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Questions and Answers
What is a key component of the Turing Test?
What is a key component of the Turing Test?
Which of the following best describes artificial intelligence?
Which of the following best describes artificial intelligence?
What does the term 'acting rationally' refer to in the context of AI?
What does the term 'acting rationally' refer to in the context of AI?
Which area of study focuses on understanding how the brain works in the context of AI?
Which area of study focuses on understanding how the brain works in the context of AI?
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What aspect of human behavior does AI seek to replicate?
What aspect of human behavior does AI seek to replicate?
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Which of these options is NOT associated with the definition of AI?
Which of these options is NOT associated with the definition of AI?
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What does the concept of 'thinking rationally' involve in AI?
What does the concept of 'thinking rationally' involve in AI?
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Which statement best aligns with the goals of cognitive science in AI?
Which statement best aligns with the goals of cognitive science in AI?
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What does acting rationally in the context of AI imply?
What does acting rationally in the context of AI imply?
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Which of the following was an early program developed during the inception of AI?
Which of the following was an early program developed during the inception of AI?
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What marked the period known as 'The AI winter'?
What marked the period known as 'The AI winter'?
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Who authored the paper titled 'Computing Machinery and Intelligence'?
Who authored the paper titled 'Computing Machinery and Intelligence'?
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Which AI development is associated with the concept of Hebbian learning?
Which AI development is associated with the concept of Hebbian learning?
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What was a significant outcome of the Dartmouth workshop in 1956?
What was a significant outcome of the Dartmouth workshop in 1956?
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Which of the following was one of the first commercial expert systems?
Which of the following was one of the first commercial expert systems?
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What limitation was noted regarding early AI systems in complex problem performance?
What limitation was noted regarding early AI systems in complex problem performance?
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What is a significant advancement in AI during the 1980s?
What is a significant advancement in AI during the 1980s?
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Which of the following is a technique used in probabilistic reasoning?
Which of the following is a technique used in probabilistic reasoning?
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What allowed the rise of AI applications during the 2000s?
What allowed the rise of AI applications during the 2000s?
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What characterizes deep learning models introduced after 2011?
What characterizes deep learning models introduced after 2011?
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Which component is NOT part of the definition of an intelligent agent?
Which component is NOT part of the definition of an intelligent agent?
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What is essential for evaluating an intelligent agent's performance?
What is essential for evaluating an intelligent agent's performance?
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What type of data has AI been significantly able to process since the 2000s?
What type of data has AI been significantly able to process since the 2000s?
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Bayesian networks are important for which aspect of AI?
Bayesian networks are important for which aspect of AI?
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What distinguishes reactive agents from other types of intelligent agents?
What distinguishes reactive agents from other types of intelligent agents?
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Which of the following characteristics describes a dynamic environment?
Which of the following characteristics describes a dynamic environment?
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What is the main function of information gathering in intelligent agents?
What is the main function of information gathering in intelligent agents?
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Which type of intelligent agent focuses on maximizing expected utility of action outcomes?
Which type of intelligent agent focuses on maximizing expected utility of action outcomes?
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What key aspect differentiates a model-based agent from a rule-based agent?
What key aspect differentiates a model-based agent from a rule-based agent?
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In which scenario would a competitive multiagent environment be observed?
In which scenario would a competitive multiagent environment be observed?
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Which evaluation aspect is NOT typically considered in intelligent agent performance evaluation?
Which evaluation aspect is NOT typically considered in intelligent agent performance evaluation?
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What distinguishes a stochastic environment from a deterministic environment?
What distinguishes a stochastic environment from a deterministic environment?
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Study Notes
Introduction to Intelligent Systems
- This presentation covers introductory topics of intelligent systems, including artificial intelligence (AI).
- The presenters are Ezequiel López-Rubio and Enrique Domínguez from the Department of Computer Languages and Computer Science at the University of Málaga, Spain.
Contents
- The presentation outlines topics that will be discussed: Artificial Intelligence (AI), History of AI, and Intelligent Agents.
- These sections cover historical developments and fundamental principles of AI.
1. Artificial Intelligence (AI)
-
Definitions:
- Acting humanly: The Turing Test (creating machines that perform functions requiring intelligence when performed by people)
- Thinking humanly: Cognitive science (study of human mind processes), Neuroscience (study of brain function).
- Thinking rationally: Aristotle's syllogisms (codifying "right thinking"), Formal Logic.
- Acting rationally: Rational agents (acting to achieve the best expected outcomes)
1. Artificial Intelligence (AI) - Acting humanly
- Can machines think?
- Can machines exhibit intelligent behavior?
- Turing Test (Process natural language, Represent knowledge, Reason, Learn, Perceive objects, Manipulate objects; NON-reproducible, NON-constructive, NO mathematical analysis; Requires a human interrogator to distinguish machine from human)
1. Artificial Intelligence (AI) - Thinking humanly
- How does a human think?
