Revolution Cognitive AI TOOLS (IBM, Google)
AI Summary
The video discusses increasing intelligence in artificial intelligence by reducing complexity through multi-agent systems. It explains how a central orchestrator agent decomposes a complex human task into simpler sub-tasks handled by specialized agents, illustrated with examples like planning a barbecue or designing a laptop. The video highlights the importance of choosing the right LLM for orchestration, as different models affect task decomposition and results. It contrasts prompt-based multi-agent systems with cognitive tool use for reasoning, referencing recent IBM research on advanced cognitive tools to enhance AI reasoning through understanding, recalling, examining answers, and backtracking.
The concept of controlling AI complexity with human supervision, game theory, and computational complexity theory is introduced. The video covers Google DeepMind’s work on enabling humans to judge complex AI through monitoring debates between AI systems, facilitating stability in decomposing complex problems for human oversight. It presents cutting-edge AI research on modularity, reasoning, and supervising superhuman AI to ensure reliable and trustworthy behavior despite high complexity. The video emphasizes ongoing exploration of cognitive architectures and multi-agent approaches as the future of AI intelligence development.