Challenge

Transcription

Challenge

Beyond Jeopardy! Challenge:
Cognitive Assistants Race
Hamid R. Motahari Nezhad, PhD, Member of IBM Academy of Technology
IBM Almaden Research Center
San Jose, CA, USA
© 2015 IBM Corporation
IBM Global Technology Outlook: Driven by Research, Drives Strategy and Innovation
2012
2013
2014
2015
Cloud
Mobile
Social
Data
Internet of Things
Volume
Velocity
Variety
Veracity
4
Confluence of
Social, Mobile, Cloud,
Big Data, IoT and Analytics
Systems of Insight
Data Transforming
Industries
Data will disrupt
entire science,
technology and
industries
Data is the world’s new natural resource!
(Ginni Rometti, IBM Annaul Report, 2014)
44 zettabytes
From the dawn of civilization until
2003, humankind generated five
exabytes of data. Now we
produce five exabytes every two
days…and the pace is
accelerating.
Eric Schmidt,
Executive Chairman, Google
unstructured data
We are here
structured data
2010
5
2015
2020
Big data creates new opportunities and challenges
90%
of the world’s data
was created in the
last two years
3M+
Apps on leading
App stores
4
80%
of the world’s data
today is
unstructured
1 Trillion
connected devices
generate 2.5
quintillion bytes
data / day
By 2017
The collective computing and storage
capacity of smartphones will surpass all
worldwide servers
A new computing paradigm is emerging
Cognitive
Systems Era
Programmable Systems Era
Tabulating
Systems Era
Cognitive Systems Era
Discovery & Recommendation
Probabilistic
Big Data
Natural Language as the Interface
Intelligent Options
6
How we got here?: Grand AI Challenges in 1980s
Challenge
Where we are today?
World Champion Chess Machine
Mathematical Discovery
Translating Telephone
Accident Avoiding Car
(Autonomous car)
Self-Organizing Systems
Self-Replicating Systems
Turing Test for Humanoid Behavior (1950-)
7
R. Reddy, Foundations and Grand Challenges of Artificial Intelligence, AI Magazine, 1988
Where we are today?: Grand AI Challenges in 1980s
8
Challenge
Where we are today?
World Champion Chess Machine
IBM DeepBlue, 1997 Won World Chess Champion
Mathematical Discovery
Open Challenge
Translating Telephone
Wildfire, Portico and Webely Translating
Assistants (1992-1994)
Accident Avoiding Car
(Autonomous car)
2004 (Desert Challenge)-2008 (Commercial)
Self-Organizing Systems
Open Challenge
Self-Replicating Systems
Open Challenge
Turing Test for Humanoid Behavior (1950-)
Attempts has been made (Eliza 1966, Goostman
2014)
Turing Test and Machine Intelligence
Can a computer think?
Are there imaginable digital computers which
would do well in the imitation game?"
The contest requires at least 30% (50%) of
judges take machine as a human!
Main variations: Standard Test (Loebner Prize), and Imitation Game
Critics: focus on human behavior, and not intelligence
AI has focused on intelligence, and on specific goals/tasks, and not on mimicking humans.
- Some human behavior are not intelligent (typing mistakes, lie, …)
- Some intelligent behavior are inhuman (solving difficult computational problems)
9
Human Intelligence in terms of Cognitive Abilities
Ability to
Achievable by machines
today?
draw abstractions from particulars.
Partially, semantic graphs*
maintain hierarchies of abstraction.
Partially, semantic graphs*
concatenate assertions and arrive at a new conclusion.
Partially, relationships present
reason outside the current context.
Not proactively
compare and contrast two representations for consistency/inconsistency.
Limited
reason analogically.
Not automated, require domain
adaptation
learn and use external symbols to represent numerical, spatial, or conceptual
information.
Better than human in symbolic rep. &
processing
learn and use symbols whose meanings are defined in terms of other learned
symbols.
Uses and processes, limited learning
invent and learn terms for abstractions as well as for concrete entities.
