chapter one

1 Intuition of AI

 

This chapter covers

  • The definition of AI as we know it
  • Intuition concepts underpinning AI and important terminology
  • Defining problem types and approaches to solve them
  • An overview of the AI algorithms discussed in this book
  • Real-world use cases for AI algorithms

Artificial intelligence is no longer a niche area of research; it is a foundational tool in modern software engineering. For professionals in the technology industry, understanding the mechanics behind these systems—rather than just their APIs—is becoming a requisite skill. This book aims to bridge that gap. You will learn exactly how different classes of AI work conceptually, alongside the basic structures of their implementations. We will trace the evolution of algorithmic intelligence, moving from the explicit logic of Search and Evolutionary Algorithms through the statistical principles of Machine Learning, and finally into the complex architectures of Deep Learning and Generative AI. By understanding these foundational principles, you will be better equipped to reason about, implement, and innovate with the systems that are reshaping the world. You can expect to learn with relatable analogies, practical examples, and hand-drawn illustrations. Let’s crack open the black box.

1.1 What is Artificial Intelligence (AI)?

1.1.1 Defining AI

1.1.2 Data is the fuel for AI algorithms

1.1.3 Algorithms are like recipes

1.1.4 Algorithms vs. models

1.2 The evolution of AI

1.3 Different types of problems

1.3.1 Search problems: Find a path to a solution

1.3.2 Optimization problems: Find a good solution

1.3.3 Prediction and classification problems: Learn from patterns in data

1.3.4 Clustering problems: Identify patterns in data

1.3.5 Deterministic models: Same result each time it’s calculated

1.3.6 Probabilistic models: Potentially different result each time it’s calculated

1.4 Intuition of AI concepts

1.4.1 Narrow intelligence: Specific-purpose solutions

1.4.2 General intelligence: Humanlike solutions

1.4.3 Super intelligence: The great unknown

1.4.4 Old AI and new AI

1.4.5 Search algorithms

1.4.6 Biology-inspired algorithms

1.4.7 Machine learning algorithms

1.4.8 Deep learning algorithms

1.4.9 Generative models

1.5 Some uses for AI algorithms

1.5.1 Agriculture: Optimizing plant growth

1.5.2 Banking: Preventing fraudulent transactions

1.5.3 Cybersecurity: Safeguarding email inboxes

1.5.4 Health care: Diagnosing patients

1.5.5 Logistics: Finding the best delivery route