Interpretable Neural Symbolic AI
Abstract
Interpretable and neural symbolic AI share a common goal: to enhance the currently opaque and brittle decision making process of deep learning methods. To address this issue, I will discuss the design of novel interpretable deep learning methods endowed with reasoning capabilities. I will then show how these methods could be applied in diverse real-world domains, ranging from answering queries on knowledge graphs to formulating conjectures in universal algebra.
Bio
Pietro Barbiero is Research Assistant at the Università della Svizzera Italiana (Switzerland). My research activity focuses on interpretable artificial intelligence, and neural-symbolic models applied to precision medicine. My current projects are related to interpretable neural reasoning, explainable AI theory, and AI-assisted conjectures for abstract mathematics.