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Introduction to Cognitive Computing the scienece behind and use of IBM Watson

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The lecture was given in a Cognitive and Analytics workshop at Indian Institute of Management. Topics covered was -
1) Understanding Natural Language Processing, Classification, Watson & its modules
2) Industry applications of Cognitive Computing
3) Understanding Cognitive Architecture
4) Understanding the disciplines / tools being used in Cognitive Science

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Introduction to Cognitive Computing the scienece behind and use of IBM Watson

  1. 1. Workshop on Cognitive and Advanced Analytics Introduction to Cognitive Computing, the science behind and use of IBM Watson AN INDUSTRY PERSPECTIVE Subhendu Dey | Senior Solution Architect, Cognitive Business Solutions, IBM PGDHRM | Indian Institute of Ranchi | August 26-29, 2016
  2. 2. Workshop on Cognitive and Advanced Analytics What we want to cover today  Understand Natural Language Processing, Classification, Watson & its modules  Industry applications of Cognitive Computing  Understanding Cognitive Architecture  Understanding the disciplines / tools being used in Cognitive Science PGDHRM | Indian Institute of Ranchi | August 26-29, 20168/28/2016 2
  3. 3. Workshop on Cognitive and Advanced Analytics Cognitive computing is built upon two main* pillars of computing science - Natural Language Processing (NLP) & Machine Learning  In the world of cognitive computing, we expect the IT systems to have one or more of the following capabilities  Understand both structured as well as unstructured content  Extract meaning out of it  Relate ingested information with own data scheme  Apply Reasoning capability  Learn with usage and  React accordingly  What is the difference between Cognitive Computing and Artificial Intelligence? Are they same? What all tasks from the above list leverage NLP? 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 3
  4. 4. Workshop on Cognitive and Advanced Analytics Let us first understand the need of NLP techniques with an example Question: In May 1898 Portugal celebrated the 400th anniversary of this explorers’ arrival in India. Who is he? 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 4 Celebrated In May 1898 400th anniversary Portugal Arrival in India Explorer In May, Gary arrived in India after he celebrated his anniversary in Portugal Arrived in Celebrated In May anniversary In Portugal India Gary Keyword matching Keyword matching Keyword matching Keyword matching Keyword matching
  5. 5. Workshop on Cognitive and Advanced Analytics Let us first understand the need of NLP techniques with an example Question: In May 1898 Portugal celebrated the 400th anniversary of this explorers’ arrival in India. Who is he? 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 5 • Search far and wide • Explore many hypothesis • Find and rank evidence Celebrated In May 1898 400th anniversary Portugal Arrival in India Explorer On the 27th of May, 1498 Vasco da Gama landed in Kappad Beach Landed in 27th May 1498 Kappad beach Vasco da Gama Geo-Spatial Reasoning Statistical Paraphrasing Temporal Reasoning
  6. 6. Workshop on Cognitive and Advanced Analytics NLP as a science has evolved over time, however there are still few challenges, and we have made decent progress in many.  Spam detection  Part of Speech tagging – identification of nouns, verbs, adjectives etc.  Named Entity Recognition – identification of person, location, organization etc. 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 6  Sentiment Analysis. E.g. movie or book review.  Co-reference resolution – i.e. mapping pronoun to a noun mentioned before.  Word sense disambiguation  Machine translation (MT)  Information Extraction (IE)  Question and Answering  Paraphrase  Summarization  Dialog Can we think of some pre-processing of text that would be essential for any of these analysis?
