This post clarifies the distinction between Large Language Models (LLMs) and AI Agents, aiming to reduce misunderstandings. LLMs are described as neural networks trained on text that predict the next word based on statistical patterns, capable of generating text, answering questions from training data, and performing tasks like summarization and translation. However, they cannot access external data, perform actions, learn post-training, or use tools. AI Agents, exemplified by Hermes-Agent, build upon LLMs by integrating tools, planning capabilities, and action cycles, enabling interaction with the external world through components like LLMs for reasoning, tools for external access, planning for task decomposition, and memory for storing intermediate results. AI
IMPACT Clarifies fundamental differences between LLMs and AI agents, aiding understanding of AI capabilities and limitations.
RANK_REASON The item explains technical concepts related to AI models.
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