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info@katrionabeales.com	
  	
   1	
  
"Intelligence	
  is	
  not	
  enough"	
  	
  
Katriona	
  Beales	
  	
  
Creative	
  AI	
  meet-­‐up,	
  The	
  Photographers	
  Gallery,	
  	
  
3rd
	
  May	
  2017	
  	
  
	
  
	
  
*CIPHER	
  (2017)	
  K.	
  Beales	
  
	
  
CIPHER	
  comes	
  out	
  of	
  my	
  ongoing	
  research	
  in	
  internet	
  addiction,	
  information	
  overload	
  and	
  ideas	
  of	
  
a	
  technological	
  sublime.	
  I’m	
  interested	
  in	
  webspace	
  as	
  a	
  psychotic	
  environment,	
  an	
  equivalent	
  to	
  
the	
  nursery	
  in	
  Charlotte	
  Perkins	
  Gillman’s	
  novella	
  ‘The	
  Yellow	
  Wallpaper’1
	
  a	
  site	
  of	
  surveillance	
  and	
  
control.	
  Webspace	
  enables	
  the	
  virtual	
  propagation	
  of	
  global	
  inequalities	
  –	
  far	
  removed	
  from	
  the	
  
dreams	
  of	
  the	
  early	
  net	
  pioneers.	
  Online,	
  we	
  actively	
  participate	
  in	
  our	
  own	
  exploitation,	
  knowingly	
  
complicit	
  in	
  algorithmic	
  systems	
  of	
  control,	
  always	
  at	
  work	
  for	
  an	
  Other.	
  
Gillman’s	
  protagonist	
  enables	
  her	
  own	
  descent	
  into	
  psychosis	
  by	
  submitting	
  to	
  the	
  administration	
  
of	
  the	
  rest	
  cure;	
  despite	
  knowing	
  it	
  is	
  causing	
  her	
  mental	
  health	
  problems.	
  She	
  is	
  unable	
  to	
  resist	
  
the	
  cultural	
  and	
  social	
  forces	
  around	
  her.	
  Similarly,	
  we	
  are	
  unable	
  to	
  resist	
  what	
  Franco	
  Bifo	
  Berardi	
  
terms	
  “the	
  constant	
  mental	
  electrocution	
  of	
  the	
  Infosphere”2
,	
  as	
  we	
  are	
  increasingly	
  submerged	
  
within	
  the	
  seductive	
  beauty	
  and	
  endless	
  imagery	
  of	
  online	
  space.	
  
	
  
CIPHER	
  is	
  an	
  ephemeral,	
  haunting	
  figure	
  of	
  a	
  woman,	
  a	
  semi-­‐translucent,	
  impermanent	
  figure.	
  
CIPHER’s	
  movements	
  are	
  based	
  on	
  a	
  1934	
  film3
	
  from	
  the	
  Wellcome	
  Collection	
  moving	
  image	
  library	
  
showing	
  female	
  acrobats	
  performing	
  various	
  contortions	
  for	
  the	
  male	
  medical	
  gaze	
  –	
  it’s	
  a	
  film	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
1
	
  
https://www.nlm.nih.gov/exhibition/theliteratureofprescription/exhibitionAssets/digitalDocs/The-­‐
Yellow-­‐Wall-­‐Paper.pdf	
  
2
	
  https://mitpress.mit.edu/books/soul-­‐work	
  
3
	
  https://archive.org/details/Fourfemaleacrobats-­‐wellcome	
  
info@katrionabeales.com	
  	
   2	
  
produced	
  by	
  a	
  professor	
  of	
  anatomy.	
  It	
  crosses	
  between	
  medical	
  &	
  voyeristic.	
  Instead	
  of	
  the	
  
sequined	
  bikini	
  worn	
  by	
  the	
  acrobats,	
  the	
  surface	
  of	
  CIPHER’s	
  body	
  is	
  a	
  morphing	
  pattern,	
  
generated	
  by	
  using	
  a	
  variation	
  of	
  Google’s	
  DeepDream	
  artificial	
  neural	
  network.	
  In	
  CIPHER	
  –	
  the	
  
activity	
  of	
  the	
  DeepDream	
  programme	
  can	
  be	
  understood	
  as	
  another	
  iteration	
  of	
  the	
  male	
  gaze	
  at	
  
work	
  on	
  the	
  female	
  body.	
  Technologies	
  are	
  not	
  neutral	
  –	
  they	
  embodied	
  the	
  ideologies	
  and	
  
politics	
  of	
  their	
  makers.	
  In	
  this	
  new	
  industrial	
  revolution,	
  many	
  of	
  the	
  same	
  inequalities	
  are	
  being	
  
further	
  entrenched.	
  Women	
  make	
  up	
  just	
  31%	
  of	
  Google’s	
  overall	
  workforce	
  (only	
  21%	
  of	
  
leadership	
  positions	
  held	
  by	
  women	
  and	
  only	
  17%	
  of	
  technology-­‐specific	
  jobs	
  held	
  by	
  women)	
  and	
  
face	
  extreme	
  gender	
  pay	
  discrimination	
  according	
  to	
  the	
  US	
  labour	
  department4
.	
  Sillicon	
  Valley’s	
  
widespread	
  tech	
  bro	
  culture	
  is	
  rampantly	
  misogynist	
  and	
  rascist.	
  Sillicon	
  Valley	
  culture	
  is	
  now	
  global	
  
culture	
  –	
  facebook	
  alone	
  in	
  the	
  fourth quarter of 2016 had 1.86 billion monthly active users.
	
