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Artificial Intelligence (AI) - Coggle Diagram
Artificial Intelligence (AI)
Understanding Intelligence
Refers to the capacity for logical reasoning, problem-solving, and adaptability.
Can be evaluated by behavior, decision-making, and human-like or rational responses.
Defining Artificial Intelligence
AI aims to create systems capable of intelligent decision-making.
Four common approaches:
Mimicking human thought
Replicating human actions
Logical reasoning (rational thought)
Optimal performance (rational behavior)
The Turing Test
Introduced by Alan Turing (1950)
Evaluates a machine’s ability to imitate human conversation convincingly
Laid the groundwork for modern AI evaluation methods
Cognitive Approaches & Strong AI
Strong AI: Machines that truly understand and reason
Inspired by human cognitive models, brain simulation, and neuroscience
Cognitive Science links mental processes with information systems
The Chinese Room Debate
Proposed by John Searle
Argues that syntactic manipulation (e.g., code) does not imply understanding
Questions the nature of machine "consciousness" and true intelligence
Rational Agents in AI
AI systems viewed as agents that choose optimal actions
Characteristics:
Autonomy
• Environmental awareness
• Adaptability
• Goal-driven behavior
Real-World Application
AI powers a range of industries and tools:
Gaming and autonomous driving
Language translation and speech recognition
Smart assistants and spam filters
Planning and logistics in operations
Interdisciplinary Foundations of AI
AI development draws upon multiple domains:
Philosophy: Reasoning and ethics
Mathematics: Logic, statistics, probability
Psychology: Human learning and perception
Computer Engineering: Speed, storage, architecture
Linguistics: Language processing
Cybernetics: Control and feedback
Economics: Decision theory and modeling
Neuroscience: Neural modeling and brain simulation