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Introduction to Artificial Intelligence
PolyU COMP5511 Lecture 1
00:00

Defining Artificial Intelligence (AI)

Artificial Intelligence is the branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. From a formal academic perspective, AI is defined as the study of Intelligent Agents: systems that perceive their environment and take actions that maximize their chances of achieving specific goals.

Core Cognitive Processes

  • Learning: The acquisition of information and rules for using the information.
  • Reasoning: Using rules (or learned patterns) to reach approximate or definite conclusions.
  • Self-Correction: The ability to evaluate performance and refine algorithms to improve accuracy.

Key Distinctions: ANI vs. AGI

Artificial Narrow Intelligence (ANI / Weak AI)

  • Definition: AI systems designed and trained for a particular task.
  • Characteristics: Operates within a limited pre-defined range or context. It simulates intelligence but lacks consciousness or genuine understanding.
  • Current Status: All currently existing AI falls into this category. Examples include Virtual Assistants (Siri, Alexa), facial recognition software, and deep learning models like AlphaGo.

Artificial General Intelligence (AGI / Strong AI)

  • Definition: A theoretical form of AI where a machine possesses the ability to understand, learn, and apply knowledge across a wide variety of tasks, indistinguishable from the human mind.
  • Characteristics: Domain-independent adaptability, common sense reasoning, and potential self-awareness.
  • Current Status: Hypothetical; currently a subject of research and science fiction.

Key Terminology

  • Intelligent Agent: An autonomous entity which observes through sensors and acts upon an environment using actuators.
  • Turing Test: A test proposed by Alan Turing (1950) to determine if a machine can exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.
  • Machine Learning (ML): A subset of AI focused on building systems that learn from data rather than being explicitly programmed.
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