Artificial Intelligence Developments Overview
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Questions and Answers

What significant event happened in 1997 regarding artificial intelligence?

  • IBM's Watson won a quiz show.
  • The Roomba vacuum cleaner was introduced.
  • Artificial intelligence entered the business world.
  • IBM Deep Blue beat Gary Kasparov in chess. (correct)

    • What characterized the second AI winter from 1987 to 1993?

  • Rapid advancements in intelligent agents.
  • High costs and inefficient results leading to funding cuts. (correct)
  • Increased government funding for AI research.
  • Lack of cost-effective expert systems.

    • Which AI implementation was first introduced to homes in 2002?

  • Google Assistant.
  • Chatbot Eugene Goostman.
  • Roomba vacuum cleaner. (correct)
  • IBM Watson.

    • In what year did IBM's Watson win the quiz show Jeopardy?

    <p>2011</p> Signup and view all the answers

    What was a key advancement in AI recognized in 2014?

    <p>Chatbot Eugene Goostman passing the Turing test.</p> Signup and view all the answers

    Which application of AI was launched by Google in 2012?

    <p>Google Now.</p> Signup and view all the answers

    What advanced technology trends began to surge from 2011 onwards?

    <p>Deep learning, big data, and artificial intelligence.</p> Signup and view all the answers

    What sensor technology allows the Roomba vacuum cleaner to navigate efficiently?

    <p>Obstacle avoidance sensors.</p> Signup and view all the answers

    Which type of AI is designed to perform specific tasks and operates under a limited set of constraints?

    <p>Artificial Narrow Intelligence (ANI)</p> Signup and view all the answers

    What is a key characteristic of Artificial General Intelligence (AGI)?

    <p>Consciousness and self-awareness</p> Signup and view all the answers

    Which of the following examples represents Artificial Narrow Intelligence (ANI)?

    <p>Voice assistants like Siri</p> Signup and view all the answers

    What distinguishes Artificial Superintelligence (ASI) from other types of AI?

    <p>It significantly surpasses human intelligence</p> Signup and view all the answers

    Which type of AI lacks memory and can only react to immediate situations?

    <p>Reactive Machines</p> Signup and view all the answers

    Which of the following is true regarding the future of Artificial General Intelligence (AGI)?

    <p>It may soon be achievable based on current advancements</p> Signup and view all the answers

    What is a limitation of Artificial Narrow Intelligence (ANI)?

    <p>It cannot learn from data</p> Signup and view all the answers

    Which feature of Artificial Superintelligence (ASI) is not present in Narrow AI?

    <p>Extraordinary problem-solving abilities</p> Signup and view all the answers

    What is considered an indicator that a computer can be classified as intelligent according to Turing's test?

    <p>A sufficient number of Player-As cannot distinguish it from a human.</p> Signup and view all the answers

    Who were the creators of the first artificial intelligence program named 'Logic Theorist'?

    <p>Allen Newell and Herbert A. Simon</p> Signup and view all the answers

    What programming languages were developed during the emergence of artificial intelligence as an academic field?

    <p>FORTRAN, LISP, and COBOL</p> Signup and view all the answers

    What significant development occurred in the field of artificial intelligence in 1966?

    <p>The first chatbot named ELIZA was created.</p> Signup and view all the answers

    What does the term 'AI winter' refer to?

    <p>A time when AI research faced a significant funding shortage.</p> Signup and view all the answers

    What was the first intelligent humanoid robot built in Japan called?

    <p>WABOT-1</p> Signup and view all the answers

    What was a key feature of the expert systems developed in 1980?

    <p>They emulated human decision-making abilities.</p> Signup and view all the answers

    What was one of the achievements of the program 'Logic Theorist'?

    <p>It proved numerous mathematics theorems.</p> Signup and view all the answers

    What does a utility function do in the context of a utility-based agent?

    <p>Assigns a score to each predicted outcome based on its desirability</p> Signup and view all the answers

    Which factor is NOT typically considered by utility-based agents when evaluating options?

    <p>Weather patterns</p> Signup and view all the answers

    What makes learning agents distinct from other types of agents?

