Transcript Lifeline - Reddy, Raj - Carnegie Mellon University

Applications of Intelligent Systems
and Robotics in Service of Society
Raj Reddy
Carnegie Mellon University
Pittsburgh
Jan 9, 2007
Keynote Speech at IJCAI 2007, Hyderabad, India
2
Outline of the Talk
 Needs
of Developing Economies
 Access
3
to Knowledge, Education and
healthcare, etc.
Minute Introduction to AI: What is it and
how it can help
 The role of AI in enabling
 access to knowledge and knowhow
 access to libraries
 access to education and learning
 access to health care
 Unfinished
research agenda of AI
Needs of the People
with Per Capita Income of Less Than $1 a Day

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Access to entertainment

watch any movie, TV show when desired

providing links to doctors and treatment at a distance

about hygiene and safe water, helping to reduce infant
mortality
Telemedicine
Access to information
Life-long learning


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independent of the limitations of language, distance, age and
physical disabilities
Price discovery
Marketing assistance

using eBay like auction exchanges

e.g. monster.com
Find jobs
They need AI and IT
but not Word, Excel and Powerpoint
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Barriers to Entry: The Digital Divide
 Connectivity Divide
 Access to free Internet for basic services?
 Computer Access Divide
 Accessibility: Less than 5 minute walk?
 Affordability: Costing less than a cup of coffee per day?
 Digital Literacy Divide
 Language Divide
 Literacy Divide
 Content Divide
 Access to information and knowledge
 Access to health care
 Access to education and learning
 Access to jobs
 Access to entertainment
 Access to improved quality of life
5
A 3-Minute Introduction to AI

What is it and how it can help

review why the world’s poor have more to gain
in relative terms by the effective use of the IT
and AI technology
6
Artificial Intelligence attempts to make
computers do things which would require
intelligence in people, i.e. any activity which
requires the use the human brain
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A Historical View of Advances in AI
1950s: Theorem Proving; Chess
 1960s: Problem Solving;
Language: Understand; Question Answering
 1970s: Speech; Vision; Expert Systems
 1980s: Robotics; Knowledge Based Systems
 1990s: Language Translation; Search
 2000s: Systems that Learn with Experience

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Some Application Domains
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Web Search : Google, Yahoo, MSN
Intelligent car
Financial planning
Manufacturing control
System diagnosis
NL communicator
Writing assistant
Knowledge-based simulation
Games
Household robot
9
Requirements for Intelligence
Learn from experience
 Exploit vast amounts of knowledge
 Exhibit Goal Directed Behavior
 Tolerate error and ambiguity in input
 Communicate with natural language
 Operate in real time, and
 Use symbols (and abstractions)

10
AI Problem Domains & Attributes
Puzzles
Knowledge
Content
Data
Rate
Response
Time
Poor
Low
Hours
Chess
Theorem Proving
Expert Systems
Natural Language
Motor Processes
Speech
Vision
Rich
High
Real Time
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Lessons from AI Experiments

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Bounded Rationality implies Opportunistic
Search
An Expert becomes a World Class Expert only
after spending at least 15 years of intensive
practice and knows 70,000+20,000 patterns
Search Compensates for Lack of Knowledge
Knowledge Compensates for Lack of Search
A Physical Symbol System is Necessary and
Sufficient for Intelligent Action
12
How Can AI Help?

Intelligent Systems in support of
Access to Knowledge and Knowhow
 Learning and Education
 Health
 Robotics for Accident Avoiding Cars,
Landmine Detection, and Disaster Recovery

13
Enabling Access to
Knowledge and Information
Village Google:
Access to Knowledge for Use in a Village
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Access to Essential Information and Advice

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Medical, Agriculture, FAQ indexed and searchable
Interactive access to Doctors, Rescue Personnel

Price discovery, crop disease information, weather
prediction
Lifelong Learning and Education
Agricultural Information
Access to Markets and Jobs
Disaster Relief and Management
Access to Newspapers, Radio and TV
Entertainment and Amusement
Communications

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Video Phone, IP Telephone, Instant Messaging
Video Email, Voice Email, Text Email
14
The Vision of a Global Knowledge Network

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Create a Knowledge Network that connects experts to the
people who need help, e.g., farmers in villages
End-users interact at Village Knowledge Centers

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Equipped with a networked computer and basic A/V equipment
Staffed by a Knowledge Officer

Humans are intrinsic to Knowledge Networks
(raw information  knowledge!)

