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AI的大模型训练和应用开发的几个主要领域
AI的大模型训练和应用开发分为几个主要领域： 1. 预训练 (Pre-training) ▪ 作用： 这是AI模型的基石，通过“压缩”整个互联网数据，让模型学习并记住海量信息，从而决定模型的智能上限和基础能力。它定义了模型的架构（如Transformer、Attention、Encoder-Decoder），并在此阶段完成绝大部分算法层面的创新。 ▪ 人才需求： 这一层主要由最顶级的AI研究员负责，他们通常需要博士学位和十年以上的深度学习、自然语言处理经验。这是一个“赢家通吃”的领域，全球只有少数大型实验室（如Google、Meta、Microsoft）和顶尖初创公司（如OpenAI、Anthropic、XAI）有能力进行基础大模型的预训练，投入巨大。 ▪ 市场价值： 对应着最高的薪酬，可达数千万美元年薪。 ▪ 转型建议： 对普通程序员而言，直接进入这一层难度极大，不建议作为主要转型方向。 2. 后训练 (Post-training) ▪ 作用： 在预训练模型的基础上，进行与人类偏好对齐的训练，使其适应特定的应用场景（如法律、数学、编程）。这包括强化学习与人类反馈对齐 (RLHF)、自学习（如教师模型教学生模型）等新方法，以使模型输出更符合人类预期和情感需求。 ▪ 人才需求： 这一层有大量的工程性问题。对于具备分布式系统经验或数据管道 (data pipeline) 经验的程序员，可以在训练框架开发等方向找到机会。 ▪ 市场价值： 薪酬潜力与模型服务层相似，可达百万美元年薪。 ▪ 转型建议： 这是一个有明确机会的转型方向，尤其是对于有强大分布式系统经验的程序员。 Read more…
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