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The Hive Think Tank: AI in The Enterprise by Venkat Srinivasan

This The Hive Think Tank talk by Venkat Srinivasan, CEO of RAGE Frameworks, focuses on successful applications of AI in the Enterprise. We start with a broad and more inclusive definition of AI in the context of enterprise business processes.
We introduce a taxonomy of AI solution methods that broaden the focus beyond a narrow focus on deep learning based on neural nets. In line with the taxonomy, we present several successful AI applications in use today at major corporations across industries including financial services, manufacturing/retail, professional services, logistics. These applications range from commercial lending, contract review, customer service intelligence, market and competitive intelligence, signals for capital markets, regulatory compliance and others.

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The Hive Think Tank: AI in The Enterprise by Venkat Srinivasan

  1. 1. ©  Rage  Frameworks  Inc,  2016.  All  rights  reserved. Enabling  the  Intelligent  Enterprise   AI  in  the  Enterprise The  Hive  Think  Tank Jan  26,  2017
  2. 2. ©  Rage  Frameworks  Inc, 2016.  All  rights  reserved. |  2 The  Resurgence  of  AI …it’s  possible Source:  The  Intelligent  Enterprise  in  the  Era  of  Big  Data,  Srinivasan,  Wiley,  2016 Google’s  DeepMind wins  historic  Go  content  4-­1 The  recent  accident  on  a  Tesla  vehicle  in   autopilot  mode
  3. 3. ©  Rage  Frameworks  Inc, 2016.  All  rights  reserved. |  3 AI  in  the  Enterprise Key  Dimensions  of  Machine  Intelligence …it’s  possible Computer  Visioning   Solutions Non-­Visioning   Solutions Computational   Statistics Knowledge   Acquisition  /   Representation Computational   Linguistics Source:  The  Intelligent  Enterprise  in  the  Era  of  Big  Data,  Srinivasan,  Wiley,  2016
  4. 4. ©  Rage  Frameworks  Inc, 2016.  All  rights  reserved. |  4 AI  in  the  Enterprise A  Taxonomy  of  Machine  Intelligence  Problem  Types   …it’s  possible Ad  Hoc  Search Clustering Prediction   [Quantitative  data] Extraction Classification   [Qualititative,  Hybrid  data] Interpretation [Natural  language, Other  data] Prediction,  Classification Artificial Intelligence (Machine  Intelligence) Intelligence  Thru Explicitly   Assumed  Models  of  Data Learn from  Data   Algorithmically Learn  to  Interpret/ Understand Meaning Source:  The  Intelligent  Enterprise  in  the  Era  of  Big  Data,  Srinivasan,  Wiley,  2016
  5. 5. ©  Rage  Frameworks  Inc, 2016.  All  rights  reserved. |  5 AI  in  the  Enterprise Machine  Intelligence  Acquisition  Methods …it’s  possible Source:  The  Intelligent  Enterprise  in  the  Era  of  Big  Data,  Srinivasan,  Wiley,  2016
  6. 6. ©  Rage  Frameworks  Inc, 2016.  All  rights  reserved. |  6 AI  in  the  Enterprise Machine  Intelligence  Acquisition  Methods …it’s  possible Pragmatics Automated Knowledge Discoverer Domain Discourse Model Public   Content Private Content RAGE KnowledgeNet™   WordNet ConceptNet FrameNet… Cognitive  Semantic  Networks Deep  Parsed  Linguistic  Maps Topic  Clusters Syntactic  Results Semantic  Roles Seed  Concept  (Optional) Knowledge  Type  Constraints Source:  The  Intelligent  Enterprise  in  the  Era  of  Big  Data,  Srinivasan,  Wiley,  2016
