From Chatbots to Augmented Conversational Assistants
From Chatbots to Augmented Conversational Assistants: An Experimental Study combining AI and Crowdsourcing Developing technology, including Natural Language Understanding and Machine Learning are taking Chatbots, Virtual Assistants, Conversational Bots… to an extent unheard of in the past. And yet, we cannot but notice that efforts are still needed for these interactive agents to attain a satisfactory level of conversational abilities. As a matter of fact, Bots still often cause a classic frustration effect when they reach limits in the scope of their powers. In order to take Bots beyond their technical boundaries: We augmented them with technologies such as advanced data processing and Artificial Intelligence, including computer vision specific capabilities to mention just a few. We designed a unique approach based on crowdsourcing methods complementing and enhancing existing techniques to AI / Deep Learning. In this talk, we will present the result of our research based on two tracks: As part as our first iteration of research (aka “AVA”), we focused our efforts on identifying the most relevant AI/ML features that bring advanced conversational capabilities to Chatbots. In a second iteration (aka “CIVA”), we concentrated our research on bringing more understanding and replying abilities to Bots defining a progressive methodology where data are improved. In this presentation you will see how we augmented Chatbots capabilities with: Machine Learning Computer vision Sentiment analysis Crowdsourcing Conversational methods
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From Chatbots to Augmented Conversational Assistants
- 1. From Chatbots to Augmented Conversational Assistants: An Experimental Study combining AI and Crowdsourcing Frederic Jacquet Chief Innovation Officer Lao-dja S. Tchala Data-Scientist
- 2. Agenda § Talk Overview: What can you expect § AI/ML to Augment Chatbots capabilities § Methods & Human to improve bots knowledge base § Perspective: Making an Inclusive Chatbot § Wraping Up
- 3. • Talk Overview: What can you expect • AI/ML to Augment Chatbots capabilities • Methods & Human to improve bot knowledge base • Perspectives: Making an Inclusive Chatbot • Wrapping Up
- 4. "By 2020, the average person will have more conversations with bots than with their spouse" Gartner 2016 Gartner's Top 10 Strategic Predictions for 2017 and Beyond: Surviving the Storm Winds of Digital Disruption
- 5. An Experimental Study combining AI and Crowdsourcing Identifying the most relevant AI/ML features that bring advanced conversational capabilities to Chatbots. Bringing more understanding and replying abilities to Bots defining a progressive methodology where data are improved based on crowdsourcing. • Methods and Human • AI/ML to Augment Bots
- 7. • Talk Overview: What can you expect • AI/ML to Augment Chatbots capabilities • Methods & Human to improve bot knowledge base • Perspectives: Making an Inclusive Chatbot • Wrapping Up
- 8. AI : Computer Vision – multiple choices • What is it? • Why? • Our hypothesis • Which technologies?
- 9. AI : Computer Vision – multiple choices • How it works?
- 10. AI : Computer Vision – multiple choices Train a model with a set of images 1 Track a hand on the images 2 Calculate the center of the cercle around the hand 3 Coding an action depending on the center’s position 4 ACTION 1 ACTION 2
- 11. AI : Computer Vision – multiple choices • Demo
- 13. AI : Computer Vision – multiple choise • Future improvements
- 14. ML : Recommendation algorithm • What is it? • Why? • Our hypothesis • Which technologies?
- 15. ML : Recommendation algorithm • How it works?
