Video Summary

What is the primary difference between AI, Machine Learning, and Deep Learning?

• AI is a type of Deep Learning, and Machine Learning is a broader concept
• Machine Learning is a type of AI, and Deep Learning is a subset of Machine Learning
• AI and Machine Learning are the same, but Deep Learning is different
• AI is a subset of Deep Learning
• Machine Learning and Deep Learning are identical

Answer explanation

Machine Learning is a subset of AI, and Deep Learning is a subset of Machine Learning, using neural networks.

Which of the following is an example of early AI technology?

• Neural networks
• Large Language Models
• Expert systems
• Deepfakes
• Reinforcement Learning

Answer explanation

Expert systems were one of the first forms of AI before machine learning became dominant.

What is the main function of Machine Learning?

• Manually programmed instructions
• Learning from data to make predictions
• Generating new content
• Running expert systems
• Storing large data sets

Answer explanation

Machine learning enables systems to learn from data without explicit programming.

What makes Deep Learning different from Machine Learning?

• Requires explicit programming
• Does not rely on data
• Uses neural networks with multiple layers
• Only used for text analysis
• Works only with labeled data

Answer explanation

Deep Learning uses multi-layered neural networks for complex pattern recognition.

Why did AI adoption accelerate in the 2010s?

• Introduction of expert systems
• Advancement in computing power and deep learning models
• Decline in interest in AI research
• Government regulations promoting AI
• Reduced need for training data

Answer explanation

Faster computing power and improved neural networks led to a major AI boom.

Which of the following best describes Foundation Models?

• AI models trained on large datasets to perform multiple tasks
• Models that only work with structured databases
• AI models that need human intervention for every decision
• Small AI models built for single-use applications
• AI models that do not require data training

Answer explanation

Foundation models like GPT-4 are trained on massive datasets and used for multiple applications.

How does Generative AI produce new content?

• It retrieves and directly copies information from existing sources
• It recombines learned patterns to predict and create new data
• It uses manual rules set by programmers
• It only mimics existing images and text without creating new data
• It uses only structured datasets for output

Answer explanation

Generative AI analyzes patterns and synthesizes new outputs rather than copying them.

What is the biggest ethical concern regarding deepfakes?

• They cannot create high-quality images
• They make it difficult to distinguish real from fake information
• They are expensive to create
• They only work with text, not images or videos
• They require explicit user approval before use

Answer explanation

Deepfakes pose misinformation risks by making fake media indistinguishable from real content.

If a company wants to improve fraud detection, which AI approach should it use?

• Generative AI
• Machine Learning
• Deepfakes
• Foundation Models
• Reinforcement Learning

Answer explanation

Machine learning can detect fraudulent patterns in financial transactions.

Which of the following is NOT a typical use case for Machine Learning?

• Predicting customer behavior
• Identifying fraudulent transactions
• Creating human-like text responses
• Designing website layouts manually
• Recommending products

Answer explanation

Website design is usually done manually or with rule-based AI, not Machine Learning.