Technology

The Evolution Of AI

A journey from neurons to networksBy Jasper Livingstone

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Length10h 56m

About this audiobook

How did intelligence evolve from biological neurons to artificial neural networks, and where might this transformation lead next? The Evolution Of AI takes you on an accessible journey through the ideas, discoveries, technologies, and human ambitions that shaped artificial intelligence. Beginning with the foundations of human intelligence and the early effort to understand how the brain processes information, the book traces the development of computing, machine learning, neural networks, deep learning, generative AI, and emerging intelligent systems. You will explore how researchers moved from rule-based programs to systems that learn from data, recognize patterns, generate language and images, solve complex problems, and increasingly work alongside people. Key developments are explained in clear language, making the history and principles of AI approachable without requiring a technical background. The book also examines the relationship between biological and artificial intelligence. What does the human brain teach us about learning? How closely do artificial neural networks resemble biological neurons? Where do the similarities end? Understanding these distinctions helps separate scientific reality from popular assumptions about intelligent machines. As AI becomes embedded in work, education, science, business, creativity, and everyday life, its evolution raises questions about human judgment, responsibility, consciousness, trust, and the future relationship between people and intelligent technology. The Evolution Of AI is for readers who want to understand where AI came from, how it reached its current capabilities, and what its continuing development means for society and the future of human intelligence.

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Audiobook details

GenreTechnology

Length10 hrs 56 mins

Narrated byListen with 1,000+ voices

FormateBook with Audio

Publish dateAug 27, 2026

LanguageEnglish

1Chapter 1: The Nature of Intelligence

2Chapter 2: From Biological Neurons to Thought

3Chapter 4: The Birth of Computing

4Chapter 5: Artificial Intelligence Becomes a Field

5Chapter 6: Rules, Symbols, and Expert Systems

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6Chapter 8: Machine Learning Changes the Approach

7Chapter 10: Deep Learning and the Data Revolution

8Chapter 11: Language Models and Generative AI

9Chapter 13: Bias, Trust, Safety, and Responsibility

10Chapter 15: The Next Evolution of Intelligence

11Chapter 1: The Nature of Intelligence

12What Do We Mean by Intelligence?

13The Major Forms of Human Intelligence

14The Biological Foundations of Intelligence

15How Intelligence Is Studied and Measured

16Task Performance and General Understanding

17Intelligence as a Human and Social Process

18Chapter 2: From Biological Neurons to Thought

19The Neuron as a Living Information Cell

20How Neurons Send Electrical and Chemical Signals

21Synapses, Plasticity, and the Biology of Learning

22From Neural Activity to Perception and Thought

23Efficiency, Adaptation, and Resilience in the Brain

24What Artificial Networks Borrowed from Biology

25Chapter 3: Logic, Mathematics, and the Dream of Thinking Machines

26From Mythical Beings to Mechanical Reasoning

27Formal Logic Turns Reasoning into Symbols

28Algorithms, Probability, and Different Kinds of Reasoning

29Babbage and Lovelace Imagine Programmable Machines

30Turing Defines the General Computing Machine

31From Computation to the Question of Machine Intelligence

32Chapter 4: The Birth of Computing

33From Counting Tools to Mechanical Calculation

34Binary Representation and the Logic of Machines

35War, Codebreaking, and the Rise of Electronic Computers

36The Stored-Program Computer

37From Vacuum Tubes to Microprocessors

38Software Creates a Platform for Artificial Intelligence

39Chapter 5: Artificial Intelligence Becomes a Field

40The Birth of Artificial Intelligence as a Scientific Field

41Symbols, Rules, and the First Model of Machine Reasoning

42Early Programs That Made Intelligence Visible

43Research Institutions, Ambitious Predictions, and Early Limits

44Chapter 6: Rules, Symbols, and Expert Systems

45The Symbolic View of Intelligence

46Knowledge Bases and Inference Engines

47Search, Problem Solving, and Decision Trees

48The Rise and Practical Use of Expert Systems

49Limitations, Decline, and Lasting Influence

50Chapter 7: Setbacks, AI Winters, and Lessons in Humility

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