So you’ve heard these AI terms and nodded along; let’s fix that

你听过这些 AI 术语并点头表示理解了吗？让我们来搞定它们

Artificial intelligence is changing the world, and simultaneously inventing a whole new language to describe how it’s doing it. Spend five minutes reading about AI and you’ll run into LLMs, RAG, RLHF, and a dozen other terms that can make even very smart people in the tech world feel insecure. This glossary is our attempt to fix that. We update it regularly as the field evolves, so consider it a living document, much like the AI systems it describes. 人工智能正在改变世界，同时也发明了一套全新的语言来描述它是如何做到这一点的。花五分钟阅读关于人工智能的内容，你就会遇到 LLM、RAG、RLHF 以及其他十几个术语，这些术语甚至会让科技界非常聪明的人感到不安。这份词汇表是我们试图解决这一问题的尝试。随着该领域的发展，我们会定期更新它，因此请将其视为一份动态文档，就像它所描述的人工智能系统一样。

AGI

通用人工智能 (AGI)

Artificial general intelligence, or AGI, is a nebulous term. But it generally refers to AI that’s more capable than the average human at many, if not most, tasks. OpenAI CEO Sam Altman once described AGI as the “equivalent of a median human that you could hire as a co-worker.” Meanwhile, OpenAI’s charter defines AGI as “highly autonomous systems that outperform humans at most economically valuable work.” Google DeepMind’s understanding differs slightly from these two definitions; the lab views AGI as “AI that’s at least as capable as humans at most cognitive tasks.” Confused? Not to worry — so are experts at the forefront of AI research. 通用人工智能（AGI）是一个模糊的术语。但它通常指在许多（如果不是大多数）任务上比普通人更有能力的人工智能。OpenAI 首席执行官山姆·奥特曼（Sam Altman）曾将 AGI 描述为“相当于你可以雇佣为同事的普通人类”。与此同时，OpenAI 的章程将 AGI 定义为“在大多数具有经济价值的工作中表现优于人类的高度自主系统”。谷歌 DeepMind 的理解与这两个定义略有不同；该实验室将 AGI 视为“在大多数认知任务上至少与人类一样有能力的人工智能”。感到困惑？别担心——处于人工智能研究前沿的专家们也一样。

AI agent

AI 智能体 (AI agent)

An AI agent refers to a tool that uses AI technologies to perform a series of tasks on your behalf — beyond what a more basic AI chatbot could do — such as filing expenses, booking tickets or a table at a restaurant, or even writing and maintaining code. However, as we’ve explained before, there are lots of moving pieces in this emergent space, so “AI agent” might mean different things to different people. Infrastructure is also still being built out to deliver on its envisaged capabilities. But the basic concept implies an autonomous system that may draw on multiple AI systems to carry out multistep tasks. AI 智能体是指一种利用人工智能技术代表你执行一系列任务的工具——它超越了基础 AI 聊天机器人的能力——例如报销费用、预订票务或餐厅座位，甚至编写和维护代码。然而，正如我们之前解释的那样，这个新兴领域中有很多变数，因此“AI 智能体”对不同的人可能意味着不同的含义。为了实现其预想的功能，基础设施也仍在建设中。但其基本概念是指一个自主系统，它可能会调用多个 AI 系统来执行多步骤任务。

API endpoints

API 端点 (API endpoints)

Think of API endpoints as “buttons” on the back of a piece of software that other programs can press to make it do things. Developers use these interfaces to build integrations — for example, allowing one application to pull data from another, or enabling an AI agent to control third-party services directly without a human manually operating each interface. Most smart home devices and connected platforms have these hidden buttons available, even if ordinary users never see or interact with them. As AI agents grow more capable, they are increasingly able to find and use these endpoints on their own, opening up powerful — and sometimes unexpected — possibilities for automation. 可以将 API 端点想象成软件背后的“按钮”，其他程序可以通过按下这些按钮来让它执行操作。开发人员使用这些接口来构建集成——例如，允许一个应用程序从另一个应用程序提取数据，或者使 AI 智能体能够在无需人工手动操作每个接口的情况下直接控制第三方服务。大多数智能家居设备和互联平台都有这些隐藏的按钮，即使普通用户从未看到或与之交互。随着 AI 智能体能力越来越强，它们越来越能够自行发现并使用这些端点，从而为自动化开启了强大且有时出人意料的可能性。

