When AI talk starts sounding like a set of incantations, it helps to return to the algorithms that search, optimization, and learning are actually built on.
The chapter turns search, heuristics, evolutionary methods, swarm intelligence, and ML basics into one map you need before modern LLM and agent systems can be judged realistically.
For interviews, it works as a short bridge between algorithmic foundations and modern AI: it helps you explain a system's strengths and weaknesses not only through the product lens, but through the mechanics underneath.
Practical value of this chapter
Algorithmic base
The chapter quickly rebuilds the main classes of AI algorithms in your head: search, heuristics, evolutionary methods, swarm intelligence, and learning.
Interview intuition
It is a practical way to talk through A*, min-max, genetic algorithms, and Q-learning without drowning in math but still showing solid engineering intuition.
Bridge to modern AI
Once this base is clear, it becomes much easier to discuss LLMs, RAG, and agent systems without treating modern AI as model magic.
Sober starting point
The book is most useful when a team first needs shared vocabulary and intuition before moving on to newer AI material.
Source
Telegram: Book Cube
Part 1 of the review covering chapters 1-6 and the core ideas of the first half.
Grokking Artificial Intelligence Algorithms
Authors: Rishal Hurbans
Publisher: Manning Publications, 2020
Length: about 350 pages
An introductory guide to core AI algorithms: search, evolutionary methods, swarm intelligence, ML, neural networks, and Q-learning. Best read as algorithmic groundwork before newer material on LLMs and generative systems.
What this book is about
This is an accessible introduction to classical artificial intelligence algorithms: from search and evolutionary methods to swarm intelligence, ML basics, neural networks, and reinforcement learning.
If you expect a guide to all of modern AI, the book will disappoint — there are no transformers or generative systems here. Its value lies elsewhere: it is a careful algorithmic foundation that newer material on LLMs, RAG, and generative systems can later build on.
Structure: chapters 1-6
What is artificial intelligence
What people usually mean by AI, how narrow AI differs from AGI, and which historical framing is the most useful to start from.
Search fundamentals
Binary search, BFS, DFS, and intuition for how to compare such algorithms by efficiency.
Smart search
Heuristic search with A* and adversarial strategies such as min-max and alpha-beta pruning.
Evolutionary algorithms
Genetic algorithms: selection, mutation, crossover, and progressive population improvement.
Advanced evolutionary algorithms
Genetic and evolutionary programming, ways to encode solutions, and the kinds of problems where these methods are most useful.
Swarm intelligence: ants
Ant colony algorithms and pheromone trails as a way to search for strong routes and combinations.
Structure: chapters 7-10
Swarm intelligence: particles
Particle swarm optimization and how local and global experience guide movement through the search space.
Machine learning
Supervised, unsupervised, and reinforcement learning; regression, classification, and clustering tasks.
Artificial neural networks
How neural networks are structured: layers, forward pass, and training through backpropagation.
Reinforcement learning with Q-learning
Q-function, reward, action choice, and the framing of problems through Markov decision processes.
Related chapter
Hands-On Large Language Models
Where to go once the classical foundation is in place and modern language models are next.
How to read this book in 2026
The book's strength is the algorithmic foundation of AI. The cost of that focus is that transformers, LLMs, and modern generative systems stay almost entirely off-screen.
This is a first step, not the end of the route: build search, evolution, swarm, and ML intuition first, then move to newer material on LLMs, RAG, and AI Engineering.
Senior engineers will find much of it too introductory. For aligning shared vocabulary and first engineering principles inside a team, though, this level is more than enough.
Related chapters
- Why engineers should know ML and AI - Provides the AI/ML overview and places this book on the broader learning map.
- Deep Learning and Data Analysis: A Practical Guide (short summary) - Builds on Grokking fundamentals and adds more hands-on practice in ML and deep learning.
- Precision and Recall Basics - Without these metrics you cannot honestly compare a classification algorithm: the chapter shows what you actually measure when you say “the model works.”
- AI Engineering (short summary) - The next step after the algorithms: what changes once a model has to be shipped to production and kept running there, not just written.
- Hands-On Large Language Models (short summary) - Bridges classical algorithmic foundations to modern LLMs, embeddings, and RAG workflows.