描述人工智能的重要术语和概念
📖 逐句对照翻译
There are several terms experts use to describe computer systems in the field of artificial intelligence.
在人工智能领域，专家们使用几个术语来描述计算机系统。
Recently, the French News Agency (AFP) defined some of the common terms and ideas used in that field.
最近，法国新闻社（AFP）对该领域使用的一些常见术语和概念进行了定义。
Here is a version for English learners:
这是一个面向英语学习者的版本：
Artificial intelligence
人工智能
The first term is “artificial intelligence.”
第一个术语是“人工智能”。
When asked what artificial intelligence is, the AI-powered ChatGPT system says that the term means “the simulation of human intelligence in machines that are programmed to think, learn and make decisions".
当被问及什么是人工智能时，人工智能驱动的ChatGPT系统表示，这个术语的意思是“通过编程让机器模拟人类的智能，使其能够思考、学习和做出决策”。
AI's main quality or characteristic is taking in large amounts of data and then processing it using methods from statistics.
AI的主要特性或特征是接收大量数据，然后运用统计学方法进行处理。
AI involves using ideas from many fields including computing, mathematics, languages, psychology, and others.
人工智能涉及运用来自计算机、数学、语言、心理学等多个领域的理念。
Currently, the technology is being used heavily for investigating health issues, translating human languages, and predicting problems in machine tools and self-driving cars.
目前，这项技术正被广泛应用于调查健康问题、翻译人类语言以及预测机床和自动驾驶汽车中的故障。
But AI is affecting many fields of business and industry.
但人工智能正在影响商业和工业的许多领域。
Algorithm
算法
A second important term is “algorithm.”
第二个重要的术语是“算法”。
An algorithm is important to all computer operations.
算法对所有计算机操作都至关重要。
It is a series of steps or instructions followed by a computer program to get a result.
它是计算机程序为获得结果而遵循的一系列步骤或指令。
Algorithms can give rules for an AI's behavior, helping it to realize the objectives of computer program developers.
算法可以为人工智能的行为提供规则，帮助其实现计算机程序开发者的目标。
Unlike a simple computer program, AI algorithms permit a computer system to “learn” for itself.
与简单的计算机程序不同，AI算法能让计算机系统自我“学习”。
Machine learning
机器学习
A third important term is “machine learning.”
第三个重要术语是“机器学习”。
Machine learning is one method that researchers have used in their efforts to produce artificial intelligence.
机器学习是研究人员用来实现人工智能的一种方法。
Machine learning lets computers learn from data without being directly programmed on what results to produce.
机器学习让计算机能够从数据中学习，而无需直接编程指定要产生的结果。
In recent years, the field of neural networks has given important results.
近年来，神经网络领域取得了重要成果。
In a neural network, connections between some nodes are strengthened and others weakened as the system learns and makes changes.
在神经网络中，随着系统的学习和改变，一些节点之间的连接会增强，而另一些则会减弱。
Learning can be "supervised."
学习可以是“有监督的”。
This means the system learns to put new data into specific groups based on a model.
这意味着系统会根据模型学习将新数据归入特定的组别。
For example, the system could learn to identify spam in an email or other messaging programs.
例如，系统可以学习识别电子邮件或其他消息程序中的垃圾邮件。
"Unsupervised" learning permits the system to independently discover new areas or ways of doing things.
“无监督”学习允许系统独立发现新的领域或做事方式。
These discoveries in the available data might not have been immediately clear.
这些在现有数据中的发现可能并非一目了然。
An example would be letting an online store identify buying trends in sales data.
一个例子是让在线商店从销售数据中识别购买趋势。
"Reinforcement" learning adds a process of repeated trial-and-error.
“强化”学习增加了一个反复试错的过程。
In this process, the system is rewarded based on its outcomes, causing it to learn and improve.
在这个过程中，系统根据其结果获得奖励，从而促使它学习和改进。
One example might be a self-driving vehicle whose objective is to reach its destination as quickly as possible but also safely.
