Chapter 11 - Artificial Intelligence

General Outcomes

 

  • Artificial Intelligence challenges include obstacles such as processing images and language, or building autonomous agents and robots.
    • Differentiate between the concepts of machine reasoning/behavior and human reasoning/behavior.
    • Identify common vocabulary concerning artificial intelligence.
    • Identify challenges with artificial intelligence concerning images and language processing.

  • Security and ethical concerns exist concerning artificial intelligence.
    • Discuss ethical and security concerns relating to artificial intelligence.

 

Learning Outcomes

By the end of this unit students should be able to:

  • Define and provide examples for foundational vocabulary terms including:
    • Agent
    • Sensor (Not bolded, but on page 562)
    • Actuator (Not bolded, but on page 562)
    • Procedural knowledge
    • Declarative knowledge
    • Strong AI
    • Weak AI
  • Identify examples of [procedural | declarative] knowledge.
  • Explain the Turing Test.
  • Define and provide examples for foundational vocabulary terms including:
    • Production System
    • Production (aka “actions”)
    • State
    • Children
    • State Space
    • Search Tree
    • breadth-first search
    • depth-first search
  • Given a simple search problem and a particular node in the search space for that problem, identify the children that can be generated.
  • Given a simple search problem, discuss the order that nodes are visited using:
    • breadth-first search
    • depth-first search
  • Provide a definition for [imitation| supervised learning | unsupervised learning | reinforcement learning].
  • Discuss a specific example of where a human is learning through [imitation | supervised learning | unsupervised learning | reinforcement learning].
  • Briefly explain the process used with [hill climbing | genetic algorithms].
    • Given a scenario to solve a problem, identify if it is using hill climbing or genetic algorithms.
  • Identify how [hill climbing | genetic algorithms] is an example of reinforcement learning.
  • Explain the concept of a perceptron.
  • Given a simple perceptron model and set of inputs, identify the output of the perceptron.
  • Given a simple perceptron model, explain the function of the perceptron (explain its outputs).
  • Explain how multiple perceptrons are combined to form an artificial neural network (ANN).
  • Describe potential positive and negative consequences of AI on society and the economy, including its impact on employment and privacy.
  • Discuss the ethical implications of AI including issues related to bias and responsibility.
  • Explain how ChatGPT works (in very general terms)
  • Evaluate the strengths and limitations of ChatGPT as a language model, including its ability to generate coherent and relevant responses, and its potential for bias or error.
  • Discuss how a tool such as ChatGPT could play a future role in domains such as customer service, language translation, and content generation, and be able to identify ethical and societal implications of such use.