No language development capability
invent and learn terms for relations as well as things
Not advanced, using symbols, not
cognitive
Gentner, D. (2003), In D. Getner & S. Goldin-Meadow (eds.), Language in Mind: Advances in the Study of Language and Thought. MIT Press. 195--235 (2003)
10
Human Intelligence vs. Machine Intelligence
Analytical Skills
Synthetic Skills
 Cognitive skills that machines excel at
would take intellectual efforts from human
 Cognitive skills that human performs
effortlessly but hard for machines with current
AI
– Mathematical calculations, making logical
decisions in complex situations, chess
 Computational Intelligence
– Manipulation of symbols through
algorithmic information processing
– The processing units (processing device)
does not know or care about the
“meaning” of symbol
– Cognition by “information processing”, or
cognition as computation
– Interpretation of subtle facial expressions,
engaging in creative conversations, etc.
 Conscious intelligence
– Symbol manipulation also happens in the lowest level
of hierarchical structure of brain function
– The higher levels of hierarchical structure of brain
function involve emergent concepts where higher level
concepts/ideas combine, and form complex organisms
(analogy with ‘cloud’, a whole, relation to air and water
molecules, component)
– It is at this level of cognition that “understanding of
meaning” arise
Ref.: Eric Lord, Science, Mind and Paranormal Experience, 2009
11
Cognitive Capability Levels Desired for an Intelligent Machine
Higher level of cognition desired for an intelligent machine
Discovery
• Create new insights and new
value
Decision
• Provide bias-free advice semiautonomously, learns, and is
proactive
Understanding
• Build and reason about models
of the world, of the user, and of
the system itself
Question
Answering
• Leverage encyclopedic domain
knowledge in context, and
interacts in natural language
Cognitive
Capability
Touring Test* (Chinese Room)
12
A major challenge towards winning Touring Test: Building knowledge bases
 “For an artifact, a computational intelligence, to be able to behave with high levels of performance on
complex intellectual tasks, perhaps surpassing human level, it must have extensive knowledge of the
domain”
 The challenge of AI in making progress toward building human-like artifacts:
– Knowledge representation, and (especially) knowledge acquisition
 Approaches
– Build a large knowledge base by reading text
– Distilling from the WWW a huge knowledge base
EDWARD A. FEIGENBAUM, Some Challenges and Grand Challenges for
Computational Intelligence, Journal of the ACM, Vol. 50, No. 1, January 2003, pp. 32–40
 Semantic Web and Linked Data methods over the last decade extensively has explored building models,
ontologies and rule-set that contributes to WWW knowledge representation
– Manual, and semi-automated, focused on curated ontologies
– Community participation in building ontologies have resulted in creation of large knowledge bases:
DBPedia, Yago, Wikidata, Freebase, MediaWiki, etc.
– Ontologies are expensive to build and scale, and are generic in nature
13
What changed the game for AI, and for the machine
intelligence was ….
Data
+
A New Computation Paradigm
Advancing Question Answering Capability
JEOPARDY! CHALLENGE
14
On February 14, 2011, IBM Watson made history . . .
15
Jeopardy Game: Automatic Open-Domain Question Answering
 Given
– Rich Natural Language Questions
– Over a Broad Domain of Knowledge
 Deliver
–
–
–
–
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Precise Answers: Determine what is being asked & give precise response
Accurate Confidences: Determine likelihood answer is correct
Consumable Justifications: Explain why the answer is right
Fast Response Time: Precision & Confidence in <3 seconds
Analyzing Jeopardy against other Question-Answering Problems
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Ref: David Ferrucci, et al. Towards the Open Advancement of Question Answering Systems.
IBM Research Report RC24789. December 2008
Jeopardy Question Examples
Broad/Open
Domain
$200
$400
The juice of this bog fruit is
sometimes used to treat
urinary tract infections
Portland, Oregon is "The
City of" these flowers
Complex
Language
$2000
High Precision
Accurate
Confidence
A little bird told us smoking
or dentures can cause this
most common fungal
infection of the mouth
Factoid
Answers
High
Speed
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$600
$800
In cell division, mitosis
splits the nucleus &
cytokinesis splits this liquid
cushioning the nucleus
Grace Murray Hopper is
credited with applying this 3letter term to a mysterious
computer problem
DeepQA in Watson:
Massively Parallel Probabilistic Evidence-Based Architecture
DeepQA generates and scores many hypotheses using an extensible collection of Natural Language Processing, Machine
Learning and Reasoning Algorithms. These gather and weigh evidence over both unstructured and structured content to
determine the answer with the best confidence.
