AIAI Seminar - 15/11/21 - Kwabena Nuamah

Title:   Deep Algorithmic Question Answering: Towards a Compositionally Hybrid AI for Algorithmic Reasoning

Abstract: 

An important aspect of artificial intelligence (AI) is the ability to reason in a step-by-step "algorithmic" manner that can be inspected and verified for its correctness. This is especially important in the domain of question answering (QA). While neural network models with end-to-end training pipelines perform well in specific applications such as image classification and language modeling, they cannot, on their own, successfully perform algorithmic reasoning, especially if the task spans multiple domains. We discuss a few notable exceptions and point out how they are still limited when the QA problem is widened to include other intelligence-requiring tasks. Our claim is that the challenge of algorithmic reasoning in QA can be effectively tackled with a "systems" approach to AI which features a hybrid use of symbolic and sub-symbolic methods including deep neural networks. 

In this talk, I propose an approach to algorithm reasoning for QA, "Deep Algorithmic Question Answering (DAQA)", based on three desirable properties: interpretability, generalizability, and robustness which such an AI system should possess. Additionally, I will discuss how we are trying to achieve these objectives in our work on the FRANK question answering system.