今年仅需这一份AI术语指南即可。
内容总结：
AI术语速查手册：从AGI到权重，一文读懂人工智能“黑话”
当前，人工智能正以前所未有的速度重塑世界，同时也催生出一套全新的行业术语。无论是产品会议、项目路演还是行业论坛，LLM（大语言模型）、RAG（检索增强生成）、RLHF（基于人类反馈的强化学习）等专业词汇频频出现，即便资深科技从业者也常感困惑。为此，我们编制了这份实时更新的AI术语速查手册，以通俗易懂的中文解读您最可能遇到的AI概念。
AGI（通用人工智能）
指在多数任务上能力超越普通人类的人工智能。OpenAI CEO阿尔特曼将其描述为“可雇佣为同事的普通人水平”，而谷歌DeepMind则认为AGI是“在大多数认知任务上至少与人类能力相当的AI”。业内尚无统一定义，即便前沿专家也对此存有分歧。
AI Agent（AI智能体）
能代替用户执行系列任务的工具，如报销、订票、编写代码等。其能力超越基础聊天机器人，可自主调用多个AI系统完成多步骤流程。目前该领域基础设施仍在建设中，不同企业对“智能体”的定义也存在差异。
API端点
软件后台的“按钮”，供其他程序调用触发功能。开发者借此搭建集成应用，智能体也可自主调用这些端点实现自动化，无需人工操作。
思维链
大模型将复杂问题分解为若干中间步骤的推理方式。虽耗时较长，但能显著提升逻辑与编程类问题的准确率。推理模型正是通过强化学习对传统大语言模型进行思维链优化的产物。
编程智能体
专门用于软件开发的AI智能体，可自主完成代码编写、测试、调试及Bug修复，减少人工重复劳动。其效率堪比不知疲倦的实习生，但仍需人类审核把关。
算力
驱动AI模型运行的核心计算能力，通常指GPU、CPU、TPU等硬件基础设施。算力是AI产业的燃料，直接决定模型训练与部署的效率。
深度学习
机器学习的分支，算法采用多层人工神经网络结构，能自动从数据中提取特征，并通过试错调整持续优化输出。其结构受人类大脑神经元连接方式启发，但需要海量数据支撑，训练成本较高。
扩散
生成式AI的核心技术，受物理学启发逐步向数据添加噪声直至“破坏”结构，再通过“逆扩散”过程从噪声中恢复原始内容，广泛应用于图像、音乐、文本生成。
蒸馏
通过“师生模型”将大模型知识迁移至小型模型的技术。开发者向“教师”模型发送请求，记录其输出后用于训练“学生”模型，使其行为逼近教师。OpenAI的GPT-4 Turbo即通过此技术实现加速。业内公司广泛使用蒸馏技术，但若从竞争对手处蒸馏通常违反服务条款。
微调
在预训练模型基础上，用特定领域数据进一步训练，以优化其在特定任务上的表现。许多AI创业公司以此为基础，结合自身专业知识构建垂直应用产品。
GAN（生成对抗网络）
由“生成器”与“判别器”两个神经网络相互博弈的机器学习框架。生成器力求产出以假乱真的数据，判别器则努力识别伪造内容。此对抗过程可自动优化输出真实性，是深度伪造工具的重要基础，但在通用AI领域效果有限。
幻觉
AI模型编造错误信息的现象。因训练数据存在知识盲区导致，可能输出误导性内容甚至危险建议（如错误医疗建议）。行业正通过开发垂直领域专用模型来降低该风险。
推理
运行AI模型使其基于训练数据预测或得出结论的过程。需先完成训练（学习数据模式），才能有效推理。不同硬件推理效率差异显著，大型模型在笔记本上运行耗时远超云端高端芯片。
中文翻译：
人工智能正在重塑世界，同时也创造了一套全新的语言来描述这一过程。如今，无论你参加产品会议、推介会还是座谈会，都会听到人们随口提及大语言模型、检索增强生成、基于人类反馈的强化学习以及其他十多个术语，这些术语甚至会让科技圈里的聪明人也感到些许不安。本词汇表旨在解决这一问题：用通俗易懂的英语定义你最可能遇到的人工智能术语——无论你是在用这些技术构建产品、进行投资，还是只是通过阅读TechCrunch或收听相关播客来努力跟上潮流。随着该领域的发展，我们会定期更新这份词汇表，因此请将其视为一份活文档，就像它所描述的人工智能系统一样。
AGI
通用人工智能（AGI）是一个模糊的术语。但它通常指在多数（甚至大多数）任务上能力超过普通人类的人工智能。OpenAI首席执行官萨姆·奥尔特曼曾将AGI描述为“相当于一个你可以雇佣为同事的中等水平人类”。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上超越人类的高度自主系统”。而谷歌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能力相当的人工智能”。感到困惑了吗？别担心——就连人工智能研究前沿的专家们也同样困惑。
