Build a Chatbot with IBM Watson - No Coding Required
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What is a "chatbot" and how does it work? In this workshop, we explored how to build a chatbot for the conversational interface, without having to write any code.
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Build a Chatbot with IBM Watson - No Coding Required
- 1. CHATBOT CHATBOT CHATBOT CHATBOT! (What is it anyway?)
- 2. Hello! I’m Charlotte Han. ● I processes data and computes digital strategies for a living ● Taiwan -> U.S. -> Germany ● Passionate about helping people understand how technologies can advance human lives ● Currently working as “Deep Learning Marketing Manager” at NVIDIA @sunsiren
- 3. Agenda ● What Is Artificial Intelligence, Machine Learning and Deep Learning ● AI in Our Personal Lives and in the Workplace ● Underlying technology of chatbots ● Let’s build a chatbot for a flower shop using IBM Watson
- 4. Source: Gartner, “Architecting the On-Demand Digital Business”; Drue Reeves, Kyle Hilgendorf, Kirk Knoernschild, August 16, 2016 By 2020, the average person will have more conversations with bots than with their spouse. Source: Gartner’s Top 10 Strategic Predictions for 2017 and Beyond
- 5. What is AI, ML and DL?
- 6. AI Is Everywhere, Touching Our Lives
- 7. Artificial Intelligence Is Sweeping Across Industries MEDICINE MEDIA & ENTERTAINMENT SECURITY & DEFENSE AUTONOMOUS MACHINES Cancer cell detection Diabetic grading Drug discovery Pedestrian detection Lane tracking Recognize traffic signs Face recognition Video surveillance Cyber security Video captioning Content based search Real time translation Image/Video classification Speech recognition Natural language processing INTERNET SERVICES
- 8. Source: Gartner, “Architecting the On-Demand Digital Business”; Drue Reeves, Kyle Hilgendorf, Kirk Knoernschild, August 16, 2016
- 9. By Better Analyzing Data, Companies Can Have: Improved Operations Increased Productivity Customers Satisfaction
- 10. Structured Data: Unstructure d Data 80% of Business- Related Data is Excel, CRM, HR systems, Financial systems, SQL Emails, images, requisitions, purchase orders, sensor data, server or web logs, audio files, video files, social media data, text files and documents
- 11. This Is When Natural Language Processing (NLP) Comes In for Text Files
- 13. This Is Why There Are Free Communication Apps • Treasure trove of different types of human communications • They can use machine learning to see patterns in how humans use their natural language
- 14. Natural Language Processing vs. AI NATURAL LANGUAGE PROCESSING
- 15. Data scientists spend 90% time here
- 16. The more data you have, the more powerful and accurate your predictive models will be.
- 17. How Do Chatbots Work? User makes a request Request analyzed by AI User info taken into account Knowledge Base Data Storage
- 18. Well-maintained knowledge base and data store will supercharge your AI brain.
- 19. (But we’ll take a shortcut with IBM Watson today; no coding or cleaning data required.)
- 20. Industry Use Cases Yes, chatbots are actually useful.
- 21. Faster Processing, Happier Customers ● Faster claims processing ● Increased customer satisfaction ● Maintain high levels of customer service ● Cost savings from automation
- 22. AI Chatbot That Speaks Emoji ● Chatbots and virtual assistants have risen in popularity in banking and other industries because advancements in AI have made them better at interacting and interpreting human language. ● The banking industry can offer advice on a larger scale and with better impact by using AI chatbots that can learn about user habits.
- 23. AI Tool Boosts Customer Service KLM’s 350+ social media service agents handle 15K requests/week. To support the volume of incoming messages, KLM uses GPU- accelerated deep learning to predict the best response. Service agents review and either approve or personalize each response. The resulting time savings allows agents to focus on customers with more pressing needs and handle more questions while maintaining high levels of customer satisfaction.
- 24. Roll up Your Sleeves! Chatbot 101 with IBM Watson Assistant Source: https://courses.competencies.ibm.com/courses/course-v1:CognitiveClass+CB0103EN+v1/
- 25. Step One Sign up for IBM Cloud: https://cloud.ibm.com/registration
- 26. Step Two: Find Watson Assistant
- 27. Step Three: Create Instance
- 29. Intents An intent is the goal of the purpose of the user’s input. Adding examples to intents helps your virtual assistants understand different ways in which people would say them. Start with “#”
- 30. Entities An entity is a portion of user’s input you can use to provide a different response to a particular intent. Adding values and synonyms to entities helps your virtual assistants learn and understand important details that your users mention. Start with “@”
- 31. Dialog Creating a dialog defines how your bot will respond to what the user is asking. Dialogues in Watson are defined through nodes. Each node has a name, a condition and one or more responses. Execution Order
- 32. #intent
- 33. Create Chitchat Intents - #greetings
- 34. Test Intents - #greetings Now, type “hello” and see what happens. What about “thank you”?
