardware cost at
about three million dollars. Its
Linpack performance stands at 80
TeraFLOPs, which is about half as fast as the cut-off line for the Top
500 Supercomputers list. According to Rennie, all content was stored
in Watson's RAM for the Jeopardy game because data stored on hard
drives would be too slow to be competitive with human Jeopardy
The sources of information for Watson include encyclopedias,
dictionaries, thesauri , newswire articles, and literary works. Watson
also used databases, taxonomies, and ontologies. Specifically, DBPedia
WordNet , and Yago were used. The
IBM team provided Watson with
millions of documents, including dictionaries, encyclopedias, and
other reference material that it could use to build its knowledge.
The computer's techniques for unraveling
Jeopardy! clues sounded
just like mine. That machine zeroes in on key words in a clue, then
combs its memory (in Watson's case, a 15-terabyte data bank of human
knowledge) for clusters of associations with those words. It
rigorously checks the top hits against all the contextual information
it can muster: the category name; the kind of answer being sought; the
time, place, and gender hinted at in the clue; and so on. And when it
feels "sure" enough, it decides to buzz. This is all an instant,
intuitive process for a human
Jeopardy! player, but I felt convinced
that under the hood my brain was doing more or less the same thing.
Watson parses questions into different keywords and sentence
fragments in order to find statistically related phrases. Watson's
main innovation was not in the creation of a new algorithm for this
operation but rather its ability to quickly execute hundreds of proven
language analysis algorithms simultaneously. The more algorithms
that find the same answer independently the more likely Watson is to
be correct. Once Watson has a small number of potential solutions, it
is able to check against its database to ascertain whether the
solution makes sense or not.
COMPARISON WITH HUMAN PLAYERS
Ken Jennings , Watson, and
Brad Rutter in their Jeopardy!
Watson's basic working principle is to parse keywords in a clue while
searching for related terms as responses. This gives Watson some
advantages and disadvantages compared with human
Watson has deficiencies in understanding the contexts of the clues. As
a result, human players usually generate responses faster than Watson,
especially to short clues. Watson's programming prevents it from
using the popular tactic of buzzing before it is sure of its response.
Watson has consistently better reaction time on the buzzer once it
has generated a response, and is immune to human players'
psychological tactics, such as jumping between categories on every
In a sequence of 20 mock games of Jeopardy, human participants were
able to use the average six to seven seconds that Watson needed to
hear the clue and decide whether to signal for responding. During
that time, Watson also has to evaluate the response and determine
whether it is sufficiently confident in the result to signal. Part of
the system used to win the
Jeopardy! contest was the electronic
circuitry that receives the "ready" signal and then examined whether
Watson's confidence level was great enough to activate the buzzer.
Given the speed of this circuitry compared to the speed of human
reaction times, Watson's reaction time was faster than the human
contestants except when the human anticipated (instead of reacted to)
the ready signal. After signaling, Watson speaks with an electronic
voice and gives the responses in Jeopardy!'s question format.
Watson's voice was synthesized from recordings that actor Jeff Woodman
made for an
IBM text-to-speech program in 2004.
Jeopardy! staff used different means to notify Watson and the
human players when to buzz, which was critical in many rounds. The
humans were notified by a light, which took them tenths of a second to
perceive . Watson was notified by an electronic signal and could
activate the buzzer within about eight milliseconds. The humans tried
to compensate for the perception delay by anticipating the light, but
the variation in the anticipation time was generally too great to fall
within Watson's response time. Watson did not attempt to anticipate
the notification signal.