  
Owen	
  Williams	
  writes	
  “The	
  order	
  of	
  words	
  in	
  a	
  sentence	
  can	
  drastically	
  alter	
  its	
  meaning,	
  but	
  a	
  
computer	
  can’t	
  intuitively	
  know	
  why	
  that’s	
  the	
  case.	
  Neural	
  networks	
  address	
  that	
  by	
  allowing	
  
computers	
  to	
  make	
  sense	
  of	
  the	
  world	
  by	
  training	
  themselves	
  in	
  what	
  they	
  see.”5
	
  
	
  
BUT	
  WHAT	
  DO	
  COMPUTERS	
  SEE?	
  	
  
Are	
  we	
  a	
  healthy	
  data	
  set?	
  
	
  
Already	
  there	
  are	
  so	
  many	
  examples	
  of	
  AI	
  recreating	
  bias	
  –	
  particularly	
  in	
  areas	
  like	
  facial	
  
recognition	
  or	
  rascist	
  bots.	
  One	
  of	
  the	
  most	
  infamous	
  examples	
  is	
  Microsoft’s	
  TAY	
  bot	
  which	
  has	
  to	
  
be	
  shut	
  down	
  in	
  16	
  hours	
  –	
  it	
  went	
  from	
  0	
  to	
  likening	
  feminism	
  to	
  cancer	
  and	
  being	
  a	
  Holocaust	
  
denier.	
  Microsoft’s	
  only	
  reply	
  was	
  “some	
  of	
  its	
  responses	
  are	
  inappropriate	
  and	
  indicative	
  of	
  the	
  
types	
  of	
  interactions	
  some	
  people	
  are	
  having	
  with	
  it.”	
  
	
  
Technology	
  reflects	
  us.	
  We	
  are	
  living	
  in	
  a	
  shift	
  in	
  the	
  historical	
  moment	
  with	
  the	
  rise	
  of	
  neo-­‐
fascism	
  to	
  dominate	
  public	
  discourse	
  again.	
  Do	
  we	
  want	
  to	
  train	
  neural	
  nets	
  on	
  this?	
  	
  
	
  
Mireille	
  Hildebrandt	
  	
  
“we	
  are	
  now	
  increasingly	
  confronted	
  with	
  mindless	
  agents	
  of	
  our	
  own	
  making,	
  capable	
  of	
  
observing	
  us	
  and	
  adapting	
  their	
  behaviour	
  to	
  the	
  feedback	
  they	
  gain	
  from	
  ours.	
  It	
  is	
  not	
  merely	
  that	
  
we	
  use	
  these	
  machines,	
  for	
  instance	
  to	
  help	
  us	
  to	
  learn	
  faster	
  or	
  more	
  efficiently,	
  they	
  also	
  use	
  us	
  
to	
  improve	
  their	
  performance.”	
  6
	
  
	
  
The	
  goal	
  of	
  much	
  artificial	
  intelligence	
  seems	
  to	
  be	
  to	
  recreate	
  human	
  intelligence	
  –	
  but	
  human	
  
intelligence	
  seems	
  to	
  be	
  understood	
  only	
  as	
  positives.	
  Part	
  of	
  the	
  problem	
  is	
  the	
  widespread	
  
preponderance	
  in	
  information	
  theory,	
  computer	
  science,	
  cognitive	
  neuroscience	
  etc	
  that	
  our	
  brains	
  
are	
  essentially	
  a	
  computer.	
  But	
  the	
  brain	
  emphatically	
  is	
  not	
  –	
  memories	
  are	
  not	
  stored	
  anywhere	
  
in	
  the	
  brain	
  –	
  relationships	
  between	
  sets	
  of	
  neurons	
  trigger	
  memories	
  in	
  a	
  way	
  that	
  neuroscientists	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
4
	
  https://www.theguardian.com/technology/2017/apr/07/google-­‐pay-­‐disparities-­‐women-­‐labor-­‐
department-­‐lawsuit	
  
5
	
  https://medium.com/conversational-­‐interfaces/how-­‐voice-­‐assistants-­‐seemingly-­‐came-­‐from-­‐
nowhere-­‐33747876b91f	
  space10	
  IKEA	
  
	
  
6
	
  Mireille	
  Hildebrandt	
  in	
  ‘Learning	
  as	
  a	
  machine:	
  crossovers	
  between	
  humans	
  and	
  machines’	
  Jan	
  
2017	
  
info@katrionabeales.com	
  	
   3	
  
still	
  don’t	
  completely	
  understand.	
  When	
  I	
  made	
  CIPHER	
  I	
  didn’t	
  know	
  what	
  I	
  was	
  doing	
  –	
  I	
  was	
  
feeling	
  in	
  the	
  dark,	
  that’s	
  how	
  much	
  of	
  what	
  I	
  do	
  feels	
  like.	
  That’s	
  learning.	
  It	
  comes	
  with	
  cycles	
  of	
  
self-­‐doubt,	
  fear,	
  failure	
  –	
  that’s	
  because	
  it	
  involves	
  risk.	
  But	
  what	
  could	
  machines	
  risk?	
  	