Chain of thought

思维链 (Chain of thought)

Given a simple question, a human brain can answer without even thinking too much about it — things like “which animal is taller, a giraffe or a cat?” But in many cases, you often need a pen and paper to come up with the right answer because there are intermediary steps. For instance, if a farmer has chickens and cows, and together they have 40 heads and 120 legs, you might need to write down a simple equation to come up with the answer (20 chickens and 20 cows). In an AI context, chain-of-thought reasoning for large language models means breaking down a problem into smaller, intermediate steps to improve the quality of the end result. It usually takes longer to get an answer, but the answer is more likely to be correct, especially in a logic or coding context. Reasoning models are developed from traditional large language models and optimized for chain-of-thought thinking thanks to reinforcement learning. 面对一个简单的问题，人类大脑甚至不需要多加思考就能回答——比如“长颈鹿和猫哪个更高？”但在许多情况下，你通常需要纸笔才能得出正确答案，因为这中间有过渡步骤。例如，如果一个农场主有鸡和牛，它们总共有 40 个头和 120 条腿，你可能需要写下一个简单的方程来得出答案（20 只鸡和 20 头牛）。在人工智能语境下，大语言模型的思维链推理意味着将问题分解为更小的中间步骤，以提高最终结果的质量。得出答案通常需要更长的时间，但答案更有可能是正确的，特别是在逻辑或编码语境下。推理模型是在传统大语言模型的基础上发展起来的，并通过强化学习针对思维链思考进行了优化。

Coding agents

编程智能体 (Coding agents)

This is a more specific concept that an “AI agent,” which means a program that can take actions on its own, step by step, to complete a goal. A coding agent is a specialized version applied to software development. Rather than simply suggesting code for a human to review and paste in, a coding agent can write, test, and debug code autonomously, handling the kind of iterative, trial-and-error work that typically consumes a developer’s day. These agents can operate across entire codebases, spotting bugs, running tests, and pushing fixes with minimal human oversight. Think of it like hiring a very fast intern who never sleeps and never loses focus — though, as with any intern, a human still needs to review the work. 这是一个比“AI 智能体”更具体的概念，指的是一个可以自主地、一步步采取行动以完成目标的程序。编程智能体是应用于软件开发的专业版本。编程智能体不仅仅是建议代码供人类审查和粘贴，它还可以自主编写、测试和调试代码，处理那些通常占用开发人员一天时间的迭代式、试错性工作。这些智能体可以在整个代码库中运行，发现错误、运行测试并推送修复程序，且只需极少的人工监督。可以把它想象成雇佣了一个从不睡觉、从不分心的超级实习生——尽管和任何实习生一样，人类仍然需要审查其工作。

Compute

计算资源 (Compute)

Although somewhat of a multivalent term, compute generally refers to the vital computational power that allows AI models to operate. This type of processing fuels the AI industry, giving it the ability to train and deploy its powerful models. The term is often a shorthand for the kinds of hardware that provides the computational power — things like GPUs, CPUs, TPUs, and other forms of infrastructure that form the bedrock of the modern AI industry. 虽然这是一个多义词，但“计算资源”（Compute）通常指使人工智能模型能够运行的关键计算能力。这种处理能力推动了人工智能行业的发展，使其能够训练和部署强大的模型。该术语通常是提供计算能力的硬件的简称——例如 GPU、CPU、TPU 以及构成现代人工智能行业基石的其他形式的基础设施。

Deep learning

深度学习 (Deep learning)

A subset of self-improving machine learning in which AI algorithms are designed with a multi-layered, artificial neural network (ANN) structure. This allows them to make more complex correlations compared to simpler machine learning-based systems, such as linear models or decision trees. The structure of deep learning algorithms draws inspiration from the interconnected pathways of neurons in the human brain. Deep learning AI models are able to identify important characteristics in data themselves, rather than requiring human engineers to define these features. 这是自我改进机器学习的一个子集，其中人工智能算法被设计为具有多层人工神经网络（ANN）结构。与线性模型或决策树等更简单的基于机器学习的系统相比，这使它们能够建立更复杂的关联。深度学习算法的结构灵感来源于人脑中神经元的互联路径。深度学习人工智能模型能够自行识别数据中的重要特征，而无需人类工程师来定义这些特征。