AI代理
AI代理指的是一种利用人工智能技术代表你执行一系列任务的工具——其能力超越了更基础的AI聊天机器人——例如处理报销、预订机票或餐厅座位，甚至编写和维护代码。然而，正如我们之前解释过的，这个新兴领域涉及众多变动因素，因此“AI代理”对不同的人可能意味着不同的东西。实现其设想能力所需的基础设施也仍在建设中。但基本概念暗示了一个自主系统，它可能利用多个AI系统来执行多步骤任务。
API端点
可以把API端点想象成软件背面的“按钮”，其他程序可以按动这些按钮来让软件执行操作。开发者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拉取数据，或者让AI代理能够直接控制第三方服务，而无需人工手动操作每个接口。大多数智能家居设备和互联平台都有这些隐藏的按钮可用，即使普通用户从未看到或与之互动。随着AI代理的能力越来越强，它们越来越能够自行发现并使用这些端点，从而为自动化开辟强大——有时甚至是意想不到——的可能性。
思维链
面对一个简单问题，人脑可以不经思考就回答——比如“哪种动物更高，长颈鹿还是猫？”但在许多情况下，你通常需要纸笔才能得出正确答案，因为存在中间步骤。例如，如果一位农夫有鸡和牛，它们共有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，对大语言模型进行思维链推理意味着将问题分解成更小、更中间的步骤，以提高最终结果的质量。获得答案通常需要更长时间，但答案更可能正确，尤其是在逻辑或编程语境下。推理模型是从传统大语言模型发展而来的，并通过强化学习针对思维链思考进行了优化。
（参见：大语言模型）
编程代理
这是一个比“AI代理”更具体的概念，“AI代理”指的是能够自主、逐步采取行动以完成目标的程序。而编程代理是应用于软件开发的专业化版本。编程代理不仅仅是建议代码供人类审查和粘贴，它可以自主地编写、测试和调试代码，处理那些通常占据开发者一天时间的迭代式、试错性工作。这些代理可以跨越整个代码库运作，发现漏洞、运行测试并推送修复，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、从不走神的极速实习生——不过，和任何实习生一样，仍然需要人类来审查其工作。
算力
尽管这是一个多义词，但算力通常指让AI模型运行所必需的关键计算能力。这种处理能力为AI行业提供动力，使其能够训练和部署其强大的模型。该术语常常是提供计算能力的硬件类型的简称——比如GPU、CPU、TPU以及其他构成现代AI行业基石的基础设施形式。
深度学习
这是自我改进型机器学习的一个子集，其中AI算法采用多层人工神经网络结构设计。这使得它们能够比基于简单机器学习的系统（如线性模型或决策树）做出更复杂的关联。深度学习算法的结构借鉴了人脑中神经元相互连接的路径。
深度学习AI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。该结构还支持能够从错误中学习，并通过重复和调整的过程来改进自身输出的算法。然而，深度学习系统需要大量数据点（数百万甚至更多）才能产生良好结果。与更简单的机器学习算法相比，它们的训练时间通常也更长——因此开发成本往往更高。
（参见：神经网络）
扩散模型
扩散模型是许多生成艺术、音乐和文本的AI模型的核心技术。受物理学启发，扩散系统通过添加噪声逐渐“破坏”数据的结构——例如照片、歌曲等——直到什么都不剩。在物理学中，扩散是自发的且不可逆的——扩散到咖啡中的糖无法恢复成方块形状。但AI中的扩散系统旨在学习一种“反向扩散”过程来恢复被破坏的数据，从而获得从噪声中恢复数据的能力。
蒸馏
蒸馏是一种使用“教师-学生”模型从大型AI模型中提取知识的技术。开发者向教师模型发送请求并记录其输出。有时会将答案与数据集进行比较以查看其准确性。然后，这些输出被用来训练学生模型，该模型被训练成近似教师的行为。
蒸馏可用于基于较大模型创建更小、更高效的模型，且蒸馏损失最小。这很可能是OpenAI开发GPT-4 Turbo（GPT-4的更快版本）的方式。
虽然所有AI公司都在内部使用蒸馏，但一些AI公司也可能利用它来追赶前沿模型。从竞争对手处进行蒸馏通常违反AI API和聊天助手的服务条款。
微调
