Joshua Starmer

In this hands-on workshop, participants will explore the cutting-edge world of Large Language Models (LLMs), Reinforcement Learning (RL), and building autonomous AI agents. Combining theory with hands-on coding examples, this session is designed to bridge the gap between theoretical concepts and real-world applications. By the end of the workshop, participants will have a solid understanding of how to build, train, and fine-tune an LLM for specific applications as well as how to increase their utility with RAG and AI Agents.

*Note: These are tentative details and are subject to change.

Although Large Language Models and AI are known to generate false and misleading responses to prompts, relatively little effort has gone into understanding how we can quantify the confidence we should have in the output from these models. In this hack session, the speaker will illustrate the problem using a simple neural network and then demonstrate two methods for quantifying our confidence in the model outputs. He will then show how these methods can be applied to Large Language Models and AI.