  7. 7. Workshop on Cognitive and Advanced Analytics Techniques of working with NLP Following the Markov Assumption, we can convert chain rule to unigram or bi-gram model or tri-gram model.  Regular Expressions (deterministic rules)  Similarity analysis – minimum edit distance (simple and weighted), typically used for spell correction  N-gram model – calculating the probability of a sentence or sequence of words. Typically very useful for  Understanding the quality of machine translation  Spell / word Correction in a sentence  Speech recognition  In mathematical terms it looks like  P(x1 , x2 ,…. xn ) = P(x1)P(x2 |x1) P(x3 |x1 , x2)…P(xn | x1 ,.., xn )  i.e. Can we realistically calculate this? 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 7
  8. 8. Workshop on Cognitive and Advanced Analytics Further usage of NLP can be seen in the areas of classification of text / document  Text classification is useful in the areas of  Assigning subject categories  Spam detection  Authorship identification  Age / gender identification  Language identification  Sentiment analysis  --- many more  Hand-written rules is the best as well as simplest but since that is not scalable supervised machine learning is adopted  Various kinds of classifiers are  Naïve Bayes  Logistic Regression  Support Vector machine (SVM)  k-Nearest neighbors 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 8
  9. 9. Workshop on Cognitive and Advanced Analytics Further usage of NLP can be seen in the areas of classification of text / document  Text classification is useful in the areas of  Assigning subject categories  Spam detection  Authorship identification  Age / gender identification  Language identification  Sentiment analysis  --- many more  Hand-written rules is the best as well as simplest but since that is not scalable supervised machine learning is adopted  Various kinds of classifiers are  Naïve Bayes  Logistic Regression  Support Vector machine (SVM)  k-Nearest neighbors 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 9 To avoid problem of floating point underflow this is practically used as
  10. 10. Workshop on Cognitive and Advanced Analytics Sentiment Analysis: as we move to unstructured content beyond the boundary of the enterprise (e.g. news) this becomes all the more important  Sentiment analysis is kind of text classification. It’s like detection of attitude.  Holder of attitude (source)  Target of attitude (aspect)  Type of attitude (simple – positive | negative, complex – scoring)  Applicable for –  Product review (find the aspects attributes like ease of use, value etc. and assign value to them)  Consumer confidence ( people have proved that twitter sentiment correlates with polling result, i.e. public opinion )  Traits in twitter (e.g. calmness) has been proven to be predictor of financial performance  Polarity analysis (positive | negative | neutral )  Again Naïve Bayes classification algorithm can be used, which is the classifier algorithm we are covering today. 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 10
  11. 11. Workshop on Cognitive and Advanced Analytics Information Extraction - basics  Regular expressions  Smart notes: create calendar event based on notes  NER  Often indexed for search  Sentiment can be associated to them  Typically done through Machine learned classifier algorithm, however some of the entities are popularly identified through exhaustive dictionary. 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 11
  12. 12. Workshop on Cognitive and Advanced Analytics Relationship Extraction - basics  What relations to extract  Automated Content Extraction (ACE)  UMLS – unified medical language system, used for Biomedical Information extraction  Database of Wikipedia relations (taken from Wikipedia info box), called DBpedia  Typically RDF triples (subject – predicate (i.e. relations) – object) – there are ~ 1B such RDF triples  Ontological relation: is-a (hypernym) and / or instance-of and many others  How to build - Hand-written rules | Supervised machine learning | Semi-supervised / un-supervised (bootstrapping, distant supervision, unsupervised learning from web)  Intuitions:  Use patterns to build relation (start with hand-written pattern, e.g. “such as”, “including”, “especially” works for “IS-A” relation.  Start with NER and then extract relation, because a pair of named entities can have a finite set of relations.  We can combine these two and make patterns (possible to be built from English thesaurus)  However all these hand-written patterns are often have low recall though may have high precision. 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 12
  13. 13. Workshop on Cognitive and Advanced Analytics Information Retrieval: The Feast or Famine model does not work well most of the times, calling for a Ranked retrieval model which is more realistic. There are several techniques in the Ranked retrieval.  Use of Jaccard Coefficient – however, this does not take into account the term frequency, hence could be misleading  Even if we use term frequency, taking it in linear proportion is not quite right.  So we use the term frequency as  This carries the problem of bag of words concept  Essentially the score becomes  General observation is rare terms are more significant  So we use document frequency (inverse) along with term frequency  Use of Cosine similarity 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 13
  14. 14. Workshop on Cognitive and Advanced Analytics What we want to cover today  Understand Natural Language Processing, Classification, Watson & its modules  Industry applications of Cognitive Computing  Understanding Cognitive Architecture  Understanding the disciplines / tools being used in Cognitive Science PGDHRM | Indian Institute of Ranchi | August 26-29, 20168/28/2016 14
