Explore a 20-minute lecture from UC Berkeley that delves into the groundbreaking "Reasoning and Tools for Forecasting" (RTF) framework, which revolutionizes AI forecasting by combining reasoning capabilities with real-time data tools. Learn how this innovative framework builds upon ReAct architecture, integrating large language models with specialized tools for dynamic system processing. Understand the hierarchical planning system where multiple ReAct agents collaborate - high-level agents managing abstract logic while lower-level agents handle specific computational tasks. Discover how this modular approach enhances predictive accuracy across various fields, from financial markets to environmental science. Examine the framework's practical applications in addressing global challenges like climate change through sophisticated simulation of interconnected environmental, economic, and social factors. Gain insights into the evolution from simple reactive models to complex multi-agent systems, and explore the current challenges in AI complexity management. Read more