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Code	
  Biology	
  and	
  (the	
  future	
  of)	
  	
  
Ar5ficial	
  Intelligence	
  
	
  
Joachim	
  De	
  Beule	
  
Recent	
  advances	
  in	
  AI	
  
	
   	
   	
   	
   	
   	
  Deep	
  learning	
  
A	
  dark	
  future	
  
	
  	
  	
  	...
 “A	
  revolu*on	
  in	
  ar*ficial	
  intelligence	
  is	
  currently	
  
sweeping	
  through	
  computer	
  science.	
  T...
“In	
  some	
  sense	
  deep	
  learning	
  is	
  what	
  happened	
  when	
  machine	
  learning	
  hit	
  big	
  data”	
...
Ref:	
  Deep	
  Learning:	
  Intelligence	
  from	
  Big	
  Data,	
  Tue	
  Sep	
  16,	
  2014,	
  Stanford	
  Graduate	
 ...
 
What’s	
  New?	
  
	
  
ü  Big	
  Data	
  
	
  
•  The	
  internet	
  &	
  Social	
  Media	
  
•  Metadata:	
  tags,	
 ...
 
What’s	
  New?	
  
	
  
ü  Big	
  Data	
  
ü  Scale	
  
	
  
•  80’s:	
  1-­‐10M	
  (106)	
  neurons/synap5c	
  connec...
 
What’s	
  New?	
  
	
  
ü  Big	
  Data	
  
ü  Scale	
  
ü  Algorithmic	
  advances	
  
	
  
•  Successive	
  layers	
...
 
What’s	
  New?	
  
	
  
ü  Big	
  Data	
  
ü  Scale	
  
ü  Algorithmic	
  advances	
  
	
  
We	
  have	
  been	
  able	
  to	
  reduce	
  the	
  word	
  error	
  rate	
  for	
  speech	
  by	
  over	
  30%	
  compar...
November	
  18,	
  2014	
  
Asked	
   whether	
   two	
   unfamiliar	
  
photos	
   of	
   faces	
   show	
   the	
   same	
  
person,	
  a	
  human	
...
Feb	
  26,	
  2015	
  
•  Isotherm	
  is	
  to	
  temperature	
  as	
  isobar	
  is	
  to?	
  (i)	
  atmosphere,	
  (ii)	
  wind,	
  (iii)	
  pre...
The	
  future?	
  
	
  
The	
  future?	
  
	
  
“I	
  am	
  in	
  the	
  camp	
  that	
  is	
  concerned	
  about	
  super	
  intelligence.	
  First	
  the	
  machines	
 ...
•  Oren	
  Etzioni	
  (Computer	
  science,	
  Univ.	
  Washington,	
  CEO	
  of	
  the	
  Allen	
  Ins5t.	
  for	
  Ar5fic...
Assump5on:	
  Deeper	
  level	
  neurons	
  are	
  more	
  “abstract”	
  
However,	
  what	
  was	
  discovered:	
  
-­‐  ...
The	
  Symbol	
  Grounding	
  Problem	
  
010000110101010
011110101010100
110100101010100
1011010101111…
Jpeg	
  
coding	
...
The	
  Symbol	
  Grounding	
  Problem	
  
010000110101010
011110101010100
110100101010100
1011010101111…
Jpeg	
  
coding	
...
The	
  Symbol	
  Grounding	
  Problem	
  
•  Categories	
  (signs	
  and	
  meanings)	
  are	
  ar5facts	
  
•  The	
  rel...
The	
  future?	
  
	
  
The	
  future?	
  
