JAIST Innovation Design Seminar Mar 1st, 2018 at JAIST Shinagawa Satellite.

1. Copyright 2018, JAIST Naoshi Uchihira.
JAIST Innovation Design Seminar
Artificial Intelligence,
Service Science, and Knowledge Science
1
2018/3/1
Naoshi Uchihira
JAIST
uchihira@jaist.ac.jp

2. 2
Artificial Intelligence and Knowledge Science
Artificial
Intelligence
Human
Intelligence
Knowledge Science

3. 3
Artificial Intelligence and Service Science
Artificial
Intelligence
Service Science
Human
Intelligence
Human
Intelligence
Value
Co-Creation

4. 4
Software Engineering and Machine Leaning
Training
Data
Model
(ex. bug estimation)
Result
Machine Learning
Metrics
Software
Development
Project
Automatically use
Interpret
Awareness triggers
Project
Manager

5. 5
How do PMs use results of machine learning?
• PMs automatically use them.
– Ex. PMs decide module test schedule based on bug
estimation results.
Data Knowledge
• PMs interpret them.
– Ex. PMs make actions for improving module
quality based on bug estimation results.
– Data Knowledge + Explicit Knowledge
• PMs use them as awareness triggers.
– Ex. PMs aware some gaps between plan and current status
based on bug estimation results.
– Data Knowledge + Tacit Knowledge
Interpretability
is needed

6. 6
Interpretability of Machine Learning
International Activities
• ICML (International Conference on Machine Learning)
ICML Workshop on Human Interpretability in Machine Learning
(WHI 2016)
https://arxiv.org/html/1607.02531
• NIPS (Neural Information Processing Systems)
Interpretable ML for Complex Systems NIPS 2016 Workshop
https://sites.google.com/site/nips2016interpretml/
• IJCAI (International Joint Conference on Artificial
Intelligence)
Explainable Artificial Intelligence (XAI)
http://home.earthlink.net/~dwaha/research/meetings/ijcai17-xai/

7. 7
Hybrid Approach is needed
Data
Knowledge
Explicit
Knowledge
Tacit
Knowledge
Decision
Making
Machine
Learning
Logical
Learning
Knowledge
Science
Empirical
Learning