  15. 15. Workshop on Cognitive and Advanced Analytics We can detect Cognitive Opportunities by keeping top three things in mind These are the items those are fundamentally changing computing in this era.  Intuitive / Value added / Pervasive System of Engagement – where the IT component can be engaged more like human in natural means (natural language, visual recognition, voice commands).  Mine huge set of structured and/or unstructured data to generate hypothesis – essentially analyzing content in a reasonable time which is humanly impossible. There could be two types of the mining of data  Industry / Org specific content mining (e.g. mining of claims data for future insight) or  Industry agnostic content mining (e.g. complaints / feedback analysis for sentiment analysis)  Evidence based decisions instead of static rules – this depends on supervised or unsupervised machine learning capability to derive decisions based on the evidence. The evidences are obtained from the structured and unstructured data analysis (as mentioned above). 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 15
  16. 16. Workshop on Cognitive and Advanced Analytics The fundamental differentiators have generated some business patterns. These are the patterns of applying cognitive techniques.  Engagement - helps build stronger relationships with constituents.  Conversational agents of human-computer communication  Human-human communication is now mediated by computers  Pictures, Videos are acting as sensors to react to event  Discovery – helps create new insights by synthesizing information.  Discovery of knowledge (personality, sentiment etc.) from natural language text  Discovery of unknown risks / opportunities from unstructured (textual) notes  Decision – helps users make more informed, evidence based decision  Manual intervention based on evidence and not rules  Move away from one-size-fit all rules and continuous update  Policy – helps users evaluate compliance of a decision to policies  De-codification of long / complex policy documents to knowledge graphs  Check Adherence to the policy  Exploration – visually depict and analyze data for clear advice  Explore the exposure to certain commodity from annual reports 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 16
  17. 17. Workshop on Cognitive and Advanced Analytics We expect cognitive technologies to change the way business is done Direct-to-customer agents Advisor-facing apps Employee-facing apps  Conversational style of interaction using text & speech  Create an individualized experience to make personalized recommendations  Invoke transactions specific to the appropriate business process 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 17 TRANSFORMED INTERACTIONS EMPOWERED ADVISORS  Front-office applications allowing advisors to readily find pertinent information for customer interaction  Integration with backend systems to provide “total view of customer”  Analytically-driven recommendations to improve customer interactions OPTIMIZED OPERATIONS  Leverage experience & best practices for improved decision-making  Access disparate systems to provide holistic view of the risk  Shift time spent finding information to making decisions & recommendations
  18. 18. Workshop on Cognitive and Advanced Analytics Now, intelligent machines simulate human brain capabilities to help solve society’s most vexing problems. Cognitive computing has indeed arrived, and its potential to transform industries around the globe is enormous.  To explore future opportunities and determine how cognitive computing is already being utilized in various industries, the IBM Institute for Business Value conducted follow up research to its initial research study.  Through a survey conducted by the Economist Intelligence Unit, we gained insights from more than 800 executives from around the world in a variety of industries, including healthcare, banking, insurance, retail, government, telecommunications, life sciences, consumer products, and oil and gas, and from supplemental desk research and interviews with subject matter experts across IBM. 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 18 Find these reports @ http://www-935.ibm.com/services/us/gbs/thoughtleadership/cognitiveindustry/
  19. 19. Workshop on Cognitive and Advanced Analytics Banking and Financial Services Industry: 67% of banking executives believe personalization is driving customer expectations Almost $1 trillion lost in the subprime mortgage crisis due to poor credit decisions 89% of those familiar with cognitive computing believe it will play a disruptive role in the banking industry  The financial services industry is experiencing multiple challenges – declining return on equity, expanding regulatory requirements, relentless security threats, demanding customer requirements, and growing non- traditional competition  At the same time, banks and other financial institutions are confronted with an ever-expanding deluge of internal and external data that might help redress challenges  Given constraints of traditional algorithm-based analytics and peoples’ ability to process information, banks have been generally unable to exploit maximum value from data  Cognitive computing expands the ability of computing exponentially, unleashing an entirely new range of business opportunities:  Organizations are able to scale and accelerate human expertise in new, powerful ways  People are able to make much better use of complex data  Bankers are able to leverage new insights to change behavior and transform their organizations 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 19 Can we think of one use case from our Banking experience?
