Basics of Artificial Intelligence

What is Artificial Intelligence (AI)?

• A method for increasing computer speed

• A type of computer hardware

• A programming language for web development

• Artificial Intelligence (AI) is the simulation of human intelligence in machines.

Name two common applications of AI in everyday life.

• Online gaming

• Social media platforms

• Virtual assistants and recommendation systems

• Weather forecasting

Explain the concept of machine learning in your own words.

• Machine learning is solely about programming computers to follow strict rules.

• Machine learning is a method where computers learn from data to make decisions or predictions without being explicitly programmed.

• Machine learning is a process where computers are taught to memorize data without any analysis.

• Machine learning is a type of artificial intelligence that requires constant human input.

What are some ethical concerns associated with AI?

• Bias, privacy, job displacement, accountability, misuse.

• Speed, scalability, user engagement, market growth

• Cost reduction, data storage, algorithm complexity

• Efficiency, transparency, user satisfaction, innovation

How can AI impact job markets and employment?

• AI has no effect on job markets whatsoever.

• AI can both displace jobs and create new opportunities, significantly impacting job markets.

• AI will only create jobs without any displacement.

• AI will eliminate all jobs in every industry.

Compare supervised and unsupervised learning techniques.

• Supervised learning uses labeled data; unsupervised learning uses unlabeled data.

• Unsupervised learning uses only labeled data for training.

• Supervised learning is faster than unsupervised learning in all cases.

• Supervised learning requires no data; unsupervised learning requires all data.

What role does data play in training AI models?

• AI models do not require data to function effectively.

• Data is only used for storage and not for training.

• Data is essential for training AI models as it allows them to learn from examples and improve their predictions.

• Data is irrelevant to the learning process of AI models.

Identify a real-world example of AI in healthcare.

• Telemedicine platforms for virtual consultations

• AI chatbots for patient scheduling

• AI algorithms for analyzing medical images in diagnostic imaging.

• Wearable fitness trackers for health monitoring

Discuss the importance of transparency in AI systems.

• Transparency is irrelevant to AI performance.

• Transparency in AI systems is essential for trust, accountability, and ethical use.

• AI systems should operate without any oversight.

• Transparency complicates the development process.

How can bias in AI algorithms affect decision-making?

• Bias in AI algorithms only affects technical performance.

• Bias in AI algorithms can improve decision-making accuracy.

• Bias in AI algorithms has no impact on outcomes.

• Bias in AI algorithms can result in unfair and discriminatory decision-making.