1 Intuition of AI

Artificial intelligence is presented as a practical, foundational capability in modern software, and this chapter builds the intuition needed to use it well. It frames AI as systems that perform tasks associated with human intelligence, then surveys the field from “old AI” (explicit logic and search) to “new AI” (learning from data) through machine learning, deep learning, and generative models. It also introduces levels of capability—narrow systems that excel at specific tasks, the aspirational goal of general intelligence, and speculative superintelligence—emphasizing that useful solutions often emerge by combining specialized components. Throughout, the chapter sets expectations for the book’s journey: start with core search and biologically inspired methods, then progress to statistical learning, neural networks, and the transformer-based models behind today’s generative AI.

The chapter grounds intuition in first principles: intelligence as autonomy and adaptability powered by data; algorithms as “recipes” that transform inputs into outputs; and the crucial distinction between algorithms (the process) and models (the learned artifact they produce). It clarifies data types (quantitative versus qualitative), how raw data becomes information and then knowledge, and why rigorous, repeatable collection and evaluation are vital to mitigate bias. It categorizes problem types—search (finding paths), optimization (seeking good-enough solutions while avoiding local optima), prediction versus classification (numbers versus labels), and clustering (discovering structure)—and contrasts deterministic models with probabilistic ones that sample from distributions. These concepts scaffold the later chapters, showing how classical search, evolutionary strategies, and swarm behaviors underpin modern learning systems.

Finally, the chapter connects ideas to real-world impact: optimizing crops with sensor data, detecting financial fraud via anomaly patterns, filtering phishing through language understanding, assisting radiology with computer vision, orchestrating delivery routes and truck packing, personalizing fitness using wearable signals, and mastering complex games through reinforcement learning and self-play. The unifying thread is converting abundant, noisy data into actionable decisions and creative outputs. With the landscape mapped and the role of data, models, and problem types clarified, the book turns next to search algorithms—the planning machinery that lets machines navigate vast choice spaces efficiently.

Examples of data around us

Qualitative data versus quantitative data

An example showing that an algorithm is like a recipe

A number-guessing-game algorithm flow chart

The evolution of AI

Levels of AI

Categorization of concepts within AI

Using data to optimize crop farming

Using machine learning for feature recognition in brain scans

Using sensors and AI to guide fitness & health

Using neural networks to learn how to play games

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