  
	
  
Hildebrandt	
  warns	
  “cyber-­‐physical	
  systems	
  that	
  integrate	
  ML	
  have	
  nothing	
  to	
  lose;	
  they	
  cannot	
  
suffer	
  or	
  fear	
  their	
  own	
  death.	
  They	
  can	
  probably	
  simulate	
  all	
  that	
  and	
  more,	
  based	
  on	
  synthetic	
  
emotions	
  and	
  affective	
  computing;	
  but	
  simulation	
  is	
  not	
  the	
  same	
  as	
  what	
  is	
  simulated.”	
  
Putting	
  this	
  in	
  human	
  terms	
  –	
  could	
  you	
  trust	
  someone	
  who	
  never	
  knew	
  fear?	
  They	
  would	
  be	
  a	
  
sociopath.	
  Yet	
  AI	
  systems	
  are	
  already	
  involved	
  in	
  many	
  aspects	
  of	
  our	
  lives	
  –	
  and	
  we	
  are	
  expected	
  
to	
  trust	
  them	
  implicitly.	
  	
  
	
  
Do	
  we	
  really	
  want	
  an	
  artificial	
  intelligence	
  modelled	
  on	
  human	
  beings?	
  
	
  After	
  the	
  chaos	
  of	
  2016	
  surely	
  we	
  need	
  to	
  rethink	
  that	
  assumption.	
  Christian	
  Villum	
  the	
  Program	
  
Director	
  at	
  the	
  Danish	
  Design	
  Centre	
  argues	
  that	
  “the	
  ideal	
  should	
  be	
  to	
  create	
  bots	
  that	
  are	
  
distinctly	
  machine	
  like,	
  so	
  that	
  even	
  as	
  they	
  become	
  increasingly	
  more	
  advanced	
  and	
  skilled,	
  they	
  
never	
  let	
  us	
  forget	
  that	
  there	
  are	
  in	
  fact	
  artificial.”7
	
  
	
  
The	
  problem	
  is	
  with	
  machine	
  learning	
  is	
  that	
  even	
  people	
  with	
  advance	
  computer	
  science	
  degrees,	
  
people	
  who	
  are	
  advanced	
  mathematicians	
  –	
  don’t	
  actually	
  understand	
  why	
  or	
  how	
  neural	
  networks	
  
come	
  to	
  the	
  conclusions	
  they	
  do.	
  We	
  set	
  the	
  parameters	
  and	
  receive	
  outcomes	
  but	
  the	
  process	
  of	
  
decision	
  making	
  within	
  the	
  neural	
  network	
  is	
  not	
  understood.	
  There	
  is	
  a	
  complete	
  opaqueness	
  at	
  
the	
  centre	
  of	
  this	
  technology,	
  a	
  total	
  lack	
  of	
  explainability.	
  How	
  can	
  we	
  intervene	
  or	
  contest	
  a	
  
decision	
  making	
  process	
  that	
  occurs	
  so	
  opaquely?	
  	
  
	
  
[That’s	
  true	
  of	
  our	
  interactions	
  with	
  people	
  –	
  no	
  one	
  –	
  not	
  even	
  those	
  closest	
  to	
  us	
  are	
  not	
  fully	
  
knowable	
  –	
  but	
  we	
  have	
  other	
  indicators	
  to	
  go	
  on	
  –	
  body	
  language	
  for	
  example.]	
  
	
  
As	
  a	
  result	
  explainability	
  is	
  core	
  to	
  how	
  many	
  companies	
  are	
  trying	
  to	
  develop	
  AI…	
  	
  
“Knowing	
  AI’s	
  reasoning	
  is	
  also	
  going	
  to	
  be	
  crucial	
  if	
  the	
  technology	
  is	
  to	
  become	
  a	
  common	
  and	
  
useful	
  part	
  of	
  our	
  daily	
  lives…	
  if	
  you	
  receive	
  a	
  restaurant	
  recommendation	
  from	
  Siri,	
  you’ll	
  want	
  to	
  
know	
  what	
  the	
  reasoning	
  was.”	
  Ruslan	
  Salakhutdinov,	
  director	
  of	
  AI	
  research	
  at	
  Apple…	
  sees	
  
explainability	
  as	
  the	
  core	
  of	
  the	
  evolving	
  relationship	
  between	
  humans	
  and	
  intelligent	
  machines.	
  
“It’s	
  going	
  to	
  introduce	
  trust,”	
  he	
  says”8
	
  Understanding	
  how	
  an	
  AI	
  has	
  come	
  to	
  a	
  certain	
  outcome	
  
may	
  not	
  be	
  a	
  problem	
  if	
  it’s	
  just	
  involved	
  in	
  suggesting	
  restaurants	
  -­‐	
  but	
  highly	
  problematic	
  I	
  would	
  
suggest	
  when	
  it’s	
  involved	
  in	
  making	
  judgements	
  on	
  credit,	
  debt,	
  healthcare…	
  	
  
	
  
As	
  for	
  “trust”	
  why	
  do	
  companies	
  such	
  as	
  Apple	
  want	
  us	
  to	
  trust	
  SIRI?	
  Or	
  Amazon	
  Alexa?	
  	