	
  
“Collec*ve	
  intelligence	
  is	
  the	
  opposite	
  of	
  ar*ficial	
  intelligence”	
  
Ø  Outer	
  world	
  onto	
  inner	
  world	
  	
  
	
  (human	
  neuronal	
  coding)	
  
Ø 	
  	
  Inner	
  worlds	
  o...
•  Semiosis	
  (life)	
  =	
  self-­‐regula5on	
  (produc5on	
  and	
  consump5on	
  of	
  variety,	
  closure)	
  
	
  
	...
•  Semiosis	
  (life)	
  =	
  self-­‐regula5on	
  (produc5on	
  and	
  consump5on	
  of	
  variety,	
  closure)	
  
•  Too...
•  Semiosis	
  (life)	
  =	
  self-­‐regula5on	
  (produc5on	
  and	
  consump5on	
  of	
  variety,	
  closure)	
  
•  Too...
•  Semiosis	
  (life)	
  =	
  self-­‐regula5on	
  (produc5on	
  and	
  consump5on	
  of	
  variety,	
  closure)	
  
•  Too...
•  Semiosis	
  (life)	
  =	
  self-­‐regula5on	
  (produc5on	
  and	
  consump5on	
  of	
  variety,	
  closure)	
  
•  Too...
“In	
  a	
  sense,	
  deep	
  learning	
  is	
  what	
  happened	
  when	
  machine	
  learning	
  hit	
  big	
  data”	
  ...
The	
  Next	
  Major	
  Transi5on?	
  
	
  
	
  
	
  
	
  
Symbolic	
  
	
  
Collec5ve	
  intelligence	
  
(deep	
  learni...
Replica*on	
  always	
  involves	
  coding!	
  
3,	
  15	
  or	
  33	
  numbers?	
  
The	
  Symbol	
  Grounding	
  Problem...
ü  	
  A	
  robot	
  may	
  not	
  injure	
  a	
  human	
  being	
  or,	
  through	
  
inac5on,	
  allow	
  a	
  human	
 ...
MIT	
  Technology	
  review,	
  Robert	
  D.	
  Hof,	
  April	
  23,	
  2014	
  
Professor	
  Geoff	
  Hinton,	
  who	
  was	
  hired	
  by	
  Google	
  two	
  years	
  ago	
  to	
  help	
  develop	
  int...
Jdb code biology and ai final
Jdb code biology and ai final
Jdb code biology and ai final
Jdb code biology and ai final
Jdb code biology and ai final
Jdb code biology and ai final
Jdb code biology and ai final
Jdb code biology and ai final
Jdb code biology and ai final
Jdb code biology and ai final
Jdb code biology and ai final
Jdb code biology and ai final
Jdb code biology and ai final
Jdb code biology and ai final
Jdb code biology and ai final
Jdb code biology and ai final
Jdb code biology and ai final
Jdb code biology and ai final
Jdb code biology and ai final
Jdb code biology and ai final
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Jdb code biology and ai final

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Slides of my talk "Code Biology and (the future of) AI" at the second international code biology conference in Jena, 16-20 June 2015

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Jdb code biology and ai final

