Inteligência Coletiva.

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Palestra de Pierre Lévy, no SENAC SP

Retirado do site do autor: http://pierrelevyblog.com/2014/03/09/the-slides-of-three-lectures-in-brazil-march-2014/

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Inteligência Coletiva.

  1. 1. Collec&ve  Intelligence   SENAC  2014   Prof.  Pierre  Lévy   Twi?er:  @plevy   Canada  Research  Chair  in  Collec&ve  Inteligence   University  of  O?awa  
  2. 2. PART  I   THE  BIG  PICTURE  
  3. 3. Collec&ve  Intelligence  Exists  Already   •  Feature  of  all  animal  socie&es:  cogni&ve  process   at  the  scale  of  the  society  itself   –  Schools  of  fishes,  beehives,  anthills,  flocks  of  birds,   herds  of  mammals,  primate  socie&es...   •  Human  collec&ve  intelligence  is  special   –  Language  and  symbolic  systems   –  Complex  social  ins&tu&ons   –  Complex  technological  systems   –  Personal  reflexive  thought   –  Cultural  evolu0on:  the  growth  of  human  collec&ve   intelligence  follows  the  improvement  of  media    
  4. 4. Algorithmic  Knowledge   Info.  econ.,  reflexive  CI,  scien&fic  humani&es   Typographic  Knowledge   Na&on-­‐states,  industry,  global  market,  natural  sciences   Literate  Knowledge   Empires,  universal  religions,  commerce,  money,  philosophy     Scribal  Knowledge   Palace-­‐temples,  agriculture,  hermeneu&cs,  systema&c  knowledge     Literate  medium–  lighter  symbols  (code  or  medium)  -­‐  augments  manipula&on   Typographic  medium–  mass  media  -­‐  automates  reproduc&on  and  diffusion   Algorithmic  medium  –  ubiquity  of  data  and  processors  -­‐    automates  manipula&on     Oral  Knowledge   Tribes,  chamanism,  hun&ng-­‐gathering,  narra&ves,  rituals   Scribal  medium–  las&ng  symbols  -­‐  augments  memory   Media,  Cultural  Evolu&on  and  Knowledge   Oral  medium–  transient  symbols  -­‐  supports  thought  and  symbolic  knowledge    
  5. 5. Research  Program:   Augmen'ng  Collec&ve  Intelligence     by  Using  the  Algorithmic  Medium   •  Compe'ng  projects:  Ar&ficial  Intelligence,   Singularity,  Post-­‐humanism,  etc.   •  Goal:  to  augment  personal  and  collec&ve   cogni&ve  processes:  percep&on,  memory,   learning,  problem  solving,  etc.   •  Strategy:  Using  the  algorithmic  medium  to   make  human  collec&ve  intelligence  reflexive  
  6. 6. VIRTUAL   Human   development   ACTUAL   Human   development   Networks  of    SIGNS   Networks  of   BEINGS   Networks  of   THINGS   Knowledge   Ethics   Empowerment   Messages   People   Equipments   Sciences   Arts   Wisdoms   Governance   Values     Rights/obliga&ons   Competences   Resolve   Finance   Technology   Health   Bio-­‐phys.  environt   Social  Roles   Trust   Social  networks   Content   Communica&on   Media   S   B   T   S   B   T   S   B   T   S   B   T   S   B   T   S   B   T   COLLECTIVE  INTELLIGENCE   INTERDEPENDENCE  
  7. 7. What  Is  an  Idea?   [Concept  +  Affect  +  Percept]  +  Social  context   Transla&on  in  the  algorithmic   [Category  +  Value  +  Data]  +  Social  context   What  is  Cogni&on?   Self-­‐reference,  growth,  reproduc&on,   evolu&on,  cross-­‐pollina&on     of  ecosystems  of  ideas  
  8. 8. Reflexive  Collec&ve  Intelligence   •  Ecosystems  of  ideas  emerging  from  collec&ve   intelligence  in  a  new  genera&on  of  social  media   •  Displayed  around  each  people,  community,   thing,  place,  work  of  the  mind...   •  Interac&on  through  holograms  in  VR  or  AR,  by   Google  glasses  or  tablet   •  Embedded  powerful  tools  for  selec&on,   explora&on,  analysis,  synthesis,  seman&c   distances  measurement   •  Symbiosis  communi&es  /  ecosystems  of  ideas     •  Symbiosis  individual  /  collec&ve  learning    
