Presentation give at Azure Saturday (https://azuresaturday.dk/) 1st Sep 2017

1. Using Machine Learning and Chatbots
to handle 1st line Technical Support
Barbara Fusinska
@BasiaFusinska

2. About me
Programmer
Machine Learning
Data Scientist
@BasiaFusinska
https://github.com/BasiaFusinska/TechSupportChatBot

3. Agenda
• What is a chatbot?
• Why would you use it?
• How to build a chatbot?
• What are available tools?
• How to apply AI in a chatbot?

4. What’s a chatbot?

5. Turing Test:
Man vs the Machine

6. The Translator

7. The companion

8. Why would
you need a
machine on
the other
side?

9. Production
Chatbots
• Customer Service / Tech
Support
• FAQ
• Scripted scenarios
• Forms replacement
• Online ordering

10. Flights Booking
Demo

11. Chatbot
Platforms
Overview
• Microsoft’s Bot Platform
• Facebook’s Bots for Messenger
• Rebot.me
• Imperson
• ChatScript
• Pandorabots

12. Microsoft Bot Framework Architecture
Receive & Send
Messages
Process Message
System Integration

13. UseCase:ITCrowd
AnsweringMachine

14. “Hello”
“Hi, something’s
wrong with my
computer”
“Hello, IT?”
“Have you tried to
turn it off an on
again?”
Turning off
worked
“You are welcome
then”
“No, no, I will do
that. Thanks.”
YES
“Is it definitely
plugged in?”
NO
“Yes, tried that and
nothing happens!”
USER CHATBOT

15. <<anything>>
<<anything>>
“Hello, IT?”
“Have you tried to
turn it off an on
again?”
Turning off
worked
“You are welcome
then”
<<anything
containing “no”>>
YES
“Is it definitely
plugged in?”
NO
<<anything
containing “yes”>>
USER CHATBOT

16. The IT Crowd Answering Machine
Demo

17. Scripted
solutions
• Conversation order
• Going off script
• Information retrieval
• Closed list of answers
• Regular expressions

18. Chatbots without machine learning

19. Natural
Language
Processing
• Information Extraction
• Guessing the context
(Intents)
• Retrieving data (Named
Entity Recognition)
• Creative Machines

20. Classification problem
Model training
Published model

21. Classification data
Source #Links #Characters ... Fake
TopNews 10 2750 … T
Twitter 2 120 … F
TopNews 235 502 … F
Channel X 1530 3024 … T
Twitter 24 70 … F
StoryLeaks 722 1408 … T
Facebook 98 230 … T
… … … … ...
Features
Labels

22. Classification problem in NLP

23. Bag of words
The quick brown fox jumps over the lazy dog
Never jump over the lazy dog quickly
Dictionary:
{brown, dog, fox, jump, jumps, lazy, never,
over, quick, quickly, the}

24. Stemming
jump, jumps => jump
quick, quickly => quick
Dictionary:
{brown, dog, fox, jump, lazy, never, over, quick,
the}

25. Stop words
a, an, are, in, is, it, the …
Dictionary:
{brown, dog, fox, jump, lazy, never, over, quick}

26. Frequency vectors
The quick brown fox jumps over the lazy dog
[1, 1, 1, 1, 1, 0, 1, 1]
Never jump over the lazy dog quickly
[0, 1, 0, 1, 1, 1, 1, 1]
Dictionary:
{brown, dog, fox, jump, lazy, never, over, quick}

27. Term Frequency - Inverse Document Frequency
𝑡𝑓 𝑡, 𝑑 = 0.5 + 0.5 ∙
𝑓𝑡,𝑑
max
𝑢𝜖𝑑
𝑓𝑢,𝑑
𝑖𝑑𝑓 𝑡, 𝐷 = log
𝐷
{𝑑𝜖𝐷: 𝑡𝜖𝑑}
𝑡𝑓𝑖𝑑𝑓 𝑡, 𝑑, 𝐷 = 𝑡𝑓(𝑡, 𝑑) ∙ 𝑖𝑑𝑓(𝑡, 𝐷)
[0.02, 0.5, ..., 0.001]

28. Applying Artificial Intelligence
Receive & Send
Messages
Process Message
System Integration
AI
(LUIS)

29. “Hi, something’s
wrong with my
computer”
“Have you tried to
turn it off an on
again?”
Already
advised
“Great, and what is
the result?”
“No, I will try it
now.”
YES
“Ok, noted. We
should schedule
the repair”
NO
“Yes, tried that and
nothing happens!”
USER CHATBOT
Not Working
Not Working
Try
“That’s great. You
are welcome then”
“I’ve just tried it
and it worked!”
Worked

30. Language Understanding Intelligent Service
Demo

31. Applying Intelligence to the Chatbot
Demo

32. LUIS
Challenges
• Training process
• Script vs Intents
• Classification errors
• Common phrases
• Hello/Goodbye
• Yes/No

33. Bot connector

36. Keep in touch
BarbaraFusinska.com
@BasiaFusinska
https://github.com/BasiaFusinska/TechSupportChatBot