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Building Cognitive Solutions with Watson APIs
The document provides an overview of cognitive solutions using Watson APIs. It discusses IBM's cognitive reference architecture, emerging cognitive patterns, best practices with Watson APIs, the art of conversation design, and future trends. A case study is presented on using Watson to help customers complete the mortgage application process for an Italian bank.
Building Cognitive Solutions with Watson APIs
- 1. Building Cognitive Solutions withWatson APIs University of Jyväskylä 2.2.2017 Jouko Poutanen Cognitive Solution Architect
- 2. Agenda • Cognitive ReferenceArchitecture • Emerging Cognitive Patterns • Best Practices with Watson APIs • The Art of Conversation Design • Future Trends
- 3. Watson in DifferentIndustries Today https://youtu.be/PujCkDAXji8
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- 5. 5 Advisors Developer Cloud Specialties Models Content Tooling Assemble Train Deploy Admin Data ServicesIngestExtract AnnotateCurate Design Engagement Discovery Decision Policy Cross Industry Editions Oncology Wealth Mgmt. Intelligence Cooking Target Industry Editions Powered by Watson Offerings App Store Healthcare Financial Svc. Travel ... Call Center User Profiling Research ... Core Offerings Watson Analytics Watson Explorer Industry Aligned Market Aligned Visualize Cognitive Services (APIs) The same services are used by business partners, customers, and IBM Developers. Watson Portfolio (partial)
- 6. © 2015 INTERNATIONALBUSINESS MACHINES CORPORATION Relationshi p Extraction Question s & Answers Languag e Detectio n Personalit y Insights Keyword Extraction Image Link Extraction Feed Detection Visual Recognition Concept Expansion Concept Insights Dialog Sentime nt Analysis Text to Speech Tradeoff Analytic s Natural Languag e Classifie r Author Extraction Speech to Text Retrieve & Rank Watson News Language Translatio n Entity Extractio n Tone Analyzer Concept Tagging Taxonomy Text Extraction Message Resonanc e Image Tagging Face Detectio n Answer Generation Usage Insights Fusion Q&A Video Augmentatio n Decision Optimizatio n Knowledge Graph Risk Stratification Policy Identificatio n Emotion Analysis Decision Support Criteria Classificatio n Knowledge Canvas Easy Adaptatio n Knowledg e Studio Service Statistic al Dialog Q&A Qualificatio n Factoid Pipeline Case Evaluation 6 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
- 7. This is theruntime architecture which showcases the components that are involved in the usage of a trained and deployed Cognitive Engagement System Cognitive-Reference Architecture IBM Architecture Center https://www.ibm.com/devops/method/content/ architecture/cognitiveArchitecture
- 8. Developer Administrator Solution User DevelopsCustom Application Componentry + UI Local User Administration Analyzes Usage Metrics IBM Administrator Manages Cloud Based Services Analyzes Usage Metrics Client Systems Data Sources Subject Matter Expert Provides context specific data Executes business transactions Content Curator Manages Corpus Content Writes/Edits Content Finds Content for Corpus Creates Training Data 8 © 2015 International Business Machines Corporation Other Services Provide additional functionality to extend the capability of the base solution Watson powered solution Responses Interactions Client Content Training Data/models Content Writer/Editor Watson High Level Reference Architecture – System Context View Process Author Creates /Updates Processes Maintains Processes
- 9. Emerging Cognitive Patterns BestPractices with Watson APIs
- 10. Cognitive technology willalso lead to industry transformation, e.g. in healthcare
- 13. The rest ofthe used materials are in the course site.
- 14. The Art ofConversation Design
- 15. • Getting theconversation design right requires information, skills and expertise • Designing effective and engaging conversational interaction that achieves your clients’ aims can be harder than it seems • It draws on skills other than ‘hands on the keyboard’ Dialog skills – Language skills – Strategic thinking – Deep knowledge of your client’s business and their customers (the end user) – Psychological insights – how people interact conversational solutions (virtual assistants) – how to establish trust and achieve behavior change Introduction
- 16. Introduction • How dowe get the conversation design right? – Carefully designing key moments in the conversational interaction – Using proactive and reactive behavior in the right balance – Proactively engaging users at the right time with key messages and questions – Using the right language – Developing the right approach to ‘chit chat’ – Leveraging profiling capability to – Keep track of things about the user and tailor the interaction to them – Gather key information about users’ interests, concerns, behaviors – Ensuring UI behavior supports the conversational interaction – And so on …
- 17. The Elements ofConversation Design – Understanding the benefits of conversational solutions (virtual assistants) – Positioning a conversational solution – Defining the purpose – Identifying the view point – Specifying the proactivity – Defining tone and personality – Designing the right approach to ‘chit chat’ – Writing for conversational interaction derive
- 18. Case Study –xCredit Prototype Client’s Problem: In Italy, the process of getting a mortgage is very long and convoluted, for both the bank customer and the branch manager. Life-time renting is common. A lot of bank customer give up part-way through the mortgage application process Client’s Vision: xCredit wants to leverage Watson technology to increase the number of customers who complete the mortgage application process, and assist branch managers in their mortgage-related work Defining the purpose
- 19. Case Study –xCredit Prototype •Initial Dialog scope – before conversation design: – Purpose: To answer questions about mortgages – Conversational elements: Intro statements, some off-topic Q&A, a simple process flow to help customers choose a mortgage Defining the purpose
- 20. Case Study –xCredit Prototype •Revised Dialog scope after conversation design: – Primary purpose: Watson should act as a facilitator in the relationship between the branch manager and the bank, to support both parties through the process – Conversation design: – Watson proactively drives the conversation with the customer, guiding them through processes, asking questions, suggesting things they might want to know about, or need to do; provides up-to-date information to keep the customer informed about the process and next steps – Watson proactively prompts the branch manager with information and reminders and the customer’s mortgage application and required next steps with customer and bank manager Defining the purpose
- 21. Case Study –xCredit Prototype •Result: – The conversational part of the solution took a leading and guiding role, with the long-tail solution providing on-topic question-answering capability – The scope leveraged the technology to address the client’s problem in a way that met the client’s vision and showed the power of cognitive technology in this context Defining the purpose
- 22. This Is theruntime architecture which showcases the components that are involved in the usage of a trained and deployed Cognitive Engagement System Cognitive Reference Architecture IBM Architecture Center https://www.ibm.com/devops/method/con tent/architecture/cognitiveArchitecture
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- 25. Hotel Concierge Poweredby Watson https://youtu.be/jC0I08qt5VU
- 26. Check Out –Project Intu • http://www.ibm.com/watson/developercloud/project-intu.html
- 27. 27 Cognitive Computing WillEvolve Over Five Dimensions What are the various types of inputs it can sense and interpret? How ubiquitous is the capability? How personalized and interactive is it? How can capability scale to meet demand? What is the degree of autonomy in learning? Scalability Evolving Dimensions Learning Ubiquity Sensing Personalized Interaction • from passive to active • interaction with each other, collective intelligence • understand the locative and temporal context • Unsupervised learning of new concepts • selftraining to be experts • Able to process e.g. video, image, audio • market place of millions of cognitive agents or avatars • personal virtual assistants • part of our daily lives • As a fabric via APIs • Cognition-as-a-Service (CaaS)