  
It’s	
  not	
  just	
  a	
  problem	
  as	
  to	
  who	
  machines	
  are	
  learning	
  from	
  but	
  WHO	
  ARE	
  machines	
  SEEING	
  
FOR?	
  
Far	
  from	
  an	
  impartial	
  observer	
  these	
  systems	
  are	
  knowing	
  us	
  on	
  behalf	
  or	
  behest	
  of	
  generally	
  a	
  
multinational	
  corporation	
  and	
  or	
  government.	
  Corporations	
  are	
  obviously	
  keen	
  to	
  develop	
  trust	
  
with	
  their	
  users	
  as	
  that	
  increases	
  sales.	
  In	
  terms	
  of	
  government	
  –	
  the	
  argument	
  is	
  generally	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
7
	
  https://medium.com/@villum/biased-­‐bots-­‐making-­‐artificial-­‐jerks-­‐a643d69a4797	
  
8
	
  https://www.technologyreview.com/s/604087/the-­‐dark-­‐secret-­‐at-­‐the-­‐heart-­‐of-­‐
ai/?utm_campaign=add_this&utm_source=facebook&utm_medium=post	
  
	
  
info@katrionabeales.com	
  	
   4	
  
employed	
  that	
  you	
  only	
  have	
  a	
  problem	
  or	
  something	
  to	
  fear	
  if	
  you	
  have	
  done	
  something	
  criminal.	
  
Unfortunately	
  the	
  definition	
  of	
  criminal	
  changes	
  with	
  who’s	
  in	
  power.	
  Graphically	
  represented	
  by	
  
the	
  changes	
  in	
  the	
  attitude	
  to	
  immigrants	
  in	
  the	
  USA	
  between	
  the	
  Trump	
  and	
  Obama	
  
administrations.	
  	
  
	
  
	
  AI	
  WILL	
  ONLY	
  ENTRENCH	
  AND	
  WIDEN	
  INEQUALITY.	
  	
  
Who	
  has	
  access	
  to	
  this	
  technology?	
  	
  
Who	
  understands	
  how	
  it	
  is	
  functioning?	
  	
  
Who	
  can	
  build	
  it?	
  	
  
Closed	
  systems	
  demand	
  compliance	
  without	
  sharing	
  power.	
  	
  
	
  
	
  
Computers	
  for	
  
peace	
  (1971)	
  	
  	
  
	
  
“Computers	
  are	
  
increasingly	
  being	
  
used	
  as	
  a	
  means	
  of	
  
oppression.	
  They	
  
are	
  at	
  the	
  heart	
  of	
  
every	
  military	
  and	
  
police	
  system.	
  
They	
  are	
  at	
  the	
  
core	
  of	
  every	
  
major	
  corporation	
  
and	
  are	
  used	
  to	
  
maximize	
  profits	
  
with	
  little	
  regard	
  
to	
  human	
  needs.”	
  
	
  	
  
	
  
	
  
	
  
	
  
	
  
	
  
Kate	
  Crawdord	
  and	
  Hito	
  Steyerl	
  –	
  Data	
  Streams	
  in	
  the	
  New	
  Enquiry:	
  on	
  the	
  fundamental	
  paradox	
  at	
  
the	
  moment…	
  	
  
	
  
“if	
  you	
  are	
  currently	
  misrecognized	
  by	
  a	
  system,	
  it	
  can	
  mean	
  that	
  you	
  don’t	
  get	
  access	
  to	
  housing,	
  
you	
  don’t	
  get	
  access	
  to	
  credit,	
  you	
  don’t	
  get	
  released	
  from	
  jail.	
  So	
  you	
  want	
  this	
  recognition,	
  but,	
  at	
  
the	
  same	
  time,	
  the	
  more	
  the	
  systems	
  have	
  accurate	
  training	
  data	
  and	
  the	
  more	
  they	
  have	
  deeper	
  
historical	
  knowledge	
  of	
  you,	
  the	
  more	
  you	
  are	
  profoundly	
  captured	
  within	
  these	
  systems….	
  We	
  are	
  
being	
  seen	
  with	
  ever	
  greater	
  resolution,	
  but	
  the	
  systems	
  around	
  us	
  are	
  increasingly	
  disappearing	
  
into	
  the	
  background.”	
  
	
  
info@katrionabeales.com	
  	
   5	
  
Hito	
  Steyerl	
  describes	
  weak	
  AI	
  systems	
  as	
  artificial	
  stupidity	
  –	
  and	
  gives	
  automation	
  as	
  an	
  example	
  
–	
  she	
  talks	
  about	
  10	
  female	
  cashiers	
  being	
  replaced	
  by	
  one	
  male	
  security	
  guard	
  guarding	
  the	
  self	
  
serve	
  checkouts.	
  How	
  intelligent	
  is	
  this	
  as	
  a	
  decision	
  making	
  process?	
  What	
  are	
  the	
  economic	
  and	
  
social	
  costs	
  of	
  higher	
  inequality?	
  Hito	
  Steyel	
  suggests	
  that	
  “The	
  more	
  “intelligent”	
  these	
  programs	
  
become,	
  the	
  more	
  social	
  fragmentation	
  will	
  increase,	
  and	
  also	
  polarization…	
  a	
  lot	
  of	
  the	
  political	
  
turmoil	
  we	
  are	
  already	
  seeing	
  today	
  is	
  due	
  to	
  artificial	
  stupidity.”	
  	
