Introduction to Artificial Intelligence

A set of flashcards covering key concepts and terminology from the lecture on Artificial Intelligence.

Artificial Intelligence (AI)

The science and engineering of making intelligent machines, as well as the study of ideas that make people intelligent.

Narrow AI

AI research that applies partial human intelligence to specific problem-solving tasks, such as Siri converting speech to text.

General AI

AI that can perform any intellectual task that a human can do, learns, reasons, adapts, and mimics human cognition.

Super AI

AI research aiming to fully replicate human intelligence, often depicted in science fiction.

The Turing Test

A test proposed by Alan Turing to evaluate whether a machine can exhibit human-like intelligence.

Machine Learning (ML)

A subset of AI that focuses on patterns learned from data rather than predefined rules.

Symbolic AI

The foundation of early AI that focuses on storing and manipulating knowledge using symbols and rules.

Recommendation Systems

AI systems used by platforms like Netflix and Amazon to analyze user data and suggest personalized content.

Self-Driving Cars

Autonomous vehicles that use AI for navigation, route planning, and obstacle avoidance.

Emotion Detection

AI technology that identifies human emotions through text analysis, often used for customer feedback.

Secure Access

Voiceprint authentication ensures secure access to devices and accounts using voice-based identification.

Robotic Automation

Use of industrial robots to perform repetitive tasks in manufacturing with precision and efficiency.

Compute Power in AI

The reliance on specialized hardware (GPUs, TPUs) and cloud computing resources to perform complex AI tasks.

Knowledge Base

A repository of domain-specific facts and rules used in expert systems.

Inference Engine

A reasoning mechanism that applies rules to a knowledge base to draw conclusions.