Foundations of Information Systems: Chapter 14 - Artificial Intelligence

These flashcards cover key concepts and terminology related to the foundations of Artificial Intelligence as discussed in the lecture notes.

Artificial Intelligence (AI)

The theory and development of information systems able to perform tasks that normally require human intelligence.

Turing Test

A test to determine if a machine can exhibit intelligent behavior indistinguishable from that of a human.

Weak AI (Narrow AI)

AI specialized in one specific task, such as chatbots or voice assistants.

Strong AI (Artificial General Intelligence)

Hypothetical AI that can think and learn like a human, across all tasks.

Machine Learning (ML)

An application of AI that enables systems to automatically learn and improve from experience without explicit programming.

Neural Network

A set of virtual neurons that simulate the human brain to help machines learn from data and make predictions.

Supervised Learning

Machine learning where a model is trained on labeled input data to predict output results.

Unsupervised Learning

A type of machine learning that finds hidden patterns in unlabelled data.

Reinforcement Learning

An AI training technique where a system learns by trial and error, receiving rewards or penalties.

Expert Systems (ES)

AI systems that transfer human expertise into a machine using predefined rules.

Algorithm Bias

Bias that occurs in machine learning due to issues in the training data or assumptions made by the developer.

Computer Vision

AI that enables computers to analyze and interpret visual data from the world.

Robotics

AI technology that controls robots to assist humans in various settings such as factories or homes.

Chatbots

AI virtual assistants that simulate human conversation through text or voice.