  
	
  
A	
  3-­‐month-­‐old	
  baby	
  was	
  barred	
  from	
  a	
  flight	
  from	
  London	
  to	
  Florida	
  on	
  suspicion	
  he	
  was	
  a	
  
terrorist.	
  The	
  baby	
  was	
  summoned	
  to	
  the	
  US	
  Embassy	
  in	
  London	
  because	
  his	
  grandfather	
  
accidentally	
  checked	
  the	
  wrong	
  box	
  on	
  a	
  visa.	
  Granddad	
  Paul	
  Kenyon	
  checked	
  “yes”	
  next	
  to	
  a	
  box	
  
on	
  the	
  form	
  that	
  read:	
  “Do	
  you	
  seek	
  to	
  engage	
  in	
  or	
  have	
  you	
  ever	
  engaged	
  in	
  terrorist	
  activities,	
  
espionage,	
  sabotage,	
  or	
  genocide?9
	
  This	
  isn’t	
  the	
  grandad	
  being	
  stupid	
  –	
  this	
  is	
  a	
  stupid	
  system.	
  If	
  
you	
  were	
  a	
  terrorist	
  would	
  you	
  tick	
  a	
  box	
  saying	
  ‘yes’	
  I	
  am	
  a	
  terrorist?	
  	
  
	
  
So	
  what	
  are	
  we	
  left	
  with?	
  What	
  can	
  we	
  do?	
  	
  
	
  
As	
  artists	
  and	
  makers	
  and	
  creatives	
  we	
  urgently	
  need	
  to	
  use	
  emergent	
  technologies	
  like	
  VR,	
  like	
  AI,	
  
like	
  machine	
  learning.	
  	
  
	
  
We	
  need	
  to	
  widen	
  the	
  discourse	
  around	
  how	
  these	
  technologies	
  are	
  constructed	
  and	
  what	
  
ideologies	
  they	
  embody.	
  	
  
	
  
	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
9
	
  http://nypost.com/2017/04/16/baby-­‐mistaken-­‐for-­‐tiny-­‐terrorist-­‐after-­‐grandpa-­‐botches-­‐visa-­‐
form/	
  
Image:	
  http://www.bbc.co.uk/news/uk-­‐35519470	
  
info@katrionabeales.com	
  	
   6	
  
	
  
CREATE	
  DIRTY	
  DATA!	
  
	
  
I	
  think	
  we	
  have	
  an	
  urgent	
  task	
  to	
  deliberately	
  create	
  streams	
  of	
  dirty	
  data.	
  	
  	
  
Refuse	
  to	
  be	
  totally	
  known	
  &	
  muddy	
  the	
  waters	
  of	
  clean	
  data	
  sets.	
  	
  
Fight	
  the	
  correlation	
  dictatorship.	
  
Invisibility	
  by	
  misinformation.	
  	
  
We	
  do	
  this	
  already	
  –	
  who	
  puts	
  their	
  real	
  date	
  of	
  birth	
  on	
  an	
  online	
  form?	
  But	
  we	
  need	
  to	
  start	
  doing	
  
this	
  actively.	
  For	
  example	
  -­‐	
  I	
  think	
  every	
  FB	
  user	
  based	
  in	
  the	
  US	
  has	
  a	
  political	
  imperative	
  to	
  miss-­‐
tag	
  all	
  their	
  photos	
  of	
  people	
  –	
  as	
  an	
  act	
  of	
  solidarity	
  -­‐	
  so	
  that	
  facial	
  recognition	
  algortihms	
  are	
  
ineffective.	
  	
  
We	
  need	
  to	
  actively	
  resist	
  algorithmic	
  systems	
  of	
  control	
  by	
  creating	
  dirty	
  data	
  sets.	
  	
  
	
  
Footnote:	
  	
  
Facebook	
  helps	
  advertisers	
  target	
  teens	
  who	
  feel	
  worthless	
  
Facebook's	
  algorithms	
  can	
  determine,	
  and	
  allow	
  advertisers	
  to	
  pinpoint,	
  "moments	
  when	
  young	
  
people	
  need	
  a	
  confidence	
  boost."	
  If	
  that	
  phrase	
  isn't	
  clear	
  enough,	
  Facebook's	
  document	
  offers	
  a	
  
litany	
  of	
  teen	
  emotional	
  states	
  that	
  the	
  company	
  claims	
  it	
  can	
  estimate	
  based	
  on	
  how	
  teens	
  use	
  the	
  
service,	
  including	
  "worthless,"	
  "insecure,"	
  "defeated,"	
  "anxious,"	
  "silly,"	
  "useless,"	
  "stupid,"	
  
"overwhelmed,"	
  "stressed,"	
  and	
  "a	
  failure."10
	
  
	
  
Katriona	
  Beales	
  2017	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
10
	
  https://arstechnica.co.uk/business/2017/05/facebook-­‐helped-­‐advertisers-­‐target-­‐teens-­‐who-­‐feel-­‐
worthless/	
  

