TOEFL 聽力練習：用超過 2,800 道附解答的 TOEFL 聽力題目和 TOEFL iBT 聽力範例準備考試，並了解我們從學生完成的 451,000 次 TOEFL 聽力練習中學到了什麼。

1. Answer: D

(D) The first print run failed the margin check. is correct because the woman states, "The copy center sent the first batch back. They said the scan code was sitting right on the border." The first print run failed the margin check.

(A) They listed the wrong meeting room is incorrect because the ecology lab is only the meeting place. She does not say the posters had the wrong room.

(B) The printer produced only six copies is incorrect because six is what her student card can pay for now, not why the first batch came back.

(C) The posters curled beside an open window is incorrect because that happened to last week's posters and is why he will bring clips.

2. Answer: C

(C) The cash is stored in a closet. is correct because the man states, "The treasurer left it in the closet with the extra paper, so I will grab it when I set up." The cash box is stored in a closet.

(A) It contains money for the printing is incorrect because he says six copies are fine for the posters. The money box is for the sign up table, not her printing.

(B) The woman will bring it from the library is incorrect because she is coming over after printing. He will grab the money box when he sets up.

(D) The treasurer will place it on the table is incorrect because the treasurer already left it in the closet, and the man will grab it.

Transcript

Man: Can you still make the sustainability club meeting later?

Woman: I think so, but I need to reprint the posters. The copy center sent the first batch back. They said the scan code was sitting right on the border.

Man: We are gathering at 5:45 in the ecology lab. I can bring table clips. Last week's posters curled near the open window.

Woman: Great. The library color printer is quick, but my student card has enough credit for only six copies. Do we need the club cash tonight?

Man: Six is fine for the posters. I still need the money box at the sign up table. The treasurer left it in the closet with the extra paper, so I will grab it when I set up.

Woman: Perfect. I will come over as soon as the printing finishes.

1. Answer: C

(C) The correct answer is To describe outcomes achieved by previous workshop attendees.. In the announcement, "Attendees from previous workshops have gone on to secure funding for independent research projects and conference travel." shows why the author or speaker mentions that detail, so "To describe outcomes achieved by previous workshop attendees." is the best explanation of its function.

(A) To list the topics that will be covered in the workshop. is incorrect because the author or speaker mentions the detail to support a different purpose, not this one

(B) To explain the eligibility requirements for attending. is incorrect because the author or speaker mentions the detail to support a different purpose, not this one

(D) To advertise new funding opportunities for the semester. is incorrect because the author or speaker mentions the detail to support a different purpose, not this one

2. Answer: C

(C) The correct answer is To invite students to a grant writing workshop.. The announcement as a whole conveys this purpose, as supported by "The graduate school is presenting a grant writing workshop this Wednesday.".

(A) To announce deadline changes for current grant applications. is incorrect because the overall purpose of the announcement is different from what this choice describes.

(B) To describe a new graduate admissions requirement. is incorrect because the overall purpose of the announcement is different from what this choice describes.

(D) To introduce a visiting professor's research seminar. is incorrect because the overall purpose of the announcement is different from what this choice describes.

Transcript

Listen to an announcement at a student center.

Attention all students! The graduate school is presenting a grant writing workshop this Wednesday at two p.m. in the student center conference room. A panel of funded researchers will walk you through the process of writing competitive grant proposals, from identifying funding sources to structuring a persuasive narrative. Attendees from previous workshops have gone on to secure funding for independent research projects and conference travel. All students considering graduate-level research are strongly encouraged to attend.

1. Answer: B

(B) The correct answer is They learn patterns from data rather than following developer-written rules. The speaker contrasts data-driven learning with explicit rule-writing.

(A) is incorrect because neural networks process multiple data types. (C) is incorrect because government certification isn't required. (D) is incorrect because processing speed isn't the distinguishing feature.

2. Answer: B

(B) The correct answer is To illustrate how neural networks learn patterns from examples rather than explicit rules. The child-learning analogy explains the training process.

(A) is incorrect because physical hardware isn't described by this analogy. (C) is incorrect because biology study advice isn't the purpose. (D) is incorrect because textbook reliability isn't argued.

3. Answer: C

(C) The correct answer is Brain-inspired computing systems, their key concepts, and their potential despite challenges. The talk covers training, deep learning, applications, and black-box concerns.

(A) is incorrect because traditional programming superiority isn't argued. (B) is incorrect because the full history of computing isn't the scope. (D) is incorrect because personal investment isn't discussed.

4. Answer: B

(B) The correct answer is Multi-layered processing enables applications like self-driving cars that interpret complex visual information. The speaker links deep layers to real-time object recognition in autonomous vehicles.

(A) is incorrect because deep learning demonstrates real capability. (C) is incorrect because programmer requirements aren't the main point. (D) is incorrect because applications extend beyond image recognition.

Transcript

Listen to a talk in a class about artificial neural networks.

Today, we're going to explore artificial neural networks, which are basically computing systems inspired by the human brain that could change how we solve problems in everything from medicine to transportation. Unlike traditional programs, where a developer writes explicit rules for every decision, neural networks learn patterns from data on their own. This makes them fundamentally different from the rule-based software most industries have relied on for decades.

Take the concept of training, for instance. Training means feeding a neural network massive amounts of data—images, text, numbers—and letting it adjust its internal connections until it can make accurate predictions or classifications. It's like teaching a child to recognize animals by showing them thousands of photos—you don't give them a list of rules about what makes a dog a dog; they just figure out the patterns by seeing enough examples. This pattern-learning ability is what makes neural networks powerful at tasks like image recognition and language translation.

Another crucial concept is deep learning. Deep learning uses neural networks with many layers—sometimes hundreds—each of which extracts increasingly complex features from the data. The first layers might detect simple edges in an image, while deeper layers combine those edges into shapes, and even deeper layers recognize entire objects. This layered processing is what allows systems like self-driving cars to identify pedestrians, traffic signs, and road markings in real time.

The potential applications are enormous—medical imaging diagnosis, fraud detection, personalized recommendations, autonomous vehicles. However, neural networks are often called black boxes because it's difficult to understand exactly how they reach their conclusions. The massive computational resources required for training, combined with concerns about bias in training data and lack of explainability, make regulation and transparency critical. Despite these challenges, neural networks remain one of the most transformative technologies in modern computing.