  1. 1. Code  Biology  and  (the  future  of)     Ar5ficial  Intelligence     Joachim  De  Beule  
  2. 2. Recent  advances  in  AI              Deep  learning   A  dark  future                Superintelligences  more  dangerous                        than  nukes   A  brighter  future                Collec5ve  intelligence  
  3. 3.  “A  revolu*on  in  ar*ficial  intelligence  is  currently   sweeping  through  computer  science.  The  technique  is   called  deep  learning  and  it’s  affec*ng  everything  from   facial  and  voice  to  fashion  and  economics.”  
  4. 4. “In  some  sense  deep  learning  is  what  happened  when  machine  learning  hit  big  data”   “Two  kinds  of  data:  raw  data  (pictures,  music,  …)  and  symbolic  data  (text)”   “With  deep  learning,  we  can  bridge  the  gap  between  the  physical  world  and  the                                world  of  compu5ng”                                    -­‐-­‐  Adam  Berenzweig,  founding  CTO  of  Clarifai  
  5. 5. Ref:  Deep  Learning:  Intelligence  from  Big  Data,  Tue  Sep  16,  2014,  Stanford  Graduate  School  of  Business     Neural  Networks  of  the  80’s    
  6. 6.   What’s  New?     ü  Big  Data     •  The  internet  &  Social  Media   •  Metadata:  tags,  transla5ons,  …   •  Mechanical  Turk   Ref:  Deep  Learning:  Intelligence  from  Big  Data,  Tue  Sep  16,  2014,  Stanford  Graduate  School  of  Business  
  7. 7.   What’s  New?     ü  Big  Data   ü  Scale     •  80’s:  1-­‐10M  (106)  neurons/synap5c  connec5ons   •  Google  Brain:  1B  (109)     (10M  video’s,  16k  computers,  3  days)   •   Adult:  100T  (1014)     •   Infant:  1Q  (1015)       Ref:  Deep  Learning:  Intelligence  from  Big  Data,  Tue  Sep  16,  2014,  Stanford  Graduate  School  of  Business  
  8. 8.   What’s  New?     ü  Big  Data   ü  Scale   ü  Algorithmic  advances     •  Successive  layers  of  learning/representa5on       •  Unsupervised  pre-­‐training      à  Structure  NN  (feature  detectors)   •  Then  supervised  back-­‐prop    à  classify/predict  labeled  data   Ref:  Deep  Learning:  Intelligence  from  Big  Data,  Tue  Sep  16,  2014,  Stanford  Graduate  School  of  Business  
  9. 9.   What’s  New?     ü  Big  Data   ü  Scale   ü  Algorithmic  advances    
  10. 10. We  have  been  able  to  reduce  the  word  error  rate  for  speech  by  over  30%  compared  to   previous  methods.  This  means  that  rather  than  having  one  word  in  4  or  5  incorrect,  now  the   error  rate  is  one  word  in  7  or  8.  While  s5ll  far  from  perfect,  this  is  the  most  drama5c  change   in  accuracy  since  the  introduc5on  of  hidden  Markov  modeling  in  1979,  and  as  we  add  more   data  to  the  training  we  believe  that  we  will  get  even  becer  results.  
  11. 11. November  18,  2014  
  12. 12. Asked   whether   two   unfamiliar   photos   of   faces   show   the   same   person,  a  human  being  will  get  it   right   97.53   percent   of   the   5me.   New   sodware   developed   by   researchers  at  Facebook  can  score   97.25   percent   on   the   same   challenge,  regardless  of  varia5ons   in  ligh5ng  or  whether  the  person   in  the  picture  is  directly  facing  the   camera.  
  13. 13. Feb  26,  2015  
  14. 14. •  Isotherm  is  to  temperature  as  isobar  is  to?  (i)  atmosphere,  (ii)  wind,  (iii)  pressure,  (iv)  la*tude,  (v)   current.     •  Iden*fy  two  words  (one  from  each  set  of  brackets)  that  form  a  connec*on  (analogy)  when  paired   with  the  words  in  capitals:  CHAPTER  (book,  verse,  read),  ACT  (stage,  audience,  play).     •  Which  is  the  odd  one  out?  (i)  calm,  (ii)  quiet,  (iii)  relaxed,  (iv)  serene,  (v)  unruffled.     •   Which  word  is  closest  to  IRRATIONAL?  (i)  intransigent,  (ii)  irredeemable,  (iii)  unsafe,  (iv)  lost,  (v)   nonsensical.     •  Which  word  is  most  opposite  to  MUSICAL?  (i)  discordant,  (ii)  loud,  (iii)  lyrical,  (iv)  verbal,  (v)   euphonious.   Ref:  arxiv.org/abs/1505.07909  :  Solving  Verbal  Comprehension  Ques5ons  in  IQ  Test  by  Knowledge-­‐  Powered  Word  Embedding  
