Important Terms and Ideas for Describing Artificial Intelligence
慢速 | 四级 难 | 753词 | 7min41s
刘立军供稿
Part I. QUESTIONS
Listen to the news and choose the best answer to each question you hear.
Q1. What is the main characteristic of artificial intelligence (AI)?
A. Translating languages for communication.
B. Relying on human intelligence to work.
C. Processing data with statistical methods.
D. Using psychology as its primary method.
Q2. What is the role of an algorithm in computer operations?
A. A simple set of instructions.
B. It helps achieve goals but does not involve learning.
C. A series of steps that helps achieve goals and allows learning.
D. It is used in traditional programs, not AI.
Q3. What is the purpose of reinforcement learning in machine learning?
A. To classify data into specific groups.
B. To improve by trial-and-error processes.
C. To identify patterns in large datasets.
D. To strengthen neural network layers.
Q4. What is the unique feature of deep learning?
A. Using many layers of neural networks.
B. Relying on unsupervised learning.
C. Avoiding the use of raw data.
D. Focusing on simple machine tasks.
Q5. What is one issue with large language models (LLMs)?
A. They cannot generate accurate answers.
B. They only focus on generating recipes.
C. They are unable to process legal questions.
D. They often create false or misleading data.
Q6. What is the ultimate goal of artificial general intelligence (AGI)?
A. Reproducing human thinking processes.
B. Translating languages with accuracy.
C. Discovering trends in large datasets.
D. Replacing humans in various fields.
Part II. TRANSCRIPT
Important Terms and Ideas for Describing Artificial Intelligence
There are several terms experts use to describe computer systems in the field of artificial intelligence. Recently, the French News Agency (AFP) defined some of the common terms and ideas used in that field. Here is a version for English learners:
intelligence n. 智力，智能
Artificial intelligence
The first term is “artificial intelligence.” 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”. (Q1) AI’s main quality or characteristic is taking in large amounts of data and then processing it using methods from statistics. AI involves using ideas from many fields including computing, mathematics, languages, psychology, and others. Currently, the technology is being used heavily for investigating health issues, translating human languages, and predicting problems in machine tools and self-driving cars. But AI is affecting many fields of business and industry.
simulation n. 模拟，仿真
Algorithm
A second important term is “algorithm.” (Q2) An algorithm is important to all computer operations. It is a series of steps or instructions followed by a computer program to get a result. Algorithms can give rules for an AI’s behavior, helping it to realize the objectives of computer program developers. Unlike a simple computer program, AI algorithms permit a computer system to “learn” for itself.
algorithm n. 算法，运算法则
Machine learning
A third important term is “machine learning.” Machine learning is one method that researchers have used in their efforts to produce artificial intelligence. Machine learning lets computers learn from data without being directly programmed on what results to produce. In recent years, the field of neural networks has given important results.
In a neural network, connections between some nodes are strengthened and others weakened as the system learns and makes changes. Learning can be “supervised.” This means the system learns to put new data into specific groups based on a model. For example, the system could learn to identify spam in an email or other messaging programs. “Unsupervised” learning permits the system to independently discover new areas or ways of doing things. These discoveries in the available data might not have been immediately clear. An example would be letting an online store identify buying trends in sales data. (Q3) “Reinforcement” learning adds a process of repeated trial-and-error. In this process, the system is rewarded based on its outcomes, causing it to learn and improve. One example might be a self-driving vehicle whose objective is to reach its destination as quickly as possible but also safely. That requirement would lead it to learn to stop at red lights although it requires additional time.
neural adj. 神经的
node n. 节点
spam n. 垃圾邮件
Deep learning
(Q4) Deep learning owes its name to its use of many layers of neural networks. Raw data is examined by each layer in turn at growing levels of abstraction. Geoffrey Hinton received the 2024 Nobel Prize in Physics. Hinton is credited with developing deep learning. Hinton received the prize along with 1980s neural-network developer John Hopfield. 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.” The method might help lead to scientific discoveries.
Language models
We now turn to large language models (LLMs). These might be the most popular example of generative AI. Large language models’ power tools like OpenAI’s ChatGPT or Google’s Gemini. Such systems are able to write long papers, answer legal questions or even produce a cake recipe based on their statistical models. But the technology is still new. (Q5) LLMs can suffer from “hallucinations” — the creation of content that is false or incorrect.
hallucination n. 幻觉，虚构内容
Artificial general intelligence
A final important term is artificial general intelligence (AGI) — one the big goals of the whole AI field. (Q6) AGI suggests the unrealized dream of a machine able to reproduce all human processes of human thinking. People who push the idea include OpenAI chief Sam Altman and his competitors at Anthropic. They consider such a system to be within reach. The goal is to use large amounts of data and processing power to train LLMs that are increasingly powerful. But critics say that LLM technology has important limits, including its ability to reason. Maxime Amblard, computing professor at France’s University of Lorraine, told AFP last year, “LLMs do not work like human beings.” Amblard added that humans, as flesh-and-blood intelligent beings, are “sense-making machines” with different abilities from today’s computer systems.
Part III. KEY
Q1. C. 细节题。题目出处：AI’s main quality or characteristic is taking in large amounts of data and then processing it using methods from statistics. 意为：AI的主要特性是接收大量数据并使用统计方法进行处理。因此答案为C。
Q2. C. 细节题。题目出处：An algorithm is ... a series of steps or instructions followed by a computer program to get a result. 以及AI algorithms permit a computer system to “learn” for itself.意为：算法是指计算机程序为获得结果而遵循的一系列步骤或指令；人工智能算法允许计算机系统“自主学习”。因此答案为C。
Q3. B. 细节题。题目出处：Reinforcement learning adds a process of repeated trial-and-error. In this process, the system is rewarded based on its outcomes, causing it to learn and improve. 意为：强化学习添加了一个反复试验的过程；在这个过程中，系统基于其结果获得奖励，从而学习和改进。因此答案为B。
Q4. A. 细节题。题目出处：Deep learning owes its name to its use of many layers of neural networks. Raw data is examined by each layer in turn at growing levels of abstraction. 意为：深度学习因其使用多层神经网络而得名；原始数据依次通过每一层进行更高层次的抽象分析。 因此答案为A。
Q5. D. 细节题。题目出处：LLMs can suffer from ‘hallucinations’ — the creation of content that is false or incorrect. 意为：LLM可能会出现‘幻觉’——即生成虚假或错误内容。因此答案为D。
Q6. A. 细节题。题目出处：AGI suggests the unrealized dream of a machine able to reproduce all human processes of human thinking. 意为：AGI表达了这样一个尚未实现的梦想——机器能够再现所有人类的思维过程。因此答案为A。
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