    <p>Their capability to learn from their experiences autonomously</p> Signup and view all the answers

    In a utility-based agent like a self-driving car, which of the following is NOT an objective it aims to optimize?

    <p>Road construction planning</p> Signup and view all the answers

    What role does the 'critic' play in a learning agent?

    <p>It provides feedback on the quality of the agent’s responses</p> Signup and view all the answers

    How do learning agents balance their need for exploration and exploitation?

    <p>By exploring new options while exploiting known knowledge for performance</p> Signup and view all the answers

    What is the function of the 'performance' element in a learning agent?

    <p>To select actions based on learned experiences</p> Signup and view all the answers

    What is a key characteristic of personalized recommendation systems in e-commerce?

    <p>They continuously store user activities for improved accuracy over time.</p> Signup and view all the answers

    Which property indicates that a search algorithm will return a solution if one exists?

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

    What does the optimality property of a search algorithm guarantee?

    <p>The solution found is the best among all possible solutions</p> Signup and view all the answers

    Which type of search utilizes additional knowledge about the problem domain?

    <p>Heuristic Search</p> Signup and view all the answers

    In what scenario is a non-heuristic search typically optimal?

    <p>In unweighted finite graphs</p> Signup and view all the answers

    What represents the maximum storage space required at any point during a search algorithm?

    <p>Space Complexity</p> Signup and view all the answers

    Which search type explores every option systematically without utilizing additional knowledge?

    <p>Uninformed Search</p> Signup and view all the answers

    What is the primary function of a heuristic in search algorithms?

    <p>To evaluate the path cost to the goal</p> Signup and view all the answers

    How does informed search compare to uninformed search in terms of efficiency?

    <p>Informed search is more efficient due to heuristic guidance</p> Signup and view all the answers

    What is the fundamental principle that governs the order of elements in a queue data structure?

    <p>First In, First Out (FIFO)</p> Signup and view all the answers

    Which operation is NOT typically associated with the queue data structure?

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

    How does breadth-first search (BFS) explore a graph?

    <p>By visiting all nodes of the current layer before moving to the next layer</p> Signup and view all the answers

    Which statement best describes the relationship between computational requirements and efficiency in search algorithms?

    <p>Efficiency takes into account both cost and performance</p> Signup and view all the answers

    In contrast to breadth-first search (BFS), which search method explores deeper into the problem space before traversing the next level?

    <p>Depth-First Search (DFS)</p> Signup and view all the answers

    What is an inherent characteristic of the stack data structure's operation?

    <p>It processes elements in a LIFO manner</p> Signup and view all the answers

    What is the purpose of the dequeue operation in a queue?

    <p>To remove and return the first element</p> Signup and view all the answers

    Which statement about admissible heuristics is true in the context of search algorithms?

    <p>They can only be used in finite search spaces</p> Signup and view all the answers

    Study Notes

    Search Algorithms

    • Search algorithms in AI help solve search problems by transforming the initial state into a desired state.
    • They employ evaluation of scenarios and alternatives to assist AI agents achieve their objective state.

    Search Algorithm Terminologies

    • Search Space: A collection of possible solutions to a problem.
    • Start State: The initial state of the agent/system where the search begins.
    • Goal State: The desired outcome or condition in the problem.
    • Goal Test: A function to decide if the current state matches the goal state.
    • Action Sequence: A series of actions that lead from the initial state to the goal state.
    • Path cost: The total cost associated with a sequence of actions from start to goal.
    • Search Tree: A tree-like representation of the possible paths from start to goal. The tree's root corresponds to the initial state and is expanded through actions.
    • Actions: The set of possible activities an agent can perform.

    Properties of Search Algorithms

    • Completeness: A search algorithm is complete if it guarantees to find a solution when one exists.
    • Optimality: A solution found by is optimal if it's guaranteed to have the lowest possible path cost among all possible solutions.
    • Time Complexity: Measures the computational time required for an algorithm to find a solution.
    • Space Complexity: The maximum memory required by an algorithm during the search.

    Types of Search Algorithms

    • Uninformed Search: These algorithms don't use any prior knowledge or heuristics.

