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Explore the cutting edge of Artificial Intelligence with "Cognitive Architectures for Language Agents (CoALA)" paper https://arxiv.org/pdf/2309.02427
Language agents are an emerging class of AI systems that leverage large language models (LLMs) to interact with the world. While LLMs alone have limitations in knowledge and reasoning, language agents connect them to internal memory and external environments, helping to ground them in existing knowledge or external observations.
Drawing on the rich history of cognitive science and symbolic artificial intelligence, the CoALA framework provides a way to organize existing language agents and plan future developments.
This podcast delves into the core concepts of CoALA, exploring how language agents are structured:
- Memory: Language agents organize information into modules, including working memory for current circumstances and long-term memories like episodic (past experiences), semantic (world facts), and procedural (rules/skills). This allows them to persist information across interactions, unlike stateless LLMs.
- Action Space: Agents interact with the world through a structured action space. This includes external grounding actions to interact with physical, digital, or human environments, and internal actions like retrieval (reading from long-term memory), reasoning (processing working memory to generate new info), and learning (modifying long-term memory or LLM parameters).
- Decision-Making: A generalized procedure structures how agents choose which actions to take, often involving planning stages to propose and evaluate actions before execution.
Join us as we uncover how CoALA provides a blueprint for building more capable agents by defining these interacting modules and processes. Discover how this framework reveals similarities and differences among prominent agents and identifies paths towards language-based general intelligence. Tune in to understand how combining the power of LLMs with structured architectures from cognitive science is shaping the future of AI.
Support the show
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By 1azSend us a text
Explore the cutting edge of Artificial Intelligence with "Cognitive Architectures for Language Agents (CoALA)" paper https://arxiv.org/pdf/2309.02427
Language agents are an emerging class of AI systems that leverage large language models (LLMs) to interact with the world. While LLMs alone have limitations in knowledge and reasoning, language agents connect them to internal memory and external environments, helping to ground them in existing knowledge or external observations.
Drawing on the rich history of cognitive science and symbolic artificial intelligence, the CoALA framework provides a way to organize existing language agents and plan future developments.
This podcast delves into the core concepts of CoALA, exploring how language agents are structured:
- Memory: Language agents organize information into modules, including working memory for current circumstances and long-term memories like episodic (past experiences), semantic (world facts), and procedural (rules/skills). This allows them to persist information across interactions, unlike stateless LLMs.
- Action Space: Agents interact with the world through a structured action space. This includes external grounding actions to interact with physical, digital, or human environments, and internal actions like retrieval (reading from long-term memory), reasoning (processing working memory to generate new info), and learning (modifying long-term memory or LLM parameters).
- Decision-Making: A generalized procedure structures how agents choose which actions to take, often involving planning stages to propose and evaluate actions before execution.
Join us as we uncover how CoALA provides a blueprint for building more capable agents by defining these interacting modules and processes. Discover how this framework reveals similarities and differences among prominent agents and identifies paths towards language-based general intelligence. Tune in to understand how combining the power of LLMs with structured architectures from cognitive science is shaping the future of AI.
Support the show