  20. 20. Workshop on Cognitive and Advanced Analytics Communication Industry: 71% of the CSP executives believe that customers demand a personalized experience today 49% of the CSP executives believe that customers demand a more seamless and consistent experience 69% of the CSP executives are actively pursuing industry model innovation 46% of the CSP executives are actively pursuing product and service innovation  The communications industry is facing multiple forces of change - evolving customer expectations, increasing OTT (over-the-top) services, rapid increase of data-intensive apps, accelerated pressure on cost reduction and higher privacy & security issues  To be successful, CSPs (communications service provider) need to develop deep capabilities around engagement of customers and other stakeholders, effective decision making, discovery of new products and services, and management of profitability  These capabilities must be able to deal with both structured and unstructured data, such as call center transcripts, and to include machine learning in M2M communications  But many CSPs lack the analytical tools and other assets needed to be a market leader. They struggle to meet customer expectations for seamless and complete service, to provide innovative services and products, and to make timely, accurate decisions  Cognitive computing can address these challenges and open up fresh opportunities for CSPs by harnessing insights hidden in data from across the organization and beyond. 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 20
  21. 21. Workshop on Cognitive and Advanced Analytics Consumer Products Goods Industry: 57% of consumer product leaders believe they are not competent enough in delivering personalized experience across all touch points  The consumer products industry is facing multiple forces of change – digitally empowered consumer, changing demographics, volatile commodity prices, disruptive competition and changing regulations  To be successful, consumer products companies need to develop deep capabilities around engagement of consumers and other stakeholders, discovery of new ideas and effective decision making  But many of them lack the analytic tools and other assets needed to be a market leader. Most of them struggle to meet consumer expectations for engagement and personalization.  Cognitive computing can address these challenges. Example being:  Helps in improving supply chain and procurement decisions  Helps develop campaign messaging for specific targeting  Helps to discover unexpected flavors which were never thought to put together before  Helps in predicting the demand for hottest products in the season 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 21
  22. 22. Workshop on Cognitive and Advanced Analytics Healthcare Industry: Outperformers are 43% more competent in consumer engagement than underperformers 167% more outperformers make innovation a major priority than underperformers 44% more outperformers are strong in decision making than underperformers # 3 killer in USAis preventable medical error in 2013 • The healthcare industry is undergoing significant change driven by six disruptive forces - rapid digitization, changing consumer expectations, regulatory complexities, increasing healthcare cost and demand, shortage of skilled resources and elevating cost pressure • To meet the implication of these forces, healthcare organizations must excel in engaging with consumers, discovering new ideas and taking effective decisions • Currently, traditional analytics capabilities are unable to exploit maximum value from the ever increasing data resource constraining organization’s achievements and performance. But cognitive computing has the ability to bridge this gap and can open up fresh opportunities for the healthcare industry. It is already helping healthcare organizations to provide personalized care, effective decisions and more innovative solutions. 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 22
  23. 23. Workshop on Cognitive and Advanced Analytics Insurance Industry: Outperformers are 65% more competent in customer engagement than underperformers 285% more outperformers make innovation a major priority 66% more outperformers are strong in decision making 98% of the insurance executives, familiar with cognitive computing, believe that it will play a disruptive role in the insurance industry  The insurance industry is facing multiple forces of change - rapid digitization, changing demographics, rising customer expectations, challenging economic environment and expanding risk of sophisticated fraud  To be successful, insurers need to develop deep capabilities around engagement of customers and other stakeholders, effective decision making, discovery of new ideas and management of profitability  But many insurers lack the analytic tools and other assets needed to be a market leader. Many insurers struggle to meet customer expectations for engagement and personalization, to calculate risk and profitability at a granular level, and to make timely, accurate decisions  Cognitive computing can address these challenges and open up fresh opportunities for insurers by creating machines that can  learn new problem domains  reason through the hypotheses  resolve ambiguity  evolve towards more accuracy and  interact in natural ways to engage discover and decide better 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 23
