Angus J. McLean

This talk explores the evolving relationship between humans and large language models (LLMs), particularly in the context of agentic systems. It argues against the hype surrounding rapid AI advancements, emphasizing that core LLM capabilities have not fundamentally changed. Instead, many current tools act as temporary fixes for inherent model limitations like data inefficiency and a lack of continuous learning. The core thesis suggests that understanding and leveraging these limitations through self-imposed constraints and diverse representation structures can lead to more effective and creative AI applications.