Since Deep Blue 's victory over
Garry Kasparov in chess in 1997, IBM
had been on the hunt for a new challenge. In 2004,
manager Charles Lickel, over dinner with coworkers, noticed that the
restaurant they were in had fallen silent. He soon discovered the
cause of this evening hiatus:
Ken Jennings , who was then in the
middle of his successful 74-game run on Jeopardy!. Nearly the entire
restaurant had piled toward the televisions, mid-meal, to watch the
phenomenon. Intrigued by the quiz show as a possible challenge for
IBM, Lickel passed the idea on, and in 2005,
IBM Research executive
Paul Horn backed Lickel up, pushing for someone in his department to
take up the challenge of playing
Jeopardy! with an
IBM system. Though
he initially had trouble finding any research staff willing to take on
what looked to be a much more complex challenge than the wordless game
of chess, eventually
David Ferrucci took him up on the offer. In
competitions managed by the United States government, Watson's
predecessor, a system named Piquant, was usually able to respond
correctly to only about 35% of clues and often required several
minutes to respond. To compete successfully on Jeopardy!, Watson
would need to respond in no more than a few seconds, and at that time,
the problems posed by the game show were deemed to be impossible to
In initial tests run during 2006 by David Ferrucci, the senior
manager of IBM's Semantic Analysis and Integration department, Watson
was given 500 clues from past
Jeopardy! programs. While the best
real-life competitors buzzed in half the time and responded correctly
to as many as 95% of clues, Watson's first pass could get only about
15% correct. During 2007, the
IBM team was given three to five years
and a staff of 15 people to solve the problems. By 2008, the
developers had advanced Watson such that it could compete with
Jeopardy! champions. By February 2010, Watson could beat human
Jeopardy! contestants on a regular basis.
Although the system is primarily an
IBM effort, Watson's development
involved faculty and graduate students from Rensselaer Polytechnic
Carnegie Mellon University , University of Massachusetts
Amherst , the
University of Southern California 's Information
Sciences Institute , the
University of Texas at Austin
University of Texas at Austin , the
Massachusetts Institute of Technology , and the
University of Trento
University of Trento ,
as well as students from
New York Medical College .
Watson demo at an
IBM booth at a trade show
IBM representatives communicated with
Harry Friedman about the possibility of having Watson compete
Ken Jennings and
Brad Rutter , two of the most successful
contestants on the show, and the program's producers agreed.
Watson's differences with human players had generated conflicts
Jeopardy! staff during the planning of the
IBM repeatedly expressed concerns that the show's
writers would exploit Watson's cognitive deficiencies when writing the
clues, thereby turning the game into a
Turing test . To alleviate that
claim, a third party randomly picked the clues from previously written
shows that were never broadcast.
Jeopardy! staff also showed concerns
over Watson's reaction time on the buzzer. Originally Watson signaled
electronically, but show staff requested that it press a button
physically, as the human contestants would. Even with a robotic
"finger" pressing the buzzer, Watson remained faster than its human
Ken Jennings noted, "If you're trying to win on the show,
the buzzer is all", and that Watson "can knock out a
microsecond-precise buzz every single time with little or no
variation. Human reflexes can't compete with computer circuits in this
regard." Stephen Baker , a journalist who recorded Watson's
development in his book Final Jeopardy, reported that the conflict
Jeopardy! became so serious in May 2010 that the
competition was almost canceled. As part of the preparation, IBM
constructed a mock set in a conference room at one of its technology
sites to model the one used on Jeopardy!. Human players, including
Jeopardy! contestants, also participated in mock games against
Watson with Todd Alan Crain of
The Onion playing host. About 100 test
matches were conducted with Watson winning 65% of the games.
To provide a physical presence in the televised games, Watson was
represented by an "avatar " of a globe, inspired by the
planet" symbol. Jennings described the computer's avatar as a "glowing
blue ball criss-crossed by 'threads' of thought—42 threads, to be
precise", and stated that the number of thought threads in the avatar
was an in-joke referencing the significance of the number 42 in
Douglas Adams ' Hitchhiker\'s Guide to the Galaxy . Joshua Davis ,
the artist who designed the avatar for the project, explained to
Stephen Baker that there are 36 triggerable states that Watson was
able to use throughout the game to show its confidence in responding
to a clue correctly; he had hoped to be able to find forty-two, to add
another level to the Hitchhiker's Guide reference, but he was unable
to pinpoint enough game states.
A practice match was recorded on January 13, 2011, and the official
matches were recorded on January 14, 2011. All participants maintained
secrecy about the outcome until the match was broadcast in February.
