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Applications of Intelligent Systems and Robotics in Service of Society Raj Reddy Carnegie Mellon 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 l Needs of Developing Economies l Access to Knowledge, 2 Outline of the Talk l Needs of Developing Economies l Access to Knowledge, Education and healthcare, etc. l 3 Minute Introduction to AI: What is how it can help l The role of AI in enabling l access to knowledge and knowhow l access to libraries l access to education and learning l access to health care l Unfinished research agenda of AI it and

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

4 Barriers to Entry: The Digital Divide l Connectivity Divide l Access to free 4 Barriers to Entry: The Digital Divide l Connectivity Divide l Access to free Internet for basic services? l Computer Access Divide l Accessibility: Less than 5 minute walk? l Affordability: Costing less than a cup of coffee per day? l Digital Literacy Divide l Language Divide l Literacy Divide l Content Divide l Access to information and knowledge l Access to health care l Access to education and learning l Access to jobs l Access to entertainment l Access to improved quality of life

5 A 3 -Minute Introduction to AI l What is it and how it 5 A 3 -Minute Introduction to AI l What is it and how it can help l 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 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 l l l 1950 s: Theorem 7 A Historical View of Advances in AI l l l 1950 s: Theorem Proving; Chess 1960 s: Problem Solving; Language: Understand; Question Answering 1970 s: Speech; Vision; Expert Systems 1980 s: Robotics; Knowledge Based Systems 1990 s: Language Translation; Search 2000 s: Systems that Learn with Experience

8 Some Application Domains l l l l l Web Search : Google, Yahoo, 8 Some Application Domains l l l l l 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 l Learn from experience l Exploit vast amounts of knowledge 9 Requirements for Intelligence l Learn from experience l Exploit vast amounts of knowledge l Exhibit Goal Directed Behavior l Tolerate error and ambiguity in input l Communicate with natural language l Operate in real time, and l Use symbols (and abstractions)

10 AI Problem Domains & Attributes Knowledge Content Puzzles Data Rate Response Time Poor 10 AI Problem Domains & Attributes Knowledge Content Puzzles Data Rate Response Time Poor Low Hours Chess Theorem Proving Expert Systems Natural Language Motor Processes Speech Vision Rich High Real Time

11 Lessons from AI Experiments l l l Bounded Rationality implies Opportunistic Search An 11 Lessons from AI Experiments l l l 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 Compensates for Lack of Search A Physical Symbol System is Necessary and Sufficient for Intelligent Action

12 How Can AI Help? l Intelligent Systems in support of Access to Knowledge 12 How Can AI Help? l Intelligent Systems in support of Access to Knowledge and Knowhow l Learning and Education l Health l Robotics for Accident Avoiding Cars, Landmine Detection, and Disaster Recovery l

13 Enabling Access to Knowledge and Information 13 Enabling Access to Knowledge and Information

Village Google: Access to Knowledge for Use in a Village l Access to Essential Village Google: Access to Knowledge for Use in a Village l Access to Essential Information and Advice l l l l l 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 l l Video Phone, IP Telephone, Instant Messaging Video Email, Voice Email, Text Email 14

The Vision of a Global Knowledge Network l Create a Knowledge Network that connects The Vision of a Global Knowledge Network l Create a Knowledge Network that connects experts to the people who need help, e. g. , farmers in villages l End-users interact at Village Knowledge Centers Equipped with a networked computer and basic A/V equipment l Staffed by a Knowledge Officer l l Humans are intrinsic to Knowledge Networks (raw information knowledge!) l Domain experts provide answers to previously unanswered questions l Answers converted into an “encyclopedia-on-demand” video documentary at higher-level centers and dubbed into local languages in each country l Also available for direct access browsing by literate and networked users 15

System Overview 16 Multi-level Information Flow - An example scenario An illiterate farmer goes 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 100 s of domain experts populate the databases, both as part of their jobs and as volunteers (say, 2 questions per week) l Hierarchical structure spanning districts, regions, countries, etc. l Outside experts interact with higher level Knowledge Officers l Builds up an ever-increasing multimedia database l l 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 Knowledge Management & Coordination (global) Of fic er s Knowledge officers and Domain Experts Knowledge Management & Coordination (global) Of fic er s World ed g e Nation Kn o wl Knowledge Management & Coordination (national level) of State Ex pe rti se 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) 17

18 Roles of Knowledge Officers Village District 3, 000 people 300, 000 people 30 18 Roles of Knowledge Officers Village District 3, 000 people 300, 000 people 30 M people 0. 3 B 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. 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 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 l 19 Farmers typically not The AI Challenges in Creating a Global Knowledge Network l 19 Farmers typically not able to tap in to existing networks Often illiterate l Rarely have relevant information or even communications accessible l l Today’s Internet and existing databases/portals are primarily intended for users literate in English and can synthesize their solutions from multiple sources