Domain experts provide answers to previously unanswered
questions

Answers converted into an “encyclopedia-on-demand” video
documentary at higher-level centers centers and dubbed into
local languages in each country

Also available for direct access browsing by literate and
networked users
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System Overview
16
Multi-level Information Flow - An example scenario
An illiterate farmer goes to a
Village Knowledge Officer
(with a computer connected
to FAO multimedia
database) and asks a
question in his or her local
language
The KO retrieves
answer from local
Multilingual database
within minutes 80 90% of the time
For the remaining 10 - 20%
of the time the KO puts up
the question to a higher
level office and gets an
answer back, typically
in less than 24 hrs
100s of domain experts
populate the databases,
both as part of their jobs
and as volunteers (say, 2
questions per week)

Hierarchical structure spanning districts, regions, countries, etc.

Outside experts interact with higher level Knowledge Officers

Builds up an ever-increasing multimedia database


Can provide static (e.g., best-practices) as well as dynamic (e.g.,
weather, prices, etc.) information
Innovative mechanisms and processes for information
digitization, exchange, analysis, and dissemination
Knowledge officers and Domain Experts
World
Knowledge Management
& Coordination (global)
Nation
Knowledge Management
& Coordination (national level)
State
Verification of Query-Answer Relevance
And RFP to domain experts
District
Translation, Information Retrieval
Village
AV data collection,
Transliteration and Transcription
Information Retrieval
Domain experts:
Volunteer to
answer at least 2 questions a week
(or part of job responsibility)
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Roles of Knowledge Officers
Village
District
3,000 people
300,000 people
30M people
0.3B people
3 Billion people
Transcription (and possibly
Transliteration)
Translation and
Information Retrieval
Verification &
RFP from Experts
Knowledge Management
& Coordination
Knowledge Analysis
and Inference
Records question of the end-user
in audio-video format. Enters text
transcription of the question.
Enters translation of questions.
Searches local language
database for answer
Need not be knowledgeable in
English.
Searches multilingual database
for answer
Sends answer after translation to
lower level
If question not among FAQs or
automated system, sends to
higher level
Region/Nation
Picks questions of critical nature
and validates the answer
provided at lower level
If critical or unanswered
question, puts up request to
experts even if not paid for
by end-user
(sub)continent
Same as next level up, but
with the range of analyses
broadened to the
region/subcontinent level
Global
Brings experts to where their
knowledge is needed.
Mobilization of resources
towards their need.
Identifies and triggers
initiatives to control
“epidemic”-like problems
(All numbers shown are for rural, developing country populations = beneficiaries)
The AI Challenges in
Creating a Global Knowledge Network

Farmers typically not able to tap in to existing
networks
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Often illiterate
Rarely have relevant information or even communications
accessible
Today’s Internet and existing databases/portals are
primarily intended for users literate in English and can
synthesize their solutions from multiple sources
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Internet Bill of Rights
Jaime Carbonell, 1994

Get the right information
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To the right people
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e.g. machine translation
With the right level of detail
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e.g. Just-in-Time (task modeling, planning)
In the right language
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e.g. categorizing, routing
At the right time
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e.g. search engines
e.g. summarization
In the right medium
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e.g. access to information in non-textual media
Relevant Technologies
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“…right information”

search engines
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“…right people”
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classification, routing
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“…right time”

anticipatory analysis
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“…right language”
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machine translation
“…right level of detail”  summarization
 speech input and output
 “…right medium”

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“…right information”
Search Engines
The Right Information
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Right Information from future Search Engines
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How to go beyond just “relevance to query” (all) and “popularity”
Eliminate massive redundancy e.g. “web-based email”
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Should not result in
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Should result in
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multiple links to different yahoo sites promoting their email, or even nonYahoo sites discussing just Yahoo-email.
a link to Yahoo email, one to MSN email, one to Gmail, one that
compares them, etc.
First show trusted info sources and user-community-vetted
sources

At least for important info (medical, financial, educational, …), I want
to trust what I read, e.g.,
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For new medical treatments