  7. 7. |  7 AI  in  the  Enterprise Machine  Intelligence  -­ Functional  Architecture …it’s  possible Source:  The  Intelligent  Enterprise  in  the  Era  of  Big  Data,  Srinivasan,  Wiley,  2016
  8. 8. ©  Rage  Frameworks  Inc, 2016.  All  rights  reserved. |  8 AI  in  the  Enterprise Machine  Intelligence  VS  Intelligent  Machines …it’s  possible Machine  Intelligence Computational   Statistics Knowledge   Acquisition  /   Representation Computational   Linguistics Source:  The  Intelligent  Enterprise  in  the  Era  of  Big  Data,  Srinivasan,  Wiley,  2016 Intelligent  Machine Ingest Process Decide Document Communicate Intelligence Analytics Integrating  into  a   Mission  Critical   Production   Business  Process
  9. 9. ©  Rage  Frameworks  Inc, 2016.  All  rights  reserved. |  9 Examples  of  Intelligent  Machines  in  the  Enterprise …it’s  possible Wealth  Management  Active  Advising   Commercial  Loan  Origination Financial  Statement   Spreading Client  Onboarding Data  Quality  Monitoring Real  Time  Intelligence  for   Cap  Markets Knowledge  Management Customer  and  Market   Intelligence RAGE  KYC  Framework   RTITM :  Credit  &   Supplier  Risk   Sales  Lead  Generation Automated  Contract  Review Customer  Service  IntelligenceAutomated  Billing  Reconciliation Supply  Chain  Cost  Audit Business   Rules  Engine Model  Engine NLP  Engine Quality   Assurance   Framework Web  Services   Engine Decision   Tree  Engine Computation al  Linguistics     Engine Model   Network   Engine Data  Access   Engine Desktop   Integration   Engine Connector   Factory   Engine Questionnair e  Engine Real  Time   Content   Integration   Engine Assignment   Engine Message   Engine External   Object   Engine Extraction   Engine Repository Intelligent   Doc  Builder   Engine User   Interface   Engine Process  Assembly  Engine
  10. 10. ©  Rage  Frameworks  Inc,  2016.  All  rights  reserved. Enabling  the  Intelligent  Enterprise   Extraction  from  Semi,  Unstructured  Documents Financial  Information
  11. 11. ©  Rage  Frameworks  Inc,  2016.  All  rights  reserved. |  11 RAGE  LiveSpread™   Process  Flow …it’s  possible Data  extraction  from   any  format  including:     pdf,  excel,  images,   paper,  web  scraping   etc. Extraction Normalization User  defined   Normalization Exceptions  and   Quality Presentation  and   Analytics Integration   Normalized  using  -­ Industry  templates;;  Pull   from  footnotes;; Footnote  interpretation   linked  to  line  items;;   30  plus  language Rules  buy  Country;; User  defined   normalization  ruleset   via  self  service   screens Exception  handling  of   data  accuracy,  in-­ built  quality   assurance  and   business  rule   compliance Presentation  of   spread  data  and   financial  ratio   calculations Integration  into   client’s  core  systems Analytics  (add  on) Credit  score  cards  and  risk  monitoring Equity  models Custom  Analytics  (M&A  deal  sourcing,  Audit  etc.)   Feature snapshot • Industry  specific  normalization  of  data • Analysis  of  revolving  credit  lines • Auditors’  opinion  on  the  financial  statements  captured • Key  break-­ups  from  notes  to  financials • Industry  ID  data:  NAICS,  SIC  or  GICS  codes • Adjustments  for  extraordinary/one-­time/non-­cash  items • Details  on  operating  leases  and  contractual  obligations • Financial  covenant  tracking  and  alerts • Automated  QA  checks • Multiple  MRA  load/delivery  options