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

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

22 “…right information” Search Engines 22 “…right information” Search Engines

The Right Information l Right Information from future Search Engines l l How to The Right Information l Right Information from future Search Engines l l How to go beyond just “relevance to query” (all) and “popularity” Eliminate massive redundancy e. g. “web-based email” l Should not result in l l Should result in l l multiple links to different yahoo sites promoting their email, or even non. Yahoo 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 l At least for important info (medical, financial, educational, …), I want to trust what I read, e. g. , l For new medical treatments First info from hospitals, medical schools, the AMA, medical publications, etc. , and l NOT from Joe Shmo’s quack practice page or from the National Enquirer. l 23

Beyond Pure Relevance in IR Current Information Retrieval Technology Only Maximizes Relevance to Query Beyond Pure Relevance in IR Current Information Retrieval Technology Only Maximizes Relevance to Query l What about information novelty, timeliness, appropriateness, validity, comprehensibility, density, medium, . . . ? ? l Novelty is approximated by non-redundancy! l l we really want to maximize: relevance to the query, given the user profile and interaction history, l l 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. 24

25 Maximal Marginal Relevance vs. Standard Information Retrieval documents query MMR Standard IR IR 25 Maximal Marginal Relevance vs. Standard Information Retrieval documents query MMR Standard IR IR

26 “…right people” Text Categorization 26 “…right people” Text Categorization

The Right People l User-focused search is key l If a 7 -year old The Right People l User-focused search is key l If a 7 -year old is working on a school project l taking good care of one’s heart and types in “heart care”, she will want links to pages like “You and your friendly heart”, l “Tips for taking good care of your heart”, l “Intro to how the heart works” etc. l NOT the latest New England Journal of Medicine article on “ Cardiological implications of immuo-active proteases”. l If a cardiologist issues the query, exactly the opposite is desired l Search engines must know their users better, and the user tasks l l 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. l l l Family group Organization group Country group Disaster affected group Stockholder group 27

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

Text Categorization Methods l Manual assignment l l Hand-coded rules l l as in Text Categorization Methods l Manual assignment l l Hand-coded rules l l as in Yahoo as in Reuters Machine Learning (dominant paradigm) Words in text become predictors l Category labels become “to be predicted” l Predictor-feature reduction (SVD, 2, …) l Apply any inductive method: k. NN, NB, DT, … l 29

30 “…right timeframe” Just-in-Time - no sooner or later 30 “…right timeframe” Just-in-Time - no sooner or later

Just in Time Information l Get the information to user exactly when it is Just in Time Information l Get the information to user exactly when it is needed Immediately when the information is requested l Prepositioned if it requires time to fetch & download (eg HDTV video) l l l requires anticipatory analysis and pre-fetching How about “push technology” for, e. g. stock alerts, reminders, breaking news? l Depends on user activity: Sleeping or Don’t Disturb or in Meeting wait your chance l Reading email now if info is urgent, later otherwise l Group info before delivering (e. g. show 3 stock alerts together) l 31

32 “…right language” Translation 32 “…right language” Translation

33 Access to Multilingual Information Language Identification (from text, speech, handwriting) l Trans-lingual retrieval 33 Access to Multilingual Information Language Identification (from text, speech, handwriting) l Trans-lingual retrieval (query in 1 language, results in multiple languages) l l l 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) l Focused on getting entities right (who, what, where, when mentioned) l l Partial on-demand translation Reading assistant: translation in context while reading an original document, by highlighting unfamiliar words, phrases, passages. l On-demand Text to Speech l l Transliteration

“…in the Right Language” l 34 Knowledge-Engineered MT Transfer rule MT (commercial systems) l “…in the Right Language” l 34 Knowledge-Engineered MT Transfer rule MT (commercial systems) l High-Accuracy Interlingual MT (domain focused) l l Parallel Corpus-Trainable MT Statistical MT (noisy channel, exponential models) l Example-Based MT (generalized G-EBMT) l Transfer-rule learning MT (corpus & informants) l l Multi-Engine MT l Omnivorous approach: combines the above to maximize coverage & minimize errors

35 “…right level of detail” Summarization 35 “…right level of detail” Summarization

36 Right Level of Detail l Automate summarization with hyperlink one-click drilldown on user 36 Right Level of Detail l Automate summarization with hyperlink one-click drilldown on user selected section(s). l 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) l EXAMPLE: A summary of a 650 -page clinical study can focus on effectiveness of the new drug for target disease l methodology of the study (control group, statistical rigor, …) l deleterious side effects if any l target population of study (e. g. acne-suffering teens, not eczema suffering adults …. depending on the user’s task or information query l