  24. 24. Workshop on Cognitive and Advanced Analytics Pharma and Life Science Industry: 145% increase in the cost of developing a drug from 2004-2014 167% more outperformers make innovation a major priority than underperformers Outperformers are 43% more competent in consumer engagement than underperformers  A new healthcare ecosystem is emerging in which life sciences organizations will play a key role across the continuum from health and wellness to preventative medicine.  The life sciences industry is facing multiple forces of change including erosion of traditional industry boundaries; rapid digitization; continued pressure on productivity as well as the need to prove the value of their drugs.  To meet the implication of these forces, life sciences organizations must excel in discovering new ideas, taking effective decisions and engaging with payer and providers and most importantly, the patient.  Currently, traditional analytics capabilities are unable to exploit maximum value from the ever increasing data resource constraining organization’s achievements and performance. But cognitive computing has the ability – in combinations with data driven analytics - to bridge this gap and can open up fresh opportunities for the life sciences industry such as accelerating discovery, modernizing clinical trials, transforming pharmacovigilance and improving adherence. 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 24
  25. 25. Workshop on Cognitive and Advanced Analytics Retail Industry: >2.5 PB of data is collected by Walmart every hour from customer transactions 69% of retail CXOs agree that customers demand more personalized experiences 59% of retail CXOs are actively pursuing industry model innovation More than half of retail executives are expected to make big decisions in strategic areas, in next 12 months  The retail industry is undergoing significant change driven by five disruptive forces – expanding customer expectations, increasing self-serve retail, rising technological advancements, falling margins and rising security breaches  To meet the implication of these forces, retailers must excel in engaging with customers, discovering new ideas and making effective decisions  Cognitive computing is helping face the challenge. Examples being –  Retailers use cognitive to understand shoppers behavior, search intent and guide them with personalized advice and accurate recommendation  Retailers use cognitive in constructing 360°view of customers and finding personality insights that helps them in designing effective campaigns  A major digital imaging company uses cognitive computing to understand customer trend and help company in making concrete improvements in products 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 25
  26. 26. Workshop on Cognitive and Advanced Analytics What we want to cover today  Understand Natural Language Processing, Classification, Watson & its modules  Industry applications of Cognitive Computing  Understanding Cognitive Architecture  Understanding the disciplines / tools being used in Cognitive Science PGDHRM | Indian Institute of Ranchi | August 26-29, 20168/28/2016 26
  27. 27. Workshop on Cognitive and Advanced Analytics It is very difficult to think of an generalized architecture for cognitive applications, as the applicability differs based on the business pattern 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 27 Security Infrastructure (Exploratory Sandbox and Runtime Cluster) Data (annotated, searchable, indexed) Contentselection, provisioning Ingestion & maintain CognitiveVisualization (search, explore, feedback) Crawl Convert Index Content Classification Rule Based Metadata Maintenance Metadata Workflow Relationship and Reasoning Feedback&Learn Content Summary Formation Probabilistic Model based Ontology Mapping Temporal Reasoning Geospatial Reasoning Other Reasoning Text Similarity Analysis Topic Cluster Content Search and Retrieve SearchAPI Context Management Orchestration Cognitive Exploration and Analysis Integration(in/out ofenterprise)
  28. 28. Workshop on Cognitive and Advanced Analytics What we want to cover today  Understand Natural Language Processing, Classification, Watson & its modules  Industry applications of Cognitive Computing  Understanding Cognitive Architecture  Understanding the disciplines / tools being used in Cognitive Science PGDHRM | Indian Institute of Ranchi | August 26-29, 20168/28/2016 28
  29. 29. Workshop on Cognitive and Advanced Analytics There are some clear disciplines of study / work in the world of cognitive computing 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 29 Typically if you are from mathematics / statistics background. Or the world of math/stat excites you. However, often there is also domain specific knowledge embedded. Typically if you are software geek and making of an interconnected, intelligent and instrumented world through real life project excites you. In case you have a strong domain of interest (may be because of your past experience).