- 35. Create Chitchat Intents - #thank_you
- 36. Test Intents - #thank_you Now, type “thank you” and see what happens.
- 37. Create Chitchat Intents - #goodbyes
- 38. Test Intents - #goodbyes Now, type “see you!” and see what happens.
- 41. @entity
- 42. Distinguish Intents with Entities ● I want flower recommendations ● Flower suggestions for boyfriend (@relationship) ● Recommend flowers for a birthday (@occasion)
- 44. Define @occasion
- 45. Test it! - flower suggestions for boyfriend - I’d like suggestions for a birthday Did Watson correctly identify both the intent and the entity?
- 47. Dialogue: Chitchat Nodes Hello, thank you and goodbye.
- 48. Create a Dialogue by default
- 49. What’s in the Welcome Node? Name of the node Condition Response Block
- 50. What’s in the “Anything Else” Node? If the conditions in the blocks above this one were false / not met, this node will be executed. Randomize order to make it more “human”
- 51. Test it! - Enter something irrelevant, such as “What!?” - What is the intent or entity identified? - What is Watson’s response? Since the condition in “welcome” was not met, Watson responded with Anything Else block. The order is randomized.
- 52. Back to Welcome Node. Change Response Hello. My name is Florence and I’m a chatbot. How can I help you? You can ask me about flower suggestions or delivery info. Personality Setting up expectations to get users back on track.
- 53. Create a Greetings Node
- 54. Create a Thank You Node
- 55. Create a Goodbyes Node
- 56. Dialogue: Domain-Specific Flower Suggestions and Delivery Info
- 57. Create Delivery Info Node
- 58. Test it! - Do you deliver? - I’d like suggestions for my boyfriend Did Watson correctly identify both the intent and the entity?
- 59. Simple Occasions
- 60. Relationship Suggestions - bf vs. hubby Same suggestion for husband and boyfriend (NOT best practice; just for simplicity’s sake!!! :) )
- 61. Why Isn’t There a Response? No response?
- 62. Jump to Relationship Node Instead
- 63. Jump to and Examine Condition(s)
- 64. Test it! - flower suggestions for my boyfriend Did Watson give you a suggestion?
- 65. To Be Continued... Follow the rest of this course about handling complex dialogue flows and deploying the chatbot to WordPress here: https://courses.competencies.ibm.com/courses/course- v1:CognitiveClass+CB0103EN+v1/
- 66. If you want to learn more about AI: Meetup.com/what-is-artificial-intelligence
- 67. Learn IT, Girl! https://www.learnitgirl.com/
Notas do Editor
- It’s an honor to be here with you. Thank you. I currently work at NVIDIA as “Deep Learning Marketing Manager”; still trying to figure out what it means. I’m learning deep. Because of NVIDIA I get to see first hand how AI is changing the world. So fast. There is so much amazing research being published every year. It blows my mind. I know you’re here for the same reason: you want to see the future. So let’s get on the journey together.
- By 2020, the average person will have more conversations with bots than with their spouse. With the rise of Artificial Intelligence (AI) and conversational user interfaces, we are increasingly likely to interact with a bot (and not know it) than ever before. The digital experience has become addictive by entering our lives through smartphones, tablets, virtual personal assistants (VPAs) or the entertainment systems in our homes and cars. I totally believe that because I don’t even have a spouse! Of course I’ll be talking more with the bots. It’s not like we’re talking to R2D2 or C-3PO, though.
- First, let’s start with some definitions… AI is a broad field of study focused on using computers to do things that require human-level intelligence. It’s been around since the 50’s, playing games like tic-tac-toe and checkers, and inspiring scary sci-fi movies. But it was limited in practical applications… ML is an approach to AI that uses statistics techniques to construct a model from observed data. It generally relies on human-defined classifiers or “feature extractors” that can be as simple as a linear regression, or the slightly more complicated “Bag of Words” analysis technique that made email SPAM filters possible. This was really handy in the late 1980’s when lots of email started showing up in your inbox But then we invented smartphones, webcams, social media services, and all kinds of sensors that generate huge mountains of data and the new challenge of understanding and extracting insights from all this “big data”. DL is a ML technique that automates the creation of feature extractors using large amounts of data to train complex “deep neural networks” DNNs are capable of achieving human-level accuracy for many tasks, but require tremendous computational power to train Several years ago, researchers started applying DNNs in a variety of areas and reporting amazing results… ============== Ref. https://en.wikipedia.org/wiki/Naive_Bayes_spam_filtering
- I was going to try to persuade you that AI is everywhere, touching our lives, but actually I’m going to try something different: raise your hand if you believe that AI is not touching your life? It must be a tough life. And you may be Amish.