  12. 12. LiveSpread Automated Receiver Fax Email Human Experts Automated   Extractor Automated Normalizer Golden   Corpus Auto Discovered   Extraction   Rules Source Docs Data  Feeds   [Acctg pkgs] • PDF  Processor • OCR  Enhancer • Computational  Geometry   Engine • Tabular  Extractor • NLP • Quality  Assurance  Rules • Traceable  links  to  source LiveSpread Upload™ Spread Data • NLP • Flexible  Normalization   Templates • Quality  Assurance  Rules Auto   Discovered   Mapping  R Human Experts Exceptions • Traceable  Linguistics   based  Deep  Learning LiveSpread™ An  Intelligent  Machine  for  Financial  Statement  Processing
  13. 13. ©  Rage  Frameworks  Inc,  2016.  All  rights  reserved. |  13 Input  Form  and  Format  Variability …it’s  possible
  14. 14. ©  Rage  Frameworks  Inc,  2016.  All  rights  reserved. |  14 Normalization  Example Non  English  Document …it’s  possible Normalized  Output Italian  document Normalization  rules
  15. 15. ©  Rage  Frameworks  Inc,  2016.  All  rights  reserved. |  15 Example  of  Extraction  from  Footnotes …it’s  possible Notes  to  the  financial  statements   (note  4) Final  Output  -­ Spreadsheet Balance  Sheet After  pulling   out  breakups   from  notes  to   the  financials Before   capturing  the   breakups Breakups  for  fixed  assets  identified  and   extracted  from  notes
  16. 16. ©  Rage  Frameworks  Inc,  2016.  All  rights  reserved. |  16 Example  of  Extraction  from  Footnotes …it’s  possible Key  breakups  for  Operating  expenses   were  pulled  from  Operating  Leases  note   as  they  were  unavailable  in  the  Income   Statement. Notes  to  the  financial  statements Final  Output  -­ Spreadsheet Income  Statement
  17. 17. ©  Rage  Frameworks  Inc,  2016.  All  rights  reserved. |  17 Normalizing  GAAP  Rules  Across  Countries …it’s  possible Final  Output  -­ SpreadsheetOriginal  Document Normalized  Metadata  – Rule  File Canadian  GAAP US    GAAP Bank  charges  map  differently  to  Interest  expenses  [As  per     Canadian  GAAP]  and  to  Other  expenses  [As  per  US  GAAP]
  18. 18. ©  Rage  Frameworks  Inc,  2016.  All  rights  reserved. Enabling  the  Intelligent  Enterprise   Classification  with  Natural  Language  Understanding Customer  Service  Intelligence ©  Rage  Frameworks  Inc,  2016.  All  rights  reserved.
  19. 19. ©  Rage  Frameworks  Inc,  2016.  All  rights  reserved. U.S.   Background  on  the  customer  data  analytics  project |  19 • The  objective:  To  aggregate  all  the  unstructured  data,  within  Seibel,  from   various  communication  types  with  the  customers,  extract,  interpret,  analyze,   and  deliver  insights  to  make  decisions  rooted  in  data  and  insights.   • Key  questions  for  the  analysis: • What  are  the  primary  reasons  reasons customers are contacted or customers contact us?  How  do  these  reasons  rank  by  volume? • What  are  the  underlying  reason  customers are contacted or customers contact us?   • Do  these  reasons  shed  light  on  the  process  elements  or  processes  that   may  be  resulting  in  repeated  customer  outreach  to  us  or  customer   dissatisfaction? • Are  there  any  inefficiencies  in  customer  service  processes,  based  on  the   service  request  fulfillment  attributes  e.g.  number  of  times  back  and  forth   communication  with  the  customers,  which  can  shed  some  light  on  the   process  inefficiencies?
