IBM Watson Developer Cloud Services Overview

January 2015

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IMPORTANT DISCLAIMER

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IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice at IBM’s sole discretion. Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision. The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract. The development, release, and timing of any future features or functionality described for our products remains at our sole discretion.

The information contained herein is IBM Confidential and may not be used or disclosed without express written permission from IBM Corporation.

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Personality Insights aka User Modeling (available in Beta now)

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What is it? Personality  profiling  to  help  engage  users  on  their  own  terms.     How does it work? The  Personality  Insights  service  uses  linguis9c  analy9cs  to  extract  a  set  of    personality  and  social  traits  from  the  way  a  person  communicates.    The  service  can  analyze  any  communica9on  the  user  makes  available  such  as  their  text  messages,  tweets,  posts,  email,  and  more.  Users  of  the  service  can  understand,  connect,  and  communicate  with  people  on  a  more  personally  tailored  level  by  analyzing  personality  and  social  traits   Example Use Cases •  Analyze  a  customer’s  twiEer  stream  to  help  a  travel  

agency  decide  between  leading  with  a  budget  or  luxury  trip  offer  

•  Improve  customer  engagement  by  personalizing  and  differen9a9ng  products  and  offerings  using  customer’s  public  communica9on  (e.g.,  twiEer  feed)    

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Question and Answer (available now)

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Understands question

Produces possible answers and evidence

Analyzes evidence

Computes confidence

Delivers response, evidence and confidence

Asks a question

Considers response and evidence

What is it?Direct  responses  to  users  inquiries  fueled  by  primary  document  sources  

How does it work?Interprets  and  answers  user  ques9ons  directly  based  on  primary  data  sources  (brochures,  web  pages,  manuals,  records,  etc.)  that  have  been  selected  and  gathered  into  a  body  of  data  or  ‘corpus’.    The  service  returns  candidate  responses  with  associated  confidence  levels  and  links  to  suppor9ng  evidence.    The  current  data  corpora  on  Bluemix  focuses  on  the  Travel  and  Healthcare  industries.  

Example Use CasesHealthcare:      

• What  is  a  stroke?    What  is  the  cause  of  Wilson  Disease?        

Travel:    • Where  is  the  best  place  to  stay  in  Prague?  

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Relationship Extraction (available in Beta now)

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What is it? From unstructured text, Relationship Extraction can extract entities (such as people, locations, organizations, events), and the relationships between these entities (such as person employed-by organization, person resides-in location) How does it work? Based on statistical modeling, Relationship Extraction performs linguistic analysis of the input text, finds spans of text that refers to entities, clusters them together to form entities, and extracts relationships between them. Relationship Extraction models are domain-specific and work best with in-domain input. Example Use Cases The output of Relationship Extraction, namely entities and their relationships, can be used to build applications such as a semantic search engine, business and competitive intelligence tools, knowledge graphs, social media monitoring tools, etc.

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Message Resonance (available in Beta now)

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What is it? Communicate  with  people  with  a  style  and  words  that  suits  them   How does it work? The  message  resonance  service  analyzes  draR  content  and  scores  how  well  it  is  likely  to  be  received  by  a  specific  target  audience.    This  analysis  is  based  on  content  that’s  been  wriEen  by  the  target  audience  itself  such  as  fans  of  a  specific  sports  team  or  new  parents.      Today,  analysis  can  be  done  against  people  ac9ve  in  cloud  compu9ng  or  discussions  but  future  versions  will  let  users  provide  their  own  community  data.   Example Use Cases Among  people  ac9ve  in  cloud  compu9ng  discussions,  op9on  A  content  is  likely  to  resonate  very  well,  op9on  B  poorly,  and  op9on  C  moderately  well.  

