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Web science AI and IA

Invited talk at Dagsthul Perspective on the Future of Web Science

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Web science AI and IA

  1. 1. Web Science and AI Fabien Gandon,
  2. 2. Webology and AI & IA
  3. 3. my (mis)understanding look into the future scoping, imagining state of field strengths and weaknesses research directions blind spots, sweet spots challenges, opportunities talk about my research
  4. 4. Web Science and AI • AI for the Web AI  Web – Connectionist AI: clustering, recommending, classifying, indexing, etc. – Symbolic AI: structured querying, reasoning, interoperating, etc. • Web for AI AI Web – Connectionist AI: Datasets, corpus, etc. ; Crowdsourced labelling – Symbolic AI: KR Standards, knowledge bases, etc. • Web and AI AI  Web Web  AI : a topic for Web Science
  5. 5. webcomputing architecture digital resources
  6. 6. 1994 - … html http url uri iri ataguaagwcagaria mwbpearlra cc/pp assx css ddrsa xml eve. exi geo api dom xform grddl inkml its cmwww ruby an.xhtml rdfa ets omr m. ok emma p3p mathml mf pics qa rif sec cont. sawsdlpng powder sml soap wsdl svg awwwttml smile rdf owlrdfssparql woff webcgm xbl xkms xlinkwscdl wsp skos ns canon. x dtxml xproc xfragxml xbase xschema xml:id xpath xpointer xquery xsignat. xbop xslt xslfo
  7. 7. e.g. impact of metamodel changes e.g. philosophical status of the Web names (URIs) web page resource value typed linkshyperlinks
  8. 8. meet WeSAI... the Web Science AI watchdog we could create
  9. 9. AI-based Web-support “The library will endure; it is the universe. (…) We walk the corridors, searching the shelves and rearranging them, looking for lines of meaning amid leagues of cacophony and incoherence, reading the history of the past and our future, collecting our thoughts and collecting the thoughts of others, and every so often glimpsing mirrors, in which we may recognize creatures of the information.” The Library of Babel --- Jorge Luis Borges
  10. 10. to google: transitive verb that means using the Google search engine to obtain information from the Web. Nominal Forms Infinitive: to google Participle: googled Gerund: googling Indicative Present I google you google he googles we google you google they google Perfect I have googled you have googled he has googled we have googled you have googled they have googled Past I googled you googled he googled we googled you googled they googled Pluperfect I had googled you had googled he had googled we had googled you had googled they had googled Future I will google you will google he will google we will google you will google they will google Future perfect I will have googled you will have googled he will have googled we will have googled you will have googled they will have googled Subjunctive Present I google you google he google we google you google they google Perfect I have googled you have googled he have googled we have googled you have googled they have googled Imperfect I googled you googled he googled we googled you googled they googled Pluperfect I had googled you had googled he had googled we had googled you had googled they had googled Conditional Present I would google you would google he would google we would google you would google they would google Perfect I would have googled you would have googled he would have googled we would have googled you would have googled they would have googled Imperative you google we Let´s google you google Progressive (Continuous) Forms Indicative Present I am googling you are googling he is googling we are googling you are googling they are googling Perfect I have been googling you have been googling he has been googling we have been googling you have been googling they have been googling Past I was googling you were googling he was googling we were googling you were googling they were googling Pluperfect I had been googling you had been googling he had been googling we had been googling you had been googling they had been googling Future I will be googling you will be googling he will be googling we will be googling you will be googling they will be googling Future perfect I will have been googling you will have been googling he will have been googling we will have been googling you will have been googling they will have been googling Conditional Present I would be googling you would be googling he would be googling we would be googling you would be googling they would be googling Perfect I would have been googling you would have been googling he would have been googling we would have been googling you would have been googling they would have been googling