  20. 20. ©  Rage  Frameworks  Inc.,  2016.  All  rights  reserved Emails Semantic  Topic  – Order  Rescheduling Semantic  Topic  -­‐ Order  Cancellation Subject: Weston  Pallet  Count From: kgxxxxx To: Exxxx Dxxxxx;  Jxxxxx fxxxxx;  Cxxx CC: Daxx Wxxxx;  Dxxx Rxxxxx Date: 2014-­‐11-­‐06  12:25:57 Hi  Eliza, The  count  for  today  is  1299  @  11:30  am The  pallet  count  is  high  with  production  requirements.  Please  cancel  Thursday  3  pm  load  4703423658.   Take  Care, Kexxxx From: Cxxxx-­‐Cxxx Sent: Thursday,  November  06,  2014  12:42  PM To: Kxxxx Gxxxxxx;  Exxxx Dxxxxx;  Jxxxxx fxxxxx;  Cxxx Cc: Daxx Wxxxx;  Dxxx Rxxxxx Subject: RE:  Weston  Pallet  Count Good  day,   Please  be  advised  that  PO#4703423658  has  been  changed  to  tomorrow  delivery  at  3pm  as  requested   in  yesterdays  email. Please  see  the  remaining  orders  for  today/tomorrow; Thank  you/Merci,   Allxxxx Mcxxxx Original  Topic  – Pallet  Count
  21. 21. Enabling  the  Intelligent  Enterprise   Natural  Language  Understanding  +  Extraction Logistics  Cost  Audit  &  Contract  Review ©  Rage  Frameworks  Inc,  2016.  All  rights  reserved.
  22. 22. ©  Rage  Frameworks  Inc,  2016.  All  rights  reserved. |  22 Intelligent  Machine  for  Cost  Audit How  machine  learning  is  applied  to  deliver  insights  and  speed-­to-­value? …it’s  possible Extract  Integrate Interpret  and   Categorize   Reconcile  and   Analytics Visualizatio n Classify Extract  content  from   wide-­variety  of   document  types Decompose   documents,  discover   taxonomy,  normalize   taxonomy Train  to  interpret  in   specific  business   context  and  extract   targeted  data  for   analytics Apply  data  mapping,   business  rules,   calculations,  models,   and  user  driven  learning • Yes  ML • Format  detection • Pixel  correction • Character   recognition • Linguistics   correction • Numeric   correction • Yes  ML • Machine  learns   from  exception   management   performed  by   humans • Yes  ML • Train  the   machine  to   interpret  based   on  business   context  not  rules • Connect  the   information  for   the  same   provision  across   documents • No  ML • RAGE   configurable   connector  factory   is  used  to  rapidly,   non-­intrusively   integrate  with   hundreds  of  data   source  (SAP,   CRM,  TMS,   Legacy  etc.).   • Yes  ML • Auto-­discover   document   structure,  key   provisions,  tables • Auto-­discover   key  concepts,   and  relationships • Assisted  ML  to   finalize  taxonomy   and  target  output Connect  with  a  variety   enterprise/legacy   systems  just  via   configuration Customizable  user   interface  developed   just  via  configuration • No  ML • Rapidly  configure custom  UI  to  display   right  charts,  visuals. • Can  be  customized   by  users   • Can  be  changed   very  rapidly  as  the   needs  change Information  flow Machine  learningOutput • Very  little  to  no  IT   time  needed   • Extract  clean   content  from   heterogeneous   quality  and  variety   (PDF  types,  images)   of  documents • Entire  document  is   read • Provisions  are   classified  based  on   language/concept   relationship  not  key   words  and  positions   • High  accuracy  of   content   categorization  as   the  search  is   business  context   (e.g.  Kroger)  driven • Human  based   exception   management   declines   dramatically.   • Custom  user   interface  to  deliver   specific  insights  that   can  be  changed   rapidly  without   coding
  23. 23. ©  Rage  Frameworks  Inc,  2016.  All  rights  reserved. RAGE  AI  Classification  and  Categorization  Process   Assisted  deep  learning  is  deployed  for  taxonomy  creation …it’s  possible Load  Document Auto  Discovery Filter  the  Auto   Discovered   Output Build   Ontology  (SI   App) Upload   Document   not  seen  by   the  system Execute  Contract   Review  Process Output  Not   Extracted Output Extracted False  Positive Partial  Match [Low  confidence   score] Accurate   Extraction Validate  the   Output Document   Decompositions
  24. 24. ©  Rage  Frameworks  Inc,  2016.  All  rights  reserved. Classification  Process  – Document  Decomposition Machine  learning  automatically  identifies  document  hierarchy  and  relationships …it’s  possible PDF  Contract  Agreement Domain  Discourse  Model Document  decomposition  helps  identify  sections,  sub-­ sections  and  their  relationship  with  each  other
  25. 25. ©  Rage  Frameworks  Inc,  2016.  All  rights  reserved. Classification  Process  – Auto  Discovery Example  to  discover  and  related  content  from  tables  (e.g.  Schedule  A  and  Invoices) …it’s  possible The  engine  parses  the  entire  table   content  even  though  there  are   multiple  variations  within  a  single   table  and  treat  each  one  of  them   separately.  The  variations  are  as   follows: Route  Information Mileage  Information Drop  Information Fees  Information Total 1 2 3 4 5 Document  Type:  Invoice 1 2 3 4 5
  26. 26. Enabling  the  Intelligent  Enterprise   Interpretation  with  Natural  Language  Understanding Real  Time  Intelligence Fund  Managers/Competitive  &  Market  Intelligence/Customer/Supplier  Risk ©  Rage  Frameworks  Inc,  2016.  All  rights  reserved.