First info from hospitals, medical schools, the AMA, medical publications, etc.
, and
 NOT from Joe Shmo’s quack practice page or from the National Enquirer.
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Beyond Pure Relevance in IR

Current Information Retrieval Technology Only
Maximizes Relevance to Query
 What about information novelty, timeliness,
appropriateness, validity, comprehensibility, density,
medium,...??
 Novelty is approximated by non-redundancy!

we really want to maximize: relevance to the query, given
the user profile and interaction history,
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P(U(f i , ..., f n ) | Q & {C} & U & H)
where Q = query, {C} = collection set,
U = user profile, H = interaction history
...but we don’t yet know how. Darn.
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Maximal Marginal Relevance vs.
Standard Information Retrieval
documents
query
MMR
Standard IR
IR
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“…right people”
Text Categorization
The Right People

User-focused search is key

If a 7-year old is working on a school project

taking good care of one’s heart and types in “heart care”, she will want links
to pages like
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“You and your friendly heart”,
“Tips for taking good care of your heart”,
“Intro to how the heart works” etc.
NOT the latest New England Journal of Medicine article on “Cardiological
implications of immuo-active proteases”.
If a cardiologist issues the query, exactly the opposite is desired
 Search engines must know their users better, and the user tasks

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Social affiliation groups for search and for automatically categorizing,
prioritizing and routing incoming info or search results. New machine
learning technology allows for scalable high-accuracy hierarchical
categorization.
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Family group
Organization group
Country group
Disaster affected group
Stockholder group
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Text Categorization
Assign labels to each document or web-page
 Labels may be topics such as Yahoo-categories
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Labels may be genres
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finance, sports, NewsWorldAsiaBusiness
editorials, movie-reviews, news
Labels may be routing codes
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send to marketing, send to customer service
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Text Categorization
Methods
 Manual assignment
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Hand-coded rules
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as in Yahoo
as in Reuters
Machine Learning (dominant paradigm)
Words in text become predictors
 Category labels become “to be predicted”
 Predictor-feature reduction (SVD, 2, …)
 Apply any inductive method: kNN, NB, DT,…

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“…right timeframe”
Just-in-Time - no sooner or later
Just in Time Information
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Get the information to user exactly when it is
needed
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Immediately when the information is requested
Prepositioned if it requires time to fetch & download
(eg HDTV video)
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requires anticipatory analysis and pre-fetching
How about “push technology” for, e.g. stock
alerts, reminders, breaking news?

Depends on user activity:
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Sleeping or Don’t Disturb or in Meeting  wait your chance
Reading email  now if info is urgent, later otherwise
Group info before delivering (e.g. show 3 stock alerts
together)
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“…right language”
Translation
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Access to Multilingual Information

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Language Identification (from text, speech, handwriting)
Trans-lingual retrieval (query in 1 language, results in
multiple languages)
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Full translation (e.g. of web page, of search results snippets,
…)
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General reading quality (as targeted now)
Focused on getting entities right (who, what, where, when
mentioned)
Partial on-demand translation
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Requires more than query-word out-of-context translation (see
Carbonell et al 1997 IJCAI paper) to do it well
Reading assistant: translation in context while reading an original
document, by highlighting unfamiliar words, phrases, passages.
On-demand Text to Speech
Transliteration
“…in the Right Language”

Knowledge-Engineered MT
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Parallel Corpus-Trainable MT
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Transfer rule MT (commercial systems)
High-Accuracy Interlingual MT (domain focused)
Statistical MT (noisy channel, exponential models)
Example-Based MT (generalized G-EBMT)
Transfer-rule learning MT (corpus & informants)
Multi-Engine MT

Omnivorous approach: combines the above to
maximize coverage & minimize errors
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“…right level of detail”
Summarization
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Right Level of Detail

Automate summarization with hyperlink one-click
drilldown on user selected section(s).