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Concept Expansion (available in Beta now)

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What is it? Maps  euphemisms  or  colloquial  terms  to  more  commonly  understood  phrases   How does it work? The  Concept  Expansion  service  analyses  text  and  interprets  its  meaning  based  on  usage  in  other  similar  contexts.    For  example,  it  could  interpret  “The  Big  Apple”  as  meaning  “New  York  City”.    It  can  be  used  to  create  a  dic9onary  of  related  words  and  concepts  so  that  euphemisms,  colloquialisms,  or  otherwise  unclear  phrases  can  be  beEer  understood  and  analyzed.     Example Use Cases “drugs”  can  be  expanded  to:  

start  seed  terms  à  motrin,  aspirin,  Keflex  

post  expansion  à  allegra,  lisinopril,  me\ormin,  aspirin,  equagesic,  cime9dine,  fiorinal,  vancomycin,  avelox,  protonix,  glimepiride,  protonix,  verapamil,  norco,  inderal,  hctz,  advair  

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What is it? Globalize  on  the  fly.  Translate  text  from  one  language  to  another.  Customize  the  transla9on  algorithm  for  your  specific  use  case,  or  use  a  generic  (news)  one.   How does it work? The  Machine  Transla9on  service  converts  text  input  in  one  language  into  a  des9na9on  language  for  the  end  user.  Today,  bi-­‐direc9onal  generic  transla9on  is  available  between  English  and  [French,  Spanish,  Portuguese];  uni-­‐direc9onal  generic  transla9on  is  available  from  Arabic  to  English.  More  languages  and  customiza9on  capabili9es  coming  soon!  

Example Use Cases •  A  news  agency  is  able  to  curate  ar9cles  from  across  the  globe,  and  present  them  to  a  local  audience  in  their  na9ve  language.    

•  An  e-­‐commerce  website  can  enable  a  truly  global  marketplace.    

•  Travel  apps  can  deliver  on  the  promise  of  a  fully  guided,  interac9ve  experience.    

Machine Translation (available in Beta now)

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Language Identification (available in Beta now)

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What is it? Iden9fies  the  language  in  which  text  is  wriEen.     How does it work? The  Language  Iden9fica9on  service  detects  the  language  in  which  text  is  wriEen.  This  helps  inform  next  steps  such  as  transla9on,  voice  to  text,  or  direct  analysis.  The  service  can  be  used  in  tandem  with  the  Machine  Transla9on  service.  Today,  the  service  can  iden9fy  15  languages  –  Arabic;  Chinese  (Simplified);  Chinese  (Tradi9onal);  Cyrillic;  Danish;  Dutch;  English;  Farsi;  Finnish;  French;  German;  Greek;  Hebrew;  Hindi;  Icelandic;  Italian;  Japanese;  Korean;  Norwegian  (Bokmal);  Norwegian  (Nynorsk);  Portuguese;  Spanish;  Swedish;  Turkish;  Urdu  

Example Use Cases A  building  block  for  Machine  Transla9on  and  future  tech  

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Visualization Rendering (available now)

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Visionary mobile prototype

What is it? Graphical  representa9ons  of  data  analysis  for  easier  understanding   How does it work? The  service  takes  input  data  and  graphically  renders  it  as  an  interac9ve  visualiza9on  which  can  range  from  a  common  business  chart  to  more  advanced  layouts.  The  visualiza9ons  can  be  easily  modified  to  match  user  needs,  visual  styling,  and  types  of  data  being  analyzed.     Example Use Cases The  service  could  represent  neighborhood  demographic  data  as  mini  pie  charts  showing  income  levels  centered  on  geographic  loca9ons  on  maps,  or  as  tree  maps  that  can  switch  from  looking  at  income  by  age  to  house  size  or  by  educa9on  level.      

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Speech-to-Text (available in beta early 1Q15)

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What is it? Automa9c  recogni9on  of  the  words  that  are  being  spoken  in  a  sound  file,  and  presents  the  highest-­‐scoring  textual  representa9on  of  that  message.   How does it work? In  addi9on  to  conver9ng  a  raw  audio  signal  into  a  best-­‐guess  of  the  words  that  are  being  spoken,  intelligence  about  the  relevant  grammar  /  how  language  is  used  within  a  specific  context  is  incorporated  to  generate  a  more  accurate  transcrip9on.  Ini9ally  coming  out  with  a  general  (news)  model  for  English;  will  eventually  be  able  to  tweak  the  algorithm  for  a  specific  use  case  (e.g.,  “Men  in  9ghts”  in  the  general  domain,  “Meningi9s”  when  customized  to  the  Medical  domain).  

Example Use Cases •  Transcrip9on  of  mee9ngs  and  conference  calls.    •  Voice-­‐control  of  applica9ons  /  embedded  devices.    •  Dicta9on  of  emails.    •  Cri9cal  building  block  for  “Speech-­‐to-­‐Speech”  transla9on.  