  30. 30. Workshop on Cognitive and Advanced Analytics There are some clear disciplines of study / work in the world of cognitive computing 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 30
  31. 31. Workshop on Cognitive and Advanced Analytics Tools from IBM. The Watson suite of products and associated services.  IBM Watson is a technology platform that uses natural language processing and machine learning to reveal insights from large amounts of unstructured data. 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 31 Watson Developer Cloud Watson DeveloperCloud enables developers and businesses of all sizes to build new cognitive applications, and add cognitive capabilities to existing applications. It provides a growing set of API’s and SDK’s, and is accessible to anyone through the Bluemix cloud environment. Watson Engagement Advisor Watson Engagement Advisor is a technology service that interacts with customers, listens to questions and offers solutions. Engagement Advisor learns with every human interaction and grows its collection of knowledge, quickly adapting to the way humans think. Watson Explorer Watson Explorer is a technology platform that accesses and analyzes structured and unstructured content. Explorer presents data, analytics and cognitive insights in a single view. Explorer gives you the information you’re looking for while uncovering trends, patterns and relationships. Watson Knowledge Studio Watson Knowledge Studio is a tool that enable subject matter experts and developers to teach Watson the linguistic nuances of industries and knowledge domains. Watson Company Analyzer Watson Company Analyzer helps you reduce the time and effort to collect, digest and synthesize information for building strategic business relationships Watson Ecosystem A breakthrough partner program to join the tens of thousands of developers who are building withWatson. From gaining insights from text to analyzing images and video, you can tap into the power of Watson APIs to build cognitive apps.
  32. 32. Workshop on Cognitive and Advanced Analytics It is very important to understand the implications of regulatory compliance and economy of choice before investing over tools and technology 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 32 • Mostly Structured few unstructured • Comparatively low volume • More control on Cognitive techniques • Typically high precision Data of Interest Internal Data External Data • Mostly unstructured / semi-structured data • Typically massive infrastructure needed except for a few cases. • Typically high precision • Overhead on data masking and re- formation.Typically seen in Q&A type solution. • Typically high recall. Data Residence Restriction, computing platformOn-premise Cloud • Look for Aggregator APIs, e.g. News on a company with Positive Sentiment. • Mostly machine learned techniques, easily scalable. • Typically high recall.
  33. 33. Workshop on Cognitive and Advanced Analytics Watson DeveloperCloud 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 33 Relationship Extraction Questions & Answers Language Detection Personality Insights Keyword Extraction Image Link Extraction Feed Detection Visual Recognition Concept Expansion Concept Insights Dialog Sentimen t Analysis Text to Speech Tradeoff Analytics Natural Language Classifier Author Extraction Speech to Text Retrieve & Rank Watson News Language Translatio n Entity Extraction Tone Analyzer Concept Tagging Taxonomy Text Extraction Message Resonance Image Tagging Face Detection Answer Generation Usage Insights Fusion Q&A Video Augmentation Decision Optimization Knowledge Graph Risk Stratification Policy Identification Emotion Analysis Decision Support Criteria Classification Knowledge Canvas Easy Adaptation Knowledge Studio Service Statistical Dialog Q&A Qualification Factoid Pipeline Case Evaluation The Waston that competed on Jeopardy! in 2011 comprised what is now a single API—Q&A—built on five underlying technologies. Since then, Watson has grown to a family of 28 APIs. By the end of 2016, there will be nearly 50 Watson APIs— with more added every year. Natural Language Processing Machine Learning Question Analysis Feature Engineering Ontology Analysis
  34. 34. Workshop on Cognitive and Advanced Analytics Foundational Technologies behind Watson Fifty (50) foundational technologies draw upon five (5) distinct field of study: Big Data & Analytics Artificial Intelligence Cognitive Experience Cognitive Knowledge Computing Infrastructure 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 34 AnaphoricCo-referencing Feature Engineering Learn to Rank Question Analysis Colloquialism Processing Feature Normalization Linguistic Analysis Question-answering Reasoning Strategies Content Management -- Versioning Focus and Spurious Phrase Resolution Logical Reasoning Analysis Recursive Neural Networks Convolutional Neural Networks HTML Page Analysis Logistical Regression Rules Processing Curation Image Management Machine Learning Scalable Search Deep Learning Information Retrieval Multi-dimensional Clustering SimilarityAnalysis Dialog Framing Knowledge (Property) Graphs MultilingualTraining Statistical Language Parsing Ellipses KnowledgeAnswering N-gram analysis (word combinations & distance) SupportVector Machines EmbeddedTable Processing Knowledge Extraction Annotators OntologyAnalysis SyllableAnalysis Ensembles and Fusion KnowledgeValidation and Extrapolation ParetoAnalysis TableAnswering Entity Resolution Language Modeling Passage Answering VisualAnalysis FactoidAnswering Latent Semantic Analysis PDF Conversion Visual Rendering Phoneme Aggregation Voice Synthesis
