Eleni Mangina, Professor, University College Dublin, Computer Science, Dublin, Ireland

Title: Data Analytics for Sustainable Global Supply Chains

Abstract: In today’s modern, fast paced and globally connected world, Supply Chains have undoubtedly become a critical component of several business operations and impact our lives every day. They are crucial to the functioning of almost all the industries and every product we use and consume everyday including tooth pastes, cars, clothes, oil and electricity are products of a Supply Chain. The last century has seen rapid economic liberalization and the opening up of new markets which has allowed companies to gain access to new markets and move their production and other key economic activities to countries which offer reduced government regulation, low production costs, cheap skilled labour among other factors which help it gain a competitive edge. This globalisation has led to the creation of a Global Supply Chain network which transcends borders and this brings with it the challenges of efficiency and an increased time to market which are critical to the success of any Supply Chain. The aim of this presentation is to investigate data from the Road Freight Transport Operations in Europe, find patterns in logistic operations and analyse them based on the efficiency in terms of the vehicle utilization or the degree of loading of vehicles during each journey and sustainability in terms of amount of Carbon-dioxide emissions per journey.

David Leslie, Director of Ethics and Responsible Innovation Research at The Alan Turing Institute and Professor of Ethics, Technology and Society, Queen Mary University of London, UK

Title:-

Abstract: TBD

Amit Sheth, Founding Director, Artificial Intelligence Institute at University of South Carolina, USA

Title: Building trustworthy Neuro-symbolic AI systems with explainability and safety: Knowledge is the key

Abstract: “Data alone is not enough.” This was the section heading in Pedro Domingos’ 2012 seminal paper. I have been a believer in this and in the duality (synergistic value) of data and knowledge for a long time. In our Semantic Search engine, commercialized in 2000, we complemented machine learning classifiers with a comprehensive WorldModel™ or knowledge bases (now referred to as knowledge graphs) for improved named entity and relationship extraction and semantic search. It was an early demonstration of the complementary nature of data-driven statistical learning (since replaced by neural networks) and knowledge-supported symbolic AI methods. In this talk, I want to observe three important issues about the Why, What, and How of using knowledge in neuro-symbolic AI systems to advance from NLP to NLU. While the transformer-based models have achieved tremendous success in many NLP tasks, the pure data-driven approach comes up short when we need NLU, where knowledge is key to understanding the language, as required for the explanation, safety, and ensuring adherence to decision-making processes that must be followed (e.g., in clinical diagnosis). Throughout the talk, I will use examples from the social good domains to demonstrate the need for “understanding” (for example, for safety with explanations) and why/how knowledge-infused learning offers better outcomes compared to data-driven only alternatives. Link to the Presentation