In a practice match before the press on January 13, 2011, Watson won
a 15-question round against
Ken Jennings and
Brad Rutter with a score
of $4,400 to Jennings's $3,400 and Rutter's $1,200, though Jennings
and Watson were tied before the final $1,000 question. None of the
three players responded incorrectly to a clue.
The first round was broadcast February 14, 2011, and the second
round, on February 15, 2011. The right to choose the first category
had been determined by a draw won by Rutter. Watson, represented by a
computer monitor display and artificial voice, responded correctly to
the second clue and then selected the fourth clue of the first
category, a deliberate strategy to find the Daily Double as quickly as
possible. Watson's guess at the Daily Double location was correct. At
the end of the first round, Watson was tied with Rutter at $5,000;
Jennings had $2,000.
Watson's performance was characterized by some quirks. In one
instance, Watson repeated a reworded version of an incorrect response
offered by Jennings. (Jennings said "What are the '20s?" in reference
to the 1920s. Then Watson said "What is 1920s?") Because Watson could
not recognize other contestants' responses, it did not know that
Jennings had already given the same response. In another instance,
Watson was initially given credit for a response of "What is leg?"
after Jennings incorrectly responded "What is: he only had one hand?"
to a clue about
George Eyser (the correct response was, "What is: he's
missing a leg?"). Because Watson, unlike a human, could not have been
responding to Jennings's mistake, it was decided that this response
was incorrect. The broadcast version of the episode was edited to omit
Trebek's original acceptance of Watson's response. Watson also
demonstrated complex wagering strategies on the Daily Doubles, with
one bet at $6,435 and another at $1,246. Gerald Tesauro, one of the
IBM researchers who worked on Watson, explained that Watson's wagers
were based on its confidence level for the category and a complex
regression model called the Game State Evaluator.
Watson took a commanding lead in Double Jeopardy!, correctly
responding to both Daily Doubles. Watson responded to the second Daily
Double correctly with a 32% confidence score.
Although it wagered only $947 on the clue, Watson was the only
contestant to miss the Final
Jeopardy! response in the category U.S.
CITIES ("Its largest airport was named for a World War II hero ; its
second largest , for a World War II battle "). Rutter and Jennings
gave the correct response of
Chicago , but Watson's response was "What
Toronto ?????" Ferrucci offered reasons why Watson would appear
to have guessed a Canadian city: categories only weakly suggest the
type of response desired, the phrase "U.S. city" did not appear in the
question, there are cities named
Toronto in the U.S. , and
Ontario has an
American League baseball team. Dr.
Chris Welty , who
also worked on Watson, suggested that it may not have been able to
correctly parse the second part of the clue, "its second largest, for
a World War II battle" (which was not a standalone clause despite it
following a semicolon , and required context to understand that it was
referring to a second-largest airport).
Eric Nyberg , a professor at
Carnegie Mellon University and a member of the development team,
stated that the error occurred because Watson does not possess the
comparative knowledge to discard that potential response as not
viable. Although not displayed to the audience as with non-Final
Jeopardy! questions, Watson's second choice was Chicago. Both Toronto
Chicago were well below Watson's confidence threshold, at 14% and
11% respectively. (This lack of confidence was the reason for the
multiple question marks in Watson's response.)
The game ended with Jennings with $4,800, Rutter with $10,400, and
Watson with $35,734.
During the introduction, Trebek (a Canadian native) joked that he had
Toronto was a U.S. city, and Watson's error in the first match
IBM engineer to wear a
Toronto Blue Jays jacket to the
recording of the second match.
In the first round, Jennings was finally able to choose a Daily
Double clue, while Watson responded to one Daily Double clue
incorrectly for the first time in the Double
Jeopardy! Round. After
the first round, Watson placed second for the first time in the
competition after Rutter and Jennings were briefly successful in
increasing their dollar values before Watson could respond.