  11. 11. Autonomy AgentWare Web Researcher, 1996 • dogs as user interface • representing an IR agent • training and retrained neural networks • sent into the Web from PC • left in a kennel: host the agent on a server and go off-line
  12. 12. Wikipedia bots • 2 187 bot tasks approved for use on the English Wikipedia • maintain 45 223 137 pages
  13. 13. web index: the state of the web country-level data on web usage, readiness, and human impact
  14. 14. Is this the Web we really want?
  15. 15. new markets e.g. world wide word market
  16. 16. Web Robots e.g. AI Watchdogs • generalize the bots of Wikipedia to Web-preserving AI • AI web kennels hosting AIs that study, monitor and report on the Web • a Web science challenge to foster beneficial AI contributions Website Down Alarm (by Tessel)
  17. 17. WeSAI could... • detect metrics manipulation • prevent vandalism • report spamming • detect cross language plagiarism • maintain digital divide indicators • perform policy-based / value-based actions • generate links, backlinks, etc. • create navigational content beyond search results • improve resilience • augment browsing experience • …
  18. 18. The Rock, by T.S. Eliot, 1934 AI and Web (science) data (science)
  19. 19. metadata overflow Card Catalog, Tim Schwartz
  20. 20. cultural data as a weapon of mass construction (autonomous “singer robots” are much less dangerous)
  21. 21. ALOOF: robots learning by reading on the Web First Object Relation Knowledge Base: 46.212 co-mentions gave 49 tools, 14 rooms, 101 “possible location” relations, Annie cuts the bread in the kitchen with her knife dbp:Knife aloof:Location dbp:Kitchen [Cabrio, Basile et al.]
  22. 22. Web&AI-based generation of candidate explanations
  23. 23. AI to assist Web Scientists and vice versa • AI to produce, curate, maintain corpora and datasets • AI for quality check, bias detection, etc. • multidisciplinary certification of training sets (quality, trust, prov)
  24. 24. bias! bias?… sniff… sad truth
  25. 25. WeSAI could... • facilitate data reuse, diffusion, sharing,… • help build datasets, corpora,… • help detect bias, links, relations, etc. • data poor, thin files • inclusion/exclusion, inequality …
  26. 26. designing benevolent AIs for the Web
  27. 27. from the notion of a “Web person” to the notion of a “Web AI”
  28. 28. from known pasts to possible futures “Then the answers, instead of coming from my likes and dislikes, come from chance operations, and that has the effect of opening me to possibilities that I hadn't considered. Chance-determined answers will open my mind to the world around.” John Cage, 1985
  29. 29. allow for different possible futures not past-driven
  30. 30. legalto read or not to read… terms and conditions of web applications 32 241 words18 301 words15 352 words 36 275 words19 972 words11 195 words < < < < <
  31. 31. 32 AT&T Privacy Bird with chirping privacy indicator by LF CRANOR
  32. 32. 33 The sound of (bad) privacyby LF CRANOR
  33. 33. 34 Privacy meters in search results Salient privacy information influences purchase decisions J. Tsai, S. Egelman, L. Cranor, and A. Acquisti. The Effect of Online Privacy Informaion on Purchasing Behavior: An Experimental Study. Information Systems Research, 2010. by LF CRANOR
  34. 34. ANSWER project: warning on tracking, security,…
  35. 35. complex web applications barely visible evolution of the place of humans in = user = processor = data
  36. 36. computational context in general • what am I being used for? what am I taking part in? • give me the big “human-computational picture”.
  37. 37. active social medias store, process, reuse, enrich, route, manage the information far beyond mere communication
  38. 38. over adaptation & filter bubble(c.f. Eli Pariser)
  39. 39. bursting bubbles
  40. 40. SEARCHING e.g. DiscoveryHub exploratory search [Marie, Gandon, Ribière.]
  41. 41. psychological feedback help reflexivity
  42. 42. AI & WEB for e-learning & serious games [Rodriguez-Rocha, Faron-Zucker et al.]
  43. 43. Educational AI e-learning for the web
  44. 44. WeSAI could... • maintain customized context description • calls attention to what is exposed & who is seeing • exemplify what can be deduced • make things visible/understandable • burst bubbles, fosters serendipity • help raise awareness, educational level, computer literacy, Web skills • report on neutrality, security etc. • enforce (human) rights • be scrutiny agent for important values
