Taking KR FOR ML a bit further

1. DEEP KNOWLEDGE
REPRESENTATION
Paola Di Maio

2. CONTENTS
SEE THE OTHER SLIDES FOR KNOWLEDGE REPRESENTATION FOR
MACHINE LEARNING
KR FOR AI , symbolic, fucnionaal, subsymbolic
MACHINE LEARNING
DEEP KNOWLEDGE REPRESENTATION
CONVERGENCE

3. DEEP KR
SAME AS KR FOR ML
BUT MORE TARGETED AT NN, AND OTHER ISSUES

4. INTRO AND OVERVIEW
A SPECIAL ISSUE * MDPI SYSTEMS 2019
A WORKSHOP, AND A TALK
COURSE NOTES (GRADUATE, POSTGRADUATE)
A course manual

5. KNOWLEDGE REPRESENTATION
WHAT IS IT
HOW HAS IT CHANGED
What is new

6. KNOWLEDGE REPRESENTATION
SYMBOLIC
SUBSYMBOLIC
BOTH OR NEITHER?
FUNCTIONAL
OTHER

7. MACHINE LEARNING
NEURAL NETWORKS
OTHER TECHNIQUEST

8. WHAT THEY MEAN KR FOR ML
KR FOR ML research is NOT STANDARD KR as before ML
THE ISSUES (FOR EXAMPLE NEELAKANTH) CURRENT KR FOR ML tackle
are different from the beginning of KR

9. WHAT I MEAN KR FOR ML
See the explainability arguments

10. DEEP
EMBEDDED
ENCRYPED
ENCODED
NOT VISIBLE AT USER LEVEL

11. ISSUES IN DEEP KR
MAKING the algorithms
TRANSPARENT
EXPLAINABLE
AUDITABLE
MAINTAINABLE
ACCOUNTABLE

12. more
See handouts