Nonetheless, the final result ended with a victory for Watson with a
score of $77,147, besting Jennings who scored $24,000 and Rutter who
The prizes for the competition were $1 million for first place
(Watson), $300,000 for second place (Jennings), and $200,000 for third
place (Rutter). As promised,
IBM donated 100% of Watson's winnings to
charity, with 50% of those winnings going to
World Vision and 50%
World Community Grid . Similarly, Jennings and Rutter
donated 50% of their winnings to their respective charities.
In acknowledgment of
IBM and Watson's achievements, Jennings made an
additional remark in his Final
Jeopardy! response: "I for one welcome
our new computer overlords ", echoing a similar memetic reference to
the episode "
Deep Space Homer " on
The Simpsons , in which TV news
presenter Kent Brockman speaks of welcoming "our new insect
overlords". Jennings later wrote an article for Slate , in which he
IBM has bragged to the media that Watson's question-answering skills
are good for more than annoying Alex Trebek. The company sees a future
in which fields like medical diagnosis , business analytics , and tech
support are automated by question-answering software like Watson. Just
as factory jobs were eliminated in the 20th century by new
assembly-line robots, Brad and I were the first knowledge-industry
workers put out of work by the new generation of 'thinking' machines.
Quiz show contestant' may be the first job made redundant by Watson,
but I'm sure it won't be the last.
John Searle argues that Watson—despite impressive
capabilities—cannot actually think. Drawing on his Chinese room
thought experiment , Searle claims that Watson, like other
computational machines, is capable only of manipulating symbols, but
has no ability to understand the meaning of those symbols; however,
Searle's experiment has its detractors .
Match Against Members Of The United States Congress
On February 28, 2011, Watson played an untelevised exhibition match
Jeopardy! against members of the United States House of
Representatives . In the first round,
Rush D. Holt, Jr. (D-NJ, a
Jeopardy! contestant), who was challenging the computer with
Bill Cassidy (R-LA, later Senator from Louisiana), led with Watson in
second place. However, combining the scores between all matches, the
final score was $40,300 for Watson and $30,000 for the congressional
IBM's Christopher Padilla said of the match, "The technology behind
Watson represents a major advancement in computing. In the
data-intensive environment of government, this type of technology can
help organizations make better decisions and improve how government
helps its citizens."
CURRENT AND FUTURE APPLICATIONS
According to IBM, "The goal is to have computers start to interact in
natural human terms across a range of applications and processes,
understanding the questions that humans ask and providing answers that
humans can understand and justify." It has been suggested by Robert
C. Weber, IBM's general counsel , that Watson may be used for legal
research. The company also intends to use Watson in other
information-intensive fields, such as telecommunications, financial
services, and government.
Watson is based on commercially available
IBM Power 750 servers that
have been marketed since February 2010.
IBM also intends to market the
DeepQA software to large corporations, with a price in the millions of
dollars, reflecting the $1 million needed to acquire a server that
meets the minimum system requirement to operate Watson.
the price to decrease substantially within a decade as the technology
Commentator Rick Merritt said that "there's another really important
reason why it is strategic for
IBM to be seen very broadly by the
American public as a company that can tackle tough computer problems.
A big slice of comes from selling to the U.S. government some of the
biggest, most expensive systems in the world."
In 2013, it was reported that three companies were working with IBM
to create apps embedded with Watson technology. Fluid is developing an
app for retailers, one called "The North Face", which is designed to
provide advice to online shoppers. Welltok is developing an app
designed to give people advice on ways to engage in activities to
improve their health. MD Buyline is developing an app for the purpose
of advising medical institutions on equipment procurement decisions.
In November 2013,
IBM announced it would make Watson's
to software application providers, enabling them to build apps and
services that are embedded with Watson's capabilities. To build out
its base of partners who create applications on the Watson platform,
IBM consults with a network of venture capital firms, which advise IBM
on which of their portfolio companies may be a logical fit for what
IBM calls the Watson Ecosystem. Thus far, roughly 800 organizations
and individuals have signed up with IBM, with interest in creating
applications that could use the Watson platform.
On January 30, 2013, it was announced that Rensselaer Polytechnic
Institute would receive a successor version of Watson, which would be
housed at the Institute's technology park and be available to
researchers and students. By summer 2013, Rensselaer had become the
first university to receive a Watson computer.
