Invited talk at Dagsthul Perspective on the Future of Web Science

1. Web Science and
AI
Fabien Gandon, http://fabien.info

2. Webology and
AI & IA

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. 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. webcomputing
architecture
digital
resources

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. e.g. impact of metamodel changes
e.g. philosophical status of the Web names (URIs)
web page resource value
typed linkshyperlinks

8. meet WeSAI...
the Web Science AI watchdog we could create

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. 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. 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. Wikipedia bots
• 2 187 bot tasks approved for
use on the English Wikipedia
• maintain 45 223 137 pages

13. web index: the state of the web
country-level data on web usage, readiness, and human impact

14. Is this the Web we really want?

15. new markets e.g. world wide word market

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)

18. 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
• …

19. The Rock,
by T.S. Eliot,
1934
AI and Web (science) data (science)

20. metadata overflow
Card Catalog, Tim Schwartz

21. cultural data as a weapon of mass construction
(autonomous “singer robots” are much less dangerous)

22. 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.]

23. Web&AI-based generation of candidate explanations

24. 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)

25. bias! bias?… sniff… sad truth

26. WeSAI could... • facilitate data reuse, diffusion, sharing,…
• help build datasets, corpora,…
• help detect bias, links, relations, etc.
• data poor, thin files
• inclusion/exclusion, inequality
…

27. designing benevolent AIs for the Web

28. from the notion of a “Web person”
to the notion of a “Web AI”

29. 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

30. allow for different possible futures not past-driven

31. 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
http://conversation.which.co.uk/technology/length-of-website-terms-and-conditions/
< < < < <

32. 32
AT&T Privacy Bird with chirping
privacy indicator by LF CRANOR

33. 33
The sound of (bad) privacyby LF CRANOR

34. 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

35. ANSWER project: warning on tracking, security,…

36. complex web applications
barely visible evolution of the place of humans in
= user
= processor
= data

37. computational context in general
• what am I being used for? what am I taking part in?
• give me the big “human-computational picture”.

38. active social medias
store, process, reuse, enrich, route, manage
the information far beyond mere communication

39. over adaptation & filter bubble(c.f. Eli Pariser)

40. bursting bubbles

41. SEARCHING
e.g. DiscoveryHub
exploratory search
[Marie, Gandon, Ribière.]

42. psychological feedback
help reflexivity

43. AI & WEB
for e-learning & serious games
[Rodriguez-Rocha, Faron-Zucker et al.]

44. Educational AI e-learning for the web

45. 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

46. 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

47. On the Web human individuals face human collectivity
design AIs to help humans face humanity (scale, diversity, speed)
on the Web

48. Weibo mid 2012: 368 million registered users
and 100 million messages each day

49. language and geographical coverage
©Denny Vrandečić

50. help translate,
foster interactions,
bridge communities, etc.

51. prevent cyberbullying

52. goal-driven agents actively participating to online
activity

53. « social » bookmark ?
massive socialization of objects & activities

54. e.g. sharing more and more
• cars (blablacar, zilokaoto, voiturelib)
• sailing (vogavecmoi.com, co-navigation.fr, equipier.fr)
• taxi (provoiturage.fr)
• packages (expediezentrevous.com)
• parking (monsieurparking.com)
• housing (AirBnB, Couchsurfing)
• storage (costockage.fr)
• funding (kisskissbankbank, kickstarter)
• offices (coworking)
• food (colunching.com, cookening.com)
• sport (Unlish.com, Cleec.com, kikourou.net)
• washing machine (lamachineduvoisin.fr)
• clothes (pretachanger.fr, vestiairecollective.com)
• worms (réseau lombricompos)
…

55. no limit to socializing

56. keep track of our digital shadows in a socio-technical mess

59. 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

60. multiply action means
multiple AIs on the Web

61. law of requisite variety
“variety absorbs variety” W.R. Ashby

62. 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?)

63. Heterogeneous AIs
Web as a distributed blackboard for many different kinds of AIs
(KRR, Learning, etc.)
Web

64. public domain Web-based AI architecture

65. Web ways applied to AI
e.g. copy-paste based contribution
/ participation to create AIs

66. 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

67. explore and expand
the range of forms
of intelligence

68. Web Sciences
impact of multidisciplinary nature on AI perspective

69. 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.

70. 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

71. artificial intelligence emotional intelligence

72. emotions, moods, feelings…

73. 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.

74. ELIZA… talking to machines

75. Web Science to identify other ways of
simulating, reproducing, engaging, …
intelligence

76. the intelligence is not in
the programming but in
the communication and
psychological design…
think of the Turing Test.

77. Wood Wide Web
mycorrhizal networks
mycorrhizal network in a forest (van der Heijden et al, 2014)

78. Connected Animals, ACI, Animal-computer
interaction, animals accessible interfaces e.g.
Herdsourcing: monitoring collective animal behavior

79. Toward a Web of Things

80. 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)

81. hybrid social constructs & equilibrium

82. EDITS
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editors by number of actsWikipedia editors, 2012
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Humans

83. 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

84. 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

85. new types of relations human-webbots

86. acceleration and alienation

87. human-friendly hybrid societies
humans in the loop… but not any loop

88. massive interaction design
ergonomics in hybrid web communities

89. IMAG_NE

90. 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.

91. 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.

92. 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

94. 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

95. ethical: keep watching?

96. tardigrades Web AIs
• 1 ° K / −458 °F / −272 °C 
• 420 °K / 300 °F / 150 °C 
• Vacuum / High pressure 
• Ionizing radiation 
• Himalayas / Deep sea 
Extremophile

97. world-wide way
as the web is spreading in the world,
the world is spreading in the web.

98. world-wild web
the natural complexity of our world
contaminates the web

99. www
mmm
world wide web
massively multidisciplinary method

100. 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/>

101. “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

102. 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