Cognitive Work Assistants - Vision and Open Challenges

© 2014 IBM Corporation
Cognitive Work Assistants:
Vision and Open Challenges
Hamid R. Motahari Nezhad
IBM Almaden Research Center,
San Jose, CA, USA
Cognitive Systems Institute

The Work Practices of Human Administrative Assistants
 Human assistant activities
– Calendaring
• Scheduling, information formatting
and preparation
– Task Management
– Email Management
• Filtering emails,
• Email classification
 Interruption management
– Mediating interruption
– Prioritizing interruptions
 Taking care of routine tasks
– Tracking
– Following up
– Travel arrangement, and
preparation
– Reminding, and organizing
– Managing work of human
• Pre-processing
• Filtering
• Prioritizing
• Compiling information and reports
2
An assistant “will remove much of the burden of administrative chores from its human user and
provide guidance, advice, and assistance in problem solving and decision making.” Gutierrze and
Hilfdalgo, 1988

Human Administrative Assistants: conceptual framework
3
T. Erickson, etc.: Assistance: The Work Practice of Human Administrative Assistants and their Implications for IT and Organizations, CSCW’08.
Blocking, Doing, Redirecting
Key to the performance of assistants

Cognitive Assistance for knowledge workers
 Cognitive case management is about providing cognitive support to knowledge workers
in handling customer cases in domains such as social care, legal, government services,
citizen services, etc.
 Handling and managing cases involves understanding policies, laws, rules, regulations,
processes, plans, as well as customers, surrounding world, news, social networks, etc.
 A cognitive agent would assist employees and customers (from each perspective)
– Assisting employees/workers by providing decision support based on understanding
the case, context, surrounding world and applicable laws/rules/processes.
– Helps employees/workers to be more productive, and effective
– Assists citizens by empowering them to know their rights and responsibilities, and
helping them to expedite the progress of the case
4
Users
Assistant
CustomersEmployees/
agents
Plansworkflows
Rules
Policies
Regulations
Templates
Instructions/
Procedures
ApplicationsSchedules
Communications such as
email, chat, social media,
etc.
Organization
Cog. Agent
Unstructured Linked Information

Cognitive Assistance: Application Domains
 Cognitive Assistance for different Occupations
– Finance
– Education
– Retail
– Healthcare (physician assistant)
– Government (case management, civilian services, intelligence, defense, etc.)
 Health Education Assistance
– Assistance to patients
– People well-being
– Public health education
 Retail
– Assistance to buyers
5

Cognitive Assistant: what is it?
 A software agent that
– “augments human intelligence” (Engelbart’s definition1 in 1962)
– Complements human by offering capabilities that is beyond the ordinary power and
reach of human (intelligence amplification)
– Performs tasks and offer assistance to human in decision making and taking actions
 A more technical definition
– Cognitive Assistant offers computational intelligence capabilities typically based on
Natural Language Processing (NLP), Machine Learning (ML) and reasoning, and
provides cognition powers that augment and scale human intelligence (Jim Spohrer)
 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
6 1 Augmenting Human Intellect: A Conceptual Framework, by Douglas C. Engelbart, October 1962

History of Cognitive Assistants from the lens of AI
7
1945
Memex (Bush)
1962
NLS/Augment
(Engelbart)
1955/6
Logic Theorist
(Newwell, Simon, 1955)
Checker Player
(Samuel, 1956)
Touring Test,
1950
Thinking machines
1966
Eliza
(Weizenbaum)
1965-1987 DENDRAL
1974-1984 MYCIN
1987 Cognitive Tutors
(Anderson)
Apple’s Knowledge
Navigator System
Expert Systems
1965-1987 1992-1998
Virtual Telephone
Assistant
Portico, Wildfire,
Webley;
Speech Recognition
Voice Controlled
2002-08
DARPA PAL
Program
CALO
IRIS

