Javen Qinfeng Shi Associate Professor, The University of Adelaide (UoA)

Director and Founder, Probabilistic Graphical Model Group, UoA

Director of Advanced Reasoning and Learning, Australian Institute of Machine Learning (AIML), UoA

WHAT IS MACHINE LEARNING? FROM THE SHALLOW END TO

DEEP GRAPH NEURAL NETWORKS

WHAT IS MACHINE LEARNING?

Using data to uncover a unknown underlying process Yaser S. Abu-Mostafa

Gives “computers the ability to learn without being explicitly programmed” Arthur Samuel, 1959

FORMULATION OF MACHINE LEARNING

EXAMPLE

DECISION FUNCTION

RECOGNISE CAT, DOG, OR SHIP

MULTI-LABEL VS SINGLE-LABEL) MULTI-CLASS

Q:

•  Thousands of machine learning algorithms out there. How can we possibly study they all?

•  Many algorithms come out every year, how do we keep up with them?

A:

•  Learning theory analyses sets of algorithms’ behaviour •  Many algorithms can be formulated in a unified

framework called Empirical Risk Minimisation (ERM).

GENERALISATION ERROR

RISK AND LOSS

ERM

LEARNING FROM DATA

WHAT IF YOU DON’T HAVE ‘BIG’ DATA? •  Startups usually do not have ‘Big’ data •  Traditional companies/businesses’ data are often not collected

or organised in a way that AI systems want •  Without big data, can you use AI? •  Yes you can! •  Bonus: it will get you more data once start using.

•  How do you guarantee performance without big data?

DOMAIN KNOWLEDGE •  It works (prior to the era of AI) •  Uncertainty (happens in some probability, rarely 100%) •  Medical •  Industry 4.0, Fish-bone diagram

•  Long time to train a person (think of how many years to train someone to be a doctor)

•  Extremely long time to accumulate (human has accumulated knowledge in thousands of years)

WHAT CLIENTS WANT? •  AI to absorb all human knowledge and experience (even the

wrong ones) •  AI can improve as the quantity and quality of the data improve •  New AI system can incorporate the previous AI systems (or IT

systems) •  With new AI system, predication and decision making should be

better than before •  Interpretable

HOW TO ALLOW AI TO ABSORB KNOWLEDGE

•  Using knowledge and rules to generate (simulate) data, and use AI system to train on them (Silly way)

•  Directly using knowledge and rules to build ‘System of Experts’ (old way, problematic)

•  Build a super machine brain that can

•  Perform both probabilistic reasoning and logic reasoning

•  Use both deep neural networks and graphical models

•  Absorb any knowledge and rules

•  Can correct wrong knowledge and rules •  Can incorporate and inherit all previous systems

•  Can discover new knowledge

•  Interpretable

GRAPHICAL MODELS

GRAPHICAL MODELS

GRAPHICAL MODELS

GRAPHICAL MODELS

APPLICATIONS •  Robotics •  Medical •  Industry 4.0 •  Fintech •  Law •  …

•  Demos

DEEP LEARNING (REPRESENTATION LEARNING)

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DEEP AUTO-SET: A DEEP AUTO-ENCODER-SET NETWORK FOR HUMAN ACTIVITY RECOGNITION USING WEARABLES

DEEP AUTO-SET: A DEEP AUTO-ENCODER-SET NETWORK FOR HUMAN ACTIVITY RECOGNITION USING WEARABLES

DEEP MIND’S GRAPH NETWORKS (OCT. 2018)