这指的是对AI模型进行进一步训练，以优化其在比之前训练重点更具体的任务或领域上的性能——通常是通过输入新的、专业化的（即面向任务的）数据。
许多AI初创公司将大语言模型作为构建商业产品的起点，但它们正竞相通过基于自身领域知识和专长进行微调，来补充早期训练周期，从而提升在目标领域或任务上的实用性。
（参见：大语言模型）
生成对抗网络
GAN，即生成对抗网络，是一种机器学习框架，支撑着生成式AI在生成逼真数据方面的一些重要进展——包括（但不限于）深度伪造工具。GAN涉及使用一对神经网络，其中一个利用其训练数据生成输出，传递给另一个模型进行评估。
这两个模型基本上被编程为试图超越对方。生成器试图让其输出通过鉴别器的检测，而鉴别器则致力于发现人工生成的数据。这种结构化的竞争可以优化AI输出，使其更加逼真，而无需额外的人工干预。尽管GAN在更具体的应用（例如生成逼真的照片或视频）中效果最佳，而非用于通用人工智能。
幻觉
幻觉是AI行业用来描述AI模型编造信息的首选术语——字面意思就是生成不正确的信息。显然，这对AI质量来说是一个巨大的问题。
幻觉产生的生成式AI输出可能具有误导性，甚至可能导致现实生活中的风险——具有潜在危险的后果（想想一个返回有害医疗建议的健康查询）。
AI编造信息的问题被认为是由训练数据中的缺口引起的。幻觉正促使人们越来越倾向于采用越来越专业化和/或垂直化的AI模型——即需要更窄专长的特定领域AI——作为减少知识缺口可能性和降低虚假信息风险的一种方式。
推理
推理是运行AI模型的过程。它指的是让模型根据之前见过的数据进行预测或得出结论。需要明确的是，没有训练就无法进行推理；模型必须先学习一组数据中的模式，然后才能有效地从这些训练数据中进行推断。
许多类型的硬件都可以执行推理，从智能手机处理器到强大的GPU，再到定制设计的AI加速器。但并非所有硬件都能同样好地运行模型。非常大的模型在笔记本电脑上预测所需的时间，与在配备高端AI芯片的云服务器上相比，会慢得多。
[参见：训练]
大语言模型
大语言模型，或称LLM，是流行AI助手所使用的AI模型，例如ChatGPT、Claude、谷歌的Gemini、Meta的AI Llama、微软的Copilot或Mistral的Le Chat。当你与AI助手聊天时，你就是在与一个大语言模型互动，它直接处理你的请求，或借助各种可用工具（如网页浏览或代码解释器）来协助处理。
大语言模型是由数十亿个数值参数（或权重，见下文）构成的深度神经网络，它们学习词语和短语之间的关系，并创建语言的表征，一种多维的词语地图。
这些模型是通过编码它们在数十亿本书籍、文章和转录文本中发现的模式而创建的。当你向大语言模型输入提示时，模型会生成最适合该提示的模式。
（参见：神经网络）
内存缓存
内存缓存指的是一个提升推理（即AI生成用户查询响应的过程）的重要过程。本质上，缓存是一种优化技术，旨在使推理更高效。AI显然由高强度的数学计算驱动，每次进行这些计算都会消耗更多电力。缓存旨在通过为未来的用户查询和操作保存特定的计算结果，来减少模型可能需要运行的计算次数。存在不同类型的内存缓存，但其中最著名的一种是KV（键值）缓存。KV缓存适用于基于Transformer的模型，通过减少生成用户问题答案所需的时间（和算法工作量）来提高效率，从而获得更快的结果。
（参见：推理）
模型上下文协议
模型上下文协议，或称MCP，是一种开放标准，允许AI模型连接到外部工具和数据——你的文件、数据库或诸如Slack和Google Drive之类的应用——而无需开发者为每一种配对情况构建自定义连接器。可以把它想象成AI的USB-C接口。Anthropic于2024年推出了MCP，随后将其移交给Linux基金会，此后被OpenAI、谷歌和微软采用，使其成为近期AI历史上传播最快的标准之一。
混合专家模型
混合专家模型是一种模型架构，它将神经网络拆分成许多更小的专业化子网络，或称“专家”，并且对于任何给定任务，只激活其中少数几个。混合专家模型并非将每个请求都路由到整个模型——就像你为每个问题召集整个办公室一样——而是有一个内置的“路由器”，只为该任务挑选合适的专家。这使得构建巨大但运行相对快速且成本低廉的模型成为可能，因为任何时候只有一小部分网络在工作。Mistral AI的Mixtral模型就是一个众所周知的例子；OpenAI较新的GPT模型也被广泛认为使用了某种形式的这种方法，尽管该公司从未正式确认过。
（参见：神经网络、深度学习）