Modern Cognitive Assistants: State of the art (2008-present)
Commercial
 Personal Assistants
– Siri, Google Now, Microsoft
Cortana, Amazon Echo,
– 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)
 Cognitive systems and platforms
– IBM Watson
– Wolfram Alpha (a computational
engine with NLP interface)
– Saffron 10
– Vicarious (Captcha)
Open Source/Research
 OAQA
 DeepDive
 OpenCog
 YodaQA
 OpenSherlock
 OpenIRIS
 iCub EU projects
 Cougaar
 Inquire* (intelligent textbook)
8
* Curated knowledge base

COGNITIVE AGENTS’
ABILITIES
What Capabilities Cognitive Agents need to have?
9

Human Intelligence in terms of Cognitive Abilities
10
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. No
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 Partially, 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)

Human Intelligence vs Machine Intelligence:
Analytical Cognition, vs. Synthetic Cognition
Analytical Skills
 Cognitive skills that machines excel
at would take intellectual efforts
from human
– 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
Synthetic Skills
 Cognitive skills that human performs
effortlessly but hard for machines with
current AI
– 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
11
Ref.: Eric Lord, Science, Mind and Paranormal Experience, 2009

Cognitive Assistant Vision: Augmenting Human Intelligence
12
Cognitive
Capability
• Create new insights and new
valueDiscovery
• Provide bias-free advice semi-
autonomously, learns, and is
proactive
Decision
• Build and reason about models
of the world, of the user, and of
the system itself
Understanding
• Leverage encyclopedic domain
knowledge in context, and
interacts in natural language
Question
Answering
Natural Language Processing and Interaction Skills
EmotionalIntelligenceSkills
SocialInteractionSkills
Touring Test
Analytical Abilities needed for a Cognitive Agent for higher order tasks

Not only personal work assistant, but a society of cogs
13
Cognitive
Agent to
Agent
Outage
Model
Consequence
Table
Financial
Cog
Enterprise
Process
Cog
Objective
Identification
Sensitivity
Analysis
PR Cog
Personal Avatar
Deep Thunder
Crew Scheduler
Feeds
Human to
Human
Cognitive
Agent to
Human
Sara’s Cog
Mobile Analytics
and Response
Two main type of cognitive
assistants: personal work
assistants, and expert cogs,
collaborate to support
human activities.
Interactions types need to be
supported:
• cog-to-cog interactions,
• human-cog interactions, and
• cog-backed human-to-
human interactions
Cogs need degrees of emotional
intelligence, and social
interaction skills to support cog-
human, and cog-backed human-
to-human interactions
Sara
Debra

A major challenge in passing Touring Test and building Cogs:
building domain 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
 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
14
EDWARD A. FEIGENBAUM, Some Challenges and Grand Challenges for
Computational Intelligence, Journal of the ACM, Vol. 50, No. 1, January 2003, pp. 32–40

Lesson Learned from Jeopardy in Watson (1)
 “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:
1. Developing a systematic approach for scalable knowledge building over large, less
reliable data sources
• Building and curating a robust, and comprehensive knowledge base and ruleset has
been a key challenge in expert systems
• Watson approach for building on massive, mixed curated and not-curated and less
reliable information sources with uncertainty has proved effective
15
Source:
Inquire Intelligent
Book

Lesson Learned from Jeopardy in Watson (2)
16
Comparison of two QA
systems with and without
confidence estimation. Both
have an accuracy of 40%.
With
perfect
confidence
estimator
Without
confidence
estimator
2. Leveraging a large number of not always accurate techniques but delivering
higher overall accuracy through understanding and employing confidence levels

Opportunity and challenge (1): explosive amount of data
17
80%
of the world’s data
today is
unstructured
90%
of the world’s data
was created in the
last two years
1 Trillion
connected devices
generate 2.5
quintillion bytes
data / day
3M+
Apps on leading
App stores

