Are you concerned with thinking or behavior?
Do you want to model humans or work from an ideal standard?
How a mere handful of matter can perceive, understand, predict, and manipulate
a world far larger and more complicated than itself.
1.1 What is AI
- The definitions on top are concerned with thought processes and reasoning, whereas the ones on the bottom address behavior.
- The definitions on the left measure success in terms of fidelity to human performance, whereas
RATIONALITY the ones on the right measure against an ideal performance measure, called rationality. - A system is rational if it does the “right thing,” given what it knows.
1.1.1 Acting humanly: The Turing Test approach
TURING TEST
- NATURAL LANGUAGE PROCESSING : natural language processing to enable it to communicate successfully in English
- KNOWLEDGE REPRESENTATION : knowledge representation to store what it knows or hears
- AUTOMATED REASONING : automated reasoning to use the stored information to answer questions and to draw new conclusions
- MACHINE LEARNING : machine learning to adapt to new circumstances and to detect and extrapolate patterns.
TOTAL TURING TEST
1.2.1 Philosophy
• Can formal rules be used to draw valid conclusions?
• How does the mind arise from a physical brain?
• Where does knowledge come from?
• How does knowledge lead to action?
• What are the formal rules to draw valid conclusions?
• What can be computed?
• How do we reason with uncertain information?
• How should we make decisions so as to maximize payoff?
• How should we do this when others may not go along?
• How should we do this when the payoff may be far in the future?
How do brains process information?
How do humans and animals think and act?
How can artifacts operate under their own control?
How does language relate to thought?
1.3.1 The gestation of artificial intelligence (1943–1955)
1.3.3 Early enthusiasm, great expectations (1952–1969)
1.3.7 The return of neural networks (1986–present)
1.4 THE STATE OF THE ART
- COMPUTER VISION : computer vision to perceive objects, and
- ROBOTICS : robotics to manipulate objects and move about.
- COGNITIVE SCIENCE
- SYLLOGISM
- LOGIC
- LOGICIST
- LIMITED RATIONALITY
- AGENT
- RATIONAL AGENT
1.2.1 Philosophy
• Can formal rules be used to draw valid conclusions?
• How does the mind arise from a physical brain?
• Where does knowledge come from?
• How does knowledge lead to action?
- RATIONALISM
- DUALISM
- MATERIALISM
- EMPIRICISM
- INDUCTION
- LOGICAL POSITIVISM
- OBSERVATION SENTENCES
- CONFIRMATION THEORY
• What are the formal rules to draw valid conclusions?
• What can be computed?
• How do we reason with uncertain information?
- ALGORITHM
- INCOMPLETENESS THEOREM
- COMPUTABLE
- TRACTABILITY
- NP-COMPLETENESS
- PROBABILITY
• How should we make decisions so as to maximize payoff?
• How should we do this when others may not go along?
• How should we do this when the payoff may be far in the future?
- UTILITY
- DECISION THEORY
- GAME THEORY
- OPERATIONS RESEARCH
- SATISFICING
How do brains process information?
- NEUROSCIENCE
- NEURON
- SINGULARITY
How do humans and animals think and act?
- BEHAVIORISM
- COGNITIVE PSYCHOLOGY
- How can we build an efficient computer?
How can artifacts operate under their own control?
- CONTROL THEORY
- CYBERNETICS
- HOMEOSTATIC
- OBJECTIVE FUNCTION
How does language relate to thought?
- COMPUTATIONAL LINGUISTICS
1.3.1 The gestation of artificial intelligence (1943–1955)
- HEBBIAN LEARNING
1.3.3 Early enthusiasm, great expectations (1952–1969)
- PHYSICAL SYMBOL SYSTEM
- MACHINE EVOLUTION
- GENETIC GENETIC
- WEAK METHOD
- EXPERT SYSTEMS
- CERTAINTY FACTOR
- FRAMES
1.3.7 The return of neural networks (1986–present)
- BACK-PROPAGATION
- CONNECTIONIST
- HIDDEN MARKOV MODELS
- DATA MINING
- BAYESIAN NETWORK
- HUMAN-LEVEL AI
- ARTIFICIAL GENERAL INTELLIGENCE
- FRIENDLY AI
1.4 THE STATE OF THE ART
- Robotic vehicles
- Speech recognition
- Autonomous planning and scheduling
- Game playing
- Spam fighting
- Logistics planning
- Robotics
- Machine Translation
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