神经网络
神经网络指的是支撑深度学习——以及更广泛地说，在大语言模型出现后整个生成式AI工具繁荣——的多层算法结构。
尽管借鉴人脑高度互联的路径作为数据处理算法的设计结构的想法可以追溯到20世纪40年代，但直到最近通过视频游戏行业发展起来的图形处理硬件（GPU）的兴起，才真正释放了这一理论的力量。这些芯片被证明非常适合训练比早期时代具有更多层的算法——使得基于神经网络的AI系统能够在许多领域取得远胜以往的性能，包括语音识别、自主导航和药物发现。
（参见：大语言模型）
开源
开源指的是软件——或者越来越多地，AI模型——其底层代码公开提供，供任何人使用、检查或修改。在AI领域，Meta的Llama系列模型是一个突出的例子；Linux是操作系统领域著名的历史类比。开源方法允许世界各地的研究人员、开发者和公司在彼此工作的基础上进行构建，加速进步，并使封闭系统难以轻易提供的独立安全审计成为可能。闭源意味着代码是私有的——你可以使用产品但看不到它的工作原理，就像OpenAI的GPT模型那样——这一区别已成为AI行业定义性的辩论之一。
并行化
并行化意味着同时做很多事情，而不是一件接一件地做——就像让10名员工同时处理一个项目的不同部分，而不是让一名员工按顺序完成所有工作。在AI中，并行化对于训练和推理都至关重要：现代GPU专门设计用于并行执行数千次计算，这是它们成为该行业硬件支柱的一个重要原因。随着AI系统变得越来越复杂，模型变得越来越大，跨多个芯片和多台机器并行化工作的能力已成为决定模型构建和部署速度及成本效益的最重要因素之一。对更好的并行化策略的研究现已成为一个独立的研究领域。
RAMageddon（内存危机）
RAMageddon是一个有趣的新词，用来描述一个正在席卷科技行业的不太有趣的趋势：随机存取存储器（RAM）芯片的持续短缺加剧——这种芯片为我们日常使用的几乎所有科技产品提供动力。随着AI行业的蓬勃发展，最大的科技公司和AI实验室——都在竞相拥有最强大、最高效的AI——正在购买大量RAM来为其数据中心供电，导致留给我们的所剩无几。这种供应瓶颈意味着剩下的RAM越来越贵。
这影响了包括游戏（许多主要公司不得不提高游戏机价格，因为为其设备寻找内存芯片更加困难）、消费电子（内存短缺可能导致十多年来智能手机出货量最大跌幅）和通用企业计算（因为这些公司无法为其自己的数据中心获得足够的RAM）等行业。价格飙升预计只会在可怕的短缺结束后停止，但不幸的是，目前几乎没有迹象表明这种情况会很快发生。
递归自我改进
像AGI一样，递归自我改进是衡量AI能达到多智能，以及它在多大程度上可以不依赖人类的一个门槛。在RSI场景中，AI模型开始在没有人类干预的情况下自我改进，导致能力和自主性的大幅加速。在某些说法中，这将是一个类似于奇点的灾难性时刻，即AI模型变得不受外部干预的影响。但RSI也描述了一种基本能力——AI模型能否设计出自己的继任者？——这使得工程师更容易尝试构建它。最近，一些AI初创公司已经开始着手构建递归自我改进模型，但大多数都否认其末日论含义，将RSI仅仅视为研究的下一个前沿。
强化学习
强化学习是一种训练AI的方式，系统通过尝试事物并因正确答案而获得奖励来学习——就像用零食训练你心爱的宠物一样，只不过这里的“宠物”是神经网络，“零食”是指示成功的数学信号。与监督学习（模型在固定的、带标签的示例数据集上训练）不同，强化学习允许模型探索其环境、采取行动，并根据收到的反馈持续更新其行为。这种方法在训练AI玩游戏、控制机器人以及最近增强大语言模型的推理能力方面已被证明特别强大。诸如基于人类反馈的强化学习（RLHF）等技术，现已成为领先AI实验室微调其模型以使其更有帮助、更准确和更安全的核心手段。
Token（令牌）
在人机通信中，存在一些明显的挑战——人们使用人类语言交流，而AI程序则通过基于数据的复杂算法过程执行任务。Token弥合了这一差距：它们是人与AI通信的基本构建块，代表被大语言模型处理或生产的数据的离散片段。它们通过一个称为分词化（tokenization）的过程创建，该过程将原始文本分解成大语言模型可以消化的小型单元，类似于编译器将人类语言翻译成计算机可以理解的二进制代码。在企业环境中，Token也决定了成本——大多数AI公司按token数量收取大语言模型使用费，这意味着企业使用得越多，支付的费用就越高。
Token吞吐量
