How AI Works: A Beginner's Guide
Welcome to this comprehensive introduction to the inner workings of artificial intelligence. In this presentation, we'll demystify how AI systems function by breaking down complex concepts into accessible explanations. By the end, you'll have a solid understanding of AI fundamentals without needing a technical background.
By Robert Domondon, RN, CCRN, CMC, CSC
What We'll Cover
1
The Foundation of AI: Data
How information becomes the building blocks of intelligence
2
Pattern Recognition
How AI systems identify meaningful connections
3
Training & Learning
The processes that allow AI to improve over time
4
Algorithms Simplified
Understanding AI's instruction sets without technical jargon
5
AI Decision-Making
How systems evaluate options and determine outcomes
Throughout this presentation, we'll use everyday analogies and visual examples to help you grasp these concepts without requiring any background in computer science or mathematics.
The Foundation: Data Is Everything
AI systems rely completely on data—they can't function without it. Think of data as the raw material that AI uses to understand the world:
  • Quantity matters: Modern AI systems often train on billions of data points (images, text snippets, videos, or numerical records)
  • Quality counts: Biased, incomplete, or inaccurate data leads to flawed AI outputs
  • Structure varies: Data can be structured (organized in databases) or unstructured (like free-form text, images, or audio)
  • Preprocessing is crucial: Raw data typically undergoes cleaning, normalization, and formatting before an AI can use it
For example, an AI that recognizes cats in photos has likely been exposed to millions of cat images labeled "cat" and millions of non-cat images labeled otherwise. Without this labeled data, the AI would have no way to learn what distinguishes cats from other objects.
Data flows into AI systems as the foundational building block for all intelligence.
The Human Connection: Our Brains vs. AI
To understand AI, it helps to compare it to human learning—though the similarities are limited:
Human Learning
  • Learn from relatively few examples
  • Can apply knowledge across domains
  • Understand context and nuance intuitively
  • Learn through diverse experiences
  • Possess consciousness and emotions
AI Learning
  • Requires massive amounts of examples
  • Usually specialized to specific tasks
  • Struggles with context outside its training
  • Learns through mathematical optimization
  • No consciousness or true understanding
While neural networks are loosely inspired by brain structure, AI systems don't "think" as humans do. They recognize statistical patterns in data without comprehending meaning in the way humans understand it.
Pattern Recognition: AI's Core Ability
What Are Patterns?
At its heart, artificial intelligence is fundamentally about identifying patterns within data. These patterns might be:
  • Visual patterns in images (facial features, objects, text)
  • Sequential patterns in text (grammar rules, topic relationships)
  • Temporal patterns in data over time (stock prices, weather changes)
  • Behavioral patterns in user actions (purchase habits, browsing history)
Much like how humans learn to recognize faces or predict what happens next in a story, AI systems analyze existing data to find recurring patterns and relationships.
From Patterns to Predictions
Once AI recognizes patterns, it can make predictions about new, unseen data:
  • Email spam filter: "This message has patterns similar to previous spam"
  • Recommendation system: "Users who liked X typically also enjoy Y"
  • Disease diagnosis: "These symptom patterns correlate with condition Z"
  • Voice assistant: "This audio pattern likely means the command 'play music'"
The accuracy of these predictions improves as the AI processes more examples and refines its understanding of the patterns in its training data.
Training: How AI Systems Learn
Step 1: Initial Setup
The AI starts with a structure (model architecture) and random parameters (weights). At this stage, it makes completely random guesses—like a baby who hasn't learned anything yet.
Step 2: Making Predictions
The AI processes training examples and makes predictions. Early on, these predictions are mostly wrong because the system hasn't learned yet.
Step 3: Measuring Errors
The system compares its predictions to the correct answers (labels) and calculates how far off it was using mathematical functions.
Step 4: Adjusting Parameters
The AI adjusts its internal parameters to reduce the error, a process called "backpropagation." This is like learning from mistakes.
Step 5: Repeat Millions of Times
Steps 2-4 repeat for millions of examples, gradually improving the AI's accuracy until it performs well enough for real-world use.
This iterative process—prediction, error measurement, adjustment—is the core of how modern AI systems learn from data. The process requires significant computing power and can take days or weeks for complex models.
Training Methods: Different Ways AI Learns
Supervised Learning
The AI learns from labeled examples where the correct answers are provided.
Example: Learning to identify cats in photos by seeing millions of images labeled "cat" or "not cat"
Human analogy: A teacher showing flashcards and correcting wrong answers
Unsupervised Learning
The AI finds patterns in data without labeled examples.
Example: Grouping customers by purchasing behavior without predefined categories
Human analogy: A child discovering that objects can be grouped by color, shape, or size without being taught these categories
Reinforcement Learning
The AI learns through trial and error with rewards for successful outcomes.
Example: An AI learning to play chess by receiving positive feedback for winning moves
Human analogy: Training a dog with treats when it performs tricks correctly
Most commercial AI applications today use supervised learning because it tends to be more reliable and predictable. However, the most advanced systems often combine multiple learning approaches. For example, OpenAI's GPT models initially use supervised learning on vast text datasets, followed by reinforcement learning based on human feedback to refine outputs.
