Artificial Intelligence Foundations
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Which of the following is a characteristic of a large language model?

  • Trained on extensive text datasets for contextual understanding (correct)
  • Operates solely on structured data
  • Educator-styled rule-based interpretation
  • Ignores morphemes and lexemes in processing

    • What is meant by ambiguity in language processing?

  • The structure of a sentence being universally accepted
  • The capability of languages to evolve over time
  • The ability of a single phrase to have multiple meanings (correct)
  • The variation in intonation while speaking

    • Which step is NOT part of the machine learning process as outlined?

  • Automating grammar correction (correct)
  • Model evaluation and improvement
  • Model training using extracted features
  • Feature extraction from the text

    • What role do stop words play in natural language processing?

    <p>They generally provide minimal useful information and are often removed</p> Signup and view all the answers

    Which element is NOT included in the elements of a language as described?

    <p>Pragmatics</p> Signup and view all the answers

    What best describes algorithmic complexity?

    <p>It assesses computational resource needs relative to input size.</p> Signup and view all the answers

    What is a characteristic of blind search techniques?

    <p>They expand states in the solution space without additional knowledge.</p> Signup and view all the answers

    Which search algorithm primarily focuses on the estimated cost from the current node to the goal?

    <p>Greedy Best-First Search</p> Signup and view all the answers

    How does A* Search improve upon Greedy Best-First Search?

    <p>It combines actual cost with estimated cost for evaluation.</p> Signup and view all the answers

    Which of the following best describes a heuristic function in search algorithms?

    <p>It estimates the cost from a given state to the goal state.</p> Signup and view all the answers

    Why can exploring all permutations be considered impractical in certain situations?

    <p>The solution space may become excessively large or complex.</p> Signup and view all the answers

    What is the main limitation of blind search techniques?

    <p>They do not use any form of optimization or guidance.</p> Signup and view all the answers

    Which strategy is characterized by expanding the least costly path in search algorithms?

    <p>Uniform Cost Search</p> Signup and view all the answers

    What distinguishes domain-specific planning from domain-independent planning?

    <p>Domain-specific planning is tailored for specific applications, while domain-independent planning can be generalized.</p> Signup and view all the answers

    Which of the following best describes satisficing planning?

    <p>A strategy that focuses on finding a solution quickly that meets basic requirements.</p> Signup and view all the answers

    What is an example of a real-world application of automated planning?

    <p>Autonomous driving systems.</p> Signup and view all the answers

    In the context of planning, what does plan-space search focus on?

    <p>Analyzing and validating various partial plans to create a complete plan.</p> Signup and view all the answers

    Which of the following is NOT a characteristic of domain-independent planning?

    <p>Requires specific knowledge of its application area.</p> Signup and view all the answers

    What is a potential advantage of using satisficing planning compared to optimal planning?

    <p>It allows for quicker decision-making under constraints.</p> Signup and view all the answers

    Which system primarily utilizes domain-independent planning techniques for organizing tasks?

    <p>Google Assistant and Amazon Alexa.</p> Signup and view all the answers

    Which of the following is NOT a concern related to transparency and explainability in AI systems?

    <p>Ensuring user privacy</p> Signup and view all the answers

    What is a primary outcome of effective scheduling in the context of planning?

    <p>It ensures actions are executed in a timely and organized manner.</p> Signup and view all the answers

    What is one potential drawback of the symbolic approach to Natural Language Processing (NLP)?

    <p>It relies on human-defined rules which limits its flexibility.</p> Signup and view all the answers

    Which AI application in the manufacturing sector focuses on identifying flaws in products?

    <p>Defect Detection</p> Signup and view all the answers

    What does the term 'algorithmic bias' refer to in the context of AI?

    <p>Perpetuation of existing societal biases in AI outputs</p> Signup and view all the answers

    Which of the following components is NOT part of the EU AI Act?

    <p>Universal ethics regulations</p> Signup and view all the answers

    In statistical NLP, which of the following is a method used to acquire rules?

    <p>Applying supervised or non-supervised machine learning</p> Signup and view all the answers

    What type of AI systems raises concerns about accountability and responsibility?

    <p>Systems that make erroneous decisions or cause harm</p> Signup and view all the answers

    Which application of AI in transportation focuses specifically on recognizing humans in the environment?

    <p>Pedestrian detection</p> Signup and view all the answers

    What characterizes a Nash equilibrium in game theory?

    <p>No player can do better by unilaterally changing their strategy.</p> Signup and view all the answers

    Which statement is true about a strict Nash equilibrium?

    <p>No player can benefit from unilaterally changing their strategy.</p> Signup and view all the answers

    What is the main challenge posed by mixed strategies in game theory?

    <p>Maintaining the secrecy of the source of randomness is difficult.</p> Signup and view all the answers

    What is the goal of mechanism design in game theory?

