GEIT 113: Introduction to Artificial Intelligence

This set of flashcards covers key vocabulary and concepts from the lecture notes on Introduction to Artificial Intelligence.

Artificial Intelligence

The ability of computers or machines to demonstrate intelligent behavior, including thinking, learning, and acting like humans.

Narrow/Weak AI

AI systems designed to handle a single, specific task, lacking the ability to perform tasks outside their programmed functionality.

General AI

A machine capable of performing many tasks and solving problems without human intervention.

Super AI

A hypothetical level of artificial intelligence where machines surpass human intelligence and abilities.

Reactive Machines

Fundamental types of AI systems that react to current situations without the ability to learn from past experiences.

Limited Memory AI

AI that learns from past data for a limited time but cannot store the experiences for future use.

Theory of Mind AI

A type of AI that understands human emotions and can adapt its behavior based on that understanding.

Self-Aware AI

A hypothetical AI that can understand its own existence and act according to human emotional cues.

Generative AI

An advanced branch of AI capable of producing high-quality, human-like written and visual content.

Machine Learning

A subfield of AI that includes methods like supervised, unsupervised, and deep learning to enable machines to learn patterns from data.

Natural Language Processing (NLP)

A branch of AI focused on the interaction between computers and humans through natural language.

Expert System

A smart computer program that simulates human expertise in a specific domain to provide recommendations.

AI Market Revenue

Projected financial growth generated by AI technologies globally, expected to reach millions by 2025.

Advantages of AI

Benefits of AI, including error reduction, virtual assistance, faster computation, and the ability to perform tasks without breaks.

Disadvantages of AI

Challenges posed by AI, including data loss risks, lack of creativity, job displacement, and high implementation costs.

Key Challenges of AI

Issues facing AI development, including building trust, handling software malfunctions, and maintaining human oversight.

AI Development Areas

Various sectors where AI technology is applied, such as expert systems, robotics, and natural language processing.