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Text-to-Speech (available in beta early 1Q15)

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What is it? Generates  an  audio  file  that  has  a  verbal  representa9on  of  the  input  text  –  complete  with  appropriate  cadence  and  intona9on,  and  the  ability  to  customize  the  pronuncia9on  of  specific  words.     How does it work? Generates  an  audio  file  that  has  a  verbal  representa9on  of  the  input  text  –  complete  with  appropriate  cadence  and  intona9on.  Ini9ally  coming  out  with  a  general  (news)  model  for  English  and  Spanish;  will  eventually  be  able  to  customize  the  pronuncia9on  of  specific  words,  and  tailor  enuncia9on  appropriate  for  a  given  domain.    Example Use Cases •  Read  texts  /  emails  aloud.    •  Assistance  tools  for  the  vision-­‐impaired.    •  Transmit  informa9on  from  a  computer  to  a  human  without  using  a  screen.    

•  Cri9cal  building  block  for  enable  “Speech-­‐to-­‐Speech  Transla9on”  

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Concept Insights (available in beta early 1Q15) What is it? Locate  relevant  documents  that  may  not  directly  men9on  your  query.   How does it work? The  Concept  Insights  service  links  documents  you  provide  with  a  pre-­‐exis9ng  graph  of  concepts  based  on  Wikipedia  (e.g.  'The  New  York  Times',  'Machine  learning',  etc.).  Two  types  of  links  are  iden9fied:  explicit  links  when  a  document  directly  men9ons  a  concept,  and  implicit  links  which  connect  your  documents  to  relevant  concepts  that  are  not  directly  men9oned  in  them.  Users  of  this  service  can  also  search  for  documents  that  are  relevant  to  a  concept  or  collec9on  of  concepts  by  exploring  the  explicit  and  implicit  links.   Example Use Cases •  A  legal  firm  could  u9lize  this  service  to  iden9fy  cases  

which  may  be  related  to  the  case  in  ques9on •  Improve  engagement  on  any  external  website    

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Visual Recognition (available in beta early 1Q15)

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What is it? Analyzes  the  visual  content  of  images  and  videos  to  understand  their  content  without  requiring  a  textual  descrip9on.   How does it work? The  Visual  Recogni9on  service  enables  you  to  analyze  the  visual  appearance  of  images  or  video  frames  to  understand  what  is  happening  in  a  scene.  Using  machine  learning  technology,  seman9c  classifiers  recognize  many  visual  en99es,  such  as  semngs,  objects,  and  events.  The  service  applies  these  pre-­‐learned  models  to  imagery  that  you  have  uploaded  to  the  service  and  returns  a  score  for  each  image  for  each  model,  indica9ng  the  likelihood  of  that  visual  element  being  present  in  the  image.   Example Use Cases •  Audio-­‐visual  indexing  and  search  of  media  archive  •  Automa9c  “Smart  Album”  genera9on  •  Marke9ng  data  analysis  from  social  media  -­‐  For  e.g.,  mine  Pinterest  images  and  perform  segmenta9on  

         

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Tradeoff Analytics (available in beta early 1Q15) What is it? Helps  make  beEer  choices  under  mul9ple  conflic9ng  goals.  Combines  smart  visualiza9ons  and  analy9cal  recommenda9ons  for  tradeoff  explora9on.   How does it work? Tradeoff  Analy9cs  helps  people  make  beEer  choices  when  mul9ple  conflic9ng  goals  come  into  play.  Tradeoff  Analy9cs  uses  Pareto  filtering  techniques  in  order  to  surface  out  only  the  op9mal  alterna9ves  across  mul9ple  criteria.  It  then  uses  various  analy9cal  and  visual  approaches  to  help  the  decision  maker  analyze  the  tradeoffs  within  the  op9mal  set  of  alterna9ves  helping  them  select  the  op9on  that  best  meets  their  preferences  and  priori9es.   Use Cases •  Front  end  sales  portal  using  Tradeoff  Analy9cs  to  empower  customers'  decision  capability  and  collects  data  on  what  customers  deem  most  important  to  product  purchases  

•  Wealth  management  using  the  technology  to  help  choose  mutual  funds