bringing ai to business intelligence

Bringing  AI  to  Business  Intelligence Integrated  Knowledge  Solu0ons  

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Agenda

• Current  BI  environment  • What  is  AI?  • AI  Technologies  • How  is  AI  being  used  by  businesses?  • Roadmap  for  bringing  AI  to  BI  • Summary  

Current  BI  Environment  

Current  BI  Environment A  sampling  of  headlines  in  various  magazines  and  blogs  

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Why  so  much  clamor  for  AI?  What  is  missing  in  BI?  Lets  look  at  BI  value  chain  

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What’s  Missing?

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•  “What  is  happening?”  •  “Why  is  it  

happening?”    Not  much  problem  there.  

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Current  BI  tools  are  good  at  answering:

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.    

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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  

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Plot  as  of  April  23,  2017  

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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  

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What  is  AI?  

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  

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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  

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.  

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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.    

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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  

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Evolu?on  of  AI  Since  1950

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AI  Technologies  

AI  Technologies

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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  

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Supervised  Learning • Training  data  comes  with  answers,  called  labels  • The  goal  is  to  produce  labels/answers  for  new  data  

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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  

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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  

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Unsupervised  Learning

• Training  data  comes  without  labels  • The  goal  is  to  group  data  into  different  categories  based  on  similari0es  

Grouped  Data  

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Unsupervised  Learning  Models • Segment/  cluster  customers  into  different  groups  • Organize  a  collec0on  of  documents  based  on  their  content  • Make  Recommenda0ons  for  products  

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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)  

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Examples  of  Deep  Learning:  Object  Detec0on  and  Labeling    

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Examples  of  Deep  Learning:  Automa0c  Descrip0on  Genera0on  of  Images  

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Example  of  Deep  Learning:  Predic0ng  Heart  A3acks  

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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  

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Neural  Transla?on

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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.  

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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    

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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.  

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How  is  AI  being  used  by  businesses?  

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Making  Recommenda?ons  using  AI

Personalized  newsfeed  on  FB  and  LinkedIn  

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AI-­‐based  Assistants

Salesforce  Einstein  iksinc@yahoo.com  

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.  

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• 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  

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How  to  Get  Started  with  AI?  

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    

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Whit  Andrews,  Gartner  VP  

Michael  Azoff,  Ovum  Principal  Analyst  

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  

Summary  

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    

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AI  Systems  are  not  Perfect

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As  John  McCarthy  so  perfectly  stated  back  in    1956  -­‐  “As  soon  as  it  works,  no  one  calls  it  AI    

anymore.”  

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