  27. 27. |  27 RTI  systematically  interprets  and  analyzes  all  publicly  and  privately  available  content  in   the  context  of  a  company,  an  industry  and  macro  environment,  to  generate  RTI  Signal Heatmaps draw  attention  to  securities  with  the  most  change  in   their  cumulative  signal  strength  highlighting  the  overall  impact   on  a  company  from  the  market  developments  around  it For  each  company,  the  RTI  Signal  can  be  further  broken  down   by  specific  business  drivers  that  may  be  impacting  a  company RTI  Signal  leads  the  stock  price  for  30  – 40%  of  the  companies   in  RAGE  portfolio  (Coverage  over  8000  companies) For  each  company,  the  cumulative  RTI  Signal  can  be  tracked   over  time  with  key  triggers  by  date 4.  alpha  – RTI  vs  Stock  price 3.  Company  view  over  time 1.  Portfolio  View 2.  Company  view  by  business drivers Stock  Price  (Log) RTI  Cumulative  Score 1.66 1.68 1.7 1.72 1.74 1.76 1.78 1.8 1.82 1.84 -­‐1.5 -­‐1 -­‐0.5 0 0.5 1 1.5 2 04/01 04/22 05/13 06/03 06/24 07/15 08/05 08/26 09/16 10/07 10/28 11/18 RTI Stock  Price  (Log)
  28. 28. RTI  is  not  a  black  box:  Drill  down  into  the  business  drivers  to  see  specific  content   pertaining  to  that  driver  deemed  relevant  by  the  RAGE  Semantics  Engine |  28 5 Expand  the  Factors  to  drill  down   into  content  pertaining  to  that   factor  
  29. 29. 29 Impact  Network  – Wal-­Mart  Stores,  Inc.  (WMT)  Plans  To  Unseat,  Inc.   (AMZN)  Prime  (Topic:  Expansion  and  Closure;;  Score:  0.3) 1st Order   Effect­mart-­stores-­inc-­wmt-­plans-­to-­unseat-­amazon-­com-­inc-­amzn-­prime/118763/ Topic:  Expansion  and  Closure Driver:  Product  Launch Sector:  Retail Primary  Impact:  Low  Medium  Positive RAGE   SI   Engine S 1 S 2 S 3 S 4 S 5 Impact  Network [Deep  Semantic  Interpretational  Map]   S 6 S 7 S1: Wal-­Mart  Stores,  Inc.   (NYSE:WMT)  plans  to  rival,  Inc.   (NASDAQ:AMZN)  with  the  launch   of  a  new  delivery  system  that   costs  less.