- Typical AI tasks include classification, pattern detection and prediction. It turns out that AI effective across many domains, and it’s transforming the way computers achieve perceptual tasks such as computer vision, pattern detection, speech recognition and behavior prediction. Some people, including Bloomberg and the World Economic Forum, have referred to it as the 4th industrial revolution. And Andrew Ng (a widely-respected Stanford University professor, founder of the Google Brain project, and co-founder of the Coursera online education platform) believes that this new deep learning approach to AI is “the new electricity.” and “Just as 100 years ago electricity transformed industry after industry, AI will now do the same.” References: https://www.bloomberg.com/news/articles/2016-05-20/forward-thinking-robots-and-ai-spur-the-fourth-industrial-revolution https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-and-how-to-respond/ Ng on AI as the new electricity: http://www.fast.ai/2016/10/11/fortune/ A few examples: Facebook’s “DeepFace” feature creates a 3D model of your face from online photos, adjusts for lighting and facial expressions, and identifies you in photos with 97% accuracy – all using deep learning. You may have experienced this when Facebook automatically alerts you that a new picture of you has been posted and gives you the option to blur out your image. References: http://www.inquisitr.com/1825367/facebook-deepface-ai-learning-your-face-in-every-uploaded-photo http://news.sciencemag.org/social-sciences/2015/02/facebook-will-soon-be-able-id-you-any-photo Microsoft’s Skype Translator performs instant translation of conversations to and from over 50 languages. If you haven’t tried this yet, it’s a really amazing way to connect and communicate with people across language barriers. References: http://www.technologyreview.com/news/534101/something-lost-in-skype-translation/ http://www.wired.com/2014/05/microsoft-skype-translate/ Other examples include medical researchers detecting genes associated with autism spectrum disorder, neuroscientists detecting and suppressing the brainwave patterns responsible for epileptic seizures, and others using deep learning to identify skin cancers, classify lung sounds, and accelerate computational drug design, saving millions in research. Imagine a day in the not-too-distant future when a personal healthcare device, like a mirror in your bathroom, puts this all together and can automatically notify you when it detects that you may have early-stage skin cancer, so you can consult with your doctor and get life-saving treatment. You can explore hundreds of deep learning use cases my team has collected at https://news.developer.nvidia.com [next] =========================== Story: Understanding Video Clarifai offers a service that rapidly analyzes images and video clips to recognize 10,000 different objects or types of scenes This capability can be used for extremely targeted advertising, for example: Showing a Starbucks ad whenever coffee appears in a video Rapidly scanning security footage Searching through your personal video archive for your child’s first steps References: http://www.technologyreview.com/news/534631/a-startups-neural-network-can-understand-video/
- Most enterprise businesses are on the path to AI already even though they might not know it. Companies are digitizing everything: supply chain, HR, finance, marketing, sales…. Digital creates data. Data requires insight. AI Deep Learning provides the tools to make the most use of that data. It’s already taking off. By 2020, 20% of companies will dedicate workers to monitor and guide neural networks. Spending on AI Technologies by companies is expected to grow to $47b in 2020 from a projected 8 billion in 2016, according to IDC. According to researcher Gartner, AI bots will power 85% of all customer service interactions by the year 2020 . In a research report to its investors, Bank of America argued that the rise of AI will lead to cost reduction and new forms of growth that could amount to $14-$33 trillion annually, in what it calls "creative disruption impact," and that's just the tip of the iceberg in some expert's view. “There are an estimated 3,000 AI startups worldwide, and many of them are building on Nvidia’s platform. They’re using Nvidia’s GPUs to put AI into apps for trading stocks, shopping online and navigating drones.” http://www.nextbigfuture.com/2016/12/nvidia-is-new-intel-and-its-chips-are.htm
- Unstructured data, especially text, images and videos contain a wealth of information. However, due to the inherent complexity in processing and analyzing this data, people often refrain from spending extra time and effort in venturing out from structured datasets to analyze these unstructured sources of data, which can be a potential gold mine.
