The potential for AI to cause job displacement.

The possibility of AI developing consciousness.

The risk of AI being used for malicious purposes.

The need for AI to be transparent and accountable.

A thought experiment that explores the ethics of self-driving cars.

A philosophical dilemma that asks whether it is morally permissible to sacrifice one person to save five others.

A scenario that illustrates the difficulty of making ethical decisions in complex situations.

A hypothetical situation that raises questions about the limits of human responsibility.

Weak AI is capable of performing specific tasks, while strong AI is capable of general intelligence.

Weak AI is based on symbolic reasoning, while strong AI is based on neural networks.

Weak AI is designed to solve problems, while strong AI is designed to create new knowledge.

Weak AI is used in applications such as facial recognition, while strong AI is used in applications such as self-driving cars.

The point at which artificial intelligence surpasses human intelligence.

The moment when AI becomes self-aware.

The time when AI becomes capable of creating new AI systems.

The period of rapid technological change that will lead to the Singularity.

A robot may not injure a human being or, through inaction, allow a human being to come to harm.

A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.

A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

All of the above.

A test that determines whether a computer can exhibit intelligent behavior.

A test that determines whether a computer can pass as human in a conversation.

A test that determines whether a computer can learn new tasks.

A test that determines whether a computer can solve complex problems.

Supervised learning requires labeled data, while unsupervised learning does not.

Supervised learning is used for classification tasks, while unsupervised learning is used for clustering tasks.

Supervised learning is more accurate than unsupervised learning.

Supervised learning is more efficient than unsupervised learning.

A neural network is a type of machine learning algorithm that is inspired by the human brain, while a decision tree is a type of machine learning algorithm that is based on a series of rules.

A neural network is used for classification tasks, while a decision tree is used for regression tasks.

A neural network is more accurate than a decision tree.

A neural network is more efficient than a decision tree.

A chatbot is a computer program that simulates human conversation, while a virtual assistant is a computer program that helps users with tasks such as scheduling appointments and finding information.

A chatbot is used for customer service, while a virtual assistant is used for personal use.

A chatbot is more intelligent than a virtual assistant.

A chatbot is more efficient than a virtual assistant.

A GAN is a type of machine learning algorithm that is used to generate new data, while a VAE is a type of machine learning algorithm that is used to compress data.

A GAN is used for image generation, while a VAE is used for text generation.

A GAN is more accurate than a VAE.

A GAN is more efficient than a VAE.

A reinforcement learning algorithm learns by interacting with its environment, while a supervised learning algorithm learns from labeled data.

A reinforcement learning algorithm is used for tasks such as playing games and controlling robots, while a supervised learning algorithm is used for tasks such as image classification and natural language processing.

A reinforcement learning algorithm is more accurate than a supervised learning algorithm.

A reinforcement learning algorithm is more efficient than a supervised learning algorithm.

A deep neural network has more layers than a shallow neural network.

A deep neural network is more accurate than a shallow neural network.

A deep neural network is more efficient than a shallow neural network.

All of the above.

A CNN is used for image processing, while a RNN is used for natural language processing.

A CNN is more accurate than a RNN.

A CNN is more efficient than a RNN.

All of the above.

A supervised learning algorithm requires labeled data, while an unsupervised learning algorithm does not.

A supervised learning algorithm is used for tasks such as image classification and natural language processing, while an unsupervised learning algorithm is used for tasks such as clustering and dimensionality reduction.

A supervised learning algorithm is more accurate than an unsupervised learning algorithm.

A supervised learning algorithm is more efficient than an unsupervised learning algorithm.