Learned Models
help combine and
weigh the Evidence
Evidence
Sources
Question
Answer
Sources
Primary
Search
Question &
Topic
Analysis
Candidate
Answer
Generation
Question
Decomposition
Hypothesis
Generation
Hypothesis
Generation
19
Answer
Scoring
Evidence
Retrieval
Hypothesis and
Evidence Scoring
Deep
Evidence
Scoring
Synthesis
Hypothesis and Evidence
Scoring
...
David Ferrucci, et al. , Building Watson: An Overview of the DeepQA Project, AI Magazine, 2010
Models
Models
Models
Models
Models
Models
Final Confidence
Merging &
Ranking
Answer &
Confidence
Question interpretation, finding, scoring and ranking
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Data as an Enabler: Where did it acquire knowledge?
Three
types of
knowledge
Domain
Data
(articles, books,
documents)
 Wikipedia
 Time, Inc.
 New York Time
 Encarta
 Oxford University
 Internet Movie Database
 IBM Dictionary
 ... J! Archive/YAGO/dbPedia…
 Total Raw Content
 Preprocessed Content
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Training and test
question sets
w/answer keys
NLP Resources
(vocabularies,
taxonomies,
ontologies)
• 17 GB
• 2.0 GB
• 7.4 GB
• 0.3 GB
• 0.11 GB
• 0.1 GB
• 0.01 GB
XXX
• 70 GB
• 500 GB
Had access to 200 million
pages of structured and
unstructured content consuming
four terabytes of disk storage
Watson Machine consisted of:
• 750 IBM Power servers, each
3.5 GHz POWER7 eight core
processor, in total, the system has
2,880 POWER7 processor threads
• 16 terabytes of RAM
Lesson Learned from Watson (1): scalable knowledge model building method
 “The Watson program is already a breakthrough technology in AI. For many years it had been largely
assumed that for a computer to go beyond search and really be able to perform complex human language
tasks it needed to do one of two things: either it would “understand” the texts using some kind of deep
“knowledge representation,” or it would have a complex statistical model based on millions of texts.”
– James Hendler, Watson goes to college: How the world’s smartest PC will revolutionize AI, GigaOm, 3/2/2013
 Breakthrough:
– Developing a systematic approach for scalable knowledge building over large, less reliable data sources,
and deploying a large array of individually imperfect algorithms to find right answers
• Building and curating a robust, and comprehensive knowledge base and ruleset is laborious, time
consuming and slow
Source:
Inquire Intelligent
Book
• Watson approach for building on massive, mixed curated and not-curated and less reliable information
sources with uncertainty has proved effective
22
Lesson Learned from Watson (2): learning-based characterization of each algorithm
Leveraging a large number of not always accurate techniques but delivering higher overall
accuracy through characterizing and employing confidence levels
Without
confidence
estimator
With
perfect
confidence
estimator
Comparison of two QA systems with
and without confidence estimation.
Both have an accuracy of 40%.