- Natural language processing (NLP) is a subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. Challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural language generation. An automated online assistant providing customer service on a web page, an example of an application where natural language processing is a major component.[1] - Wikipedia
- The ultimate goal of NLP is to the fill the gap how the people communicate (natural language) and what the computer understands (machine language). Although, some people talk like robots. “We need to ETL the data from the warehouse so we can move it into the Hadoop Cluster!”.
- AI teaches systems to do intelligent things Machine Learning teaches systems to do intelligent things that can learn from experience NLP teaches systems to be intelligent, learn from experience and can analyze, understand and generate human language.
- Data scientists spend 90% of their time getting and cleaning data. Only when the data is prepped can they get to work with identifying patterns and making predictions. They can’t go straight into predicting the future without understanding the data. Your team has to better understand the past to be able to predict the future.
- Knowledge Base - It contains the database of information that is used to equip chatbots with the information needed to respond to queries of customers request. Data Store - It contains interaction history of chatbot with users. NLP Layer - It translates users queries (free form) into information that can be used for appropriate responses. Application Layer - It is the application interface that is used to interact with the user. Chatbots learn each time they make interaction with the user trying to match the user queries with the information in the knowledge base using Machine Learning.
- BNP Paribas Cardif Industry(ies): FSI Data: structured and un-structured Products: 2 TITAN X for POC Summary The insurance industry hasn’t changed much in that it still relies largely on evidence-based, non-standardized documents (paper, scans, photos, etc) in its contract management processes. Processing this type of documentation is often manual, tedious, and time consuming for both the insurer and the insured. ‘Cardif Forward’ is BNP Paribas Cardif’s innovative digitization plan with AI being a key element of the plan. Thanks to artificial intelligence, the insurer will be able to automatically analyze documents and make monthly loan repayments without waiting for all supporting documents. This will allow a third of clients to receive immediate approval. Problem When facing unexpected events, customers expect their insurer to support them as quickly as possible. However, claims management may require different levels of checks and validations before a claim can be approved and a payment can be made. With the new practices and behaviors generated by the digital economy, this process needs adaptation thanks to data science to meet the new needs and expectations of customers. The insurance industry still relies largely on evidence-based, non-standardized documents (paper, scans, photos, etc) in its management processes. E.g. medical reports for credit insurance; RIB to control operations; death certificate or work stoppage to validate a claim. Solution ‘Cardif Forward’ is BNP Paribas Cardif’s development plan for 2017-2020 and marks the 3rd phase in the digitization of the company. Cardif wants to remain a leading-edge company in term of customer experience and is using AI and DL to develop internal expertise to automatically recognize and process documents digitized by the insured. Result(s) The receipt, validation and processing of the contents of these documents are often manual and therefore long and tedious for the insured (numerous round trips) and costly for the company. The AI solution will save money and reduce the complexity of our contract management Impact Expected: -Faster claims processing -Increased customer satisfaction -Maintain high levels of customer service -Cost savings from automation About the Customer Protecting people and their property at every stage of their lives. As a global specialist in personal insurance, BNP Paribas Cardif serves 90 million clients in 36 countries across Europe, Asia and Latin America. More Information https://twitter.com/bnpp_cardif/status/847354298891517953?lang=en https://www.forbes.com/sites/blakemorgan/2017/07/25/how-artificial-intelligence-will-impact-the-insurance-industry/#6b4bb5226531 https://www.theguardian.com/sustainable-business/2017/jan/28/insurance-company-lemonde-claims
- Capital One Industry(ies): Financial Services Data: Text Products: GPUs on AWS Summary Fintech analysts, Juniper Research, estimates the number of mobile banking users will reach 2b by the year 2021. So, it’s no surprise to see the rising popularity of Chatbots in the finance industry. Through convenience and ease-of-use, Chatbots optimize digital services at scale. Chatbots are convenient and easy for customers to use. And, with the ability to automate operations, to reach more customers chatbots are streamlining and optimizing digital services. Capital One is piloting an SMS text-based intelligent assistant named Eno. Eno uses GPU-powered deep learning to respond to natural language text messages from customers inquiring about their accounts. Customers text Eno to track their balance, recent charges, or to pay their bill. Eno takes mobile banking to the next level, which is just a text message away. Problem Solution