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Author's review of the book and key content highlights.
Hunting Electric Sheep: The Big Book of Artificial Intelligence
Authors: Sergey Sergeevich Markov
Publisher: markoff.science (free digital edition), DMK Press (print edition)
Length: 1352 pages (568 + 784, two volumes)
Sergei Markov's book about the evolution of AI: from ancient computing ideas and the perceptron to AlexNet, deep learning and modern intelligent systems.
OriginalWhy is the book useful for a systems engineer?
The book shows that AI architectures evolve in waves, not linearly: ideas return in new technical contexts.
The history of neural networks is revealed through people and engineering solutions, and not just through mathematical formulas.
The material helps to connect research, product practice and system design into a single picture.
Focusing on the long horizon is useful for architects who need to understand not only the current state, but also trends.
AI's historical arc in the book
Antique and mechanical calculators
The first attempts to formalize intelligence and automate calculations.
Early AI and cybernetics
McCulloch and Pitts neuron, Rosenblatt perceptron, hopes and first limitations.
Skepticism and local breakthroughs
Periods of “AI winter”, development of learning algorithms and the return of backpropagation.
AlexNet and the new wave
The start of the “deep learning revolution”, after which AI entered mainstream products.
Modern stage
Foundation models, agent-based scenarios and the transition from AI demos to AI systems in production.
Related chapter
AlphaGo
A documentary case study on how games have accelerated the practical progress of AI.
People and ideas that shaped the industry
One of the strengths of the book is its emphasis on the people who consistently built the foundation of the modern AI ecosystem: from the early researchers of neural networks and the perceptron to the authors of the algorithms that paved the way for deep learning. This format makes the story less “flat”: you can see which ideas have stood the test of time and which ones turned out to be dead ends.
Related chapter
AI Engineering
Practices of creating production systems on top of foundation models.
Architectural implications for system design
The evolution of AI depends not only on algorithms, but also on infrastructure: compute, networks, storage, development tools.
Games (chess, Go) are useful as engineering testing grounds: they accelerate the emergence of practical architectural solutions.
Product AI requires balancing accuracy, cost, latency and reliability, rather than maximizing a single metric.
Historical context helps us better evaluate hype and make more sustainable technology decisions.
Where to read and what to discover nearby
Author's website (free electronic version)
PDF by volume, EPUB and FB2 versions of the book are available.
Printed edition in DMK Press
Card of the paper two-volume edition and annotation from the publisher.
To continue the route, see Hands-On LLM, Prompt Engineering for LLMs And The Thinking Game.