23
Towards Mass Computing as the Shared Characteristic of Recent Computing
Paradigm Shifts
Building Stronger
Super Computers
Cloud Computing
Scalable Computing over
Massive Commodity Hardware
Crowd Computing
Big
Data
Advanced
Analytics
Watson: Mass computing applied to AI
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Advanced individual
algorithms
Complex array of algorithms applied to make
sense of data, and offer cognitive assistance
Discovery
Decision
Understanding
Question
Answering
BEYOND JEOPARDY!: COGNITIVE COMPUTING
COGNITIVE ASSISTANTS, HEALTHCARE, WATSON SERVICES, AND DEEP LEARNING
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Cognitive System
A Cognitive System offers computational capabilities typically based on Natural Language Processing (NLP),
Machine Learning (ML), and reasoning chains, on large amount of data, which provides cognition powers that
augment and scale human expertise
2 Generates and
evaluates
evidence-based
hypothesis
1 Understands
natural language
and human
communication
Watson
3 Adapts and learns
from user
selections and
responses
26
Cognitive Systems do actively
discover, learn and act
© 2012 International Business Machines Corporation
Gartner Hype Cycle of Technology 2015
Nuance’s Survey of 1000 users found…
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Cognitive Assistant
 A software agent (cog) that
– “augments human intelligence” (Engelbart’s definition1 in 1962)
– Performs tasks and offer services (assists human in decision making and taking actions)
– Complements human by offering capabilities that is beyond the ordinary power and reach of human (intelligence
amplification)
 A more technical definition
– Cognitive Assistant offers computational capabilities typically based on Natural Language Processing (NLP),
Machine Learning (ML), and reasoning chains, on large amount of data, which provides cognition powers that
augment and scale human intelligence
 Getting us closer to the vision painted for human-machine partnership in 1960:
– “The hope is that, in not too many years, human brains and computing machines will be coupled together very
tightly, and that the resulting partnership will think as no human brain has ever thought and process data in a way
not approached by the information handling machines we know today”
“Man-Computer Symbiosis , J. C. R. Licklider IRE Transactions on Human Factors in Electronics, volume HFE-1,
pages 4-11, March 1960
28
1
Augmenting Human Intellect: A Conceptual Framework, by Douglas C. Engelbart, October 1962
History of Cognitive Assistants from the lens of AI
Thinking machines
DARPA PAL
Program
Expert Systems
Touring Test,
1950
Logic Theorist
(Newwell, Simon, 1955)
1965-1987 DENDRAL
1974-1984 MYCIN
IRIS
Checker Player
(Samuel, 1956)
NLS/Augment
(Engelbart)
Memex (Bush)
1987 Cognitive Tutors
(Anderson)
Eliza
(Weizenbaum)
Apple’s Knowledge
Navigator System
1945
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1955/6
1962
1966
1965-1987
Virtual Telephone
Assistant
CALO
Portico, Wildfire,
Webley;
Speech Recognition
Voice Controlled
1992-1998
2002-08
Modern Cognitive Assistants: State of the art (2008-present)
Commercial
Open Source/Research
 Personal Assistants and Bots
 OAQA
– Siri, Google Now, Microsoft
Cortana, Amazon Echo, FB M
– Braina, Samsung's S Voice,
LG's Voice Mate, SILVIA, HTC's
Hidi, Nuance’ Vlingo
– AIVC, Skyvi, IRIS, Everfriend,
Evi (Q&A), Alme (patient
assistant)
– Viv (Global Brain as a Service)
– x.ai, Telegram bots
 Cognitive and intelligent
platforms
– IBM Watson
– Wolfram Alpha
– Saffron 10
– Vicarious (Captcha)
 DeepDive
 OpenCog
 YodaQA
 OpenSherlock
 OpenIRIS
 iCub EU projects
 Cougaar
 Inquire* (intelligent textbook)
* Curated knowledge base
30
Intelligent Assistants: Related App Landscape
31
A Society of Interacting Cognitive Agents (Bots) and Humans
Cognitive
Agent to
Agent
Human-Cog interaction
Cognitive
Agent to
Human
Human to
Human
Natural Language
Planning a Vacation
Trip
Cog-Cog interaction
Natural Language, or ACL?
ACL: Agent Communication Language, KQML, etc.
Cog-mediated Human Interaction
Provider
Cogs
Travel Cog 1
Health
Agent
Personality
Mediated and facilitated by Cogs
Weather
Cog
Insight Cog.
Considering preferences,
experience, conditions, cost,
Availability, etc.
Natural Language-ACL-Natural Language
Travel Cog 2
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eAssistant: a cognitive assistant for the enterprise
 A mobile intelligent assistant for the enterprise that assist a user (worker) to be
more productive by supporting following a methodology of monitor, process,
recommend and do actions with the following capabilities
– Understands human language
– Monitors input channels including email, calendar chat and enterprise
information sources
– Builds a model of the user and the world, and is situational aware (context)
– Offer assistance by
• Pre-processing information, and presenting information in desired format
• Categorizing and filtering information
• Gathering and organizing information
• Scheduling meetings
• Identifying requests, and organizing to-dos of its human subject
• Assists in performing tasks such as organizing events, travel assistant, and
learns new tasks
• And, suggest taking certain actions to its human subject that supports
increasing productivity, and growth
33
eAssistant: Cognitive Assistant Types in Work Environment
 Personal (employee) eAssistant
– Personal eAssistants have access to the data space (and applications) that the principal has access to with the same
level of visibility
– While eAssistant is proactive in making suggestions, it takes action under the control and direction of the principal
 Assistant’s eAssistant
– An assistant to Human Assistants helping them to become more productive, and focus on work that require human
judgment
 Expert/Process eAssistants
– Assistants that are experts in a specific domain such as travel policy, human resources, etc.