Information Structuring and Summarization l Hierarchical multi-level pre-computed summary structure, or on-the-fly drilldown expansion Information Structuring and Summarization l Hierarchical multi-level pre-computed summary structure, or on-the-fly drilldown expansion of info. Headline <20 words Abstract 1% or 1 page l Summary 5 -10% or 10 pages l Document 100% l l l Scope of Summary l l l 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) 37

Document Summarization Types of Summaries Task for Filtering Query-free (focused) INDICATIVE Query-relevant (generic) Filter Document Summarization Types of Summaries Task for Filtering Query-free (focused) INDICATIVE Query-relevant (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 38

39 “…right medium” Finding information in Non-textual Media 39 “…right medium” Finding information in Non-textual Media

Indexing and Searching Non-textual (Analog) Content Speech text (speech recognition) l Text speech l Indexing and Searching Non-textual (Analog) Content Speech text (speech recognition) l Text speech l l TTS: FESTVOX by far most popular high-quality system Handwriting text (handwriting recognition) l Printed text electronic text (OCR) l Picture caption key words (automatically) for indexing and searching l Diagram, tables, graphs, maps caption key words (automatically) l 40

41 AI and Access to Libraries The Million Book Digital Library Project 41 AI and Access to Libraries The Million Book Digital Library Project

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One Step at a Time… l Million Book DL l Only about 1% of One Step at a Time… l Million Book DL l Only about 1% of all the world’s books l 12 M l Library of Congress 30 M l OCLC catalog 42 M l l Harvard University All Multilingual Books ~100 M At the rate of digitization of the last decade it would take a 100 years!

Million Book Project: Issues l Time l l At one page per second (20, Million Book Project: Issues l Time l l 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 l l Even in India and China the cost will be $1 B l l 100 M books at US$100 per book would coat $10 B The annual cost is currently expected to be close $10 M per year with support from US, India and China. Selection l Selection of appropriate books for scanning is time consuming and expensive

Million Book Project: Issues (cont) l Logistics l l Meta Data l l Each Million Book Project: Issues (cont) l Logistics l l Meta Data l l 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 l Essential for searching, translation and summarization l Many languages don’t have OCR

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

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Million Book Project: AI Research Challenges l Multilingual Information Retrieval l Translation l Summarization Million Book Project: AI Research Challenges l Multilingual Information Retrieval l Translation l Summarization l Reading Assistant using Multi Lingual Speech Synthesis and Translation (e. g. for news paper DL) l Easy to use interfaces for Billions l Providing Access to Billions everyday l Distributed Cached Servers in every region

54 AI and Education 54 AI and Education

Intermediate Examination 2006 Urban – Rural Divide 55 Intermediate Examination 2006 Urban – Rural Divide 55

Intermediate Examination 2006 Differences in Performance of Different Social Groups – Percent Failing 56 Intermediate Examination 2006 Differences in Performance of Different Social Groups – Percent Failing 56

Intermediate Examination 2006 57 Differences in Performance of Different Social Groups Intermediate Examination 2006 57 Differences in Performance of Different Social Groups

Performance in EAMCET 2006 Rural Urban Divide 58 Performance in EAMCET 2006 Rural Urban Divide 58

59 Large Variation in School Quality of schools where NOT a SINGLE student got 59 Large Variation in School Quality of schools where NOT a SINGLE student got more than 75% marks and more than 50% of all taking exam failed l No. l 360 in 2004, and l 965 in 2006 l Intensity of problem is almost twice in rural areas compared to urban areas

60 Large Variation in College Quality l Even bright fail! 1345 students who got 60 Large Variation in College Quality l 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 l Of these 1345, 222 had >90% in two subjects and 53 in three subjects l 253 colleges where failing rate is more than 75% l 239 colleges where not a single student gets more than 75% l 829 colleges where less than 5% students passing with more than 75% (state avg. is 22%) l Intensity of problem is almost twice for colleges in rural areas compared to colleges in urban areas l

61 Problems with Current System l Focus on national best with consequent neglect of 61 Problems with Current System l Focus on national best with consequent neglect of local best l l Urban students with access to tuition and coaching get the highest ranks in national tests Schools in remote villages Lack of quality teachers l No coaching centers l Deprived of competitive atmosphere l l No system to nurture talent who do best in such difficult situations l Financial issues often prohibit the brightest rural students from attending the best universities