  15. 15. The  future?    
  16. 16. The  future?    
  17. 17. “I  am  in  the  camp  that  is  concerned  about  super  intelligence.  First  the  machines  will  do  a  lot  of  jobs  for   us  and  not  be  super  intelligent.  That  should  be  posi*ve  if  we  manage  it  well.  A  few  decades  a[er  that,   though,  the  intelligence  is  strong  enough  to  be  a  concern.  I  agree  with  Elon  Musk  and  some  others  on   this  and  don't  understand  why  some  people  are  not  concerned.”   Stephen  Hawking  (hcp://www.bbc.com/news/technology-­‐30290540)        "The  development  of  full  ar*ficial  intelligence  could  spell  the  end  of  the  human  race  […]              It  would  take  off  on  its  own,  and  re-­‐design  itself  at  an  ever  increasing  rate  […]              Humans,  who  are  limited  by  slow  biological  evolu*on,  couldn't  compete,  and  would  be                        superseded.”  
  18. 18. •  Oren  Etzioni  (Computer  science,  Univ.  Washington,  CEO  of  the  Allen  Ins5t.  for  Ar5ficial  Intelligence):      “The  popular  dystopian  vision  of  AI  is  wrong  for  one  simple  reason:  it  equates  intelligence  with   autonomy.  That  is,  it  assumes  a  smart  computer  will  create  its  own  goals,  and  have  its  own  will,  and  will   use  its  faster  processing  abili*es  and  deep  databases  to  beat  humans  at  their  own  game.  It  assumes   that  with  intelligence  comes  free  will,  but  I  believe  those  two  things  are  en*rely  different”     •  Michael  Licman  (AI,  Brown  Univ.,  former  program  chair  for  the  Ass.  of  the  Advancmnt  of  AI):    “There  are  indeed  concerns  about  the  near-­‐term  future  of  AI  —  algorithmic  traders  crashing  the   economy,  or  sensi*ve  power  grids  overreac*ng  to  fluctua*ons  and  shucng  down  electricity  for  large   swaths  of  the  popula*on.  [...]  These  worries  should  play  a  central  role  in  the  development  and   deployment  of  new  ideas.  But  dread  predic*ons  of  computers  suddenly  waking  up  and  turning  on  us  are   simply  not  realis*c.”     •  Yann  LeCun  (Facebook’s  director  of  research,  one  of  the  world’s  top  experts  in  deep  learning):    “Some  people  have  asked  what  would  prevent  a  hypothe*cal  super-­‐intelligent  autonomous   benevolent  A.I.  to  “reprogram”  itself  and  remove  its  built-­‐in  safeguards  against  gecng  rid  of  humans.   Most  of  these  people  are  not  themselves  A.I.  researchers,  or  even  computer  scien*sts.”     •  Andrew  Ng  (founded  Google’s  Google  Brain  project,  now  Chief  Scien5st  at  Baidu):    “Computers  are  becoming  more  intelligent  and  that’s  useful  as  in  self-­‐driving  cars  or  speech   recogni*on  systems  or  search  engines.  That’s  intelligence,”  he  said.  “But  sen*ence  and  consciousness  is   not  something  that  most  of  the  people  I  talk  to  think  we’re  on  the  path  to.”  
  19. 19. Assump5on:  Deeper  level  neurons  are  more  “abstract”   However,  what  was  discovered:   -­‐  A  single  neuron's  feature  is  no  more  interpretable  as  a   meaningful  feature  than  a  random  set  of  neurons.     -­‐  NN’s  do  not  "unscramble"  the  data  by  mapping  features   to  individual  neurons  in  say  the  final  layer.  The   informa5on  that  the  network  extracts  is  just  as  much   distributed  across  all  of  the  neurons  as  it  is  localized  in  a   single  neuron.     -­‐  Furthermore,  Every  deep  neural  network  has  "blind   spots"  in  the  sense  that  there  are  inputs  that  are  very   close  to  correctly  classified  examples  that  are   misclassified.  
  20. 20. The  Symbol  Grounding  Problem   010000110101010 011110101010100 110100101010100 1011010101111… Jpeg   coding   01000001 01000011 01010100 ASCII   coding   CAT   Deep  NN   Harnad,  S.  (1990)  
