T4: Machine Reasoning: Technology, Dilemma and Future

Nan Duan, Duyu Tang, Ming Zhou

Live Session 1: Nov 19, Live Session 1: Nov 19 (09:00-10:00 UTC) [Join Zoom Meeting]
Live Session 2: Nov 20, Live Session 2: Nov 20 (01:00-02:00 UTC) [Join Zoom Meeting]
Abstract: Machine reasoning research aims to build interpretable AI systems that can solve problems or draw conclusions from what they are told (i.e. facts and observations) and already know (i.e. models, common sense and knowledge) under certain constraints. In this tutorial, we will (1) describe the motivation of this tutorial and give our definition on machine reasoning; (2) introduce typical machine reasoning frameworks, including symbolic reasoning, probabilistic reasoning, neural-symbolic reasoning and neural-evidence reasoning, and show their successful applications in real-world scenarios; (3) talk about the dilemma between black-box neural networks with state-of-the-art performance and machine reasoning approaches with better interpretability; (4) summarize the content of this tutorial and discuss possible future directions.

Time Event Hosts
Nov 19, (09:00-10:00 UTC) Q&A Nan Duan, Duyu Tang and Ming Zhou
Nov 20, (01:00-02:00 UTC) Q&A
Information about the virtual format of this tutorial: This tutorial has a prerecorded talk on this page (see below) that you can watch anytime during the conference. It also has two live sessions that will be conducted on Zoom and will be livestreamed on this page. Additionally, it has a chat window that you can use to have discussions with the tutorial teachers and other attendees anytime during the conference.