2024-11-22 chatgpt # Redefining AI Agents: Neural-Symbolic Approaches and Beyond ### 3-Sentence Summary The speaker discusses their evolving understanding of AI agents, emphasizing the limitations of traditional agent definitions as simple LLMs with tool-calling capabilities. They advocate for a broader, neural-symbolic approach that integrates neural networks with symbolic logic to create more robust agentic workflows. This perspective reflects a blend of historical AI philosophies and modern neural network advancements to enhance decision-making and flexibility in AI systems. ### Detailed Summary The video provides an informal overview of the speaker's current thoughts on AI agents and their broader conceptual framework. They begin by reflecting on the popular but narrow definition of agents as LLMs capable of tool-calling loops, such as the React agent model. React agents, derived from a structured methodology, prompt LLMs to reason iteratively and use external tools for answering queries. While effective, this model is considered limiting when applied to real-world production systems. The speaker critiques this standard narrative and introduces a more inclusive definition of agents as "neural-symbolic systems." This definition combines neural network capabilities (e.g., LLMs, embedding models) with symbolic approaches (e.g., handwritten rules, code execution). They provide historical context, tracing symbolic AI's roots to logical frameworks like Aristotle's syllogisms and contrasting it with neural AI, which emerged with concepts like Rosenblatt's perceptron. Neural-symbolic architectures balance the strengths of both approaches, enabling AI systems to process complex queries using both pre-trained neural models and symbolic logic for decision-making. The discussion extends to examples of advanced workflows that blend neural and symbolic methods. For instance, embedding models can classify user intents to directly trigger specific toolsets, bypassing LLM decision-making in some cases. This hybrid methodology improves efficiency, adaptability, and control in AI applications. The speaker concludes by highlighting the need for a flexible definition of agents that goes beyond current practices and embraces diverse technologies to optimize decision-making processes. ### Nested Outline - **Introduction** - Overview of the speaker's thoughts on AI agents. - Personal updates influencing their work. - Goal: Broadening the understanding of agents. - **Traditional Agent Definition** - Explanation of React agents. - Iterative reasoning and tool-calling methodology. - Example scenario: Apple remote query solved through multiple reasoning steps. - Critique of limitations in production use. - **Proposed Definition: Neural-Symbolic Systems** - Combining neural and symbolic approaches. - Historical context: - Symbolic AI (1940s-70s): Logic-based systems, ontologies, and rules. - Neural AI: Rosenblatt's perceptron and the emergence of neural networks. - Defining "agentic workflows" as hybrid systems. - **Neural-Symbolic Architectures in Practice** - Symbolic components: - Code execution, handwritten rules, and logical structures. - Neural components: - LLMs and neural networks for flexible decision-making. - Example: - Embedding models classifying user intents for efficient tool routing. - **Advantages of Neural-Symbolic Systems** - Flexibility in decision-making. - Enhanced control over workflows. - Scalability across diverse use cases. - **Future Directions** - Need for a broader, more inclusive agent definition. - Plans for more structured content on this topic. - **Conclusion** - Summary of ideas presented. - Promise of future updates. ### Table: Comparison of Agent Definitions | **Aspect** | **Traditional Agents** | **Neural-Symbolic Agents** | |---------------------------|----------------------------------------------|----------------------------------------------------| | **Core Model** | LLMs with tool-calling capabilities | Neural networks + symbolic logic | | **Reasoning** | Iterative reasoning within a single loop | Flexible workflows integrating neural-symbolic steps | | **Historical Roots** | Modern LLM frameworks (e.g., React agents) | Rooted in both symbolic AI (1940s-70s) and neural AI | | **Decision-Making** | Restricted to tool-calling | Embedding models, LLMs, and symbolic triggers | | **Use Case Scope** | Narrowly defined scenarios | Broad and adaptable across domains | | **Efficiency** | May require multiple loops | Optimized workflows with embedded logic | | **Flexibility** | Limited to LLM functionality | Combines symbolic rules with neural adaptability | | **Examples** | React agents | Neural-symbolic