  45. 45. AI to help humans face humanity “To see a world in a grain of sand And a heaven in a wild flower, Hold infinity in the palm of your hand, And eternity in an hour. (…)” Auguries of Innocence, William Blake
  46. 46. On the Web human individuals face human collectivity design AIs to help humans face humanity (scale, diversity, speed) on the Web
  47. 47. Weibo mid 2012: 368 million registered users and 100 million messages each day
  48. 48. language and geographical coverage ©Denny Vrandečić
  49. 49. help translate, foster interactions, bridge communities, etc.
  50. 50. prevent cyberbullying
  51. 51. goal-driven agents actively participating to online activity
  52. 52. « social » bookmark ? massive socialization of objects & activities
  53. 53. e.g. sharing more and more • cars (blablacar, zilokaoto, voiturelib) • sailing (,, • taxi ( • packages ( • parking ( • housing (AirBnB, Couchsurfing) • storage ( • funding (kisskissbankbank, kickstarter) • offices (coworking) • food (, • sport (,, • washing machine ( • clothes (, • worms (réseau lombricompos) …
  54. 54. no limit to socializing
  55. 55. keep track of our digital shadows in a socio-technical mess
  56. 56. WeSAI could... • maintain social overview, detect over-socialization • foster linkage, interactions and convergence • prevent bullying, harassment, suicide,… • protect children, data poor, thin files,… • fight divide, polarization and radicalization • bridge, translate, check, report, augment… posts • act as diplomats (diplomatic AIs on the Web) • remind past events, posts, opinions,… help us with the history /memory • help us scale to the world-wide web scale
  57. 57. multiply action means multiple AIs on the Web
  58. 58. law of requisite variety “variety absorbs variety” W.R. Ashby
  59. 59. Web Science and Distributed AI (multi-agent systems) • “re-decentralize the Web, give everybody his Web site back” TimBL • more than AI : distributed AI – Web should embrace multi agent systems & autonomous agent – Bridge Web Science and AAMAS communities (co-locate?)
  60. 60. Heterogeneous AIs Web as a distributed blackboard for many different kinds of AIs (KRR, Learning, etc.) Web
  61. 61. public domain Web-based AI architecture
  62. 62. Web ways applied to AI e.g. copy-paste based contribution / participation to create AIs
  63. 63. WeSAI could... • evolve on the next Web as in a multi-agent system • have a lot of AIs friends with a different skills • have to form collaboration, follow social rules,… • be born, edited, crossed, bred on the Web • socialy maintained, copied, versioned
  64. 64. explore and expand the range of forms of intelligence
  65. 65. Web Sciences impact of multidisciplinary nature on AI perspective
  66. 66. extreme speed dating of multiple disciplines align multidisciplinary facets of AI and Web Science e.g. cognitive approaches of intelligent agents, e.g. social science approaches based on multi-agent systems, e.g. HCI work in each domain, etc.
  67. 67. Translate expertise of each domain into AIs that help (assisting, reporting, educating) Computer science, Policy and Law, Network engineering, Economics, Social Science, Psychology, Mathematics, Media studies, etc. Web
  68. 68. artificial intelligence emotional intelligence
  69. 69. emotions, moods, feelings…
  70. 70. WiFi-SM Christophe Bruno Wi-Fi device that one can fix on any part of the body, and whose function is to “share the pain of the world” analyzing RSS feeds.
  71. 71. ELIZA… talking to machines
  72. 72. Web Science to identify other ways of simulating, reproducing, engaging, … intelligence
  73. 73. the intelligence is not in the programming but in the communication and psychological design… think of the Turing Test.
  74. 74. Wood Wide Web mycorrhizal networks mycorrhizal network in a forest (van der Heijden et al, 2014)
  75. 75. Connected Animals, ACI, Animal-computer interaction, animals accessible interfaces e.g. Herdsourcing: monitoring collective animal behavior
  76. 76. Toward a Web of Things
  77. 77. WeSAI could... • benefit from WS study of intelligence in the wild • be built from broader definitions of intelligence • be built on new kinds of artificial intelligence • interact with many forms of intelligence • use new intelligent interactions (e.g. AI nudge) // paving the WAI (web augmented interaction)
  78. 78. hybrid social constructs & equilibrium
  79. 79. EDITS 0 500000 1000000 1500000 2000000 2500000 3000000 3500000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 editors by number of actsWikipedia editors, 2012 0 500000 1000000 1500000 2000000 2500000 3000000 3500000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 Humans
