[T BUFFET] 노정석 대표의 'How Computers Understand Humans'

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  • 1. How  computers     understand  human   2013.06.12   노정석  
  • 2. Brief  History   •  16  year-­‐long  entrepreneurial  journey   •  4  @mes  of  founding  ventures   – Each  of  which  went  IPO,  bankrupt,  M&A  with   Google,  …   •  3  @mes  of  working  in  conglomerates   •  10+  companies  of  angel-­‐inves@ng   – Many  cases  of  successful  exits  such  as  Ticket   Monster,  Dialoid,  PaprikaLab  …   •  1  venture-­‐incuba@on  company   – Fast  Track  Asia    
  • 3. Xenters SKC INTERVENTURE Companies  I   founded Companies  I   worked  for Companies  I   invested Went  public  in  2002 Went  bankrupt  2004 Acquired  by  Google  in  2008 Currently  in  love  with  
  • 4. All  the  beginning  :  Feb.  1994   Sun  sparc  sta@on  2   baram.kaist.ac.kr   chester@baram.kaist.ac.kr   KUS  
  • 5. 2  big  ques@ons  à  1  big  ques@on   •  The  end  of  oil  era   – Sustainability  of  complex  society   •  The  crea@on  of  human-­‐level  ar@ficial  intelligence   – When?  How?      
  • 6. What  is  value  crea@on?  
  • 7. Value  crea@on  is  ‘bringing  order  to  chaos’  itself.   Source:  ‘Extropy’  by  Kevin  Kelly  
  • 8. Evolu@on  is  all  about  organizing  informa@on   ‘beder’  
  • 9. Adding  Neocortex  was  all  the  beginning.  
  • 10. Evolu@on  of  compu@ng  
  • 11. Ar@ficial  intelligence  will  be  a  new  epoch   for  evolu@on   Neocortex  will  have  a  new  extender.  
  • 12. Ray  calls  it  ‘Singularity’  
  • 13. How  computers     understand  human   2013.06.12   노정석  
  • 14. Ques@on  #1   이런 날이 올거라고 생각하시는 분 ?  
  • 15. Ques@on  #2   언제 즈음 나올 것 같나요 ?     1.  5년내   2.  10년내   3.  50년내   4.  인간의 신의 창조물이다. 그런날은 결코 오지 않는 다.  
  • 16. Answers   #1.  Very  soon      (2029,  Ray  Kurzweil)   #2.  prac@cal  level  ?  In  5  to  10  years                    human  level?    In  20~30  years  
  • 17. Human  vs.  Computer  
  • 18. IBM  watson  
  • 19. IBM  watson   •  200m  pages  of  document  (4TB)   •  A  cluster  of  90  IBM  Power  750  Servers   – 10Racks   – 2880  Power7  processor  cores   – 16  TB  of  RAM   •  It  can  process  500  GB  of  data  in  a  second     – Equivalent  to  1M  books   •  It  costs  3M  USD,  the  94th  fastest  supercomputer  
  • 20. How  does  ‘it’  work?  
  • 21. Siri  
  • 22. Siri  
  • 23. How  does  ‘it’  work?   User   Speech  Recogni@on   Natural  Language   Understanding Dialogue  Manager     Natural  Language   Genera@on Text-­‐to-­‐Speech   Synthesis
  • 24. How  does  ‘it’  work?  
  • 25. Con@nuous  Speech  Recogni@on Dialogue  Management Task  Comple@on
  • 26. “국립중앙박물관으로 가는 길을 알려줘” Recognized-­‐Speech  =     “국립중앙박물관으로 가는      길을 알려줘” find_route  (      from=here,      to=“국립중앙박물관”   ) Confirm(        first_candidate=“국립중앙박물관”,      first_geocode=“용산구 용산동6가 168-­‐6”,      second_candidate=“국립국악박물관”,      second_geocode=“서초구 서초동 700”   )   “서울 용산구에 있 는 국립중앙박물 관으로 가는 길을 원하십니까?
  • 27. “응,  실시간 교통 정보 이용해서 경 로 찾아줘” Recognized-­‐Speech  =  “응,  실 시간 교통 정보 이용해서 경로 찾아줘” find_route(      from=here,      to=“국립중앙박물관”,          search_opIon=USE_RTTI   )   start_navigaIon(      string=Default      opIon=USE_RTTI   ) “실시간 교통정 보를 이용하여 길 안내를 시작합니 다. *  RTTI  :  Real-­‐Time  Traffic  Informa3on    
  • 28. Acoustic Model Language Model Builder Blog Twitter News Crawler Acoustic DB Language Model Acoustic Model Trainer Decoder Text Analysis (Grapheme-to-Phoneme) Dictation
  • 29. Really  simple^2    explana@on     …    
  • 30. Human  brain   •  Neocortex    :  80%  of  brain  mass   •  Simple  homogeneous  circuit  structure   – Brain  is  very  plas@c!   – Use  it  or  Lose  it  /  Fire  together,  wire  together   •  300M  modules,    100  neurons  per  each  module   •  Each  module  is  one  padern  recognizer   •  Connec@on  maders,  100  trillion     – Learning  makes  connec@ons   – A  lot  of  redundancy  
  • 31. Padern  recogni@on  in  neocortex  
  • 32. What  are  the  recently  solved  problems?   •  Search  (Google  Knowledge  Graph)   •  Con@nuous  Speech  Recogni@on   •  Speech  Synthesis   •  Machine  Transla@on   •  Natural  Language  Understanding   – Deep  Q&A   – Task  Comple@on   •  Gene  predic@on  
  • 33. What  has  changed  in  the  last  decade?   •  All  the  theories  are  nearly  30~40  year-­‐old   already-­‐solved-­‐problems  mathema@cally,  only   the  prac@cal  implementa@on  started  working   recently.   •  What  is  the  main  factor  that  enabled  this  *leap*?    
  • 34. Where  we  are  now   •  June  25,  2012   •  Lead  by  Andrew  NG   –  Standford  professor   –  Coursera  founder   •  1000  computers  with  16,000  processors   •  10m  200x200  s@ll  cuts  from  youtube  to  neural   networks  for  3  days   •  More  than  1  billion  connec@ons   •  S@ll  long  way  to  go  for  complete  visual  cortex   simula@on.  Maybe  in  a  decade?  
  • 35. Key  Takeaways   •  Computer  is  not  just  aiding  tool  any  more,  it’s   becoming  intelligence.   – Most  of  white  collar  work  will  go  away.   – The  real  meaning  of  big  data  is  …     •  Do  not  step  away  with  fear,  embrace  it  more!   – Think  like  computer  scien@st.   – You  can  hire  thousands  of  knowledge  workers   with  nearly  zero  price.  
  • 36. Key  Takeaways   •  Computer  is  not  just  aiding  tool  any  more,  it’s   becoming  intelligence.   – Most  of  white  collar  work  will  go  away.   – The  real  meaning  of  big  data  is  …     •  Do  not  step  away  with  fear,  embrace  it  more!   – Think  like  computer  scien@st.   – You  can  hire  thousands  of  knowledge  workers   with  nearly  zero  cost.  
  • 37. Cri@cal  intersec@on  right  ahead   We’re  now  here,   *again*  
  • 38. 2  different  species,     that  were  originally  one,  human.  
  • 39. Think  big!