Speaker: Jin Song Dong (National University of Singapore)

Time: 16:00 - 18:00 p.m., Oct 8, 2024, GMT+8

Venue: Lecture hall 1126, Science Building #1 (Yanyuan)

Abstract: 

Machine Learning (ML) systems have become increasingly integral to safety and security-critical applications. However, a significant challenge arises from the inherent lack of explainability and verifiability in many ML systems. Our recent research has focused on addressing this issue by developing a Trusted ML system. The initial segment of this presentation delves into the "Silas: Trusted Machine Learning System," an initiative that seamlessly integrates open machine learning with formal automated reasoning (www.depintel.com). In the subsequent part of the discussion, we explore the reasoning capabilities of LLM (encompassing ChatGPT3.5 and GPT4). Specifically, we discuss the approaches to link LLM with formal reasoning techniques, aiming to establish a framework for trusted LLM agents. As a practical demonstration, we will present the application of probabilistic reasoning, machine learning, LLM, and computer vision to sports analytics and share the vision of a new international sports analytics conference series (https://formal-analysis.com/isace/2025/).

Source: School of Computer Science, PKU