5天学习AI Agent：Day 1 智能体与大语言模型简介

Day 1: Introduction to AI Agents and Large Language Models (LLMs)

Goal: Gain a foundational understanding of AI agents, their key components, and the role LLMs (like GPT-4) play in forming the core intelligence of AI agents.

Topics:What are AI Agents?

Definition: 

• AI agents are autonomous systems that perceive their environment, make decisions, and take actions to achieve specific goals. They can be either task-driven (e.g., virtual assistants) or adaptive, continuously learning from their experiences.

Key Concepts:

• Perception: How agents gather information from their environment.
• Action: How agents perform tasks or communicate.
• Learning: Agents’ ability to improve performance through experience (via reinforcement learning, supervised learning, etc.).
• Decision-Making: The logic or algorithms agents use to decide the next step or action.

Basic Concepts of Agents in AI:

• Perception: The sensory input or the way agents understand their surroundings. For LLM-based agents, this could be text, images, or other forms of input.
• Action: The responses or outputs agents give based on the input and their goals.
• Learning: How agents can improve over time using various learning models.
• Decision-Making: The process through which agents select actions based on their knowledge and objectives.

How LLMs like GPT-4 Form the “Brain” of AI Agents:

LLMs as the Brain: Large Language Models, like GPT-4, process text, generate responses, and make decisions, making them well-suited to serve as the core intelligence of AI agents.

Transformer Architecture: LLMs are based on the transformer model, which processes information in parallel and captures long-term dependencies in text.

Pre-Training and Fine-Tuning:

• Pre-Training: The model is trained on massive datasets to learn general language understanding.
• Fine-Tuning: The pre-trained model is further trained on specific tasks (e.g., customer service) to refine its responses for real-world applications.

第一天：智能体和大语言模型简介

目标：掌握智能体（AI Agents）的基础概念，了解其核心组成部分，以及大语言模型（如 GPT-4）在智能体扮演的核心智能角色。

1.什么是智能体（AI Agents）？

定义：智能体是一种自主系统，能够感知环境、做出决策并执行相应的行动，以实现特定目标。智能体可以是任务驱动型的（如虚拟助手），也可以是自适应型的，能够通过持续学习不断优化自身能力。

关键概念：

• 感知（Perception）
• 行动（Action）
• 学习（Learning）
• 决策（Decision-Making）

2.智能体的基本概念：

• 感知（Perception）：指智能体获取环境信息的方式。对于基于大语言模型（LLM）的智能体，感知可以表现为文本输入、图像或其他形式的数据输入。
• 行动（Action）：指智能体基于输入信息和自身目标做出的响应或输出。
• 学习（Learning）：指智能体如何利用各种学习方法（如监督学习、强化学习）不断优化自身的能力和表现。
• 决策（Decision-Making）：指智能体如何根据已有知识和目标选择最优行动策略。

3.大语言模型（如GPT-4）如何成为智能体的“核心大脑”：

LLMs作为“大脑”：大语言模型（如GPT-4）能够处理文本、生成响应、执行推理任务，因此非常适合作为AI代理的核心智能模块。

Transformer结构：大语言模型基于Transformer架构，能够并行处理信息，并捕捉文本中的长距离依赖关系，从而提高语言理解和生成能力。

预训练与微调（Pre-Training and Fine-Tuning）：

• 预训练（Pre-Training）：大模型在大规模数据集上进行训练，以学习通用的语言理解能力。
• 微调（Fine-Tuning）：在特定任务（如客服、医疗咨询等）上对预训练模型进行进一步训练，使其在特定领域的应用更具针对性和精准性。