Applications of Intelligent Systems and Robotics in Service of Society - PowerPoint PPT Presentation

Loading...

PPT – Applications of Intelligent Systems and Robotics in Service of Society PowerPoint presentation | free to download - id: 3bb2e-NDZhY



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Applications of Intelligent Systems and Robotics in Service of Society

Description:

Keynote Speech at IJCAI 2007, Hyderabad, India. 2. Outline of the Talk ... Labels may be topics such as Yahoo-categories. finance, sports, News ... – PowerPoint PPT presentation

Number of Views:465
Avg rating:3.0/5.0
Slides: 81
Provided by: RahulT5
Learn more at: http://www.rr.cs.cmu.edu
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Applications of Intelligent Systems and Robotics in Service of Society


1
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 to Knowledge, Education and healthcare,
    etc.
  • 3 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

3
Needs of the People with Per Capita Income of
Less Than 1 a Day
  • Access to entertainment
  • watch any movie, TV show when desired
  • Telemedicine
  • providing links to doctors and treatment at a
    distance
  • Access to information
  • about hygiene and safe water, helping to reduce
    infant mortality
  • Life-long learning
  • independent of the limitations of language,
    distance, age and physical disabilities
  • Price discovery
  • Marketing assistance
  • using eBay like auction exchanges
  • Find jobs
  • e.g. monster.com
  • They need AI and IT
  • but not Word, Excel and Powerpoint

4
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 worlds 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

7
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

8
Some Application Domains
  • 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
  • Knowledge Data Response Content
    Rate Time
  • Poor Low Hours
  • Puzzles
  • Chess
  • Theorem Proving
  • Expert Systems
  • Natural Language
  • Motor Processes
  • Speech
  • Vision

Rich High Real
Time
11
Lessons from AI Experiments
  • Bounded Rationality implies Opportunistic Search
  • An Expert becomes a World Class Expert only after
    spending at least 15 years of intensive practice
    and knows 70,00020,000 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

14
Village Google Access to Knowledge for Use in a
Village
  • Access to Essential Information and Advice
  • Medical, Agriculture, FAQ indexed and searchable
  • Interactive access to Doctors, Rescue Personnel
  • Lifelong Learning and Education
  • Agricultural Information
  • Price discovery, crop disease information,
    weather prediction
  • Access to Markets and Jobs
  • Disaster Relief and Management
  • Access to Newspapers, Radio and TV
  • Entertainment and Amusement
  • Communications
  • Video Phone, IP Telephone, Instant Messaging
  • Video Email, Voice Email, Text Email

15
The Vision of a Global Knowledge Network
  • 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
  • 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

16
System Overview
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

17
Knowledge officers and Domain Experts
World
Knowledge Management Coordination (global)
Nation
Knowledge Management Coordination (national
level)
Expertise of Knowledge Officers
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)
18
Roles of Knowledge Officers
District
(sub)continent
Global
Village
Region/Nation
3,000 people Transcription (and possibly
Transliteration)
300,000 people Translation and Information
Retrieval
0.3B people Knowledge Management Coordination
30M people Verification RFP from Experts
3 Billion people Knowledge Analysis and Inference
Records question of the end-user in
audio-video format. Enters text transcription of
the question. Searches local language database
for answer Need not be knowledgeable in English.
Enters translation of questions. Searches
multilingual database for answer Sends answer
after translation to lower level If question not
among FAQs or automated system, sends to higher
level
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
Same as next level up, but with the range of
analyses broadened to the region/subcontinent
level
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)
19
The AI Challenges in Creating a Global Knowledge
Network
  • Farmers typically not able to tap in to existing
    networks
  • Often illiterate
  • Rarely have relevant information or even
    communications accessible
  • Todays Internet and existing databases/portals
    are primarily intended for users literate in
    English and can synthesize their solutions from
    multiple sources

20
Internet Bill of RightsJaime Carbonell, 1994
  • Get the right information
  • e.g. search engines
  • To the right people
  • e.g. categorizing, routing
  • At the right time
  • e.g. Just-in-Time (task modeling, planning)
  • In the right language
  • e.g. machine translation
  • With the right level of detail
  • e.g. summarization
  • In the right medium
  • e.g. access to information in non-textual media

21
Relevant Technologies
  • search engines
  • classification, routing
  • anticipatory analysis
  • machine translation
  • summarization
  • speech input and output
  • right information
  • right people
  • right time
  • right language
  • right level of detail
  • right medium

22
right information Search Engines

23
The Right Information
  • Right Information from future Search Engines
  • How to go beyond just relevance to query (all)
    and popularity
  • Eliminate massive redundancy e.g. web-based
    email
  • Should not result in
  • multiple links to different yahoo sites promoting
    their email, or even non-Yahoo sites discussing
    just Yahoo-email.
  • Should result in
  • 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.,
  • For new medical treatments
  • First info from hospitals, medical schools, the
    AMA, medical publications, etc. , and
  • NOT from Joe Shmos quack practice page or from
    the National Enquirer.
  • Maximum Marginal Relevance
  • Novelty Detection
  • Named Entity Extraction

24
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,
  • P(U(f i , ..., f n ) Q C U H)
  • where Q query, C collection set,
  • U user profile, H interaction history
  • ...but we dont yet know how. Darn.

