The talk consists of lessons learned from developing chatbots for clients, practical tooling, and resources required for creating a usable chatbot.

1. Chatbots: From Simple
to Complex
Peeter Piksarv
Data Scientist @Mooncascade
PyData Tartu Kickoff meetup Monday, 2 April 2018peeter@mooncascade.com

2. 2016: The Year of Chatbots
2017 2018
●
1966 ELIZA
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1995 A.L.I.C.E.
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2010 Siri
●
2016 Facebook Messenger platform
Microsoft Bot Framework

3. Why Chatbots?
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Automated customer service
●
Personal assistants
●
Messaging platform as an alternative UI
●
Focused tool
●
Games and toys

4. Examples
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Siri, Google Assistant, Cortana, Amazon Alexa
●
Microsoft's Xiaolce (Chinese)
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x.ai Amy -- scheduling assistant
●
DoNotPay -- contesting parking tickets
●
Poncho -- weather forecast bot

5. NLP Tools for Chatbots
LUIS
IBM Watson Assistant
Amazon Lex

6. Working Principles
●
Intents
– Mapping of user input
to actions
– min 10-20 example
utterances per intent
●
Context
●
Slots / Entities
– Required information
for current action
– Locations
– Date/time

7. Where to start?
●
Purpose / use case?
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Supported languages?
●
Platform? -- limitations and features
●
Data? -- previous (chat) interactions

8. ●
When using external NLP tools, keep your dev-
staging-production-cycle
●
Adding voice support adds extra errors
90%-90%-90% -> 73%
●
Self improvement is tricky to get right
(remember Tay?)
●
Manage expectations, better to start simple!

9. Personal examples
●
Tune: Slack tunebot
https://help.tune.com/marketing-console/slack-tunebot/
Go, Python, NLP by Dialogflow
●
Merck bot
Internal tool for interacting with Salesforce
MS Bot Framework, LUIS

10. Reading Materials
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Chatbots Magazine: chatbotsmagazine.com
●
Chatbot's Life: chatbotslife.com
●
Recast.AI newsletter
●
The UX of Chatbots: uxofchatbots.com

11. Thank you!