Purpose Driven: summaries are in service of an
information need, not one-size fits all (as in Shaom’s
outline and the DUC NIST evaluations)

EXAMPLE: A summary of a 650-page clinical study can focus on
effectiveness of the new drug for target disease
 methodology of the study (control group, statistical rigor,…)
 deleterious side effects if any
 target population of study (e.g. acne-suffering teens, not eczema
suffering adults ….depending on the user’s task or information
query

Information Structuring and
Summarization
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Hierarchical multi-level pre-computed summary
structure, or on-the-fly drilldown expansion of info.
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Headline <20 words
Abstract 1% or 1 page
Summary5-10% or 10 pages
Document
100%
 Scope of Summary
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Single big document (e.g. big clinical study)
Tight cluster of search results (e.g. vivisimo)
Related set of clusters (e.g. conflicting opinions on how to cope
with Iran’s nuclear capabilities)
Focused area of knowledge (e.g. What’s known about Pluto?
Lycos has good project in this via Hotbot)
Specific kinds of commonly asked information(e.g. synthesize a
bio on person X from any web-accessible info)
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Document Summarization
Types of Summaries
Task
INDICATIVE
for Filtering
Query-relevant
Query-free
(focused)
(generic)
Filter search engine
results
Short abstracts
Solve problems for busy
professionals
Executive
summaries
(Do I read further?)
CONTENTFUL
for reading in lieu of
full doc
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“…right medium”
Finding information in Non-textual Media
Indexing and Searching
Non-textual (Analog) Content
Speech  text (speech recognition)
 Text  speech
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TTS: FESTVOX by far most popular high-quality
system
Handwriting  text (handwriting recognition)
 Printed text  electronic text (OCR)
 Picture  caption key words (automatically) for
indexing and searching
 Diagram, tables, graphs, maps  caption key
words (automatically)

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AI and Access to Libraries
The Million Book Digital Library Project
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One Step at a Time…

Million Book DL
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Only about 1% of all the world’s books

Harvard University
12M
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Library of Congress
30M
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OCLC catalog 42M

All Multilingual Books ~100M
At the rate of digitization of the last decade it
would take a 100 years!
Million Book Project: Issues

Time
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At one page per second (20,000 pages per day
shift), it will take 100 years (200 working days per
year) to scan a million books of 400 pages each
Cost

100M books at US$100 per book would coat $10B

Even in India and China the cost will be $1B

The annual cost is currently expected to be close
$10M per year with support from US, India and
China.
Selection

Selection of appropriate books for scanning is time
consuming and expensive
Million Book Project: Issues (cont)

Logistics
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Meta Data
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Each containers hold 10,000 to 20,000 books.
Shipping and handling costs about $10,000
Accessing and/or creating Meta data requires
professionals trained in Library science
Optical Character Recognition Technology

Essential for searching, translation and
summarization

Many languages don’t have OCR
Million Book Project: Status
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18 Centers in India
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22 centers in China

1 Center in Egypt
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15 Centers in Poland
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Planned : Australia

Over 1,400,000 books scanned
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Over 250,000+ accessible on the web
Title
Author
Language
Subject
Publisher
Year
Abstract
Rig Veda
Pandit Sriram Sharma Acharya
Sanskrit
Philosophy
Sanskriti Sansthan Bareli
47
Rig Veda is the oldest of the
Vedas. The Rig Veda is the
oldest book in Sanskrit or any
Indo-European language. Many
great Yogis and scholars who
have understood the
astronomical references in the
hymns, date the Rig Veda as
before 4000 B.C., perhaps as
early as 12,000. Modern
western scholars date it around
1500 B.C., though recent
archaeological finds in India
(like Dwaraka) now appear to
require a much earlier date
Title
Author
Language
Subject
Publisher
Year
Abstract
48
Elementary Treatise on the
Wave-Theory of Light
Humphery Lloyd, D.D, D.C.L
English
Physics
Longmans, Green & Co
1873
This book deals with the
various aspects of the wave
theory of light. It is a critical
work which contains an
analytical discussion of the
most recent researches in
Optics. It presents a clear and
connected view of the
subject.
Title
Author
Language
Subject
Publisher
Year
Abstract
49
Mudalayiram Mulamum
Periya Jeeyar
Tamil
Religion
Sri Vaishnava Sampirathaya
Sanjeevikiri Sabayai
1909
This volume is written in Tamil.
It provides a detailed account
of the origin of Vaishnava and
is written by Periya Jeeyar. .
50
Title
Author
Language
Subject
Publisher
Year
Abstract
Gulzar-A-Badesha
Khader Badesha
Urdu
Literature
Namipress, Chennai
1919
Literature
51
Title
Author
Language
Subject
Publisher
Year
Abstract
Jawahar Ali Joyviyah
Dr.Ilyas lomas
Arabic
Metrology
Bakri and Issa
1876
It is a book on Metrology, a
study of measurements
Title
Author
Language
Subject
Publisher
Year
Abstract
52
Structure Des Molecules
Victor Henri
French
Chemistry
Taylor and Francis
1925
This is a unique book that
explicates, in detail, the
structure of molecules and
touches upon certain specific
characteristics of molecules
with particular reference to
Benzene
Million Book Project:
AI Research Challenges
Multilingual Information Retrieval
 Translation
 Summarization
 Reading Assistant using Multi Lingual
Speech Synthesis and Translation (e.g. for
news paper DL)
 Easy to use interfaces for Billions
 Providing Access to Billions everyday

