When privacy matters! A series of challenges for chatbots in data-sensitive businesses such as healthcare and finance by Christophe Willemsen Meetup: Integration of Chatbots in Healthcare and BFSI, Dubai, 1.11.2018

1. When privacy matters !
Chatbots in data-sensitive businesses
GraphAware, world’s #1 Neo4j consultancy
Creators of Hume - AI for Humans
graphaware.com
@graph_aware
Christophe Willemsen
CTO GraphAware
@ikwattro

2. ● A word on data privacy
● Build your own : expectations vs reality
● Natural Language Processing
● Challenges with NLP
● Sentiment
● Q & A
Outline

3. A word on data privacy

4. ● Where data is sent ?
● What is sensitive ?
● Are your users aware ?
Data Privacy

5. Build your own :
expectations

6. Business guy

7. Business guy I WANT A
CHATBOT

8. IT Department

9. IT Department

10. DataScience ARMY

11. Build your own :
expectations

12. ● Creating a Chatbot with Deep Learning, Python, and TensorFlow
● How Chatbots Are Learning Emotions Using Deep Learning
● How I Used Deep Learning To Train A Chatbot To Talk Like Me
● Neuralconvo - Chatting with a Deep learning brain
● ...
Current conversation topics

13. ● Creating a Chatbot with Deep Learning, Python, and TensorFlow
● How Chatbots Are Learning Emotions Using Deep Learning
● How I Used Deep Learning To Train A Chatbot To Talk Like Me
● Neuralconvo - Chatting with a Deep learning brain
● …
● Training your Machine Learning models on the blockchain
● Conversational Artificial Intelligence in the FinTech
Current conversation topics

14. Build your own :
reality

15. Conversation Flow

16. ● It is the identification and categorization of what a user online
intended or wanted when they typed their query
Intent detection

17. Conversation Flow

18. Business guy

19. ● It is the identification and categorization of what a user online
intended or wanted when they typed their
● Intent detection using semantically enriched word embeddings
● Zero-shot User Intent Detection via Capsule Neural Networks
● A Bi-model based RNN Semantic Frame Parsing Model for Intent
Detection and Slot Filling
Intent detection

20. Business guy

22. ● Naive Bayes classifier
Intent detection

23. Intent detection

24. Build your own :
Challenges with NLP

25. ● Bot : Hi, I am your health assistant, how can I help you ?
● User : I have a cough
Named Entity Recognition

26. ● Bot : Hi, I am your health assistant, how can I help you ?
● User : I have a cough
Named Entity Recognition

27. ● Bot : Hi, I am your health assistant, how can I help you ?
● User : I have a cough
Named Entity Recognition
SYMPTOM

28. Named Entity Recognition
Finding data

29. Named Entity Recognition
Finding data

30. Named Entity Recognition
Finding data

31. Named Entity Recognition
Finding data

32. hume.ga/ner

33. Build your own :
Challenges with NLP

34. Coreference Resolution
● Bot : Hi, I am your health assistant, how can I help you ?
● User : I have pain in the neck, it is really burning

35. Coreference Resolution
● Bot : Hi, I am your health assistant, how can I help you ?
● User : I have pain in the neck, it is really burning
Refers to

36. Coreference Resolution

37. Coreference Resolution

38. Build your own :
Challenges with NLP

39. Sentiment Analysis
● Sentiment analysis aims to determine the attitude of a speaker,
writer, or other subject with respect to some topic or the overall
contextual polarity or emotional reaction to a document,
interaction, or event

40. Sentiment Analysis

41. Sentiment Analysis

42. Sentiment Analysis
● If chatbots are not your primary mean of communication, it
means you failed at other points ( the user could not find the
information online )
● A typical negative, neutral, positive polarity does not reflect the
reality

43. Sentiment Analysis
angry
Very angry
I’m gonna find your family

44. Build your own :
Challenges with NLP

45. Expectations vs reality
● Bot : I am your financial assistant, how can I help you ?
● User : Hi, I could not login into the online banking system

46. Expectations vs reality
● Bot : I am your financial assistant, how can I help you ?
● User : Hi, I could not login into the online banking system
● The app doesn’t work, dunno man, wtf ?

47. Expectations vs reality
● Bot : I am your health assistant, how can I help you ?
● User : Hi, I have a cough

48. Expectations vs reality
● Bot : I am your health assistant, how can I help you ?
● User : Hi, I have a cough
● ‫ﺳﻌﺎل‬ ‫ﻋﻧدي‬ ، ً‫ﺎ‬‫ﻣرﺣﺑ‬

49. Expectations vs reality
● Bot : I am your health assistant, how can I help you ?
● User : Hi, I have a cough
● ‫ﺳﻌﺎل‬ ‫ﻋﻧدي‬ ، ً‫ﺎ‬‫ﻣرﺣﺑ‬
● Most NLP Processing techniques for English do not work for
Arabic
● Arabic has 40 dialects
● POS tagging is dependant on the domain
● etc...

50. Build your own :
So what’s the point now ?

51. Data-privacy is a trade-off
● When you make the choice of data-privacy, you have trade-offs
○ You do not sit on the shoulders on giants anymore
○ The cost of research will exponentially surpass the usage cost
of a third-party solution
○ You have the sole responsibility of the satisfaction of the
customers

52. It has to work 100% of the times

53. “I don’t understand your
question”
is not an answer

54. It’s an insult !

55. Integrate all the things
● Intent Detection
● Natural Language Processing
● Custom Named Entity model training
● Co-reference resolution training
● English, Arabic, German support
● Graph-based context builder

56. hume.ga
graphaware.com

58. Questions ?