Summer Hangouts: Jingye Yang and Carlos Schmidt-Padilla

Introduction to LLMs and Introduction to Causal Inference

August 03, 2023 11:00 am - 1:30 pm

Jingye Yang and Carlos Schmidt-Padilla

RDDSX space outside the Collaborative Classroom

Van Pelt-Dietrich Library

Time	Instructor	Title	Description
11:00 – 12:00	Jingye Yang	Introduction to LLMs	Ever wonder how ChatGPT seem to ‘understand’ what you’re saying? This friendly talk will dive into the world of large language models. We’ll break down how these AI brains learn to chat just like us, their cool uses (think helping with work or writing a novel), and the challenges we face with them, like making sure they’re used responsibly.
12:00 – 12:30		Lunch
12:30 – 1:30	Carlos Schmidt-Padilla	Introduction to Causal Inference in person only	In this tutorial, we will provide a comprehensive explanation of causal inference, which involves understanding and determining the cause-and-effect relationship between variables or events. We will explore how this concept is applicable to various fields, including technology (such as A/B testing), health sciences, and the social sciences. By delving into these applications, we will gain insights into how causal inference plays a crucial role in understanding and making informed decisions based on causal relationships in diverse domains.

Time

Instructor

Title

Description

11:00 – 12:00

Jingye Yang

Introduction to LLMs

Ever wonder how ChatGPT seem to ‘understand’ what you’re saying? This friendly talk will dive into the world of large language models. We’ll break down how these AI brains learn to chat just like us, their cool uses (think helping with work or writing a novel), and the challenges we face with them, like making sure they’re used responsibly.

12:00 – 12:30

Lunch

12:30 – 1:30

Carlos Schmidt-Padilla

Introduction to Causal Inference

in person only

In this tutorial, we will provide a comprehensive explanation of causal inference, which involves understanding and determining the cause-and-effect relationship between variables or events. We will explore how this concept is applicable to various fields, including technology (such as A/B testing), health sciences, and the social sciences. By delving into these applications, we will gain insights into how causal inference plays a crucial role in understanding and making informed decisions based on causal relationships in diverse domains.