Individual cognitive
agents
Assistant’s Cognitive
Agents
Cognitive Assistant Platform
34
Systems of cognitive agents that
collaborate effectively with one
another to support human activities.
Expert Cognitive
Agents
Interactions types need to be supported:
• cog-to-cog interactions,
• human-cog interactions, and
• cog-backed human-to-human interactions
Actionable Statement Identification Over Unstructured Conversations
Email, Chat, and Calendaring apps are
the most used channels for doing work
in the enterprise
Addressing the work organization and
management for Knowledge workers:
monitoring communication channels (email,
chat), and:
-
35
capturing, prioritizing and organizing work
of a worker
Identifying actionable statements
(requests, commitments, questions) and
track them over the course of
conversations
Cognitive Assistant for Task management
Task, commitment and task lifecycle extraction from workers interactions over email and chat
36
Anup K. Kalia, Hamid R. Motahari Nezhad, Claudio Bartolini, Munindar P. Singh: Monitoring Commitments in People-Driven Service Engagements. IEEE SCC
2013: 160-167
Opportunity and challenge (2): cognitive methods and tools
Cognitive Computing as a Service: Watson in IBM BlueMix
Available today
User Modeling
Personality profiling to help engage users on their own terms.
Message Resonance
Communicate with people with a style and words that suits them
Concept Expansion
Maps euphemisms to more commonly understood phrases
Relationship Extraction
Intelligently finds relationships between sentences components
Machine Translation
Translate text from one language to another.
Question and Answer
Direct responses to users inquiries fueled by primary document sources
Visualization Rendering
Graphical representations of data analysis for easier understanding
Language Identification
Coming
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Concept Analytics
Question Generation
Speech Recognition
Text to Speech
Tradeoff Analytics
Medical Information Extraction
Semantic Expansion
Policy Knowledge
Ontology Creation
Q&A in other languages
Policy Evaluation
Inference detection
Social Resonance
Answer Assembler
Relationship identification
Dialog
Machine Translation (French)
Smart Metadata
Visual Recommendation
Industry accelerators
Identifies the language in which text is written
37
Deep Learning
Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in
data by using multiple processing layers with complex structures, or otherwise composed of multiple non-linear transformations
(Wikipedia Definition)
 Most successful form of deep learning algorithms
– Artificial Neural Networks (ANNs)
• We have had good algorithm for training ANNs with 1-hidden layer, Deep Learning focuses on training ANNs with
multiple hidden layers, at scale
 Deep learning methods use a cascade of many layers of nonlinear processing units for feature extraction and transformation
 Are hierarchical in learning the model (different levels of abstractions), favor unsupervised (and probabilistic) feature
extraction and transformation
 Other examples: Deep Boltzmann Machine (DBM), a binary pair-wise Markov random field, Multi-layer Kernel Machines
(MKM), Deep Q-Networks (Google DeepMind)
 Applications
– Speech recognition, Image recognition, NLP
 Libraries
– Torch, Theano, OpenNN, TensorFlow (Google), etc.
 Latest Development
– Modeling Order in Neural Word Embeddings at Scale, 32nd International Conference on Machine Learning, 2015.
– Digital Reasoning: training largest neural network with 160 Billion parameters (Google’s record was 11.2 Billion)
– Achieved 85.8% accuracy, up from 76.2% (Google), with few hours/days of training time on commodity hardware
38
Todai Challenge: Tokyo University Entrance Exam
39
Japan National Institute of Informatics, http://21robot.org/
Open Challenges (1)
 Building the knowledge base and Training Cognitive Agents
– How does User Train the Cog?
– How to build a user model for the Cog?
