AI Learning Quiz

How do most AI models learn to perform tasks?

• By being programmed with every possible answer.

• By being trained on large collections of data called datasets.

• By watching videos on the internet.

• By interviewing human experts.

What is a "dataset" in the world of AI?

• A complicated math problem.

• A collection of text, images, or other information on a specific topic.

• A network of connected computers.

• A list of rules for the AI to follow.

Which type of AI training uses a dataset where every piece of data is already labeled or identified?

• Unsupervised Training

• Reinforcement Training

• Supervised Training

• Automatic Training

What is it called when an AI makes up information that is incorrect or strange because it has moved away from the data it was trained on?

• A bug

• An error

• A Hallucination

• A Glitch

In a Generative Adversarial Network (GAN), which part is like an "artist" trying to create new, realistic images?

• The Discriminator

• The Encoder

• The Critic

• The Generator

What is "bias" in AI?

• When an AI system unfairly prefers some things over others, leading to unfair decisions.

• The speed at which an AI can process information.

• A special programming language used for AI.

• When an AI makes a random mistake.

What is the question or instruction you give to an AI like ChatGPT called?

• A command

• An order

• A prompt

• A request

Which of these is a major ethical concern related to AI?

• The AI might become too creative.

• The AI could learn to do math too quickly.

• The AI could be used to create "deep fakes" to fool people.

• The AI might use too much electricity.

In a GAN, what is the job of the "Discriminator"?

• To create new images.

• To try and tell if an image created by the Generator is real or fake.

• To choose the colors for the image.

• To save the final image.

What does "labeling a dataset" mean?

• Printing out all the data and putting it in a folder.

• Giving each piece of data a name or value that identifies what it is.

• Deleting any information that isn't useful.

• Checking if the dataset is good or bad.