Sample Study Guide: Fundamentals of Artificial
Intelligence
Overview: This document provides a foundational look at AI, covering its history, core
subfields, and common algorithms.
1. Key Definitions
●​ Artificial Intelligence (AI): The simulation of human intelligence processes by
machines, especially computer systems.
●​ Machine Learning (ML): A subset of AI that involves the use of data and algorithms
to imitate the way that humans learn, gradually improving its accuracy.
●​ Deep Learning: A specialized form of ML that uses neural networks with many
layers (hence "deep") to analyze complex data.
2. The Three Types of AI
Type
Description
Current
Status
Narrow AI
(ANI)
Designed to perform a single task (e.g., facial
recognition, Siri).
Available
Now
General AI
(AGI)
AI that can understand, learn, and apply knowledge in a
way indistinguishable from a human.
Theoretical
Super AI
(ASI)
AI that surpasses human intelligence across all fields.
Theoretical
3. Core Algorithms to Know
1.​ Linear Regression: Used for predicting a continuous value (e.g., house prices).
2.​ Decision Trees: A flowchart-like structure used for classification and prediction.
3.​ Neural Networks: Inspired by the human brain, these are used for image recognition
and natural language processing.
4. Ethical Considerations
●​ Bias in Data: If the training data is biased, the AI's decisions will be too.
●​ Job Displacement: The automation of tasks previously handled by humans.
●​ Transparency: The "Black Box" problem, where it's difficult to see how an AI
reached a specific conclusion.