On February 6, 2014, it was reported that
IBM plans to invest $100
million in a 10-year initiative to use Watson and other IBM
technologies to help countries in Africa address development problems,
beginning with healthcare and education.
On June 3, 2014, three new Watson Ecosystem partners were chosen from
more than 400 business concepts submitted by teams spanning 18
industries from 43 countries. "These bright and enterprising
organizations have discovered innovative ways to apply Watson that can
deliver demonstrable business benefits", said Steve Gold, vice
IBM Watson Group. The winners were Majestyk Apps with their
adaptive educational platform, FANG (Friendly Anthropomorphic
Networked Genome); Red Ant with their retail sales trainer; and
GenieMD with their medical recommendation service.
On July 9, 2014,
Genesys Telecommunications Laboratories announced
plans to integrate Watson to improve their customer experience
platform, citing the sheer volume of customer data to analyze is
Watson has been integrated with databases including Bon Appétit
magazine to perform a recipe generating platform.
Watson is being used by Decibel, a music discovery startup, in its
app MusicGeek which uses the supercomputer to provide music
recommendations to its users. The use of the artificial intelligence
of Watson has also been found in hospitality industry. GoMoment uses
Watson for its Rev1 app, which gives hotel staff a way to quickly
respond to questions from guests. Arria NLG has built an app that
helps energy companies stay within regulatory guidelines, making it
easier for managers to make sense of thousands of pages of legal and
OmniEarth, Inc. uses Watson computer vision services to analyze
satellite and aerial imagery, along with other municipal data, to
infer water usage on a property-by-property basis, helping water
districts in drought-stricken California improve water conservation
In September 2016, Condé Nast has started using IBM's Watson to help
build and strategize social influencer campaigns for brands. Using
software built by
IBM and Influential, Condé Nast's clients will be
able to know which influencer's demographics, personality traits and
more best align with a marketer and the audience it is targeting.
In May 2017,
IBM partnered with the
Pebble Beach Company to use
Watson as a concierge . Watson's artificial intelligence was added to
an app developed by Pebble Beach and was used to guide visitors around
the resort. The mobile app was designed by
IBM iX and hosted on the
IBM Cloud. It uses Watson's Conversation applications programming
In healthcare, Watson's natural language, hypothesis generation, and
evidence-based learning capabilities are being investigated to see how
Watson may contribute to clinical decision support systems for use by
medical professionals. To aid physicians in the treatment of their
patients, once a physician has posed a query to the system describing
symptoms and other related factors, Watson first parses the input to
identify the most important pieces of information; then mines patient
data to find facts relevant to the patient's medical and hereditary
history; then examines available data sources to form and test
hypotheses; and finally provides a list of individualized,
confidence-scored recommendations. The sources of data that Watson
uses for analysis can include treatment guidelines, electronic medical
record data, notes from physicians and nurses, research materials,
clinical studies, journal articles, and patient information. Despite
being developed and marketed as a "diagnosis and treatment advisor",
Watson has never been actually involved in the medical diagnosis
process, only in assisting with identifying treatment options for
patients who have already been diagnosed.
In February 2011, it was announced that
IBM would be partnering with
Nuance Communications for a research project to develop a commercial
product during the next 18 to 24 months, designed to exploit Watson's
clinical decision support capabilities. Physicians at Columbia
University would help to identify critical issues in the practice of
medicine where the system's technology may be able to contribute, and
physicians at the
University of Maryland would work to identify the
best way that a technology like Watson could interact with medical
practitioners to provide the maximum assistance.
In September 2011,
WellPoint announced a partnership to
utilize Watson's data crunching capability to help suggest treatment
options to physicians. Then, in February 2013,
Watson its first commercial application, for utilization management
decisions in lung cancer treatment at Memorial Sloan–Kettering
Cancer Center .
IBM announced a partnership with
Cleveland Clinic in October 2012.