 Adaptation and training of Cogs for a new domain
– How to quickly train a cog for a new domain? Current approaches is laborious and tedious.
 Performance Dimensions, and Evaluation Framework
– Metrics, testing and validating functionality of Cog
– Are controlled experiments possible?
– Do we need to test in Real environment with Real users
 User adoption/trust, and privacy
–
–
–
Can I trust that the Cog did what I told/taught/think the Cog did?
Is the Cog working for me?
Issues of privacy, privacy-preserving interaction of cogs.
 Team vs. Personal Cogs
–
–
Training based on best practices vs. personalized instruction
Imagine Teams of Cogs working with teams of Human Analysts
 Symbiosis Issues
–
40
What is best for the human to do? What is best for the cog?
Open Challenges (2)
 Teaching the Cog what to do
– Learning from demonstration, Learning from documentation
– Telling the Cog what to do using natural language
– Interactive learning where the Cog may ask questions of the trainer
– How does the Cog learn what to do, reliably?
– Active learning where the Cog improves over time
• Moving up the learning curve (how does Cog understand the goal/desired end state?)
• Adapts as the environment (e.g., data sources and formats change)
– On what conditions should the Cog report back to the Human?
– Task composition (of subtasks) and reuse
– Adaptation of past learning to new situation
 Proactive Action taking
– Initiating actions based on learning and incoming requests
• E.g., deciding what information sources to search for the request , issuing queries, evaluating
responses
– Deciding on next steps based on results or whether it needs further guidance from Human
 Personal knowledge representation and reasoning
– Capturing user behavior, interaction in form of personal knowledge
– Ability to build knowledge from various structured and unstructured information
– AI Principle: expert knows 70,000+/- 20,000 information pieces, and human tasks involves 1010
rules
(foundation of AI, 1988)
Open Challenges (3)
 Context understanding, and context-aware interaction
– Modeling the world of the person serving, including all context around the work/task, and
being able to use the contextual and environmental awareness to proactively and reactively
act on behalf of the user
 Learning to understand the task and plan to do it
– Understanding the meaning of tasks, and coming up with a response (e.g.. How many
people replied to an invite over email, accepting the offer, without asking the Cog to do so),
or suggestions on how to achieve it (based on any new information discovered by the Cog)
 Cognitive Speech recognition, or other human-computer interfaces for communicating with Cogs
– Improving the speech-to-text techniques, and personalized, semantic-enriched speech
understanding
– Non-speech based approaches for communicating with humans
Summary and Conclusions
 Data is the world’s new natural resource (Ginni Rometti, IBM’s CEO)
–New data types and feeds becoming available: personal data marketplace
 Data is an enabler of machine intelligence
– Companies are offering free services to gather more data
– Data owners will rule the future of IT!
 Machine Intelligence and AI is offered as a Service (like other business functionalities)
– It has implications for countries and companies that do not own data/services
 Big data and AI advances also means the need for training skilled workers
–Education programs at universities have to be created on big data, analytics and cognitive
computing
– In the US, there is estimate of the need for thousands of data scientists, and analytics over next
5-10 years
 Cognitive Computing, and Deep Learning are hot areas of research
 Natural Language will be the new interface, Cognitive Assistants with NL interfaces with be
commonplace in next 5-10 years
43
The call for Defining a National Grand Challenge
 Choosing a challenge of national importance and visibility
 Involving big data analytics and AI challenges, that are achievable within a given
timeframe
 Should involves all lifecycle of data acquisition, data pre-processing and
transformation, knowledge base building, AI / cognitive computing, and adaptive
and deep learning
– Should exhibits characteristics of big data, and intelligence
 Partnership among universities, industry and government agencies
44
Questions?
THANK YOU!
45
BACKUP
46
Where Watson Fits
Efficient decision support over unstructured (and structured) content
Deeper Understanding,
Higher Precision and Broader,
Timely Coverage at lower costs
Shallow Understanding
Low Precision
Broad Coverage
Unstructured Data
 Broad, rich in context
 Rapidly growing, current
 Invaluable yet under utilized
47
Deeper Understanding but Brittle
High Precision at High Cost
Narrow Limited Coverage
Structured Data
 Precise, explicit
 Narrow, expensive

Challenge

Transcription

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