再说一次，token是AI语言模型在处理之前将语言分解成的文本小块——通常是单词的一部分而不是整个单词；为了理解AI工作负载，它们大致相当于“单词”。吞吐量指的是在给定时间段内可以处理多少数据，因此token吞吐量本质上是衡量一个系统一次可以处理多少AI工作的指标。高token吞吐量是AI基础设施团队的一个关键目标，因为它决定了一个模型可以同时为多少用户提供服务，以及每个用户收到响应的速度有多快。AI研究员安德烈·卡帕西曾描述过当他的AI订阅闲置时感到焦虑——这呼应了他读研究生时昂贵计算机硬件未被充分利用的感受——这种情绪捕捉了为什么最大化token吞吐量已成为该领域某种执念的原因。
训练
开发机器学习AI涉及一个称为训练的过程。简单来说，这指的是将数据输入模型，以便模型能够从模式中学习并生成有用的输出。本质上，这是系统响应数据特征的过程，使其能够调整输出以接近目标——无论是识别猫的图片还是按需创作俳句。
训练可能成本高昂，因为它需要大量输入，并且所需的数据量一直在上升——这就是为什么混合方法，例如使用目标数据微调基于规则的AI，可以帮助控制成本，而无需完全从头开始。
[参见：推理]
迁移学习
一种技术，使用先前训练好的AI模型作为起点，为不同但通常相关的任务开发新模型——使得之前训练周期中获得的知识得以重用。
迁移学习可以通过缩短模型开发周期来提高效率。当开发模型所用任务的数据有限时，这也可能很有用。但重要的是要注意，这种方法有其局限性。依赖迁移学习获得通用能力的模型，可能需要在额外数据上进行训练，才能在其专注领域表现良好。
（参见：微调）
验证损失
验证损失是一个告诉你AI模型在训练过程中学习得如何的数字——数值越低越好。研究人员密切关注它，将其作为一种实时的成绩单，用于决定何时停止训练、何时调整超参数，或者是否调查潜在问题。它有助于标记的关键问题之一是过拟合，即模型记住了其训练数据，而不是真正学习可以泛化到新情况的模式。可以把它想象成一个真正理解材料的学生与一个只是记住了去年考试内容的学生之间的区别——验证损失有助于揭示你的模型正在变成哪一种。
权重
权重是AI训练的核心，因为它们决定了在用于训练系统的数据中，不同特征（或输入变量）被赋予多少重要性（或权重）——从而塑造AI模型的输出。
换句话说，权重是定义在给定的训练任务中，数据集中什么最突出的数值参数。它们通过对输入进行乘法运算来实现其功能。模型训练通常从随机分配的权重开始，但随着过程的展开，模型会调整权重，以试图得到更接近目标的输出。
例如，一个用于预测房价的AI模型，在目标位置的历史房地产数据上进行训练，可能会包含诸如卧室和浴室数量、房产是独栋还是半独栋、是否有停车位、车库等特征的权重。
最终，模型附加到每个这些输入上的权重，反映了它们基于给定数据集对房产价值的影响程度。
本文会定期更新新的信息。
英文来源：
Artificial intelligence is rewriting the world, and simultaneously inventing a whole new language to describe how it’s doing it. Sit in on any product meeting, pitch, or panel these days, and you’ll hear people toss around LLMs, RAG, RLHF, and a dozen other terms that can make even very smart people in the tech world feel a little insecure. This glossary is our attempt to fix that: pain-English definitions of the AI terms you’re most likely to actually run into, whether you’re building with this stuff, investing in it, or just trying to keep up by reading TechCrunch or listening to related podcasts. We update it regularly as the field evolves, so consider it a living document, much like the AI systems it describes.
