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Ai Chapter 1

TermDefinition
Intelligence The ability to learn from experience
Human Intelligence The ability to analyze and conclude
Artificial Intelligence (AI) A science that aims to build systems that simulate human behavior in learning
AI Theory: Thinking Rationally Defines AI as computational studies that make it possible to perceive
AI Theory: Thinking Humanly Defines AI as the automation of activities related to human thinking.
AI Theory: Acting Rationally Defines AI as the study of intelligent behavior in industry.
AI Theory: Acting Humanly Defines AI as creating machines that can perform activities requiring intelligence.
AI Foundation: Philosophy Established concepts like rationality; Aristotle formulated elementary rules.
AI Foundation: Mathematics Provides probability
AI Foundation: Psychology Studies human vision and perception; treats the brain as an information-processing system.
AI Foundation: Neuroscience Studies the nervous system and brain; inspired neural network models.
Weak AI (Artificial Narrow Intelligence ANI)
Strong AI (Artificial General Intelligence AGI)
Artificial Super Intelligence (ASI) Hypothetical AI beyond human-level intelligence
AI Characteristic: Automation Technology performs tasks automatically with minimal human intervention. Examples: machines transporting packages in factories
AI Characteristic: Reliability and Accuracy AI enhances data analysis
AI Characteristic: Availability AI systems can operate continuously. Example: 24/7 chatbot customer service.
AI Characteristic: Efficiency AI processes large
AI Characteristic: Risk Mitigation AI replaces humans in dangerous situations. Examples: bomb disposal robots
AI Application: Speech Recognition Converts spoken words into text and responses. Example: smartphone personal assistants.
AI Application: Computer Vision Enables machines to interpret images and videos. Examples: facial recognition apps
AI Application: Natural Language Processing (NLP) Reads
AI Application: Robotics Designs intelligent machines to assist humans in daily life and dangerous environments. Examples: robots in manufacturing
AI History: 1943 Warren McCulloch & Walter Pitts Proposed first model of artificial neural networks (neurons as on/off states).
AI History: 1950 Alan Turing Published "Computing Machinery and Intelligence"; proposed the Turing Test.
AI History: 1960 Lotfi Zadeh Published "Fuzzy Sets
AI History: 1956–1970 John McCarthy Developed Lisp language; proposed "Advice Taker" program.
AI History: 1956–1970 Frank Rosenblatt Developed the perceptron
AI History: 1970–1980 Expert Systems Dendral (chemistry expert system)
AI History: Mid-1980s Advances in machine learning
AI History: 1982 John Hopfield Introduced Hopfield neural networks.
AI History: 1986 Rumelhart Hinton
AI History: 1999 Deep learning techniques advanced; neural networks began competing with support vector machines.
Turing Test Proposed by Alan Turing to measure machine intelligence through text-based interaction.
Turing Test Requirement: Natural Language Processing Program must understand and generate language.
Turing Test Requirement: Knowledge Representation Program must store and analyze information to answer questions.
Turing Test Requirement: Machine Learning Program must adapt and learn during conversation.
Created by: user-1993104
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