Seminar at Nanyang Technological University
Human-Machine Teaming in Practice: Bayesian Networks as a Collaborative Approach to Artificial Intelligence
Thursday, March 28, 2019, 1:00 p.m. – 4:00 p.m.
Tan Chin Tuan Lecture Theatre, LT2
Nanyang Technological University, Singapore
“Currently, Bayesian Networks have become one of the most complete, self-sustained and coherent formalisms used for knowledge acquisition, representation and application through computer systems.” (Bouhamed et al., 2015)
In this seminar, we illustrate how scientists in many fields of study — rather than only computer scientists — can employ Bayesian networks as a very practical form of Artificial Intelligence for exploring complex problems. We present the remarkably simple theory behind Bayesian networks and then demonstrate how to utilize them for research and analytics tasks.
However, key to unlocking the full potential of Bayesian networks is recognizing their capacity for human-machine teaming. On the one hand, Bayesian networks can perform reasoning tasks that no human could ever perform. On the other hand, Bayesian networks can directly incorporate human causal knowledge, which computers cannot generate independently. As a result, Bayesian networks are a symbiosis based on mutual learning.
Outline of Talk
- The promise, the peril, and the limitations of Artificial Intelligence
- Human cognitive limitations & biases in reasoning
- Human-Machine Teaming: Combining the knowledge and reasoning capabilities of humans and computers.
Background: A Conceptual Map of Analytic Modeling and Reasoning
- Inference type: probabilistic vs. deterministic
- Model purpose: observational vs. causal inference
- Model source: data vs. theory
Introducing Bayesian Networks as a Research Framework
- Under the hood: the simple math of Bayesian networks
- Key advantages of Bayesian networks as a modeling framework
Bayesian Networks in Practice (with Software Demonstrations):
- Knowledge Encoding & Probabilistic Inference
- Introductory example: Prosecutor's Fallacy
- Reinventing the Delphi Method: sparse knowledge for sound reasoning under extreme uncertainty
- Knowledge Discovery for Classification/Prediction
- Optimizing medical diagnostics with Bayesian networks and information-theoretic criteria
- Knowledge Discovery for Human Interpretation
- Visualizing the data-generating process, not just the data
- 2D/3D/VR Visualization of network structures
- Knowledge Encoding + Knowledge Discovery for Causal Inference
- Simpson's Paradox rears its ugly head
- Human reason to the rescue!
Mr Stefan Conrady
Managing Partner, Bayesia USA & Bayesia Singapore
Stefan Conrady has over 20 years of experience in decision analysis, market intelligence, and product strategy with Fortune 100 companies in North America, Europe, and Asia. Today, in his role as Managing Partner of Bayesia USA and Bayesia Singapore, he is recognized as a thought leader in applying Bayesian networks for research, analytics, and reasoning.
Dr How Meng-Leong
Research Fellow, Office of Education Research, NIE/NTU
Meng-Leong graduated in 2015 with a PhD in Education from Monash University, Australia. He is a Research Fellow in the Centre of Research in Pedagogy and Practice (CRPP) in the Office of Educational Research (OER), National Institute of Education. He enjoys using Machine Learning to analyse “small data” for Unified Analytics from multiple research projects, typically with small numbers of participants (each class with around 30 students) which can be used to generate a combined meta-analysis for educational stakeholders.
This event is organised by the Office of Education Research, National Institute of Education, Nanyang Technological University, Singapore. Please direct enquiries to email@example.com or call +65-67903865.