Mixture of Predictive Agents Architecture

In this video, the narrator introduces the Mixture of Predictive Agents (MoPA) architecture, which leverages the wisdom of many AI agents to predict outcomes by averaging outputs from multiple large language models (LLMs). The primary focus is on predicting Bitcoin prices. The process begins by fetching Bitcoin price data from the CoinGecko API, which is then cached in a JSON file to avoid API rate limits. The architecture allows for the use of various models, including LLaMA, GPT-4, Claude 3.5 Sonnet, and others, by sending a custom prompt to each model in a loop. The models’ predictions are aggregated to form an average prediction for the next 24 hours. The system is designed to be flexible, allowing easy switching between different models. Despite some initial inaccuracies, such as overly optimistic predictions, the architecture demonstrates the potential of using multiple models to improve predictive accuracy. The video also highlights the ease of setting up and testing the MoPA system, making it a valuable tool for those interested in AI-driven predictions. Additionally, the video is sponsored by HubSpot, which offers an ebook on enhancing productivity using AI.