25
Maximal Marginal Relevance vs. Standard
Information Retrieval
documents
query
MMR
Standard IR
IR
26
right peopleText Categorization

27
The Right People
  • User-focused search is key
  • If a 7-year old is working on a school project
  • taking good care of ones heart and types in
    heart care, she will want links to pages like
  • 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
  • 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.
  • Family group
  • Organization group
  • Country group
  • Disaster affected group
  • Stockholder group

28
Text Categorization
  • Assign labels to each document or web-page
  • Labels may be topics such as Yahoo-categories
  • finance, sports, News?World?Asia?Business
  • Labels may be genres
  • editorials, movie-reviews, news
  • Labels may be routing codes
  • send to marketing, send to customer service

29
Text Categorization
Methods
  • Manual assignment
  • as in Yahoo
  • Hand-coded rules
  • 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,

30
right timeframeJust-in-Time - no sooner or
later

31
Just in Time Information
  • Get the information to user exactly when it is
    needed
  • Immediately when the information is requested
  • Prepositioned if it requires time to fetch
    download (eg HDTV video)
  • requires anticipatory analysis and pre-fetching
  • How about push technology for, e.g. stock
    alerts, reminders, breaking news?
  • Depends on user activity
  • Sleeping or Dont 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)
  • Info directly relevant to users current task ?
    immediately

32
right languageTranslation

33
Access to Multilingual Information
  • Language Identification (from text, speech,
    handwriting)
  • Trans-lingual retrieval (query in 1 language,
    results in multiple languages)
  • Requires more than query-word out-of-context
    translation (see Carbonell et al 1997 IJCAI
    paper) to do it well
  • Full translation (e.g. of web page, of search
    results snippets, )
  • General reading quality (as targeted now)
  • Focused on getting entities right (who, what,
    where, when mentioned)
  • Partial on-demand translation
  • Reading assistant translation in context while
    reading an original document, by highlighting
    unfamiliar words, phrases, passages.
  • On-demand Text to Speech
  • Transliteration

34
in the Right Language
  • Knowledge-Engineered MT
  • Transfer rule MT (commercial systems)
  • High-Accuracy Interlingual MT (domain focused)
  • Parallel Corpus-Trainable MT
  • 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

35
right level of detailSummarization

36
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
    Shaoms 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 users task or information query

37
Information Structuring and Summarization
  • Hierarchical multi-level pre-computed summary
    structure, or on-the-fly drilldown expansion of
    info.
  • Headline lt20 words
  • Abstract 1 or 1 page
  • Summary 5-10 or 10 pages
  • Document 100
  • Scope of Summary
  • 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 Irans nuclear
    capabilities)
  • Focused area of knowledge (e.g. Whats 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)

38
Document Summarization
  • Types of Summaries

39
right mediumFinding information in
Non-textual Media

40
Indexing and Searching Non-textual (Analog)
Content
  • Speech ? text (speech recognition)
  • Text ? speech
  • 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)

41
AI and Access to LibrariesThe Million Book
Digital Library Project

42
(No Transcript)
43
One Step at a Time
  • Million Book DL
  • Only about 1 of all the worlds books
  • Harvard University 12M
  • Library of Congress 30M
  • OCLC catalog 42M
  • All Multilingual Books 100M
  • At the rate of digitization of the last decade it
    would take a 100 years!

44
Million Book Project Issues
  • Time
  • 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 US100 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

45
Million Book Project Issues (cont)
  • Logistics
  • Each containers hold 10,000 to 20,000 books.
    Shipping and handling costs about 10,000
  • Meta Data
  • Accessing and/or creating Meta data requires
    professionals trained in Library science
  • Optical Character Recognition Technology
  • Essential for searching, translation and
    summarization
  • Many languages dont have OCR

46
Million Book Project Status
  • 18 Centers in India
  • 22 centers in China
  • 1 Center in Egypt
  • 15 Centers in Poland
  • Planned Australia
  • Over 1,400,000 books scanned
  • Over 250,000 accessible on the web

47
(No Transcript)
48
(No Transcript)
49
(No Transcript)
50
(No Transcript)
51
(No Transcript)
52
(No Transcript)
53
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
55
Intermediate Examination 2006 Urban Rural
Divide
56
Intermediate Examination 2006 Differences in
Performance of Different Social Groups Percent
Failing
57
Intermediate Examination 2006 Differences in
Performance of Different Social Groups
58
Performance in EAMCET 2006 Rural Urban Divide
59
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 gt90 in two subjects and
    53 in three subjects
  • 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
  • Urban students with access to tuition and
    coaching get the highest ranks in national tests
  • Schools in remote villages
  • 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 SystemAdmission 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

64
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 mathematicians 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
65
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 todays highly competitive job
    market
  • Imperfect credit market for higher secondary
    education-
  • Have you heard of bank loan for coaching
    classes for 11th and 12th, JEE, EMCET, AIEEE

66
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

67
AI and Development of Soft Skills
  • Soft skills have become key to success in todays
    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

68
AI and Healthcare
69
(No Transcript)
70
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

71
(No Transcript)
72
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

73
A Call to Action to AI Researchers In India
74
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

75
Unfinished Research Agenda for AI
  • spoken language understanding,
  • dialog modeling,
  • multimedia synthesis and language generation,
  • multi-lingual indexing and retrieval,
  • language translation, and
  • summarization.

76
Next Steps
  • 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 information
    retrieval

77
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

78
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

79
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

80
Conclusions
  • As we enter the Second 50 Years AI RD, 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
About PowerShow.com