    <p>To create rules that align individual incentives with system objectives.</p> Signup and view all the answers

    In a second-price auction, which statement describes the bidding strategy that bidders are incentivized to adopt?

    <p>Bid their maximum willingness to pay.</p> Signup and view all the answers

    What is the primary purpose of online state estimation in a robotic system?

    <p>To continuously determine the most likely current state of the system.</p> Signup and view all the answers

    What is a characteristic of partially observable environments in AI and robotics?

    <p>There are limitations to the information that can be perceived.</p> Signup and view all the answers

    Which of the following best describes the concept of a belief state in particle filters?

    <p>A probability distribution representing multiple hypotheses about the robot's location.</p> Signup and view all the answers

    What conditions must be fulfilled for every game to have at least one Nash equilibrium?

    <p>The game must have a finite number of players and strategies.</p> Signup and view all the answers

    What is true about a weak Nash equilibrium?

    <p>There is an alternative strategy that gives the same payout.</p> Signup and view all the answers

    In state representation, what aspect does dynamic state consider that kinematic state does not?

    <p>The forces and masses affecting the object's movement.</p> Signup and view all the answers

    What is a fundamental issue associated with perception in robotic systems?

    <p>Inferring unobservable facts from observable data.</p> Signup and view all the answers

    What role do particles play in the function of particle filters?

    <p>They represent guesses about the robot's possible locations.</p> Signup and view all the answers

    Which of the following is NOT an issue related to perception in robotic systems?

    <p>Capability to accurately estimate physical forces.</p> Signup and view all the answers

    How does the Bayes filter update its belief state?

    <p>By incorporating new sensor measurements and previous beliefs.</p> Signup and view all the answers

    What characterizes continuous measurements in contrast to discrete measurements?

    <p>Continuous measurements represent data that can take any value within a range.</p> Signup and view all the answers

    Study Notes

    Artificial Intelligence Foundations

    • Artificial intelligence (AI) studies the synthesis and computational agents that act intelligently. AI aims to create computers and programs capable of intelligent action.

    What is AI?

    • AI is the theory and development of computer systems that perform tasks typically requiring human intelligence.

    Examples of Intelligence

    • Logical reasoning
    • Problem-solving
    • Creativity
    • Planning

    Narrow vs. General AI

    • Narrow AI requires reconfiguration or a new algorithm for each new task (e.g., chess, speech recognition, facial recognition).

    • General AI applies intelligent systems to any problem, applying knowledge across a range of tasks.

    • The primary research areas of AI are reasoning, learning, problem-solving, and perception.

    Applications of AI

    • Robotics (industrial, autonomous, domestic)
    • Industrial automation (intelligent control, safety, security)
    • Healthcare (drug design, operating room robotics)
    • Games (NPCs, virtual, augmented reality)
    • Other areas (education, agriculture, personal assistance)

    Intelligent Agents

    • An agent is anything perceiving its environment through sensors and acting upon it using actuators.
    • Intelligent agents perceive, reason, and act autonomously to achieve goals.
    • The percept sequence is the complete history of all data received by an agent from its sensors.

    Agent Rationality

    • A rational agent acts to achieve the best possible outcome based on its knowledge and goals, aiming to maximize its performance.
    • Prior knowledge, performable actions, percept sequence to date, and success criteria are important factors.
    • Observability is crucial, and partially observable environments present challenges.

    Discrete vs. Continuous Environments

    • Discrete environments have a finite number of actions and states (e.g., chess).
    • Continuous environments have infinite possibilities of actions or states (e.g., tennis).
    • Adversarial environments involve competition between agents, while benign environments do not.

    Types of Agents

    • Reflex agents use the current percept.
    • Model-based reflex agents keep track of environment state.
    • Goal-based agents plan actions to achieve goals.
    • Utility-based agents maximize utility.
    • Learning agents adapt over time.

    Search Techniques

    • Search is a powerful technique for solving AI problems, often formulated as directed graphs.
    • The goal is to find the correct action sequence to achieve the goal while respecting constraints.
    • Blind search explores without prior knowledge.
    • Informed search uses heuristic functions to guide the search (e.g., Breadth-First Search, Depth-First Search).

    Modelling Challenges

    • Algorithmic complexity measures the computational resources needed by an algorithm based on the input size (often represented using Big O notation).

    Knowledge and Reasoning

    • Knowledge in AI refers to the data, information, and concepts used by AI systems to understand the world and solve problems.
    • Reasoning is the process of drawing conclusions using available knowledge.

    Knowledge-Based Agents

    • A knowledge-based agent uses a knowledge base to make decisions, reason, and solve problems, maintaining and updating its knowledge base.

    Knowledge Representation

    • A knowledge base contains statements (sentences) in a representation language.
    • Axioms are sentences not derived from other entities.
    • TELL operations add new sentences.
    • ASK operations query the knowledge base.