 Distributed
Cached Servers in every region
54
AI and Education
Intermediate Examination 2006
Urban – Rural Divide
67
61
56
52
48
35
30
27
18
15
8
4
Passing
First division
More than 75%
Rural
More than 90%
Urban
First divisionmaximum of a
district
First divisionminimum of a
district
55
Intermediate Examination 2006
Differences in Performance of Different
Social Groups – Percent Failing
59
60
46
43
43
34
27
FC
BC
SC
ST
Muslim
Others
Total
56
Intermediate Examination 2006
57
Differences in Performance of Different
Social Groups
13
7
6
40
5
4
28
20
FC
BC
2
2
10
8
SC
ST
75 % or more
17
Muslim
more than 90 %
Others
23
Total
Performance in EAMCET 2006
Rural Urban Divide
72
70
65
Percent share
62
38
35
30
28
Avg. of Math+Sci EAMCET rank less EAMCET rank less EAMCET rank less
greater than 94.5%
than 5,000
than 10,000
than 50,000
Rural
Urban
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Large Variation in School Quality

No. of schools where NOT a SINGLE
student got more than 75% marks and
more than 50% of all taking exam failed
 360
in 2004, and
 965 in 2006

Intensity of problem is almost twice in
rural areas compared to urban areas
60
Large Variation in College Quality

Even bright fail!
1345 students who got more than 90% in Math in SSC failed in
either math A or B in year I or year II
 Of these 1345, 222 had >90% in two subjects and 53 in three
subjects



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253 colleges where failing rate is more than 75%
239 colleges where not a single student gets more than
75%
829 colleges where less than 5% students passing with
more than 75% (state avg. is 22%)
Intensity of problem is almost twice for colleges in rural
areas compared to colleges in urban areas
61
Problems with Current System

Focus on national best with consequent neglect of local
best


Schools in remote villages

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

Urban students with access to tuition and coaching get the
highest ranks in national tests
Lack of quality teachers
No coaching centers
Deprived of competitive atmosphere
No system to nurture talent who do best in such difficult
situations

Financial issues often prohibit the brightest rural students from
attending the best universities
62
Problems with Current System (Cont)

Lack access to quality colleges

Lack proper guidance, motivation and peer
groups

Inadequate support from families

Poverty prevents access to coaching classes,
tutoring etc

Poverty compels them to seek work to for
livelihood rather than proceed to college
essential for reaching their full potential
63
Current System
Admission to Engineering and Medicine

Coaching for 11th and 12th (costs 60K to
120/240K), Kota, Hyderabad, Delhi,

Unaffordable to many

Teaching to test
 Not

broad education
Revised pattern of JEE seems not to
diminish the importance of coaching
Focus
During Formative Years

Right guidance and environment during formative
years

This is what famous mathematician Hardy says
about mathematics genius Srinivas Ramanujan
The years between eighteen and twenty-five are the critical years
in the mathematician’s career and that the real tragedy is not that
Ramanujan died early, but during these years his genius was
misdirected, sidetracked, and to some extent even distorted
64
Problems with Current System
Wastage of precious time
 commuting (lot of time in to-and-fro, may be
1-4 hours a day)
 only two semesters in a year
 Lack focus on development of soft skills, a key
to success in today’s highly competitive job
market
 Imperfect credit market for higher secondary
education
Have you heard of bank loan for “coaching classes”
and 12th, JEE, EMCET, AIEEE
for 11th
65
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How AI can Help?
Creating a New Affirmative Action Plan For The
Socially Disadvantaged?
 Data Mining: Local Best instead of National Best
 Intelligent Tutoring Systems (AI Meets Cognitive
Science) : Variable Duration Learning