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.
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.
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.
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.
(See: Large language model)
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.
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.
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. The structure also supports algorithms that can learn from errors and, through a process of repetition and adjustment, improve their own outputs. However, deep learning systems require a lot of data points to yield good results (millions or more). They also typically take longer to train compared to simpler machine learning algorithms — so development costs tend to be higher.
(See: Neural network)
Diffusion
Diffusion is the tech at the heart of many art-, music-, and text-generating AI models. Inspired by physics, diffusion systems slowly “destroy” the structure of data — for example, photos, songs, and so on — by adding noise until there’s nothing left. In physics, diffusion is spontaneous and irreversible — sugar diffused in coffee can’t be restored to cube form. But diffusion systems in AI aim to learn a sort of “reverse diffusion” process to restore the destroyed data, gaining the ability to recover the data from noise.
Distillation
Distillation is a technique used to extract knowledge from a large AI model with a ‘teacher-student’ model. Developers send requests to a teacher model and record the outputs. Answers are sometimes compared with a dataset to see how accurate they are. These outputs are then used to train the student model, which is trained to approximate the teacher’s behavior.
Distillation can be used to create a smaller, more efficient model based on a larger model with a minimal distillation loss. This is likely how OpenAI developed GPT-4 Turbo, a faster version of GPT-4.
While all AI companies use distillation internally, it may have also been used by some AI companies to catch up with frontier models. Distillation from a competitor usually violates the terms of service of AI API and chat assistants.
Fine-tuning
This refers to the further training of an AI model to optimize performance for a more specific task or area than was previously a focal point of its training — typically by feeding in new, specialized (i.e., task-oriented) data.
Many AI startups are taking large language models as a starting point to build a commercial product but are vying to amp up utility for a target sector or task by supplementing earlier training cycles with fine-tuning based on their own domain-specific knowledge and expertise.
(See: Large language model [LLM])
GAN
A GAN, or Generative Adversarial Network, is a type of machine learning framework that underpins some important developments in generative AI when it comes to producing realistic data — including (but not only) deepfake tools. GANs involve the use of a pair of neural networks, one of which draws on its training data to generate an output that is passed to the other model to evaluate.
The two models are essentially programmed to try to outdo each other. The generator is trying to get its output past the discriminator, while the discriminator is working to spot artificially generated data. This structured contest can optimize AI outputs to be more realistic without the need for additional human intervention. Though GANs work best for narrower applications (such as producing realistic photos or videos), rather than general purpose AI.
Hallucination
Hallucination is the AI industry’s preferred term for AI models making stuff up — literally generating information that is incorrect. Obviously, it’s a huge problem for AI quality.
Hallucinations produce GenAI outputs that can be misleading and could even lead to real-life risks — with potentially dangerous consequences (think of a health query that returns harmful medical advice).
The problem of AIs fabricating information is thought to arise as a consequence of gaps in training data. Hallucinations are contributing to a push toward increasingly specialized and/or vertical AI models — i.e. domain-specific AIs that require narrower expertise — as a way to reduce the likelihood of knowledge gaps and shrink disinformation risks.
Inference
Inference is the process of running an AI model. It’s setting a model loose to make predictions or draw conclusions from previously seen data. To be clear, inference can’t happen without training; a model must learn patterns in a set of data before it can effectively extrapolate from this training data.
Many types of hardware can perform inference, ranging from smartphone processors to beefy GPUs to custom-designed AI accelerators. But not all of them can run models equally well. Very large models would take ages to make predictions on, say, a laptop versus a cloud server with high-end AI chips.
[See: Training]
Large language model (LLM)
Large language models, or LLMs, are the AI models used by popular AI assistants, such as ChatGPT, Claude, Google’s Gemini, Meta’s AI Llama, Microsoft Copilot, or Mistral’s Le Chat. When you chat with an AI assistant, you interact with a large language model that processes your request directly or with the help of different available tools, such as web browsing or code interpreters.
LLMs are deep neural networks made of billions of numerical parameters (or weights, see below) that learn the relationships between words and phrases and create a representation of language, a sort of multidimensional map of words.
These models are created from encoding the patterns they find in billions of books, articles, and transcripts. When you prompt an LLM, the model generates the most likely pattern that fits the prompt.