62 Problems with Current System (Cont) l Lack access to quality colleges l Lack 62 Problems with Current System (Cont) l Lack access to quality colleges l Lack proper guidance, motivation and peer groups l Inadequate support from families l Poverty prevents access to coaching classes, tutoring etc l 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 l Coaching for 11 th and 63 Current System Admission to Engineering and Medicine l Coaching for 11 th and 12 th (costs 60 K to 120/240 K), Kota, Hyderabad, Delhi, l Unaffordable l Teaching l Not to many to test broad education l Revised pattern of JEE seems not to diminish the importance of coaching

Focus During Formative Years l Right guidance and environment during formative years l This Focus During Formative Years l Right guidance and environment during formative years l 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 l commuting (lot of time in Problems with Current System Wastage of precious time l commuting (lot of time in to-and-fro, may be 1 -4 hours a day) l only two semesters in a year l Lack focus on development of soft skills, a key to success in today’s highly competitive job market l Imperfect credit market for higher secondary educationl Have you heard of bank loan for “coaching classes” and 12 th, JEE, EMCET, AIEEE for 11 th 65

66 How AI can Help? Creating a New Affirmative Action Plan For The Socially 66 How AI can Help? Creating a New Affirmative Action Plan For The Socially Disadvantaged? l Data Mining: Local Best instead of National Best l Intelligent Tutoring Systems (AI Meets Cognitive Science) : Variable Duration Learning l l l Online Reading Tutors Online Math Tutors Intelligent Monitoring Systems Early Detection of Promising Students and Problem Students thru Progress Monitoring l Process Improvement l

AI and Development of Soft Skills l Soft skills have become key to success AI and Development of Soft Skills l Soft skills have become key to success in today’s highly competitive job market l Develop Intelligent Tutoring Systems for: l Communication skills/language proficiency l Interpersonal Interaction and Negotiation l Personality traits/sociability l Teamwork l Work ethic l Courtesy l Self-discipline, self-esteem and self-confidence l Presentation skills 67

68 AI and Healthcare 68 AI and Healthcare

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PCtvt UI Design for Use by Illiterate Persons l. An Illiterate person needs a PCtvt UI Design for Use by Illiterate Persons l. An Illiterate person needs a more powerful PC than a Ph. D! l. If not e-mail, use voice-mail l. Replace Text Help by Video Help l. Radically simple design l. One minute learning time l. Two click model l. Three modes of communication: Video, Audio and Text l. Both Synchronous and Asynchronous l. All-Iconic interfaces l. Multiple input modalities l. TV-remote, Speech I/O, Keyboard, Mouse or Cell phone 70

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72 AI and e. Learning Give man a fish and you will feed him 72 AI and e. Learning 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) l How to teach an illiterate villager who has never seen a computer to effectively use PCtvt? l l Self-evident, intuitive interfaces l l l Just in Time learning l l l Two clicks to most applications Learning time – less than five minutes to happiness 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 l Teach the Teacher Programs l

73 A Call to Action to AI Researchers In India 73 A Call to Action to AI Researchers In India

India Has 21 Official Languages! We need to Break the Language Barrier! Language barriers 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 • 74

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

Next Steps 76 l Create technologies and solutions for overcoming the language barrier l Next Steps 76 l Create technologies and solutions for overcoming the language barrier l Create toolkits for rapid acquisition of new language capabilities l Character codes, optical character recognition, speech synthesis, translation, search engines, text mining, summarization, language tutoring, etc. l Capture data, information and knowledge from masses l Make fundamental advances in language processing algorithms, e. g. , l Deal with 1000 times more data l Conceptual advance in semantic retrieval information

The Educational Plan l Training a generation of researchers to explore many techniques in The Educational Plan l Training a generation of researchers to explore many techniques in many languages l Training innovators and entrepreneurs in applications of language technology l Training scholars in each country to be expert in language technology l Training individuals in foreign languages and cultures 77

The Research Plan l Analogy to Human Genome Project l Meticulous core-science based fundamentals The Research Plan l Analogy to Human Genome Project l Meticulous core-science based fundamentals l Researcher toolkits for known methodologies l Architecture supporting diversity of methodologies l Long planning horizon to support development of novel and radical approaches l Quantitative evaluation against a standard of steadily accumulating improvements in performance 78

Impact and Benefits l greater participation in global economy l preserve local languages and Impact and Benefits l greater participation in global economy l preserve local languages and cultures l promote greater communication and understanding among states and individuals l With over 100 orphan languages, each country of the world needs these tools in its own enlightened self interest l International focus and multinational involvement will establish India as a world leader in this important technology 79

Conclusions As we enter the Second 50 Years AI R&D, we need to ask 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 l Proactive Development of Intelligent Systems for l l l Access to Knowledge and Know how Learning and Education Health Robotics for l l l Accident Avoiding Cars Landmine Detection, and Disaster Rescue and Recovery 80