  35. 35. Workshop on Cognitive and Advanced Analytics Alchemy Language AlchemyLanguage is a collection of APIs that offer text analysis through natural language processing. The AlchemyLanguageAPIs can analyze text and help you to understand its sentiment, keywords, entities, high-level concepts and more.  Entity Extraction  Sentiment Analysis  Emotion Analysis  Keyword Extraction  Concept Tagging  Relation Extraction  Taxonomy Classification  Author Extraction  Language Detection  Linked Data Support 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 35
  36. 36. Workshop on Cognitive and Advanced Analytics Conversation Add a natural language interface to your application to automate interactions with your end users. Common applications include virtual agents and chat bots that can integrate and communicate on any channel or device.  Watson combines a number of cognitive techniques to help you build and train a bot - defining intents and entities and crafting dialog to simulate conversation. The system can then be further refined with supplementary technologies to make the system more human-like or to give it a higher chance of returning the right answer. Watson Conversation allows you to deploy a range of bots via many channels, from simple, narrowly focused Bots to much more sophisticated, full-blown virtual agents across mobile devices, messaging platforms like Slack, or even through a physical robot. 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 36
  37. 37. Workshop on Cognitive and Advanced Analytics Personality Insights Uncover a deeper understanding of people's personality characteristics, needs, and values to drive personalization.  Personality Insights extracts and analyzes a spectrum of personality attributes to help discover actionable insights about people and entities, and in turn guides end users to highly personalized interactions. The service outputs personality characteristics that are divided into three dimensions: the Big 5, Values, and Needs. While some services are contextually specific depending on the domain model and content, Personality Insights only requires a minimum of 3500+ words of any text.  Some usages:  Targeted marketing  Customer acquisition  Customer care  Personal connections  Resume writing  ………. 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 37
  38. 38. Workshop on Cognitive and Advanced Analytics Visual Recognition Understand the contents of images. Create custom classifiers to develop smart applications.  Visual Recognition allows users to understand the contents of an image or video frame, answering the question: “What is in this image?”  Submit an image, and the service returns scores for relevant classifiers representing things such as objects, events and settings.  What types of images are relevant to your business? How could you benefit from understanding and organizing those images based on their contents?  With Visual Recognition, users can automatically identify subjects and objects contained within the image and organize and classify these images into logical categories. 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 38
  39. 39. Workshop on Cognitive and Advanced Analytics Alchemy Data News AlchemyData provides news and blog content enriched with natural language processing to allow for highly targeted search and trend analysis. Now you can query the world's news sources and blogs like a database.  AlchemyData News indexes 250k to 300k English language news and blog articles every day with historical search available for the past 60 days.  One can query the News API directly with no need to acquire, enrich and store the data yourself - enabling you to go beyond simple keyword-based searches. 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 39
  40. 40. Workshop on Cognitive and Advanced Analytics Tradeoff Analytics Helps users make better choices to best meet multiple conflicting goals.  Tradeoff Analytics is a service that helps people make decisions when balancing multiple objectives. The service uses a mathematical filtering technique called “Pareto Optimization,” that enables users to explore tradeoffs when considering multiple criteria for a single decision.  It can help bank analysts or wealth managers select the best investment strategy based on performance attributes, risk, and cost.  It can help consumers purchase the product that best matches their preferences based on attributes like features, price, or warranties.  Additionally, Tradeoff Analytics can help physicians find the most suitable treatment based on multiple criteria such as success rate, effectiveness, or adverse effects 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 40
  41. 41. Workshop on Cognitive and Advanced Analytics Questions 8/28/2016 PGDHRM | Indian Institute of Ranchi | August 26-29, 2016 41

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