0:00 Overview of Current Work: The video introduces the presenter's ongoing work on restructuring the conceptualization, teaching, and application of AI agents. It emphasizes the broader context of AI agents beyond current popular definitions. The speaker mentions personal reasons for the absence of consistent posting: the birth of his son, and work on other projects over aelo.
0:01 React Agent Review (0:02:05): The presenter briefly revisits the ReAct (Reasoning and Acting) agent, a foundational structure for modern LLM-based agents.
0:02 React Defined (0:02:17): ReAct involves giving an LLM an input (text), access to tools, and prompting it to go through multiple reasoning steps before generating an output.
0:07 Limitations of Current Agent Definitions: The presenter believes that defining agents solely as LLMs capable of calling tools is too restrictive for production use, citing broader literature definitions.
0:07 Neurosymbolic Architecture (0:08:00): The presenter highlights the "Miracle" paper's definition of agents as a "neurosymbolic architecture" that combines neural networks and symbolic AI.
0:09 Symbolic AI (0:09:25): Symbolic AI, the traditional AI approach from the mid-20th century, relies on written rules, ontologies, and logical functions for achieving AGI. An example provided is Aristotle's syllogistic logic (major premise, minor premise, conclusion).
0:12 Connectionist AI (0:12:38): Connectionist AI, now known as neural AI, focuses on building AI systems based on brain mechanisms.
0:14 Example of Semantic Router: Demonstrates use of semantic routers (embedding models) which are neuron-network based. Input examples can be provided, to trigger a surge.
0:20 Defining Agents as Neural + Symbolic (0:19:54): The presenter's preferred definition of agents incorporates both neural networks (LLMs) and symbolic components (handwritten code). This definition is rooted in the history of AI and allows for flexibility in system design.
0:25 More Flexible Definition (0:25:21): The video then expands, describing how to use alternate tools to follow on, when agents are intended for different purposes. If one tool is for reading news, the other is for a math question, for example.
0:27 Alternative Agent Architectures (0:27:19): Suggests that simply thinking of an agent as a LM with tool calls in a loop, is not always an effective approach.
0:28 Conclusion and Future Content (0:27:56): The presenter concludes by stating their approach to agents differs from the standard narrative, but hopes to provide more structured content on the topic soon.