  80. 80. toward a Web linking, harnessing, combining very different types of intelligence • human intelligence, write access, • Social Web, crowdsourcing, social media, human computing, wisdom of crowd, social intelligence • Webbots, software agents, artificial intelligences, multi-agent systems • Web of Objects, ambient intelligence, ubiquitous Web, smart objects • Herdsourcing, animal intelligence, connected animals
  81. 81. Toward a Web of Hybrid Societies • hybrid societies of natural intelligence and artificial intelligence on the Web • study and design these societies, their normative systems, rules, etc. • different forms of intelligence to cooperate including different kinds of AI
  82. 82. new types of relations human-webbots
  83. 83. acceleration and alienation
  84. 84. human-friendly hybrid societies humans in the loop… but not any loop
  85. 85. massive interaction design ergonomics in hybrid web communities
  86. 86. IMAG_NE
  87. 87. Consider a Web of intelligence linking  Web of Animals: connected animals to predict earthquakes  Web of Things, Web of Sensors  Web of AIs: reasoning systems, learning systems, etc.  Web of People: experts from all over the world (i.e. some expert always awake // crowdsourcing & Q&A expert routing) … to form a collective intelligent decision system.
  88. 88. Web Science and (AI & IA) two fields born in the 50s: – AI for Artificial Intelligence (McCarthy et al., 1955) – IA for Intelligence Amplification (Ashby, 1956) and Intelligence Augmentation (Engelbart, 1962) build a research program on both and on the worldwide dimension that the Web brings.
  89. 89. WeSAI could... • engage in very different form of interactions • handle massive inter-actions • form and manage hybrid societies • be the result of a WebSci & AI & IA joint research
  90. 90. Design AI to provide the immune system of the Web “That is why it is so important not only to have excellent treatment but also to try to get back the immune defense, because there you have a natural defense that takes place everywhere.” Luc Montagnier
  91. 91. ethical: keep watching?
  92. 92. tardigrades Web AIs • 1 ° K / −458 °F / −272 °C  • 420 °K / 300 °F / 150 °C  • Vacuum / High pressure  • Ionizing radiation  • Himalayas / Deep sea  Extremophile
  93. 93. world-wide way as the web is spreading in the world, the world is spreading in the web.
  94. 94. world-wild web the natural complexity of our world contaminates the web
  95. 95. www mmm world wide web massively multidisciplinary method
  96. 96. WeSAI could... • defend the values we want to defend • assist the governance we need to setup • maintain visualizations we need to understand • automate part of our methods • help scale multi-disciplinary interactions • build and maintain boundary artefacts • enforce a constructive design of the Web • <YOUR DAGSTHUL IDEA HERE/>
  97. 97. “a Web Science research agenda must account for the fact that the long term potential of the Web is to augment and link all forms of intelligence” Peter Steiner, The New Yorker, 1993 Kaamran Hafeez, The New Yorker, 2015 I pass CAPTCHAs, Nobody knows I’m a bot 2018
  98. 98. some material I reused Fabien Gandon, A Survey of the First 20 Years of Research on Semantic Web and Linked Data, (To Appear in ISI special issue on “Linked Open Data” 2018) Fabien Gandon, Alain Giboin. Paving the WAI: Defining Web-Augmented Interactions, Web Science 2017 (WebSci17), Jun 2017 Fabien Gandon. The three 'W' of the World Wide Web callfor the three 'M‘ of a Massively Multidisciplinary Methodology, 10th International Conference, WEBIST 2014. Fabien Gandon, Michel Buffa, Elena Cabrio, Olivier Corby, Catherine Faron Zucker, et al. Challenges in Bridging Social Semantics and Formal Semantics on the Web. International Conference, ICEIS 2013 Fabien Gandon. Combining reactive and deliberative agents for complete ecosystems in infospheres. IEEE/WIC International Conference on Intelligent Agent Technology (IAT), Oct 2003 Fabien Gandon. Distributed Artificial Intelligence And Knowledge Management: Ontologies And Multi-Agent Systems For A Corporate Semantic Web. Web. Université Nice Sophia Antipolis, PhD Thesis, 2002 HTTP://FABIEN.INFO

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  • arjenpdevries

    Jun. 27, 2018
  • halani

    Jun. 29, 2018

Invited talk at Dagsthul Perspective on the Future of Web Science


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