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Questions and Answers
What does a graphical model represent?
The conditional independence structure between random variables.
What defines strong AI?
Artificial intelligence that matches or exceeds human intelligence.
What is the focus of the situated approach in agent design?
Behaving usefully in an environment and basic perceptual and motor skills.
What is embodied cognition?
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What does AI winter refer to?
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What is inference?
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What is deductive reasoning?
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What characterizes inductive reasoning?
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What is analogy in cognitive processes?
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What is Artificial Intelligence?
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Within artificial intelligence, a __________ is one that maximizes its expected utility, given its current knowledge.
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What is the Turing Test designed for?
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What is Natural Language Processing?
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Define Intelligent Agent.
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What does Knowledge Representation (KR) involve?
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What is Automated Reasoning?
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What is Machine Learning?
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What is Computer Vision?
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Define Robotics.
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What is Cognitive Science?
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What are Syllogisms?
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What is Logic?
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One of the schools of thought in the philosophy of mathematics, putting forth the theory that mathematics is an extension of logic is called _________.
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An agent that acts to achieve the best or expected outcome is called a __________.
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The idea of __________ suggests that rationality in decision-making is based on available information.
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Descartes was a strong advocate of the power of reasoning in understanding the world, a philosophy now called _________.
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In addition to rationalism, Descartes was also a proponent of __________.
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Define Materialism.
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What is Empiricism?
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The Principle of ________ says that general rules are acquired by repeated associations.
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What is Logical Positivism?
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Observation Sentences hold that all knowledge can be characterized by logical theories connected to __________.
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Define Confirmation Theory.
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What is an Algorithm?
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What is the Incompleteness Theorem?
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Define Computability.
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What is Intractability?
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What is NP-Complete (NP-C)?
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The third great contribution of mathematics to AI is the theory of _________.
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What is Utility?
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Define Decision Theory.
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What is a Game in terms of utility?
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What does Game Theory study?
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Define Operations Research.
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What are Markov Decision Processes (MDPs)?
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What is Satisficing?
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Define Neuroscience.
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What is a Neuron?
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What is Technological Singularity?
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Define Behaviorism.
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What is Cognitive Psychology?
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What does Cognitive Science examine?
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What is Control Theory?
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Intelligence could be created by the use of _____________ containing appropriate feedback loops.
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Wiener's book ___________ became a bestseller and introduced the idea of intelligent machines.
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Modern control theory aims to maximize an ___________ over time.
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Define Computational Linguistics.
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What does Knowledge Representation (KR) involve?
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Who is Hebb?
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What is a Physical Symbol System?
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What is Lisp?
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Microworlds are limited problems that appeared to require intelligence to solve, known as __________.
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What is Adaline?
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Define Perceptron.
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What does the Perceptron Convergence Theorem state?
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The illusion of unlimited computational power is also referred to as ____________.
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Define Genetic Algorithms.
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What defines Weak Methods?
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What are Expert Systems?
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MYCIN incorporated a calculus of uncertainty called __________.
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What are Frames in AI?
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What is Back-Propagation?
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Define Connectionist.
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What are Hidden Markov Models?
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What is Data Mining?
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What is a Bayesian Network?
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What is Human-Level AI?
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What does HLAI stand for?
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What is Artificial General Intelligence?
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Define Friendly AI.
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What is Epistemology?
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Define Bayesian Inference.
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What is Optimization?
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Define Hebbian Theory.
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What is Neuroevolution?
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What is an Evolutionary Algorithm?
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Define Evolutionary Computation.
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What is a Markov Model?
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What is the Markov Property?
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What is Stochastic Calculus?
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Study Notes
Artificial Intelligence
- Concerns the creation of machines that exhibit intelligence.
- Encompasses various subfields including machine learning, robotics, and natural language processing.
Rational Agent
- Defined as an agent maximizing expected utility based on knowledge.
- Acts to achieve the best outcome in scenarios with uncertainty.
Turing Test
- Provides a framework for defining machine intelligence.
- Assesses whether a machine's behavior is indistinguishable from that of a human.
Natural Language Processing (NLP)
- Combines computer science and linguistics to enable machines to understand and interact using human languages.
Intelligent Agent
- Autonomous entities that perceive their environment and act towards achieving specified goals.
Knowledge Representation (KR)
- Translates information into symbols for effective inference and information creation.
Automated Reasoning
- Aims to understand various dimensions of thought processes through computer science and mathematical logic.
Machine Learning
- Involves algorithm design for computers to adapt behaviors based on empirical data from sensors and databases.
Computer Vision
- Involves methods for processing and understanding images and high-dimensional data to derive actionable information.
Robotics
- Focuses on designing and operating autonomous machines with sensory feedback and information processing capabilities.
Cognitive Science
- Interdisciplinary field integrating AI models and psychological techniques to study human cognition.
Syllogisms
- Logical structures providing patterns for valid arguments and conclusions based on premises.
Logic
- Explores valid reasoning strategies and identifies forms of logical fallacies.
Logicism
- Philosophical stance suggesting that mathematics is reducible to logic.
Bounded Rationality
- Concept stating that individuals' rational decision-making is limited by available information and cognitive capacity.
Rationalism
- Philosophy emphasizing reasoning as a key understanding tool, associated with thinkers like Descartes and Aristotle.