  9. 9. Seman0c  Sphere  (2020)   Reflexive  collec&ve  intelligence   World  Wide  Web  (2000)   Global  hypermedia  public  sphere   Internet  (1980)   Computer  networks   Digital  computers  (1950)   Electronic  circuits   Universal  addressing  of  operators   Universal  addressing  of  data   Universal  addressing  of  metadata  (concepts)   Layers  of  the  Algorithmic  Medium   Local  addressing  of  data  and  operators   © Pierre Lévy 2012 License Apache 2.0
  10. 10. PART  II   TRAINING  FOR  COLLECTIVE   INTELLIGENCE  
  11. 11. •  Indirect  self-­‐organized  communica&on:   Individuals  communicate  by  modifying   their  data-­‐environment  (s&mergy).   •  Awareness  of  situa&ons,  contexts,   communi&es,  local  memories   •  Don’t  waste  the  &me  of  others  :   ignorance,  redundance,  irrelevance   Network  Awareness  
  12. 12. •  Ac&ons  in  the  digital  medium:  subscribe,  buy,   comment,  record,  broadcast,  hyperlink,  tag,   approve/like,  par&cipate  to  a  group,   communicate,  etc.   •  Every  digital  ac&on  sculpts  the  collec&ve  memory.   •  We  are  all  poten&ally  readers,  spectators,   authors,  cri&ques,  editors,  publishers,  curators,   librarian  and  co-­‐responsible  of  the  collec&ve   memory.   •  Knowledge  society  ci&zenship:  help  /  orient   others.   Personal  responsability     of  collec0ve  memory  
  13. 13. Cri0cal  Evalua0on  of  Sources   •  There  is  no  objec&vity   1.  Orienta0on:  problems,  ques&ons,  agendas   2.  Frame:  breadth  and  cuing  of  the  context   3.  Narra0ve:  who  are  the  actors,  the   «  vic&ms  »,  the  beneficiaries...?  The  same   event  can  be  told  in  many  different  ways.   4.  Norms:  tacit,  explicit   •  Mul&ply  and  cross-­‐check  the  sources  
  14. 14. Six  Ques0ons  on  Transparency   1.  Who?   Iden&fy  the  source  (people,  ins&tu&ons,  schools  of   thought...)   2.  Where  does  the  money  come  from?   How  many,  from  what  sources?   3.  Why?   What  are  the  ques&ons  /  problems  /  agendas  (poli&cal,   economic,  theore&cal,  religious,  etc.)  of  the  source?   4.  References?   Are  the  sources  of  the  source  clearly  iden&fied?     5.  When   Find  the  origin  in  &me  of  an  idea  or  informa&on.  What  are   the  «  temporal  lines  »  of  ideas  or  events.   6.  Is  the  source  transparent  on  1,  2,  3,  4,  5?    
  15. 15. Personal  Knowledge  Management  1  (PKM)   1.  A?en&on  management   –  Define  interests,  priori&es,  areas  of  exper&se   (acquired  and  aimed).     Stay  focused,  avoid  distrac&on,  keep  in  mind  the  big   picture   2.  Connec&on  to  valuable  sources   –  Informa&on  streams  from  people  and  ins&tu&ons   3.  Gathering  and  aggrega&on  of  data  streams   4.  Filtering   –  Manual  and  automa&c  (according  to  1)   5.  Categoriza&on   –  Tagging,  folksonomies,  classifica&ons,  ontologies  
  16. 16. Personal  Knowledge  Management  2  (PKM)   1.  Recording  for  long-­‐term  memory     –  Data  cura&on,  social  bookmarking,  ar&cles,  notes  and   library  management  tools,  cloud  memory   2.  Synthesis   –  Blog  posts,  ar&cles,  wiki  entries   3.  Sharing,  communica&on   –  Pos&ng  result  of  4,  5,  6,  7  on  social  media.  Replies,   crea&ve  dialogue   4.  Reassess:  a?en&on  management,  connec&ons,   categoriza&on,  PKM  tools  

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