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.