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Katriona Beales - Intelligence is not enough - Creative AI meetup

  • 1. info@katrionabeales.com     1   "Intelligence  is  not  enough"     Katriona  Beales     Creative  AI  meet-­‐up,  The  Photographers  Gallery,     3rd  May  2017         *CIPHER  (2017)  K.  Beales     CIPHER  comes  out  of  my  ongoing  research  in  internet  addiction,  information  overload  and  ideas  of   a  technological  sublime.  I’m  interested  in  webspace  as  a  psychotic  environment,  an  equivalent  to   the  nursery  in  Charlotte  Perkins  Gillman’s  novella  ‘The  Yellow  Wallpaper’1  a  site  of  surveillance  and   control.  Webspace  enables  the  virtual  propagation  of  global  inequalities  –  far  removed  from  the   dreams  of  the  early  net  pioneers.  Online,  we  actively  participate  in  our  own  exploitation,  knowingly   complicit  in  algorithmic  systems  of  control,  always  at  work  for  an  Other.   Gillman’s  protagonist  enables  her  own  descent  into  psychosis  by  submitting  to  the  administration   of  the  rest  cure;  despite  knowing  it  is  causing  her  mental  health  problems.  She  is  unable  to  resist   the  cultural  and  social  forces  around  her.  Similarly,  we  are  unable  to  resist  what  Franco  Bifo  Berardi   terms  “the  constant  mental  electrocution  of  the  Infosphere”2 ,  as  we  are  increasingly  submerged   within  the  seductive  beauty  and  endless  imagery  of  online  space.     CIPHER  is  an  ephemeral,  haunting  figure  of  a  woman,  a  semi-­‐translucent,  impermanent  figure.   CIPHER’s  movements  are  based  on  a  1934  film3  from  the  Wellcome  Collection  moving  image  library   showing  female  acrobats  performing  various  contortions  for  the  male  medical  gaze  –  it’s  a  film                                                                                                                   1   https://www.nlm.nih.gov/exhibition/theliteratureofprescription/exhibitionAssets/digitalDocs/The-­‐ Yellow-­‐Wall-­‐Paper.pdf   2  https://mitpress.mit.edu/books/soul-­‐work   3  https://archive.org/details/Fourfemaleacrobats-­‐wellcome  
  • 2. info@katrionabeales.com     2   produced  by  a  professor  of  anatomy.  It  crosses  between  medical  &  voyeristic.  Instead  of  the   sequined  bikini  worn  by  the  acrobats,  the  surface  of  CIPHER’s  body  is  a  morphing  pattern,   generated  by  using  a  variation  of  Google’s  DeepDream  artificial  neural  network.  In  CIPHER  –  the   activity  of  the  DeepDream  programme  can  be  understood  as  another  iteration  of  the  male  gaze  at   work  on  the  female  body.  Technologies  are  not  neutral  –  they  embodied  the  ideologies  and   politics  of  their  makers.  In  this  new  industrial  revolution,  many  of  the  same  inequalities  are  being   further  entrenched.  Women  make  up  just  31%  of  Google’s  overall  workforce  (only  21%  of   leadership  positions  held  by  women  and  only  17%  of  technology-­‐specific  jobs  held  by  women)  and   face  extreme  gender  pay  discrimination  according  to  the  US  labour  department4 .  Sillicon  Valley’s   widespread  tech  bro  culture  is  rampantly  misogynist  and  rascist.  Sillicon  Valley  culture  is  now  global   culture  –  facebook  alone  in  the  fourth quarter of 2016 had 1.86 billion monthly active users.   Owen  Williams  writes  “The  order  of  words  in  a  sentence  can  drastically  alter  its  meaning,  but  a   computer  can’t  intuitively  know  why  that’s  the  case.  Neural  networks  address  that  by  allowing   computers  to  make  sense  of  the  world  by  training  themselves  in  what  they  see.”5     BUT  WHAT  DO  COMPUTERS  SEE?     Are  we  a  healthy  data  set?     Already  there  are  so  many  examples  of  AI  recreating  bias  –  particularly  in  areas  like  facial   recognition  or  rascist  bots.  One  of  the  most  infamous  examples  is  Microsoft’s  TAY  bot  which  has  to   be  shut  down  in  16  hours  –  it  went  from  0  to  likening  feminism  to  cancer  and  being  a  Holocaust   denier.  Microsoft’s  only  reply  was  “some  of  its  responses  are  inappropriate  and  indicative  of  the   types  of  interactions  some  people  are  having  with  it.”     Technology  reflects  us.  We  are  living  in  a  shift  in  the  historical  moment  with  the  rise  of  neo-­‐ fascism  to  dominate  public  discourse  again.  Do  we  want  to  train  neural  nets  on  this?       Mireille  Hildebrandt     “we  are  now  increasingly  confronted  with  mindless  agents  of  our  own  making,  capable  of   observing  us  and  adapting  their  behaviour  to  the  feedback  they  gain  from  ours.  It  is  not  merely  that   we  use  these  machines,  for  instance  to  help  us  to  learn  faster  or  more  efficiently,  they  also  use  us   to  improve  their  performance.”  6     The  goal  of  much  artificial  intelligence  seems  to  be  to  recreate  human  intelligence  –  but  human   intelligence  seems  to  be  understood  only  as  positives.  