Crash Course Computer Science 2 Quiz: Artificial Intelligence and Machine Learning

What is the difference between artificial intelligence and machine learning?

• Artificial intelligence is the same as machine learning

• Artificial intelligence is only used in robotics

• Machine learning is the broader concept of machines being able to carry out tasks in a way that we would consider 'smart'

• Artificial intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider 'smart', while machine learning is a subset of AI that allows machines to learn from data and improve over time without being explicitly programmed.

Explain the concept of supervised learning in machine learning.

• The model is trained on an unlabeled dataset and learns to make predictions based on input data.

• The model is not trained on any dataset and makes predictions randomly.

• The model is trained on a labeled dataset but does not learn to make predictions based on input data.

• The model is trained on a labeled dataset and learns to make predictions based on input data.

What is the role of neural networks in artificial intelligence?

• Control physical robots in the real world

• Create virtual reality environments

• Process complex data and learn patterns to make decisions or predictions

• Generate random numbers for decision making

How does reinforcement learning work in the context of machine learning?

• It uses a fixed set of rules to make decisions and does not adapt based on feedback

• It relies on human input to make decisions and improve its performance

• It works by analyzing historical data and making predictions based on patterns

• It learns from the consequences of its actions through a system of rewards and punishments.

Discuss the importance of data in training machine learning models.

• Data is crucial for training machine learning models.

• Data has minimal impact on the training of machine learning models

• Machine learning models can be trained without using any data

• Data is not important for training machine learning models

Explain the concept of unsupervised learning in machine learning.

• Unsupervised learning involves the use of reinforcement learning algorithms.

• The model is trained on labeled data to find patterns and relationships on its own.

• It is a type of machine learning where the model is provided with the correct output during training.

• The model is trained on unlabeled data to find patterns and relationships on its own.

What are the ethical considerations surrounding the use of artificial intelligence?

• Considerations may include weather patterns, animal migration, and geological formations.

• Considerations may include cooking recipes, gardening tips, and travel recommendations.

• Considerations may include privacy, bias, accountability, transparency, and impact on employment.

• Considerations may include fashion trends, celebrity gossip, and sports scores.

Discuss the concept of deep learning and its significance in artificial intelligence.

• Deep learning is a type of shallow learning that uses only one layer of neural networks.

• Deep learning is a form of unsupervised learning that does not require any data to train the algorithms.

• Deep learning is insignificant in artificial intelligence and has no practical applications.

• Deep learning is a subset of machine learning that uses neural networks with multiple layers to learn from data. It is significant in artificial intelligence because it allows for the development of more complex and sophisticated algorithms that can learn and make decisions on their own.

How can bias be addressed in machine learning algorithms?

• Using biased and limited training data

• Using diverse and representative training data, regularly evaluating and updating the model, and implementing fairness constraints in the algorithm

• Never updating the model

• Ignoring fairness constraints in the algorithm