(See: Neural network)
Memory cache
Memory cache refers to an important process that boosts inference (which is the process by which AI works to generate a response to a user’s query). In essence, caching is an optimization technique, designed to make inference more efficient. AI is obviously driven by high-octane mathematical calculations and every time those calculations are made, they use up more power. Caching is designed to cut down on the number of calculations a model might have to run by saving particular calculations for future user queries and operations. There are different kinds of memory caching, although one of the more well-known is KV (or key value) caching. KV caching works in transformer-based models, and increases efficiency, driving faster results by reducing the amount of time (and algorithmic labor) it takes to generate answers to user questions.
(See: Inference)
Model Context Protocol (MCP)
Model Context Protocol, or MCP, is an open standard that lets AI models connect to outside tools and data — your files, databases, or apps like Slack and Google Drive — without a developer building a custom connector for every single pairing. Think of it as a USB-C port for AI. Anthropic introduced MCP in 2024 and later handed it over to the Linux Foundation, and it’s since been adopted by OpenAI, Google, and Microsoft, making it one of the fastest-spreading standards in recent AI history.
Mixture of Experts (MoE)
Mixture of Experts is a model architecture that splits a neural network into many smaller specialized sub-networks, or “experts,” and only activates a handful of them for any given task. Rather than routing every request through the entire model — like calling in your whole office for every question — an MoE model has a built-in “router” that picks just the right specialists for the job. This makes it possible to build enormous models that stay relatively fast and cheap to run, since only a fraction of the network is doing work at any one time. Mistral AI’s Mixtral model is a well-known example; OpenAI’s newer GPT models are also widely believed to use some version of this approach, though the company has never officially confirmed it.
(See: Neural network, Deep learning)
Neural network
A neural network refers to the multi-layered algorithmic structure that underpins deep learning — and, more broadly, the whole boom in generative AI tools following the emergence of large language models.
Although the idea of taking inspiration from the densely interconnected pathways of the human brain as a design structure for data processing algorithms dates all the way back to the 1940s, it was the much more recent rise of graphical processing hardware (GPUs) — via the video game industry — that really unlocked the power of this theory. These chips proved well suited to training algorithms with many more layers than was possible in earlier epochs — enabling neural network-based AI systems to achieve far better performance across many domains, including voice recognition, autonomous navigation, and drug discovery.
(See: Large language model [LLM])
Open source
Open source refers to software — or, increasingly, AI models — where the underlying code is made publicly available for anyone to use, inspect, or modify. In the AI world, Meta’s Llama family of models is a prominent example; Linux is the famous historical parallel in operating systems. Open source approaches allow researchers, developers, and companies around the world to build on top of one another’s work, accelerating progress and enabling independent safety audits that closed systems cannot easily provide. Closed source means the code is private — you can use the product but not see how it works, as is the case with OpenAI’s GPT models — a distinction that has become one of the defining debates in the AI industry.
Parallelization
Parallelization means doing many things at the same time instead of one after another — like having 10 employees working on different parts of a project at the same time instead of one employee doing everything sequentially. In AI, parallelization is fundamental to both training and inference: modern GPUs are specifically designed to perform thousands of calculations in parallel, which is a big reason why they became the hardware backbone of the industry. As AI systems grow more complex and models grow larger, the ability to parallelize work across many chips and many machines has become one of the most important factors in determining how quickly and cost-effectively models can be built and deployed. Research into better parallelization strategies is now a field of study in its own right.
RAMageddon
RAMageddon is the fun new term for a not-so-fun trend that is sweeping the tech industry: an ever-increasing shortage of random access memory, or RAM chips, which power pretty much all the tech products we use in our daily lives. As the AI industry has blossomed, the biggest tech companies and AI labs — all vying to have the most powerful and efficient AI — are buying so much RAM to power their data centers that there’s not much left for the rest of us. And that supply bottleneck means that what’s left is getting more and more expensive.
That includes industries like gaming (where major companies have had to raise prices on consoles because it’s harder to find memory chips for their devices), consumer electronics (where memory shortage could cause the biggest dip in smartphone shipments in more than a decade), and general enterprise computing (because those companies can’t get enough RAM for their own data centers). The surge in prices is only expected to stop after the dreaded shortage ends but, unfortunately, there’s not really much of a sign that’s going to happen anytime soon.
Recursive self-improvement
Like AGI, recursive self-improvement is a threshhold for how smart AI can get, and how little it may rely on humans. In the RSI scenario, AI models start improving themselves without human intervention, leading to a huge acceleration in capabilities and autonomy. In some tellings, this would be a cataclysmic moment akin to the singularity, a moment when AI models become immune to outside intervention. But RSI also describes a basic capability — can an AI model design its own successor? — which makes it much easier for engineers to try to build it. A number of recent AI startups have set out to build recursively self-improving models, but most of them dismiss the apocalyptic implications, presenting RSI as simply the next frontier for research.