Part  of  the  problem  is  the  widespread   preponderance  in  information  theory,  computer  science,  cognitive  neuroscience  etc  that  our  brains   are  essentially  a  computer.  But  the  brain  emphatically  is  not  –  memories  are  not  stored  anywhere   in  the  brain  –  relationships  between  sets  of  neurons  trigger  memories  in  a  way  that  neuroscientists                                                                                                                   4  https://www.theguardian.com/technology/2017/apr/07/google-­‐pay-­‐disparities-­‐women-­‐labor-­‐ department-­‐lawsuit   5  https://medium.com/conversational-­‐interfaces/how-­‐voice-­‐assistants-­‐seemingly-­‐came-­‐from-­‐ nowhere-­‐33747876b91f  space10  IKEA     6  Mireille  Hildebrandt  in  ‘Learning  as  a  machine:  crossovers  between  humans  and  machines’  Jan   2017  
  • 3. info@katrionabeales.com     3   still  don’t  completely  understand.  When  I  made  CIPHER  I  didn’t  know  what  I  was  doing  –  I  was   feeling  in  the  dark,  that’s  how  much  of  what  I  do  feels  like.  That’s  learning.  It  comes  with  cycles  of   self-­‐doubt,  fear,  failure  –  that’s  because  it  involves  risk.  But  what  could  machines  risk?       Hildebrandt  warns  “cyber-­‐physical  systems  that  integrate  ML  have  nothing  to  lose;  they  cannot   suffer  or  fear  their  own  death.  They  can  probably  simulate  all  that  and  more,  based  on  synthetic   emotions  and  affective  computing;  but  simulation  is  not  the  same  as  what  is  simulated.”   Putting  this  in  human  terms  –  could  you  trust  someone  who  never  knew  fear?  They  would  be  a   sociopath.  Yet  AI  systems  are  already  involved  in  many  aspects  of  our  lives  –  and  we  are  expected   to  trust  them  implicitly.       Do  we  really  want  an  artificial  intelligence  modelled  on  human  beings?    After  the  chaos  of  2016  surely  we  need  to  rethink  that  assumption.  Christian  Villum  the  Program   Director  at  the  Danish  Design  Centre  argues  that  “the  ideal  should  be  to  create  bots  that  are   distinctly  machine  like,  so  that  even  as  they  become  increasingly  more  advanced  and  skilled,  they   never  let  us  forget  that  there  are  in  fact  artificial.”7     The  problem  is  with  machine  learning  is  that  even  people  with  advance  computer  science  degrees,   people  who  are  advanced  mathematicians  –  don’t  actually  understand  why  or  how  neural  networks   come  to  the  conclusions  they  do.  We  set  the  parameters  and  receive  outcomes  but  the  process  of   decision  making  within  the  neural  network  is  not  understood.  There  is  a  complete  opaqueness  at   the  centre  of  this  technology,  a  total  lack  of  explainability.  How  can  we  intervene  or  contest  a   decision  making  process  that  occurs  so  opaquely?       [That’s  true  of  our  interactions  with  people  –  no  one  –  not  even  those  closest  to  us  are  not  fully   knowable  –  but  we  have  other  indicators  to  go  on  –  body  language  for  example.]     As  a  result  explainability  is  core  to  how  many  companies  are  trying  to  develop  AI…     “Knowing  AI’s  reasoning  is  also  going  to  be  crucial  if  the  technology  is  to  become  a  common  and   useful  part  of  our  daily  lives…  if  you  receive  a  restaurant  recommendation  from  Siri,  you’ll  want  to   know  what  the  reasoning  was.”  Ruslan  Salakhutdinov,  director  of  AI  research  at  Apple…  sees   explainability  as  the  core  of  the  evolving  relationship  between  humans  and  intelligent  machines.   “It’s  going  to  introduce  trust,”  he  says”8  Understanding  how  an  AI  has  come  to  a  certain  outcome   may  not  be  a  problem  if  it’s  just  involved  in  suggesting  restaurants  -­‐  but  highly  problematic  I  would   suggest  when  it’s  involved  in  making  judgements  on  credit,  debt,  healthcare…       As  for  “trust”  why  do  companies  such  as  Apple  want  us  to  trust  SIRI?  Or  Amazon  Alexa?     It’s  not  just  a  problem  as  to  who  machines  are  learning  from  but  WHO  ARE  machines  SEEING   FOR?   Far  from  an  impartial  observer  these  systems  are  knowing  us  on  behalf  or  behest  of  generally  a   multinational  corporation  and  or  government.  Corporations  are  obviously  keen  to  develop  trust   with  their  users  as  that  increases  sales.  In  terms  of  government  –  the  argument  is  generally                                                                                                                   7  https://medium.com/@villum/biased-­‐bots-­‐making-­‐artificial-­‐jerks-­‐a643d69a4797   8  https://www.technologyreview.com/s/604087/the-­‐dark-­‐secret-­‐at-­‐the-­‐heart-­‐of-­‐ ai/?utm_campaign=add_this&utm_source=facebook&utm_medium=post    