      • Breadth-First Search (BFS): Explores the search tree level by level, guaranteed to find the shortest path if one exists but has high space complexity
      • Depth-First Search (DFS): Explores a branch as deep as possible before backtracking. Can be incomplete if the tree is infinite, efficient if the solution is close to the starting node.
      • Depth-Limited Search (DLS): Combines DFS with a boundary or limit to the depth; it is complete when the search space is finite.
      • Uniform Cost Search (UCS): Searches the tree based on path cost, always choosing the node with the lowest cost first, thus finding the optimal path when costs are non-negative.
      • Bidirectional Search: Run two searches simultaneously (forward and backward), stopping when the search spaces overlap. Fast and efficient memory-wise.

    • Informed Search: These algorithms use prior knowledge (heuristics) to guide the search process.

      • Greedy Best-First Search (GBS): Selects the node that seems best at each step. In cases of non-optimal solutions
      • A* Search: Uses a heuristic function to estimate cost of the best path and expands the node with the lowest estimated total cost. Guarantees optimality under certain conditions.

    Iterative Deepening Depth-First Search (IDDFS)

    • Iterative deepening search combines DFS and BFS, incrementally increasing the depth limit until the goal is found.
    • This balances the good memory efficiency of DFS with the completeness of BFS.
    • It's useful for large search spaces with an unknown goal depth.
    • Runs simultaneous forward and backward searches, starting from the initial and goal states, respectively
    • Stops when the search spaces overlap, finding a solution usually more quickly and efficiently.

    The Queue Data Structure

    • In computer science, a queue is a linear data structure where elements are added at the rear and removed from the front. It follows the First-In-First-Out (FIFO) principle.
    • Basic Operations: Enqueue (Adding to the rear), Dequeue (Removing from the front)

    The Stack Data Structure

    • Stacks are linear data structures implementing the Last-In-First-Out (LIFO) principle. Elements are added and removed from the top.
    • Basic Operations: Push (Adding to the top), Pop (Removing from the top)

    Knowledge Based Agents

    • Knowledge-based agents: These are Al systems that reason and make decisions using a well-organized collection of facts and rules stored in a knowledge base.
    • Operations: Knowledge-based agents can "tell" (add to knowledge), "ask" (query), and "perform" (act).
    • Architecture: Two components: Knowledge base for facts, rules and heuristics and an inference engine to make deductions and draw inferences from stored knowledge.

    Levels of Knowledge-Based Agents

    • Declarative Knowledge: States facts and information, without specifying how it should be used.
    • Procedural Knowledge: Contains instructions on how to perform a task or solve a problem.
    • Knowledge about Knowledge (Meta Knowledge): Understanding relationships between different pieces of information.
    • Heuristic Knowledge: Rules of thumb based on experience, often used in complex situations.

    Approaches to Design Behavioral Systems

    • Declarative Approach: Tells the system what is true about its environment; facts and rules are inserted, the system reasons to infer new knowledge.
    • Procedural Approach: Provides a set of algorithms or procedures dictating the agent's behavior in the environment. Less flexible, but faster and more efficient for specific tasks.

    Knowledge Representation Techniques

    • Logical Representation: A well-defined syntax for logical relationships and meaning to symbols. Typically uses propositional logic in simpler forms and First-Order Logic (FOL) in more complex scenarios.
    • Semantic Networks: Graphical representation where nodes are concepts and links represent relationships between them (inheritance reasoning).
    • Frames: Hierarchically structured knowledge representations, where elements (slots) have attributes and values.
    • Production Rules: if-then rules used to encode knowledge and guide decision making.

    Reasoning

    • Deductive Reasoning: If premises are true, the conclusion must be true.
    • Inductive Reasoning: Draws a general conclusion from specific observations. The truth of the premises suggests the conclusion, but does not guarantee it.
    • Abductive Reasoning: Starts with observations and finds the best explanation(s) for these observations.
    • Common Sense Reasoning: Everyday experiences and heuristic knowledge guide reasoning.

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    Description

    Test your knowledge on key developments in artificial intelligence from the late 20th century to the present. This quiz covers significant milestones, technology trends, and different types of AI, including Narrow, General, and Superintelligence. Challenge yourself and see how much you know about AI's evolution and its impact on technology.

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