Abstract:The talk investigates the current state of the art and the main research directions for common sense reasoning on natural language texts: one of the holy grails of A.I. We will start with a brief history of the area and continue with the approaches and results using end-to-end machine learning: the successes, failures and the issue of explainability. We’ll briefly consider why rule based reasoning has not been successful for the common sense problem. Next we will investigate the current hybrid systems combining machine learning with rule-based reasoning and describe both the approach of adding rules to machine learning systems and the opposite approach of adding machine learning to rule-based systems. The last part of the talk goes into the examples and details of the hybrid common sense system our research group is working on, along with the practical open questions on the path.

Bio: Tanel Tammet is a full professor of applied artificial intelligence at the Tallinn Uni of Technology. He got his MSc from the Uni of Tartu and PhD from the Chalmers Uni of Technology. His core research area is automated reasoning: developing the theory and implementing several leading provers for both classical first order logic and different nonclassical logics. He has also worked on industrial IT systems, government information systems, cyber security, machine learning, data integration and visualization for both large corporate databases and crowd-sourced geodata. His current line of work is building hybrid – machine learning plus automated reasoning – systems for explainable commonsense reasoning over natural language texts.

More information about the event: https://courses.cs.ut.ee/t/esscass2022/

ESSCaSS 2022 has been supported by the University of Tartu ASTRA Project PER ASPERA (European Regional Development Fund).