- How does the brain work?
- Validation of human mind theories.
- Prediction and comparison with human behavior (Cognitive Science).
- Identification with neural behavior and implementation (Neuroscience)
1. Artificial Intelligence (AI) - Thinking rationally
- Aristotle's syllogisms.
- Codify "right thinking."
- Logic (Notation, Inference rules).
- An automated process?
1. Artificial Intelligence (AI) - Acting rationally
- Rational behavior (Act so as to achieve the best expected outcome, Achieve one's goals efficiently, Environment.)
- Agent (Entity whose actions are based on the perceived information).
2. History of AI
-
Inception (1943-1956):
- W. McCulloch & W. Pitts (1943): Early work in neural networks.
- Hebbian learning (1949).
- SNARC - First neural network computer (1950).
- A. Turing: "Computing Machinery and Intelligence" (1950): Can machines think?, Turing test.
- E. Dijkstra (Can submarines swim?)
2. History of AI - Early enthusiasm (1952-1969)
- First programs (General Problem Solver, Checkers Program, Geometry Theorem Prover).
- Dartmouth workshop (1956): 10 people during 2 months.
- Lisp (John McCarthy, 1958).
- Genetic algorithms (1958-59).
- Friedberg – Machine evolution.
- Neural networks (1962): Widrow – Adalines, Rosenblatt – Perceptron
2. History of AI - Expert systems (1969-1986)
- "A dose of reality" (1966-1973): Overconfidence, Illusion of unlimited computational power.
- Criticisms: (Lighthtill report, 1973) and lack of funding (“The AI winter”).
- Limitations of basic structures.
- Computational complexity.
- General-purpose programs (weak methods): Domain-specific programs.
- Expert systems (1969): DENDRAL, MYCIN, Prolog
- RI – First commercial expert system (1982): Digital Equipment Corporation (McDermott); saved more than $40 million / year.
2. History of AI - Probabilistic reasoning and ML (1980-1990s)
- Return of neural networks: Back-propagation algorithm, Rumelhart & McClelland (1986).
- Probability in AI.
- More scientific approach.
- Benchmark datasets and competitions.
- Hidden Markov models (mathematical theory + training).
- Bayesian networks (uncertain knowledge representation + algorithms).
- Reunification of subfields (Computer vision, speech recognition, natural language processing,... New applications and faster deployment).
2. History of AI - Big Data and Deep Learning (2000s)
- Appearance of large datasets (e.g., World Wide Web).
- Unstructured data (text, images, audio, video).
- New algorithms to deal with unlabeled data.
- AI starts attracting commercial attention (e.g., Watson systems (IBM).
- Deep learning (2011-): Multiple layers (convolutional neural networks).
- More applications (machine translation, medical diagnosis, game playing).
- DL relies heavily on powerful hardware (GPUs).
- The present (and future): Next generation of conversational assistants (Chatbots), Generative AI (multimodal).
3. Intelligent Agents
- Definition: Agent: entity which acts based on its perception of its environment.
- Environment, Sensors, Actuators (f: P* → A)
3. Intelligent Agents - Definition 2
- Intelligent agent (Optimizing its expected performance): According to the received percepts, it selects the right actions.
- Doing the right thing.
- Goals (desirable states).
- Evaluation of agent's performance.
3. Intelligent Agents - Omniscience, learning and autonomy
- Intelligent ≠ Omniscient (Not all relevant information can be perceived).
- Information gathering (exploration).
- Outcomes might be different from expectation.
- Autonomous (learning), Behavior determined by its own experience.
3. Intelligent Agents - Specification
- Performance evaluation.
- Environment.
- Actuators.
- Sensors.
3. Intelligent Agents - Properties of task environments
- Observable (partially / fully).
- Single agent / multiagent (Competitive / Cooperative).
- Deterministic / stochastic.
- Episodic / sequential.
- Static / dynamic.
- Discrete / continuous.
- Known / unknown.
3. Intelligent Agents - Structure
- Artificial intelligence (Find way to implement f: P* → A)
- Architecture + Program = Agent
3. Intelligent Agents - Kinds of intelligent agents
- Rule-based (reactive)
- Model-based.
- Goal-based (Search and planning).
- Utility-based (Maximize the expected utility).
3. Intelligent Agents - Reactive
- Actions depend on current percepts, ignoring the rest of the percept history.
3. Intelligent Agents - Model-based
- The current state depends on the percept history.
3. Intelligent Agents - Goal-based
- Search and planning.
3. Intelligent Agents - Utility-based
- Maximize the expected utility of action outcomes.
3. Intelligent Agents - Learning agents
- Performance standard, Critic, Learning, Problem Generator
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Description
This quiz explores the foundational concepts of intelligent systems, particularly focusing on artificial intelligence (AI) and its historical context. Topics include definitions, the Turing Test, cognitive science, and the principles of rational agents. Delve into the world of AI and understand its impact on technology and society.