一个例子可能是自动驾驶汽车，其目标是尽快到达目的地，同时确保安全。
That requirement would lead it to learn to stop at red lights although it requires additional time.
这一要求将促使它学会在红灯前停下，即便这会耗费额外的时间。
Deep learning
深度学习
Deep learning owes its name to its use of many layers of neural networks.
深度学习之所以得名，是因为它使用了多层神经网络。
Raw data is examined by each layer in turn at growing levels of abstraction.
原始数据依次经过每一层的检查，抽象层次逐渐提高。
Geoffrey Hinton received the 2024 Nobel Prize in Physics.
杰弗里·辛顿获得了2024年诺贝尔物理学奖。
Hinton is credited with developing deep learning.
辛顿被誉为深度学习之父。
Hinton received the prize along with 1980s neural-network developer John Hopfield.
辛顿与20世纪80年代神经网络开发者约翰·霍普菲尔德共同获得了该奖项。
Francis Bach, head of France's SIERRA statistical learning laboratory, said this about deep learning: "The more layers you have, the more complex behavior can become, and the more complex the behavior can be, the easier it is to learn a desired behavior efficiently."
法国SIERRA统计学习实验室主任弗朗西斯·巴赫曾这样评价深度学习：“层数越多，行为可以变得越复杂，而行为越复杂，就越容易高效地学习到期望的行为。”
The method might help lead to scientific discoveries.
这种方法可能有助于带来科学发现。
Language models
语言模型
We now turn to large language models (LLMs).
我们现在转向大型语言模型（LLMs）。
These might be the most popular example of generative AI.
这些可能是生成式人工智能最受欢迎的例子。
Large language models power tools like OpenAI’s ChatGPT or Google’s Gemini.
大型语言模型为OpenAI的ChatGPT或谷歌的Gemini等工具提供动力。
Such systems are able to write long papers, answer legal questions or even produce a cake recipe based on their statistical models.
这类系统能够撰写长篇论文、回答法律问题，甚至根据其统计模型生成蛋糕食谱。
But the technology is still new.
但这项技术仍处于新兴阶段。
LLM’s can suffer from "hallucinations"- the creation of content that is false or incorrect.
LLM 可能会出现“幻觉”——即生成虚假或错误的内容。
Artificial general intelligence
通用人工智能
A final important term is artificial general intelligence (AGI) - one the big goals of the whole AI field.
最后一个重要术语是人工通用智能（AGI）——这是整个人工智能领域的重大目标之一。
AGI suggests the unrealized dream of a machine able to reproduce all human processes of human thinking.
AGI意味着一个尚未实现的梦想，即一种能够复现人类所有思维过程的机器。
People who push the idea include OpenAI chief Sam Altman and his competitors at Anthropic.
推动这一理念的人包括OpenAI的首席执行官萨姆·奥特曼及其在Anthropic的竞争对手。
They consider such a system to be within reach.
他们认为这样的系统是可以实现的。
The goal is to use large amounts of data and processing power to train LLMs that are increasingly powerful.
目标是利用大量数据和计算能力来训练越来越强大的LLMs。
But critics say that LLM technology has important limits, including its ability to reason.
但批评者认为，LLM技术存在重要局限，包括其推理能力。
Maxime Amblard, computing professor at France's University of Lorraine, told AFP last year, "LLMs do not work like human beings."
法国洛林大学的计算机教授马克西姆·安布拉尔去年对法新社表示：“大型语言模型的工作原理与人类不同。”
Amblard added that humans, as flesh-and-blood -intelligent beings, are "sense-making machines" with different abilities from today's computer systems.
安布拉尔还指出，人类作为有血有肉的智能生物，是“意义建构的机器”，其能力与当今的计算机系统不同。
I’m Anna Matteo.
我是安娜·马特奥。
And I’m John Russell.
我是约翰·拉塞尔。
Pierre Celerier reported on this story for Agence France-Presse.
皮埃尔·塞莱里耶为法新社报道了这一事件。
John Russell adapted it for VOA Learning
约翰·拉塞尔为VOA学习频道改编了它。
