Compared with rule-based AI technologies, the emerging machine learning opened the door for machine acting more like human: natural interactions, engagement, personalization, versatility. This talk will introduce those breakthroughs: 1. methods of learning structured behavior; 2. theory of universal probabilistic language model; 3. practices of deep learning to understand voice and text. In civil aviation, the autopilot developed from CAT I to III. We borrow this term to introduce the possible capacities of conversational AI now and future, and finally how works at convospot.io aligned with this vision.

1. convospot
Conversational AI
from CAT I to III
@homerquan
homer@convospot.io

2. ©Reflen Inc 2017 - confidential and property
Conversational AI
Cognition
Knowledge
Language
Understanding
Language
Synthetic
• How to model knowledge and cognition
• How to get and update those models

3. ©Reflen Inc 2017 - confidential and property
Rule-based chatbot

4. ©Reflen Inc 2017 - confidential and property
How voice assistant works
Source: http://www.macworld.com/article/3067407/ios/meet-viv-the-new-voice-
assistant-from-the-creators-of-siri.html
Entity TaggingNLP + Query plan
Pattern match

5. ©Reflen Inc 2017 - confidential and property
Knowledge from data sources
Source: Google Knowledge Graph Search
Dynamic info

6. ©Reflen Inc 2017 - confidential and property
More or less UX tricks
Source: Cheat Sheet: All Facebook Chatbot Interactions - chatbot magazine
Combined with ML improvement in NLP
They are already very interesting and powerful!
e.s.p Alexa’s voice understanding

7. Challenges
• How to easily acquire cognition and knowledge
• Knowledge with reasoning
• Limited knowledge, new concept

8. ©Reflen Inc 2017 - confidential and property
CAT I,II,III
• CAT I Understand language commands e.g., Siri,
Viv, Alexa
• CAT II Natural interaction with portable knowledge
and memory (knowledge)
• CAT III Reasoning and acquiring new knowledge
proactively (knowledge & cognition)

9. ©Reflen Inc 2017 - confidential and property
Universal Language Model
• A Neural Knowledge Language Model
• Merge of graph model and probabilistic model to represent
knowledges in our language.
e.g., Composing graphical models with neural networks for structured
representations and fast inference

10. ©Reflen Inc 2017 - confidential and property
Method of learning structured behavior
• Learning alone timeline: Long short-term memory (LSTM)
• Dynamic Memory: Memory Network
• Demo: http://yerevann.com/dmn-ui/#/

11. ©Reflen Inc 2017 - confidential and property
Practices of deep learning
• Trained by raw data (end-to-end)
(*But NLP is still necessary)
• Using fast inference to update the model
• Invert Reinforce Learning (IRL) for apprenticeship
learning
(convospot.io)

12. https://goo.gl/7K05nf
Reference papers

13. ©Reflen Inc 2017 - confidential and property
• A channel to deliver conversational AI
• A platform to digitalize knowledges

14. ©Reflen Inc 2017 - confidential and property
Team
Homer Quan
Founder and CEO
www.homerquan.me
Qiang Liu
Mentor on AI
www.cs.dartmouth.edu/~qliu/
Techstars Alumni (2015)
Yahoo Search Entity Graph
Yahoo News
Yahoo E-commerce
Yahoo Local
Assistant Professor
Postdoc