The company has sent Watson to the
Cleveland Clinic Lerner College of
Case Western Reserve University , where it will increase
its health expertise and assist medical professionals in treating
patients. The medical facility will utilize Watson's ability to store
and process large quantities of information to help speed up and
increase the accuracy of the treatment process. "Cleveland Clinic's
IBM is exciting because it offers us the
opportunity to teach Watson to 'think' in ways that have the potential
to make it a powerful tool in medicine", said C. Martin Harris, MD,
chief information officer of Cleveland Clinic.
MD Anderson Cancer Center began a pilot program to
further the center's "mission to eradicate cancer". However, after
spending $62 million, the project did not meet its goals and it has
been put on hold.
On February 8, 2013,
IBM announced that oncologists at the Maine
Center for Cancer Medicine and Westmed Medical Group in New York have
started to test the Watson supercomputer system in an effort to
recommend treatment for lung cancer.
On July 29, 2016,
IBM and Manipal Hospitals (a leading hospital
chain in India), announced launch of
IBM Watson for Oncology, for
cancer patients. This product provides information and insights to
physicians and cancer patients to help them identify personalized,
evidence-based cancer care options. Manipal Hospitals is the second
hospital in the world to adopt this technology and first in the world
to offer it to patients online as an expert second opinion through
On January 7, 2017,
IBM and Fukoku Mutual Life Insurance entered into
a contract for
IBM to deliver analysis to compensation payouts via its
IBM Watson Explorer AI, this resulted in the loss of 34 jobs and the
company said it would speed up compensation payout analysis via
analysing claims and medical record and increase productivity by 30%.
The company also said it would save ¥140m in running costs.
It is said that
IBM Watson will be carrying the knowledge-base of
1000 cancer specialists which will bring a revolution in the field of
IBM is regarded as a disruptive innovation . However the
stream of oncology is still in its nascent stage.
IBM WATSON GROUP
On January 9, 2014
IBM announced it was creating a business unit
around Watson, led by senior vice president Michael Rhodin. IBM
Watson Group will have headquarters in New York 's
Silicon Alley and
will employ 2,000 people.
IBM has invested $1 billion to get the
division going. Watson Group will develop three new cloud -delivered
services: Watson Discovery Advisor, Watson Engagement Advisor, and
Watson Explorer. Watson Discovery Advisor will focus on research and
development projects in pharmaceutical industry , publishing , and
biotechnology , Watson Engagement Advisor will focus on self-service
applications using insights on the basis of natural language questions
posed by business users, and Watson Explorer will focus on helping
enterprise users uncover and share data-driven insights based on
federated search more easily. The company is also launching a $100
million venture fund to spur application development for "cognitive"
applications. According to IBM, the cloud-delivered enterprise-ready
Watson has seen its speed increase 24 times over—a 2,300 percent
improvement in performance, and its physical size shrank by 90
percent—from the size of a master bedroom to three stacked pizza
Virginia Rometty said she wants Watson to generate $10
billion in annual revenue within ten years.
Watson is being used via
IBM partner program as a Chatterbot to
provide the conversation for children's toys.
In 2015, the engineering firm ENGEO created an online service via the
IBM partner program named GoFetchCode. GoFetchCode applies Watson's
natural language processing and question-answering capabilities to the
International Code Council 's model building codes.
Ashok Goel , professor at Georgia Tech, used Watson to create a
virtual Teaching Assistant to assist students in his class.
Initially, Goel did not reveal the nature of "Jill", which was created
with the help of a few students and IBM. Jill answered questions where
it had a 97% certainty of an accurate answer, with the remainder being
answered by human assistants.
The research group of Sabri Pllana developed an assistant for
learning parallel programming using the
IBM Watson. A survey with a
number of novice parallel programmers at the Linnaeus University
indicated that such assistant will be welcome by students that learn
In August 2016,
IBM announced it would be using Watson for weather
forecasting . Specifically, the company announced they would use
Watson to analyze data from over 200,000 Weather Underground personal
weather stations , and data from other sources, as a part of project
Deep Thunder .
On February 5–6, 2017, tax preparation company H
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IBM Watson: The Face of Watson on
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