Reinforcement learning
Reinforcement learning is a way of training AI where a system learns by trying things and receiving rewards for correct answers — like training your beloved pet with treats, except the “pet” in this scenario is a neural network and the “treat” is a mathematical signal indicating success. Unlike supervised learning, where a model is trained on a fixed dataset of labeled examples, reinforcement learning lets a model explore its environment, take actions, and continuously update its behavior based on the feedback it receives. This approach has proven especially powerful for training AI to play games, control robots, and, more recently, sharpen the reasoning ability of large language models. Techniques like reinforcement learning from human feedback, or RLHF, are now central to how leading AI labs fine-tune their models to be more helpful, accurate, and safe.
Token
When it comes to human-machine communication, there are some obvious challenges — people communicate using human language, while AI programs execute tasks through complex algorithmic processes informed by data. Tokens bridge that gap: they are the basic building blocks of human-AI communication, representing discrete segments of data that have been processed or produced by an LLM. They are created through a process called tokenization, which breaks down raw text into bite-sized units a language model can digest, similar to how a compiler translates human language into binary code a computer can understand. In enterprise settings, tokens also determine cost — most AI companies charge for LLM usage on a per-token basis, meaning the more a business uses, the more it pays.
Token throughput
So again, tokens are the small chunks of text — often parts of words rather than whole ones — that AI language models break language into before processing it; they are roughly analogous to “words” for the purposes of understanding AI workloads. Throughput refers to how much can be processed in a given period of time, so token throughput is essentially a measure of how much AI work a system can handle at once. High token throughput is a key goal for AI infrastructure teams, since it determines how many users a model can serve simultaneously and how quickly each of them receives a response. AI researcher Andrej Karpathy has described feeling anxious when his AI subscriptions sit idle — echoing the feeling he had as a grad student when expensive computer hardware wasn’t being fully utilized — a sentiment that captures why maximizing token throughput has become something of an obsession in the field.
Training
Developing machine learning AIs involves a process known as training. In simple terms, this refers to data being fed in in order that the model can learn from patterns and generate useful outputs. Essentially, it’s the process of the system responding to characteristics in the data that enables it to adapt outputs toward a sought-for goal — whether that’s identifying images of cats or producing a haiku on demand.
Training can be expensive because it requires lots of inputs, and the volumes required have been trending upwards — which is why hybrid approaches, such as fine-tuning a rules-based AI with targeted data, can help manage costs without starting entirely from scratch.
[See: Inference]
Transfer learning
A technique where a previously trained AI model is used as the starting point for developing a new model for a different but typically related task — allowing knowledge gained in previous training cycles to be reapplied.
Transfer learning can drive efficiency savings by shortcutting model development. It can also be useful when data for the task that the model is being developed for is somewhat limited. But it’s important to note that the approach has limitations. Models that rely on transfer learning to gain generalized capabilities will likely require training on additional data in order to perform well in their domain of focus
(See: Fine tuning)
Validation loss
Validation loss is a number that tells you how well an AI model is learning during training — and lower is better. Researchers track it closely as a kind of real-time report card, using it to decide when to stop training, when to adjust hyperparameters, or whether to investigate a potential problem. One of the key concerns it helps flag is overfitting, a condition in which a model memorizes its training data rather than truly learning patterns it can generalize to new situations. Think of it as the difference between a student who genuinely understands the material and one who simply memorized last year’s exam — validation loss helps reveal which one your model is becoming.
Weights
Weights are core to AI training, as they determine how much importance (or weight) is given to different features (or input variables) in the data used for training the system — thereby shaping the AI model’s output.
Put another way, weights are numerical parameters that define what’s most salient in a dataset for the given training task. They achieve their function by applying multiplication to inputs. Model training typically begins with weights that are randomly assigned, but as the process unfolds, the weights adjust as the model seeks to arrive at an output that more closely matches the target.
For example, an AI model for predicting housing prices that’s trained on historical real estate data for a target location could include weights for features such as the number of bedrooms and bathrooms, whether a property is detached or semi-detached, whether it has parking, a garage, and so on.
Ultimately, the weights the model attaches to each of these inputs reflect how much they influence the value of a property, based on the given dataset.
This article is updated regularly with new information.