Online Reading Tutors
Online Math Tutors
Intelligent Monitoring Systems


Early Detection of Promising Students and Problem
Students thru Progress Monitoring
Process Improvement
AI and Development of Soft Skills
Soft skills have become key to success in
today’s highly competitive job market
 Develop Intelligent Tutoring Systems for:

 Communication skills/language proficiency
 Interpersonal Interaction and Negotiation
 Personality traits/sociability
 Teamwork
 Work ethic
 Courtesy
 Self-discipline, self-esteem and self-confidence
 Presentation skills
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AI and Healthcare
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PCtvt UI Design
for Use by Illiterate Persons
An Illiterate person needs a more
powerful PC than a PhD!
If not e-mail, use voice-mail
Replace Text Help by Video Help
Radically simple design
One minute learning time
Two click model
Three modes of communication: Video,
Audio and Text
Both Synchronous and Asynchronous
All-Iconic interfaces
Multiple input modalities
TV-remote, Speech I/O, Keyboard, Mouse
or Cell phone
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AI and eLearning


Give man a fish and you will feed him for a day. Teach
man to fish and you will feed him for life. (Old Chinese
Proverb -- Lao Tzu)
How to teach an illiterate villager who has never seen a
computer to effectively use PCtvt?

Self-evident, intuitive interfaces
Two clicks to most applications
 Learning time – less than five minutes to happiness


Just in Time learning
Immersive Interactive Simulated Environments
 Short video clips: Instant access to information through vast video
digital libraries in local languages


Interactive Problem Solving
Intensive programs for educating the local expert, the Village
Information Officer
 Teach the Teacher Programs

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A Call to Action to
AI Researchers In India
India Has 21 Official Languages!
We need to Break the Language Barrier!
•
•
•
•
•
Language barriers can significantly slow
down the economic growth
Globalization requires cross-border and
cross-language communication
Eliminate cultural and social barriers
Access to rare (and potentially beneficial)
knowledge requires eliminating the
language divide
Preservation of minority languages, cultures
and heritage
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Unfinished Research Agenda for AI
spoken language understanding,
 dialog modeling,
 multimedia synthesis and language
generation,
 multi-lingual indexing and retrieval,
 language translation, and
 summarization.

Next Steps
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Create technologies and solutions for
overcoming the language barrier
 Create toolkits for rapid acquisition of new
language capabilities

 Character
codes, optical character recognition,
speech recognition, speech synthesis,
translation, search engines, text mining,
summarization, language tutoring, etc.
Capture data, information and knowledge
from masses
 Make fundamental advances in language
processing algorithms, e.g.,

 Deal with 1000 times more data
 Conceptual advance in semantic
retrieval
information
The Educational Plan
 Training
a generation of researchers to
explore many techniques in many
languages
 Training innovators and entrepreneurs in
applications of language technology
 Training scholars in each country to be
expert in language technology
 Training individuals in foreign languages
and cultures
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The Research Plan
Analogy to Human Genome Project
Meticulous core-science based fundamentals
Researcher toolkits for known methodologies
Architecture supporting diversity of
methodologies
 Long planning horizon to support
development of novel and radical approaches
 Quantitative evaluation against a standard of
steadily accumulating improvements in
performance




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Impact and Benefits
greater participation in global economy
 preserve local languages and cultures
 promote greater communication and
understanding among states and
individuals
 With over 100 orphan languages, each
country of the world needs these tools in
its own enlightened self interest

 International
focus and multinational
involvement will establish India as a world
leader in this important technology
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Conclusions
As we enter the Second 50 Years AI R&D, we
need to ask how our work can help Society at
large and People at the bottom of the pyramid in
particular
 Proactive Development of Intelligent Systems for





Access to Knowledge and Know how
Learning and Education
Health
Robotics for



Accident Avoiding Cars
Landmine Detection, and
Disaster Rescue and Recovery
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