Introduction to Algorithms: The Recipes of AI
"An algorithm is just a recipe—a series of precise steps that transforms inputs into outputs."
What Algorithms Do
In non-technical terms, AI algorithms are simply sets of instructions that tell the computer how to process data and make decisions. Different types of algorithms are suited for different tasks:
  • Classification algorithms sort items into categories (Is this email spam or not?)
  • Regression algorithms predict numerical values (What will this house sell for?)
  • Clustering algorithms group similar items together (Which customers have similar preferences?)
  • Natural language algorithms process and generate human language (How should I respond to this question?)
The Algorithm Spectrum
Algorithms range from simple decision trees (following basic if-then rules) to complex neural networks with billions of parameters. The right algorithm depends on the task, available data, and required performance.
Though algorithms can seem magical, they're ultimately mathematical tools with specific strengths and limitations. No algorithm works for every problem—choosing the right one is part of the art of AI development.
Neural Networks: AI's Powerhouse
Neural networks are the most powerful and widely-used class of algorithms in modern AI. While they sound complex, the basic concept can be understood without deep technical knowledge:
Input Layer
Raw data enters the network here. For an image, each pixel might be an input. For text, each word or character could be an input.
Hidden Layers
Multiple layers of interconnected "neurons" process the data, each extracting increasingly complex patterns. Early layers might detect edges in an image; deeper layers might recognize faces.
Output Layer
The final layer produces the result—whether that's a classification ("this is a dog"), a prediction ("the stock will rise"), or generated content (like text or images).
The "deep" in "deep learning" refers to having many hidden layers—modern systems may have hundreds. Each connection between neurons has a weight that gets adjusted during training. With billions of these connections, neural networks can represent extremely complex patterns, which is why they've revolutionized AI capabilities in the past decade.
How AI Makes Decisions
AI decision-making is fundamentally different from human reasoning. When an AI "decides," it's typically following these steps:
  1. Input processing: The system receives data (an image, text, sensor readings, etc.)
  1. Feature extraction: It identifies relevant patterns and characteristics in the input
  1. Probability calculation: It computes the likelihood of different possible outputs
  1. Selection: It chooses the output with the highest probability or value
For example, when a voice assistant decides what you said, it calculates probabilities for different possible phrases based on the audio patterns and context, then selects the most likely interpretation.
Unlike humans, AI doesn't have intuition, emotional reasoning, or consciousness. It's making mathematical calculations based on patterns it observed during training—though the results can sometimes appear remarkably human-like.

Confidence Scores
Most AI systems assign "confidence scores" to their decisions—essentially percentages indicating how certain the AI is about its answer. For example:
  • Image is a cat: 97% confident
  • Image is a dog: 2% confident
  • Image is a rabbit: 1% confident
These scores help developers set thresholds for when the AI should act versus when it should indicate uncertainty.
The Limitations of AI
Only As Good As Its Data
AI systems inherit biases, gaps, and errors present in their training data. If the data doesn't represent diverse situations or populations, the AI will perform poorly for underrepresented groups.
Example: Facial recognition systems trained primarily on light-skinned faces perform worse on dark-skinned faces.
Lacks True Understanding
AI recognizes patterns without comprehending meaning. It doesn't truly "understand" concepts the way humans do.
Example: A language model might write eloquently about quantum physics without actually understanding the science—it's recognizing and reproducing language patterns.
Struggles with Context
AI often fails when faced with situations significantly different from its training data or requiring common sense reasoning.
Example: An AI might excel at chess but be unable to play if the board is slightly modified, while a human would easily adapt.
Lacks Transparency
Many AI systems (especially neural networks) function as "black boxes"—even their creators can't fully explain why they make specific decisions.
Example: A loan approval AI might reject an applicant without being able to provide clear, understandable reasons.
Understanding these limitations is crucial as AI becomes more integrated into critical systems. The most effective applications of AI typically pair machine capabilities with human oversight.
Key Takeaways: Understanding How AI Works
1
Data Is the Foundation
AI systems learn from data—the quality, quantity, and diversity of that data determine their capabilities and limitations.
2
Pattern Recognition Is AI's Core Skill
AI excels at finding patterns in massive datasets and using those patterns to make predictions about new inputs.
3
Training Is How AI Learns
Through iterative processes of prediction and correction, AI systems gradually improve their performance on specific tasks.
4
Algorithms Are AI's Recipes
Different algorithmic approaches solve different types of problems, with neural networks being especially powerful for complex tasks.
5
AI Decision-Making Is Probabilistic
AI systems make decisions by calculating probabilities based on patterns in their training data, not through human-like reasoning.
"AI systems don't 'think' like humans do, but they can perform amazing feats of pattern recognition that complement human capabilities."
Understanding how AI works—even at a non-technical level—helps us use these tools more effectively and set realistic expectations about their capabilities and limitations.