Cognitive Computing as a Service: Watson in IBM BlueMix
18
Visualization Rendering
Graphical representations of data analysis for easier understanding
User Modeling
Personality profiling to help engage users on their own terms.
Language Identification
Identifies the language in which text is written
Machine Translation
Translate text from one language to another.
Concept Expansion
Maps euphemisms to more commonly understood phrases
Message Resonance
Communicate with people with a style and words that suits them
Question and Answer
Direct responses to users inquiries fueled by primary document sources
Relationship Extraction
Intelligently finds relationships between sentences components
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
Available today
Opportunity and challenge (2): cognitive methods and tools

Open Challenges (1)
19
 Building the knowledge base and Training Cognitive Agents
– How does User Train the Cog?
– How does User Delegate to 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
– 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

Learning from Jeopardy Challenge
 Back in 2006, DeepQA (Question Answering) involved addressing key challenges
 Feb 27-28, 2008, a group of researchers and practitioners from industry, academia and
government met to discuss state of the Question Answering (QA) field
 The result was the development of a document (published in 2009) that included
– Vision for QA systems, and DeepQA
– Development of challenge problems with measurable dimensions
– Approach to open collaboration
– Open collaboration model
 Defining Performance
Dimensions
 Challenge Problem Set
Comparison
22

Call for Enabling Open Collaboration Model on Cognitive Assistants
 The Open Collaboration Model enables sharing
knowledge, expertise, datasets, progress and
devising interoperable solution components
– Challenge Problem Set Comparison
– Defining and Developing Performance
Dimensions
– Open platform for sharing data, testbed and
comparative analysis
 Building on Watson Ecosystem for Partners and
Watson University Program for academic partners
for towards Cognitive Assistant Open Collaboration
Platform, or similar open platforms for collaboration
– Building on open source cog projects
23
Building open platforms similar to Watson Content
Marketplace, Watson Ecosystem, and Watson University
Programs

THANK YOU!
Questions?
24

BACKUP
25

Example: Automatic Task Extraction and Management
Over Unstructured Communications
26
Email, Chat, and Calendaring apps are
the most used channels for doing work
in the enterprise
The goal of this project was to monitor
Communication channels (email, chat)
To capture and organize work of an
Employee (tasks)
Lifecycle of a typical task:
Create, Active, Complete, Cancel

Cognitive Assistant for Task management
 Processing text of conversations (email, chat, etc) to extract and manage task
lifecycle
27
Anup K. Kalia, Hamid R. Motahari Nezhad, Claudio Bartolini, Munindar P. Singh: Monitoring Commitments in
People-Driven Service Engagements. IEEE SCC 2013: 160-167
Deep Parsing of text

Where did it acquire knowledge?
• Wikipedia
• Time, Inc.
• New York Time
• Encarta
• Oxford University
• Internet Movie Database
• IBM Dictionary
• ... J! Archive/YAGO/dbPedia…
• Total Raw Content
• Preprocessed Content
28
• 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
Three
types of
knowledge
Domain
Data
(articles, books,
documents)
Training and test
question sets
w/answer keys
NLP Resources
(vocabularies,
taxonomies,
ontologies)

Use Case: Work Assistant Example for knowledge workers
 Assume an executive admin is managing an event organization process for their
department
– Step 1: sending invite to an event to employees in their department, through
email and requests for RSVP
• Cog (1): Q&A ability for the admin: How many have confirmed, how many
pending, how many not answered
• Cog (2): Predictive analytics: how many will eventually RSVP?
• Cog (3): Diagnostic analytics: why some not accepted (customers in case
of marketing case)?
– Step 2: Ordering place, food, transportation, etc
• Cog (1): tracking of the process steps, which vendor have replied, which
ones pending, have questions, etc.
• Cog (2): keeping track of synchronization and consistency (dates, amounts,
numbers, etc.) among different steps
– Step 3: Pre-event steps (self-discipline, and organization)
• Reminding people who have RSVPed
• Compiling and sending logistic information (from different steps)
– Learning changes to the process
29

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