  • 4. info@katrionabeales.com     4   employed  that  you  only  have  a  problem  or  something  to  fear  if  you  have  done  something  criminal.   Unfortunately  the  definition  of  criminal  changes  with  who’s  in  power.  Graphically  represented  by   the  changes  in  the  attitude  to  immigrants  in  the  USA  between  the  Trump  and  Obama   administrations.        AI  WILL  ONLY  ENTRENCH  AND  WIDEN  INEQUALITY.     Who  has  access  to  this  technology?     Who  understands  how  it  is  functioning?     Who  can  build  it?     Closed  systems  demand  compliance  without  sharing  power.         Computers  for   peace  (1971)         “Computers  are   increasingly  being   used  as  a  means  of   oppression.  They   are  at  the  heart  of   every  military  and   police  system.   They  are  at  the   core  of  every   major  corporation   and  are  used  to   maximize  profits   with  little  regard   to  human  needs.”                   Kate  Crawdord  and  Hito  Steyerl  –  Data  Streams  in  the  New  Enquiry:  on  the  fundamental  paradox  at   the  moment…       “if  you  are  currently  misrecognized  by  a  system,  it  can  mean  that  you  don’t  get  access  to  housing,   you  don’t  get  access  to  credit,  you  don’t  get  released  from  jail.  So  you  want  this  recognition,  but,  at   the  same  time,  the  more  the  systems  have  accurate  training  data  and  the  more  they  have  deeper   historical  knowledge  of  you,  the  more  you  are  profoundly  captured  within  these  systems….  We  are   being  seen  with  ever  greater  resolution,  but  the  systems  around  us  are  increasingly  disappearing   into  the  background.”    
  • 5. info@katrionabeales.com     5   Hito  Steyerl  describes  weak  AI  systems  as  artificial  stupidity  –  and  gives  automation  as  an  example   –  she  talks  about  10  female  cashiers  being  replaced  by  one  male  security  guard  guarding  the  self   serve  checkouts.  How  intelligent  is  this  as  a  decision  making  process?  What  are  the  economic  and   social  costs  of  higher  inequality?  Hito  Steyel  suggests  that  “The  more  “intelligent”  these  programs   become,  the  more  social  fragmentation  will  increase,  and  also  polarization…  a  lot  of  the  political   turmoil  we  are  already  seeing  today  is  due  to  artificial  stupidity.”       A  3-­‐month-­‐old  baby  was  barred  from  a  flight  from  London  to  Florida  on  suspicion  he  was  a   terrorist.  The  baby  was  summoned  to  the  US  Embassy  in  London  because  his  grandfather   accidentally  checked  the  wrong  box  on  a  visa.  Granddad  Paul  Kenyon  checked  “yes”  next  to  a  box   on  the  form  that  read:  “Do  you  seek  to  engage  in  or  have  you  ever  engaged  in  terrorist  activities,   espionage,  sabotage,  or  genocide?9  This  isn’t  the  grandad  being  stupid  –  this  is  a  stupid  system.  If   you  were  a  terrorist  would  you  tick  a  box  saying  ‘yes’  I  am  a  terrorist?       So  what  are  we  left  with?  What  can  we  do?       As  artists  and  makers  and  creatives  we  urgently  need  to  use  emergent  technologies  like  VR,  like  AI,   like  machine  learning.       We  need  to  widen  the  discourse  around  how  these  technologies  are  constructed  and  what   ideologies  they  embody.                                                                                                                         9  http://nypost.com/2017/04/16/baby-­‐mistaken-­‐for-­‐tiny-­‐terrorist-­‐after-­‐grandpa-­‐botches-­‐visa-­‐ form/   Image:  http://www.bbc.co.uk/news/uk-­‐35519470  
  • 6. info@katrionabeales.com     6     CREATE  DIRTY  DATA!     I  think  we  have  an  urgent  task  to  deliberately  create  streams  of  dirty  data.       Refuse  to  be  totally  known  &  muddy  the  waters  of  clean  data  sets.     Fight  the  correlation  dictatorship.   Invisibility  by  misinformation.     We  do  this  already  –  who  puts  their  real  date  of  birth  on  an  online  form?  But  we  need  to  start  doing   this  actively.  For  example  -­‐  I  think  every  FB  user  based  in  the  US  has  a  political  imperative  to  miss-­‐ tag  all  their  photos  of  people  –  as  an  act  of  solidarity  -­‐  so  that  facial  recognition  algortihms  are   ineffective.     We  need  to  actively  resist  algorithmic  systems  of  control  by  creating  dirty  data  sets.       Footnote:     Facebook  helps  advertisers  target  teens  who  feel  worthless   Facebook's  algorithms  can  determine,  and  allow  advertisers  to  pinpoint,  "moments  when  young   people  need  a  confidence  boost."  If  that  phrase  isn't  clear  enough,  Facebook's  document  offers  a   litany  of  teen  emotional  states  that  the  company  claims  it  can  estimate  based  on  how  teens  use  the   service,  including  "worthless,"  "insecure,"  "defeated,"  "anxious,"  "silly,"  "useless,"  "stupid,"   "overwhelmed,"  "stressed,"  and  "a  failure."10     Katriona  Beales  2017                                                                                                                   10  https://arstechnica.co.uk/business/2017/05/facebook-­‐helped-­‐advertisers-­‐target-­‐teens-­‐who-­‐feel-­‐ worthless/