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Bringing AI to Business Intelligence

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Intro to artificial intelligence for business intelligence

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Bringing AI to Business Intelligence

  1. 1. Bringing  AI  to  Business   Intelligence Integrated  Knowledge  Solu0ons   h3ps://iksinc.wordpress.com/home/   iksinc@yahoo.com  
  2. 2. Agenda • Current  BI  environment   • What  is  AI?   • AI  Technologies   • How  is  AI  being  used  by  businesses?   • Roadmap  for  bringing  AI  to  BI   • Summary  
  3. 3. Current  BI  Environment  
  4. 4. Current  BI  Environment A  sampling  of  headlines  in  various  magazines  and  blogs   iksinc@yahoo.com  
  5. 5. Why  so  much   clamor  for  AI?   What  is  missing   in  BI?  Lets  look   at  BI  value  chain   iksinc@yahoo.com   What’s  Missing?
  6. 6. iksinc@yahoo.com  
  7. 7. •  “What  is  happening?”   •  “Why  is  it   happening?”     Not  much  problem  there.   iksinc@yahoo.com   Current  BI  tools  are  good  at  answering:
  8. 8. However,  there  are  inherent  dangers  in  manually  searching   for  rela0ons/pa3erns/trends  in  charts/dashboards.     Analysts'  biases  are  unavoidable;  A  study  at  Bayer  about  10   years  ago  found  that  70%  of  analysis  could  not  be  replicated   by  changing  the  analyst.     iksinc@yahoo.com  
  9. 9. Gartner  defines  prescrip0ve  analy0cs  as:  “…the  applica0on  of  logic  and   mathema0cs  to  data  to  specify  a  preferred  course  of  ac0on.  While  all   types  of  analy0cs  ul0mately  support  be3er  decision  making,   prescrip0ve  analy0cs  outputs  a  decision  rather  than  a  report,  sta0s0c,   probability  or  es0mate  of  future  outcomes.”     AI   iksinc@yahoo.com  
  10. 10. Plot  as  of  April  23,  2017   iksinc@yahoo.com  
  11. 11. Predic?ve  vs.  Prescrip?ve  Analy?cs Predic0ve  analy0cs  is  simply  focused  on  the  outcome  –  good  for  a  sports  be3or   Prescrip0ve  analy0cs  is  what  the  coaching  staff  needs   iksinc@yahoo.com  
  12. 12. What  is  AI?  
  13. 13. What  Characterizes  Intelligence • Ability  to  interact  with  real  world   •  To  perceive,  understand,  and  act   • Searching  the  best  solu0on   • Reasoning  and  planning   •  Modeling  the  environment   •  Solving  new  problems,  planning,  and  making  decisions   •  Ability  to  deal  with  uncertain0es   • Learning  and  adapta0on   iksinc@yahoo.com  
  14. 14. What  is  AI? • The  term  ar0ficial  intelligence  was  coined   by  John  McCarthy  circa  1956.  He  defined   it  as  “the  science  and  engineering  of   making  intelligent  machines”   Ar0ficial  intelligence  is  technology  that  appears  to  emulate   human  performance  typically  by  learning,  coming  to  its  own   conclusions,  appearing  to  understand  complex  content,  engaging   in  natural  dialogs  with  people,  enhancing  human  cogni0ve   performance  (also  known  as  cogni0ve  compu0ng)  or  replacing   people  on  execu0on  of  non-­‐rou0ne  tasks.     Gartner  Defini0on   iksinc@yahoo.com  
  15. 15. Weak  AI • Also  known  as  Narrow  AI   •  a  descrip0ve  term  used  for  AI  that  can  demonstrate  human   like  intelligence,  but  only  for  a  specific  task  or  tasks.   Majority  of  today's  AI  systems  fall  in  this  category.   iksinc@yahoo.com  
  16. 16. Ar?ficial  General  Intelligence  (AGI) • Also  known  as  Strong  AI   •  a  term  used  to  describe  a  certain  mind-­‐set  of   ar0ficial  intelligence  development.  Strong  AI’s  goal   is  to  develop  ar0ficial  intelligence  to  the  point   where  the  machine’s  intellectual  capability  is   func0onally  equal  to  a  human’s.     iksinc@yahoo.com  
