Cognitive Assistants - Opportunities and Challenges - slides

© 2014 IBM Corporation

Cognitive Assistants:Opportunities and Challenges

Hamid R. Motahari Nezhad

IBM Almaden Research Center,

San Jose, CA, USA

With Inputs and Contributions from:

Jim Spohrer, IBM Research

Frank Stein, IBM Analytics CTO


Cognitive Assistant: what is it?

A software agent 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

2 1 Augmenting Human Intellect: A Conceptual Framework, by Douglas C. Engelbart, October 1962


Human Intelligence in terms of Cognitive Abilities

3

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 Partially, using symbols, not

cognitiveGentner, 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)


History of Cognitive Assistants from the lens of AI

4

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

– Saffron 10

– Vicarious (Captcha)

Open Source/Research

OAQA

DeepDive

OpenCog

YodaQA

OpenSherlock

OpenIRIS

iCub EU projects

Cougaar

Inquire* (intelligent textbook)

5

* Curated knowledge base


Cognitive Assistant Vision: Augmenting Human Intelligence

6

CognitiveCapability

• 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


Building a Society of Cognitive Agents

7

Cognitive Agent to

Agent

Outage Model

Consequence Table

Smart Swaps

Lighting

Objective Identification

Sensitivity Analysis

Sentiment Analysis

Systems of specialized cognitive agents that collaborate effectively with one another

Cognitive agents that collaborate effectively with people through natural user interfaces

A nucleus from which an internet-scale cognitive computing cloud can be built

Personal Avatar

Deep Thunder

Crew Scheduler

News

Human to Human

Cognitive Agent to Human

Watson

Mobile Analytics

and Response

E:/Cognitive Computing Demo - Emergency Planning.wmv

E:/Cognitive Computing Demo - Emergency Planning.wmv


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 (taking care of routine task), and

effective

– Assists citizens by empowering them by knowing their rights and responsibilities,

and helping them to expedite the progress of the case

8

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


Learning from an experience: 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

9


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:

– Developing a systematic approach for scalable knowledge building over large, less

reliable data sources, and deploying a large array of individually imperfect techniques to

find right answers

• 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

10

Source:

Inquire Intelligent

Book


Lesson Learned from Jeopardy in Watson (2)

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Comparison of two QA

systems with and without

confidence estimation. Both

have an accuracy of 40%.

With perfect confidence estimator

Without confidence estimator

Leveraging a large number of not always accurate techniques but delivering

higher overall accuracy through understanding and employing confidence levels


Opportunity assessment (1): building knowledge from data

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80%of the world’s data

today is unstructured

90% of the world’s data was created in the

last two years

1 Trillionconnected devices

generate 2.5 quintillion bytes

data / day

3M+Apps on leading

App stores


Cognitive Computing as a Service: Watson in IBM BlueMix

13

Visualization RenderingGraphical representations of data analysis for easier understanding

User ModelingPersonality profiling to help engage users on their own terms.

Language IdentificationIdentifies the language in which text is written

Machine TranslationTranslate text from one language to another.

Concept ExpansionMaps euphemisms to more commonly understood phrases

Message ResonanceCommunicate with people with a style and words that suits them

Question and AnswerDirect responses to users inquiries fueled by primary document sources

Relationship ExtractionIntelligently 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 assessment (2): cognitive techniques and tools


Open Challenges (1)

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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


THANK YOU!Questions?

17

Data & Analytics

Cognitive Assistants - Opportunities and Challenges - slides