- 16. ML : Recommendation algorithm • General Qi Qj Qk Full Text Full Text Full Text Ai Aj Ak Ai Aj Ak Ai Qi Aj Qj Ak Qk Qi Qk Key_word_i1 Key_word_i2 Key_word_k1 Key_word_k2 User send message 1 Bot answers 2 Mapping 3 Key words extraction 4 Model prediction 5 Qi Qk Ai Aj • Helpdesk use case : equipment's recommendation Qj Full Text Full Text Full Text Qi Qk Manager Equipment_i1 Equipment_i2 Equipment_k1 Equipment_k2 Authentificated users send messages 1 Key words extraction 4 Developer Administrator Qi Qk 2 3 Rôle prediction 5 Administrator Developer Administrator tips Bot predicts the user’s rôle and proposes supplement equipments 6 Equipment_i1 Equipment_i2 You must be an Administrator You might also need Administrator
- 17. ML : Recommendation algorithm • Demo
- 19. ML : Recommendation algorithm • Future improvements
- 20. • Talk Overview: What can you expect • AI/ML to Augment Chatbots capabilities • Methods & Human to improve bot knowledge base • Perspectives: Making an Inclusive Chatbot • Wrapping Up
- 21. Humans’ contribution • What is it? • Why? • Our hypothesis • Which technologies?
- 22. Humans’ contribution • How it works?
- 23. Humans’ contribution Qi Qj Qk Ai Aj Ak Qi Ai Qi1 Qi1 Qi1 Ai Qi1 Qi1 Ai Ai Ai Qi Ai Qi1 Qi1 Qi1 Ai Qi1 Qi1 Ai Ai Ai Qi Ai Qi1 Qi1 Ai Qi1 Ai Ai Augmenting Data 1 Cleaning Data 2 Feeding the bot 3 Ask for clarification Reformulating answers Adaptive answers
- 27. Humans’ contribution • Future improvements
- 28. Adaptive answers • What is it? • Why? • Our hypothesis • Which technologies?
- 29. Adaptive answers • How it works?
- 30. Adaptive answers Qi Qj Qk Full Text Full Text Full Text Ai Aj Ak Ai Aj Ak Ai Aj Ak Ai Ak User send message 1 Bot answers 2 Training Data from user 3 Model predict which answers the user may like or not 4 Model training 5 Aj Al Ak New answers’ prediction True values Predcitons Memory to use in next conversations Al Ak 6
- 33. Adaptive answers • Future improvements
- 34. Reformulating answers • What is it? • Why? • Our hypothesis • Which technologies?
- 35. Reformulating answers • How it works?
- 36. Reformulating answers Qj User send message 1 Ai Ai1 Ai2 Ai3 Ai Ai1 Ai2 Ai3 Bot chose a response and send it to the user 2 The already given responses are removed from the ones the bot will chose from 3 Qj Ai Ai1 Ai2 Ai3 OR 4
- 39. Reformulating answers • Future improvements
- 40. Ask for clarification • What is it? • Why? • Our hypothesis • Which technologies?
- 41. Ask for clarification • How it works?
- 42. Ask for clarification Qi 2 questions are similar 0 Qi Qi1 Qi1 Qj Qj1 Qj1 Qi Qj Qf Qe Qf User ask one of them 1 Key_word_i1 Key_word_i2 Key_word_k1 Key_word_k2 Key_word_i1 Key_word_i2 Key_word_k1 Key_word_k2 Keys words are extracted from each of those questions 2 Keys words are used to ask clarification to the user 3 Do you mean Key_word_i1 Key_word_i2 Key_word_k1 Key_word_k2 or Key_word_i1 Key_word_i2 Key_word_k1 Key_word_k2 ?
- 43. Ask for clarification • Demo
- 45. Ask for clarification • Future improvements
- 46. • Talk Overview: What can you expect • AI/ML to Augment Chatbots capabilities • Methods & Human to improve bot knowledge base • Perspectives: Making an Inclusive Chatbot • Wrapping Up
- 47. Inclusive Chatbots Because texting or talking may prove to be temporarily or permanently challenging because of morphology specifics, trauma, mobility deficiency, stroke, diseases or any deficiency.
- 48. • Talk Overview: What can you expect • AI/ML to Augment Chatbots capabilities • Methods & Human to improve bot knowledge base • Perspectives: Making an Inclusive Chatbot • Wrapping Up
- 49. Feedback / Questions Do you have any questions? Your feedback is important to us. Don’t forget to rate and review the sessions.