  17. 17. Ar?ficial  Super  Intelligence  (ASI) •  A  term  used  for  AI  of  the  future.  It  will  be  be  superior  to   any  level  of  human  intelligence  and  will  (poten0ally),  if   allowed,  be  in  complete  control  of  its  own  decision  making.     “It  seems  probable  that  once  the  machine  thinking   method  had  started,  it  would  not  take  long  to   outstrip  our  feeble  powers...  They  would  be  able  to   converse  with  each  other  to  sharpen  their  wits.  At   some  stage  therefore,  we  should  have  to  expect  the   machines  to  take  control.”     Alan  Turing,  the  'godfather  of  AI'   from  Nick  Bostrom’s  latest  book:  ‘Superintelligence:  Paths,  Dangers,   Strategies   iksinc@yahoo.com  
  18. 18. Evolu?on  of  AI  Since  1950 iksinc@yahoo.com  
  19. 19. AI  Technologies  
  20. 20. AI  Technologies iksinc@yahoo.com  
  21. 21. Learning  in  AI •  Most  domina0ng  subfield  of  AI  today.  Machine  learning  is   concerned  with  making  computers  learn  to  make  predic0ons/ decisions  without  explicitly  programming  them.  Rather  a  large   number  of  examples  of  the  underlying  task  are  shown  to   op0mize  a  performance  criterion  to  achieve  learning.   •  Two  major  styles  of  machine  learning:  Supervised  and   unsupervised   iksinc@yahoo.com  
  22. 22. Supervised  Learning • Training  data  comes  with  answers,  called  labels   • The  goal  is  to  produce  labels/answers  for  new  data   iksinc@yahoo.com  
  23. 23. Supervised  Learning  Models • Classifica0on  models   • Predict  whether  a  customer  is   likely  to  be  lost  to  compe0tor   • Tag  objects  in  a  given  image   • Determine  whether  an   incoming  email  is  spam  or  not   iksinc@yahoo.com  
  24. 24. Supervised  Learning  Models • Regression  models   • Predict  credit  card  balance  of   customers   • Predict  the  number  of  'likes'   for  a  pos0ng   • Predict  peak  load  for  a  u0lity   given  weather  informa0on   iksinc@yahoo.com  
  25. 25. Unsupervised  Learning • Training  data  comes   without  labels   • The  goal  is  to  group  data   into  different  categories   based  on  similari0es   Grouped  Data   iksinc@yahoo.com  
  26. 26. Unsupervised  Learning  Models • Segment/  cluster  customers  into   different  groups   • Organize  a  collec0on  of   documents  based  on  their  content   • Make  Recommenda0ons  for   products   iksinc@yahoo.com  
  27. 27. Deep  Learning • It’s  a  subfield  of  machine  learning  that  has  shown   remarkable  success  in  dealing  with  applica0ons  requiring   processing  of  pictures,  videos,  speech,  and  text.     • Deep  learning  is  characterized  by:   •  Extremely  large  amount  of  data  for  training   •  Neural  networks  with  exceedingly  large  number  of  layers   •  Training  0me  running  into  weeks  in  many  instances     •  End  to  end  learning  (No  human  designed  rules/features  are  used)   iksinc@yahoo.com  
  28. 28. Examples  of  Deep  Learning:  Object   Detec0on  and  Labeling     iksinc@yahoo.com  
  29. 29. Examples  of  Deep  Learning:  Automa0c   Descrip0on  Genera0on  of  Images   iksinc@yahoo.com  
  30. 30. Example  of  Deep  Learning:  Predic0ng   Heart  A3acks   iksinc@yahoo.com  
  31. 31. Natural  Language  Processing  &     Speech  Recogni?on •  NLP  &  speech  recogni0on  are  those  subfields  of  AI  that  make  it  possible   for  machines  to  communicate  with  humans  by  understanding  wri3en  or   spoken  text   •  The  text  could  be  structured  or  unstructured.     •  These  two  subfields  of  AI  are  finding  many  applica0ons  in  the  industry  to   build  new  UIs  that  are  proving  more  effec0ve.     •   Alexa,  Cortana,  Siri  are  all  examples  of  these  AI  technologies.   •  IBM  Watson  is  another  example  of  using  NLP  to  assist  in  evidence  based   medicine   iksinc@yahoo.com  