    First Order Logic (FOL)

    • A formal language for representing objects, functions, and relationships in an environment.
    • FOL models include objects, constants, functions, and relations to describe the environment.

    Logical Inference

    • Logical inference is the process of drawing conclusions based on some known information and reasoning.
    • Desirable properties for inference algorithms include soundness (deriving only entailed conclusions) and completeness (deriving all entailments).

    Planning

    • Planning is the process of creating a strategy to achieve a desired goal state by choosing actions that maximize the probability of success.
    • Planning frequently involves identifying tasks, actions, and their ordering.
    • Planning differs from scheduling in that planning focuses on identifying actions, while scheduling focuses on the timing of those actions.

    AI Planning Techniques

    • Domain-specific planning is tailored to a particular domain (e.g., a game).
    • Domain-independent planning uses techniques applicable to various domains.
    • Planning techniques include state-space search and plan-space search to find solutions.

    Satisficing vs. Optimal Planning

    • Satisficing planning aims to find a good enough solution, often prioritizing speed.
    • Optimal planning aims to find the best possible solution under specific criteria (e.g., minimum steps, lowest cost).

    Contingent Planning

    • Addresses uncertainty by considering different possible outcomes of actions. The plans also account for how actions could affect future outcomes.

    Conformant Planning

    • Plans that work regardless of the uncertain outcome.
    • The agent will plan without knowing precisely how the environment or future will unfold.

    Temporal Planning

    • Includes planning with constraints
    • Includes numeric resources like fuel for travel

    Uncertainty and Probability in AI

    • Stochastic events and partial observability introduce uncertainty into AI systems.
    • Probability distributions help model uncertainty, enabling a more robust estimate of future outcomes given current conditions.
    • Bayes Networks are representations for probability distributions.

    Time, Resources, and Exogenous Events

    • Real-world planning often involves temporal constraints, resource limitations, and unforeseen events that are not predictable.

    Machine Learning

    • A sub-field of AI focused on developing models from data.

    Classification vs. Regression

    • Classification aims to categorize data into discrete classes.
    • Regression aims to predict continuous values.

    Linear Regression

    • A simple, widely used algorithm for regression.
    • Attempts to fit a straight line through data points to predict values.

    Reinforcement Learning

    • A machine learning method for training agents to make decisions in an environment to maximize rewards. The agent learns through trial and error based on feedback responses.

    Active and Passive Reinforcement Learning

    • Passive reinforcement learning involves learning a value function given a pre-determined policy, providing a method for evaluating actions.
    • Active RL agents modify their environment to learn about it and improve their decision-making.

    Markov Decision Processes (MDP)

    • A formal framework for modelling sequential decision-making problems with uncertainty.

    Exploration-Exploitation Trade-Off

    • Exploration involves trying unseen actions/paths to maximize potential gain, while exploitation involves prioritizing actions already known to enhance rewards.
    • The balance between exploration and exploitation must be considered to achieve long-term, optimized performance.

    Computer Vision

    • Extracts information, analyzes, and interprets visual data (images/videos).
    • Used in object recognition, image processing, analysis, detection.

    Thresholding

    • A simple way to classify or make decisions on data by comparing values against a fixed value.
    • Widely used in image processing and anomaly detection.

    Law and Ethics in AI

    • The ethical considerations in AI systems including bias, transparency, accountability, data privacy.

    Natural Language Processing (NLP)

    • A branch of AI focused on enabling computers to understand, interpret, and generate human language.

    Semantic Networks

    • Represents knowledge using interconnected nodes and edges to show relationships.
    • Traversing nodes in this network, depending on the edge type, determines information relationships and associations.

    Description Logics

    • A formal language to build and link categories.

    Online State Estimation

    • Algorithms predicting the most likely state of systems from real-time data.
    • Particle filters estimate the belief state, updating probabilistic estimations over time.

    State Representation

    • Models describing the state of a system.
    • Includes kinematic (position, velocity) as well as dynamic representations of the state.

    Alternative Systems for Knowledge Representation

    • Semantic networks use nodes and edges to describe relationships.
    • Description logics provide a formal means for constructing and combining categories and their relationships.

    Multi-Agent Systems

    • Systems where more than one agent interacts with each other.

    Zero-Sum Games

    • Games where one player's gain precisely equals another player's loss.

    Minimax Algorithm

    • Often applied to games of strategy.
    • Determines the best move for a player under the assumption that the opponent will play the worst possible move.

    Alpha-Beta Pruning

    • A method to optimize the minimax search algorithm, reducing computational load.

    Game Theory

    • Game theory examines strategic interactions between multiple entities (players).

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    Foundations of Artificial Intelligence PDF

    Description

    Explore the fundamentals of artificial intelligence, including its definition, examples of intelligence, and the distinction between narrow and general AI. This quiz covers the key applications and research areas of AI, such as robotics and healthcare.

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