In March 2017, Capital One launched a pilot of Eno (“One” spelled backwards), an SMS text-based intelligent assistant. Eno uses artificial intelligence to respond to natural language text messages and emojis from users about their money. Result(s) With Eno, customers can stay on top or their Capital One credit card and bank accounts, through text or emoji. You can text Eno things like, “What’s my balance?” or “How much credit do I have?” or “What are my recent charges?” and Eno will respond instantly with the information. Customers can also pay their credit card bill by simply texting “Eno, pay my bill.” In a bid to make the experience more human, Eno has also been programmed to recognize certain "emojis”. For example, users can prompt Eno to show them their account balance by sending the "bag of money" emoji or they can confirm a payment through the "thumbs up" emoji. Impact -Eno helps customers stay on top of their accounts, anywhere, anytime. -Right now, Eno is available to a small pilot of customers – Capital One is keeping a waitlist for customer who are interested in getting in the next wave. -At this time, Eno cannot transfer funds. Capital One is working on developing new capabilities, and transferring money is one of them – but it’s not in the starting lineup of features. -Overall: ---chatbots and virtual assistants have risen in popularity in banking and other industries because advancements in AI have made them better at interacting and interpreting human language. ---the banking industry can offer advice on a larger scale and with better impact by using AI chatbots that can learn about user habits. About the Customer Capital One Financial Corporation is a bank holding company specializing in credit cards, home loans, auto loans, banking and savings products headquartered in McLean, CA. Capital One is the eighth-largest commercial bank in the United States when ranked by assets and deposits and is ranked 9th on the list of largest banks in the United States by total assets. The bank has 755 branches and 2,000 ATMs. It is ranked #100 on the Fortune 500 #17 on Fortune's 100 Best Companies to work for list, and conducts business in the United States, Canada, and the United Kingdom. The company helped pioneer the mass marketing of credit cards in the 1990s, and it is one of the largest customers of the United States Postal Service due to its direct mail credit card solicitations. In 2015, it was the 5th largest credit card issuer by purchase volume, after American Express, JP Morgan Chase, Bank of America, and Citigroup. More Information https://www.capitalone.com/applications/eno/ https://www.abe.ai/blog/10-big-banks-using-chatbots-boost-business/ https://www.juniperresearch.com/press/press-releases/mobile-banking-users-to-reach-2-billion-by-2020 Oct. 2016: “New research from leading Fintech analysts, Juniper Research, finds that over 2bn mobile users will have used their devices for banking purposes by the end of 2021, compared to 1.2bn this year (2016) globally. Growth in mobile banking is being driven by consumer adoption of banking apps the changing way consumers manage their finances.”
- DIGITALGENIUS – KLM Industry: Transportation Data: KLM’s historical data Products: NVIDIA TITAN X GPUs for training. NVIDIA GPUs on AWS cloud with CUDA for production workloads and inference. SUMMARY KLM’s 350 social media service agents engage in 15K conversations each week on channels like Facebook Messenger, Twitter and Whatsapp, 24/7. To support the overwhelming volume of messages, KLM uses GPU-accelerated deep learning from DigitalGenius to predict the best response to an incoming message and shows it to a contact center agent for approval or personalization before sending it to the customer. The resulting time savings for KLM service agents means they can focus on customers with more pressing needs and handle a greater volume of questions while still maintaining a high degree of customer satisfaction. Challenge -KLM has >22 million social-media followers who engage with the airlines on various platforms >100,000 times a week. -KLM’s team of 350 social media service agents engage in 15,000 conversations a week across all its social platforms, offering 24/7 service in 10 languages. Solution To contend with the overwhelming volume of messages, KLM turned to DigitalGenius and AI. DigitalGenius uses NVIDIA TITAN X GPUs DigitalGenius uses NVIDIA TITAN X GPUs to train its deep learning neural networks on the company’s historical data (>60,000 KLM questions and answers). Production workloads for enterprise customers run on NVIDIA GPUs in the AWS cloud, with the CUDA parallel computing platform providing acceleration. When a new message comes in via a digital channel such as email, chat, social media or text, DigitalGenius’ deep learning model takes a couple of actions: ------It predicts and auto-fills metadata related to the incoming message. ------It predicts the best response to the incoming message and shows it to the contact center agent for approval or personalization before sending it to the customer Result Huge time savings for customer service agents, who can instead focus on customers with more pressing or complicated needs. Impact By applying AI, KLM can handle a greater volume of questions while still maintaining its personal approach and speed The rapid increase in the collection of historical customer data and advances in NVIDIA hardware have combined to make AI-powered customer service practical for the first time.