  32. 32. iksinc@yahoo.com  
  33. 33. iksinc@yahoo.com  
  34. 34. Neural  Transla?on iksinc@yahoo.com  
  35. 35. Named-­‐En?ty  Recogni?on •  It’s  a  subfield  of  NLP;  Named   En0ty  Recogni0on  (NER)  labels   sequences  of  words  in  a  text   which  are  the  names  of  things,   such  as  person  and  company   names,  or  gene  and  protein   names.     •  Helpful  for  automa0c  informa0on   extrac0on  to  build  rela0onships   between  different  en00es.  Think   of  Jeopardy.   iksinc@yahoo.com  
  36. 36. Op?miza?on  and  Planning • Lots  of  overlap  with  opera0ons   research   • AI  centric  op0miza0on  methods:   •  Gene0c  algorithms   •  Based  on  natural  selec0on  in  a  popula0on   •  Simulated  annealing   •  Based  on  crystal  forma0on  in  solids  through   cooling     iksinc@yahoo.com  
  37. 37. Op?miza?on  and  Planning • Ant  colony  op0miza0on   •  Based  on  how  ants  leave  markers  for  other  ants   • Par0cle  swarm  op0miza0on   •  Based  on  behavior  of  the  flock  of  birds,  pool  of   fishes  etc.   iksinc@yahoo.com  
  38. 38. How  is  AI  being  used  by   businesses?  
  39. 39. iksinc@yahoo.com  
  40. 40. Making  Recommenda?ons  using  AI Personalized  newsfeed  on  FB  and  LinkedIn   iksinc@yahoo.com  
  41. 41. AI-­‐based  Assistants Salesforce  Einstein  iksinc@yahoo.com  
  42. 42. ORION,  an  acronym  that  stands  for   On-­‐Road  Integrated  Op0miza0on  and   Naviga0on,  is  perhaps  the  largest   commercial  analy0cs  project  ever   undertaken.  It’s  required  well  over  a   decade  to  build  and  roll  out,  and   more  than  $250  million  of  investment   by  UPS.   Savings  in  driver  produc0vity  and   fuel  economy:  $300  -­‐  $400  million  a   year,  100  million  fewer  miles  driven   and  a  resul0ng  cut  in  carbon   emissions  of  100,000  metric  tons  a   year.   iksinc@yahoo.com  
  43. 43. •  Vetride  system  is  an  another  example  of  prescrip0ve   analy0cs  developed  by  a  local  company   •  The  system  is  being  used  at  64  VA  sites  all  over  USA   and  is  installed  approximately  in  1200  vehicles   •  Provides  veterans  transporta0on  at  demand   op0mizing  a  number  of  parameters  with  many   constraints   iksinc@yahoo.com  
  44. 44. iksinc@yahoo.com  
  45. 45. How  to  Get  Started  with  AI?  
  46. 46. Some  Poten?al  Sugges?ons • Candidate  task  characteris0cs  for  ini0al  AI  projects:   •  Complexity  level  :  low  to  medium   •  High  volume  and  repe00ve   •  No  legal  or  ethical  risks   •  Fair  level  of  user  interac0on,  internal  or  external     iksinc@yahoo.com   Whit  Andrews,   Gartner  VP   Michael  Azoff,   Ovum  Principal   Analyst  
  47. 47. Three  Phase  Process • Prepara0on  phase   •  Structure  preparedness     •  Organiza0onal  preparedness   •  Knowledge  preparedness   • Buy-­‐in  and  value  crea0on   •  Create  awareness  and  value  proposi0on   •  Brainstorm  project  ideas   •  Demo  value   • Organiza0on  wide  adop0on   iksinc@yahoo.com  
  48. 48. Summary  
  49. 49. Summary • AI  is  set  to  play  a  big  role  in  businesses  across  a  wide   spectrum   • Tune  out  the  hype  and  focus  on  how  you  can  ini0ate  a   low  risk,  low  complexity  project  to  get  started.   • Be  op0mis0c,  that  is  an  AI  trait     iksinc@yahoo.com  
  50. 50. AI  Systems  are  not  Perfect iksinc@yahoo.com  
  51. 51. As  John  McCarthy  so  perfectly  stated  back  in     1956  -­‐  “As  soon  as  it  works,  no  one  calls  it  AI     anymore.”   iksinc@yahoo.com  
  52. 52. iksinc@yahoo.com  

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