  30. 30. 30 Real  Time  Intelligence RTI  Signal  Leads  Stock  Price  -­ Wal-­Mart  Stores,  Inc.  [WMT.N] ©Rage  Frameworks  Inc,  2016.  All  rights  reserved. Business Driver  -­ Same   Store  Sales Jan 7th,  2015  -­ RetailNext -­ Foot  traffic  dropped  8.3   percent  during  November  and   December  versus  a  year  ago  at   the  specialty  stores  and  large   retailers  . 0 5 10 15 20 25 30 35 55 60 65 70 75 80 85 90 95 Alpha  Signal  Rating Stock  Price Business Driver  – Consumer   Confidence   Oct 15th,  2015  –   -­ Improving  views  of  personal   finances  signal  the  turmoil  in   financial  markets  and  slowdown   in  hiring  is  not  affecting consumer psyches,   which bodes well  for  sustained   gains  in consumer spending. Business Driver  -­ Expansion July  22,  2015  – The    new  1.2-­million-­square-­foot   center  is  part  of  a  "next-­ generation" network to  support   Walmart's  rapidly  growing  e-­ commerce  business.  It  features   state-­of-­the-­art  automation  and   warehousing  systems. Business Driver  – Retail  Sales Jan  20th,  2016  – Americans  spent  $626.1  billion  in  the holiday  season,  representing  a  3.7   percent  increase  on  a  year-­over-­year basis  when including online sales. Signal Stock Price
  31. 31. Enabling  the  Intelligent  Enterprise   AI  in  the  Enterprise Summary ©  Rage  Frameworks  Inc,  2016.  All  rights  reserved.
  32. 32. ©  Rage  Frameworks  Inc, 2016.  All  rights  reserved. |  32 AI  in  the  Enterprise Machine  Intelligence  Acquisition:  Method  Fit …it’s  possible Source:  The  Intelligent  Enterprise  in  the  BigData Era,  Srinivasan,  Wiley,  2016 n How  important  is  it  to  start  with  a  high  level  of  accuracy  [precision   and  recall]?    How  expensive  is  a  mistake?    Both  false  positive  and   false  negative. n How  much  variability  is  there  in  the  underlying  phenomenon  and   therefore  data?    The  larger  the  variability  like  unstructured  text,  the   training  sample  needs  to  be  extremely  large  to  get  reasonable  results n Can  you  live  with  a  black  box?    Do  you  need  transparency  in  the   engine’s  reasoning?    Do  you  need  to  trace  its  reasoning  so  you  can   understand  ‘causality’? n Random  Forests  [Breiman]  and  Natural  Language  Understanding   [RAGE  AI™]  are  traceable  methods.    High  levels  of  variability  and/or   high  cost  of  mistakes  strongly  imply  traceable  and  transparent   methods.
  33. 33. ©  Rage  Frameworks  Inc, 2016.  All  rights  reserved. |  33 Summary …it’s  possible n AI  seems  to  be  back  in  full  force  and  this  time  getting  integrated  into  the   mainstream n Big  Data.    The  ability  to  analyze  entire  populations  vs  samples  has  allowed   assumption-­free  algorithmic  approaches  to  flourish  vs  the  traditional  ‘data   model’.    We  are  letting  the  data  tell  us  the  story  vs  assuming  prior  behavior  of   data;;  but  key  challenges  wrt text  are  context,  language  and  traceability n Deep  learning  with  deep  linguistic  parsing  in  context  will  allow  us  to  create   ‘natural  language  understanding’  in  machines  vs  just  ‘natural  language   processing’ n AI  vs  Machine  Intelligence.    AI  =  Automation  including  knowledge-­based  tasks.     Machine  Intelligence  =  embedding  intelligence  and  learning  from  data  and   experts  continuously  to  enable  AI. n With  all  these  advances,  enterprise  business  architecture  will  change   dramatically.    Execution  will  be  largely  thru  Intelligent  Machines.    Design  will  be   machine  informed.    The  rate  of  change  in  the  role  of  humans  will  accelerate. Source:  The  Intelligent  Enterprise  in  the  Era  of  Big  Data,  Srinivasan,  Wiley,  2016
  34. 34. ©  Rage  Frameworks  Inc,  2016.  All  rights  reserved. Enabling  the  Intelligent  Enterprise   AI  in  the  Enterprise The  Hive  Think  Tank Jan  26,  2017