Exploring Artificial Intelligence

What is the definition of Artificial Intelligence?

• Artificial Intelligence is a type of software that only plays games.

• Artificial Intelligence refers to the use of robots in manufacturing processes.

• Artificial Intelligence is the simulation of human intelligence processes by machines, especially computer systems.

• Artificial Intelligence is the ability of machines to perform physical tasks only.

What are the main types of AI agents?

• Unsupervised agents

• Supervised agents

• Hybrid agents

• Reactive agents, limited memory agents, theory of mind agents, self-aware agents.

How does a reactive agent differ from a deliberative agent?

• A reactive agent plans extensively before acting, while a deliberative agent reacts instantly.

• A reactive agent uses complex algorithms, while a deliberative agent relies on simple rules.

• A reactive agent acts immediately based on stimuli, while a deliberative agent reasons and plans before acting.

• A reactive agent is slower to respond, while a deliberative agent acts quickly based on intuition.

What are some common applications of AI in everyday life?

• Virtual assistants, recommendation systems, chatbots, smart home devices, navigation apps.

• Social media networks

• Video streaming services

• Online shopping platforms

What ethical considerations should be taken into account when developing AI?

• User engagement metrics

• Data collection efficiency

• Fairness, transparency, privacy, societal impact, safety, and security.

• Profit maximization

How can AI impact job markets and employment?

• AI can both displace jobs and create new employment opportunities.

• AI will only create jobs in technology sectors.

• AI has no effect on job markets whatsoever.

• AI will eliminate all jobs without creating any new ones.

What is the role of machine learning in AI?

• Machine learning is solely focused on hardware improvements.

• Machine learning is used to eliminate the need for AI.

• Machine learning is a type of AI that does not require data.

• Machine learning enables AI to learn from data and improve its performance autonomously.

What are the potential risks associated with AI technology?

• Improved decision-making capabilities

• Increased job security

• Enhanced privacy protection

• Potential risks associated with AI technology include bias, job displacement, privacy issues, security vulnerabilities, and misuse.

How can bias in AI systems be addressed?

• Bias in AI systems can be addressed by using diverse training data and implementing fairness algorithms.

• Relying solely on user feedback for improvements

• Using only synthetic data for training

• Ignoring the training data quality

What is the significance of transparency in AI decision-making?

• Transparency reduces the efficiency of AI systems.

• Transparency in AI decision-making is significant for trust, accountability, and understanding.

• Transparency is only important for data privacy.

• Transparency is irrelevant to user experience.