Explore a comprehensive 35-minute video lecture on automated AI agent design systems that delves into the development of self-designing artificial intelligence frameworks. Learn about the novel approach where AI agents utilize advanced language models to generate specialized module architectures across domains like physics, chemistry, and biology. Understand the iterative collaboration process where expert modules independently tackle tasks, undergo peer review, and refine solutions through constructive feedback. Discover how the framework implements a decision module that aggregates refined solutions for optimized outcomes. Follow along with practical demonstrations including GitHub code examples, Python implementations for review agents, and GPT-4 code explanations. Master the fundamentals of agent templates, search functions for agent refinement, and real-world applications in the LLMAgentBase class. Gain insights into cutting-edge developments in automated AI systems, including a special focus on new open research tools for accelerating scientific discovery. Based on research from "Automated Design of Agentic Systems" (arXiv:2408.08435), this technical deep-dive provides hands-on exposure to building scalable, self-improving AI architectures. Read more