Dualism
- Descartes' belief in a non-physical component of the mind that exists outside of nature's laws.
Materialism
- Contrasts dualism, positing that the mind arises entirely from physical brain operations.
Empiricism
- Philosophy asserting knowledge originates from sensory experiences.
Induction
- Principle stating general rules emerge from repeated associations.
Logical Positivism
- Merges empiricism and rationalism to form a unified epistemological framework.
Confirmation Theory
- Explores how knowledge is gained through experiential evidence.
Algorithm
- Step-by-step procedures for performing calculations and solving problems.
Incompleteness Theorem
- Gödel's assertion on the limitations of formal axiomatic systems in arithmetic.
Computability
- Relates to problem-solving efficiency and correspondence to algorithms.
Intractability
- Describes theoretically solvable problems that are practically too complex to address.
NP-Complete (NP-C)
- Class of decision problems where solutions can be verified quickly but locating those solutions is not efficient.
Probability
- A fundamental mathematical concept crucial for AI, highlighted by Gerolamo Cardano's work.
Utility
- Mathematical representation of preferred outcomes, refined through the work of economists.
Decision Theory
- Integrates probability and utility theories to inform decision-making under uncertainty.
Game Theory
- Studies strategic decision-making, focusing on the interactions between rational agents.
Operations Research
- Applies analytical methods to improve decision-making and management practices.
Markov Decision Processes (MDPs)
- Mathematical framework for modeling decisions with both random and controlled outcomes.
Satisficing
- Strategy aiming for adequate solutions rather than optimizing for the best outcome.
Neuroscience
- Study of the brain and nervous system's structure and functions.
Neuron
- Specialized cell responsible for processing and transmitting information through electrical signals.
Technological Singularity
- Theoretical future point when machines surpass human intelligence, creating unpredictable advancements.
Behaviorism
- Approach dismissing mental processes based on the reliability of introspection.
Cognitive Psychology
- Explores how humans process internal mental activities, including perception and problem-solving.
Control Theory
- Engineering discipline focused on managing dynamical systems to maintain desired outputs.
Homeostatic Devices
- Mechanisms conceptualized to achieve stable adaptive behavior through feedback loops.
Cybernetics
- Research field established by Wiener, exploring control and communication in machines.
Objective Function
- Central goal in control theory aimed at maximizing a defined quantity over time.
Computational Linguistics
- Combines linguistics and computer science to model natural language computationally.
Hebb’s Principle
- Demonstrates how neurons adapt by modifying connection strengths during learning.
Physical Symbol System
- Composes symbols that convey information through structured manipulation.
Lisp
- Long-standing AI programming language characterized by its distinct syntax.
Microworlds
- Limited problem domains selected for AI development, focusing on specific intelligence tasks.
Adaline
- A basic neural network model consisting of weights and activation functions.
Perceptron
- Binary classification algorithm utilizing linear functions for decision-making.
Back-Propagation
- Common neural network training approach for refining outputs to minimize errors.
Genetic Algorithms
- Heuristic search methodologies inspired by natural evolutionary processes for optimization.
Neural Networks
- AI models mimicking brain functionality by interconnecting simpler units to achieve complex behaviors.
Expert Systems
- AI systems replicating human expertise to solve complicated problems through reasoning.
Certainty Factors
- Uncertainty calculus introduced in MYCIN for medical diagnosis assessments.
Frames
- AI data structures representing stereotyped situations to form comprehensive knowledge concepts.
Connectionist Models
- Approaches modeling cognition and behavior as emergent from networks of interconnected units.
Hidden Markov Models
- Statistical models assuming unobserved states in a Markov process framework.
Data Mining
- Discovery of patterns in large data sets using AI, machine learning, and statistical methods.
Bayesian Network
- Probability model representing variable dependencies through a directed acyclic graph.
Human-Level AI
- Concept aiming to develop machines capable of human-like learning, thinking, and creativity.
Strong AI
- Represents AI with capabilities matching or exceeding human cognitive skills.
Nouvelle AI
- 1980s research approach focusing on insect-level intelligence viability over human-level performance.
Situated Approach
- Agent design methodology emphasizing environmental interaction over abstract reasoning.
Embodied Cognition
- Cognitive processes simulating bodily functions directly rather than through logical abstractions.
AI Winter
- Periods of reduced interest and funding in AI research, characterized by cycles of hype and disappointment.### Inference
- Inference involves drawing logical conclusions from premises that are known or assumed to be true.
- The conclusion reached through inference is referred to as an idiomatic.
Deductive Reasoning
- Deductive reasoning is the process of moving from general statements to reach a logically certain conclusion.
- It operates by using true premises to arrive at a conclusion that is also true.
Inductive Reasoning
- Inductive reasoning constructs or evaluates propositions based on observations from individual instances within a specific class.
- This reasoning is distinct from arriving at a general conclusion through specific examples.
Analogy
- Analogy is a cognitive process that transfers information or meaning from one subject (the source) to another (the target).
- It highlights connections between different concepts by drawing parallels.
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Test your knowledge on key concepts in artificial intelligence with these flashcards. Each card provides essential definitions and terms that are fundamental to understanding AI. Perfect for students or enthusiasts looking to enhance their understanding of this crucial field.