  21. 21. The  Symbol  Grounding  Problem   010000110101010 011110101010100 110100101010100 1011010101111… Jpeg   coding   01000001 01000011 01010100 ASCII   coding   CAT   Human   coding   Human   coding   Deep  NN   Human   Qualifica5on  or  Semiosis   Harnad,  S.  (1990)  
  22. 22. The  Symbol  Grounding  Problem   •  Categories  (signs  and  meanings)  are  ar5facts   •  The  rela5on  between  them  is  arbitrary   •  They  are  realized  by  agents  performing  semiosis     Diagram  of  Self-­‐regula5on  
  23. 23. The  future?    
  24. 24. The  future?     “Collec*ve  intelligence  is  the  opposite  of  ar*ficial  intelligence”  
  25. 25. Ø  Outer  world  onto  inner  world      (human  neuronal  coding)   Ø     Inner  worlds  onto  each  other        (collec5ve  intelligence)     Ø     Collec5ve  intelligence  onto  inner        world    
  26. 26. •  Semiosis  (life)  =  self-­‐regula5on  (produc5on  and  consump5on  of  variety,  closure)                               Self-­‐regulatory  system  (Agent)  
  27. 27. •  Semiosis  (life)  =  self-­‐regula5on  (produc5on  and  consump5on  of  variety,  closure)   •  Tool  usage  (supplementa5on  of  variety)  
  28. 28. •  Semiosis  (life)  =  self-­‐regula5on  (produc5on  and  consump5on  of  variety,  closure)   •  Tool  usage  (supplementa5on  of  variety)   •  Extension  and  specializa5on  (constraints)    “Now,  as  the  Internet  revolu*on  unfolds,  we  are  seeing  not  merely  an  extension  of   mind  but  a  unity  of  mind  and  machine,  two  networks  coming  together  as  one.”     [Deepstuff,  May  25,  2015]  
  29. 29. •  Semiosis  (life)  =  self-­‐regula5on  (produc5on  and  consump5on  of  variety,  closure)   •  Tool  usage  (supplementa5on  of  variety)   •  Extension  and  specializa5on  (constraints)   •  Coordina5on  (conven5onal  codes)                         Agent  1   Agent  2  
  30. 30. •  Semiosis  (life)  =  self-­‐regula5on  (produc5on  and  consump5on  of  variety,  closure)   •  Tool  usage  (supplementa5on  of  variety)   •  Extension  and  specializa5on  (constraints)   •  Coordina5on  (conven5onal  codes)       à  Metasystem  or  “Major  Transi5on”                       Agent  1   Agent  2   Meta   agent  
  31. 31. “In  a  sense,  deep  learning  is  what  happened  when  machine  learning  hit  big  data”   “Two  kinds  of  data:  raw  data  (pictures,  music,  …)  and  symbolic  data  (text)”   “With  deep  learning,  we  can  bridge  the  gap  between  the  physical  world  and  the    world  of  compu5ng”                                      -­‐-­‐  Adam  Berenzweig,  founding  CTO  of  Clarifai  
  32. 32. The  Next  Major  Transi5on?           Symbolic     Collec5ve  intelligence   (deep  learning)   Physical     Collec5ve  ac5ng   (da5ng,  vo5ng,  …)   Informa5on  seeking   ac5ng   Tagging  and  training  
  33. 33. Replica*on  always  involves  coding!   3,  15  or  33  numbers?   The  Symbol  Grounding  Problem  Harnad,  S.  (1990)  
  34. 34. ü   A  robot  may  not  injure  a  human  being  or,  through   inac5on,  allow  a  human  being  to  come  to  harm.   ü  A  robot  must  obey  the  orders  given  to  it  by  human   beings,  except  where  such  orders  would  conflict  with  the   First  Law.   ü  A  robot  must  protect  its  own  existence  as  long  as  such   protec5on  does  not  conflict  with  the  First  or  Second   Laws.  
  35. 35. MIT  Technology  review,  Robert  D.  Hof,  April  23,  2014  
  36. 36. Professor  Geoff  Hinton,  who  was  hired  by  Google  two  years  ago  to  help  develop  intelligent  opera5ng   systems,  said  that  the  company  is  on  the  brink  of  developing  algorithms  with  the  capacity  for  logic,   natural  conversa5on  and  even  flirta5on.     “Basically,  they’ll  have  common  sense”     “Thought  vectors,  Hinton  explained,  work  at  a  higher  level  by  extrac5ng  something  closer  to  actual   meaning”  

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