This article discusses the challenges and methods for teaching a language model to generate humor. It details the use of specific rubrics to evaluate comedic content and describes the data collection process from various platforms like Twitter and TikTok. The author shares successes and failures in refining the model's ability to produce funny responses.

The article critiques various AI platforms, highlighting design flaws and performance issues. It uses humor and slang to express dissatisfaction, particularly focusing on poor visual aesthetics and functionality. Each platform is rated, with some described as “cooked” or a “digital war crime.”

The article discusses an experiment using reinforcement learning to generate humor, specifically aiming to create the funniest joke with the help of GPT-4. It explores the intricacies of humor generation and the effectiveness of AI in crafting jokes that resonate with human audiences.