AI Foundations

Demystifying the Technology Behind the Revolution
First Edition 2026 · Montbleau.ca Academic Press
A comprehensive, accessible introduction to artificial intelligence for readers who want clarity over hype. Written by educator and researcher Pierre Montbleau, this book explains how AI works, what it can and cannot do, and why it matters for all of us.

Author and publisher

Pierre Montbleau Montbleau.ca Academic Presspierre@montbleau.ca · montbleau.ca/academic-press
© 2026 Montbleau.ca Academic Press. All rights reserved.
AI Concepts in plain language 14 Chapters plus conclusion Hands-on Exercises and reflections Ethics Woven throughout

About the book

The journey begins with the fundamentals: what AI is, where it came from, and how Narrow AI differs from the imagined General AI. Readers then explore machine learning, deep learning, neural networks, data quality, language systems, computer vision, reinforcement learning, generative models, and the organizational realities of using AI responsibly.
Beyond the technology, the book addresses the ethical questions raised by AI: bias and discrimination, black-box systems, job transformation, privacy, regulation, environmental impact, psychology, and geopolitics.

What readers will find

Clear foundations
Core AI concepts explained without heavy mathematical notation or unnecessary jargon.
Practical frameworks
Guidance for scoping AI projects, assessing organizational readiness, and avoiding common failure modes.
Responsible AI lens
Case studies and discussion of algorithmic bias, explainability, regulation, safety, and societal impact.

Inside the book

Chapter 1
The AI Landscape — From Myth to Reality

Chapter 4

Reinforcement Learning & Computer Vision

Chapter 7

Limitations, Bias, and the Black Box

Chapter 10

The AI Ecosystem, Funding, and Next Steps

Chapter 13

AI & Geopolitics
Chapter 2
Machine Learning & Deep Learning

Chapter 5

Defining and Scoping Your AI Project

Chapter 8

Why AI Projects Fail and How to Prevent It

Chapter 11

AI and the Environment

Chapter 14

The Future You Build
Chapter 3
Language, Planning, and Robotics

Chapter 6

Organizational Readiness to Real-World Impact

Chapter 9

Acceptability, Explainability, and the Law

Chapter 12

The Psychology of AI

Hands-On Learning & Practical Exercises

AI Foundations goes beyond theory by providing practical exercises, case studies, review questions, and reflection activities designed to help readers understand AI concepts and apply them in real-world situations.
AI Around You
Identify and categorize AI systems you encounter in daily life, from recommendation engines to virtual assistants and computer vision applications.
Rule-Based vs Machine Learning
Compare traditional programming approaches with machine learning systems by designing solutions to real-world problems such as spam detection.
Bias Detection Workshop
Explore how algorithmic bias emerges in AI systems and learn strategies to improve fairness and accountability.

Organizational Readiness Assessment

Evaluate an organization's readiness for AI adoption by examining data quality, skills, governance, leadership support, and infrastructure.

AI Project Scoping

Transform vague business objectives into measurable AI initiatives using practical project-planning frameworks and templates.

Explainability & Transparency

Examine the challenges of black-box AI systems and evaluate approaches to improve transparency and trust.

Responsible AI Case Studies

Analyze real-world examples involving hiring algorithms, healthcare systems, facial recognition, and criminal justice applications. Learn to identify stakeholders, risks, benefits, and ethical considerations.

Build vs Buy Decision Framework

Evaluate whether organizations should build AI solutions internally, purchase commercial products, or partner with external providers.

What Readers Will Gain

  • Critical thinking about AI technologies
  • Practical project planning and governance skills
  • Understanding of AI risks and limitations
  • Responsible AI and ethical decision-making frameworks
  • Confidence in evaluating AI opportunities and challenges
  • Real-world readiness for AI-driven workplaces

Ethics Woven Throughout

Ethical considerations are not confined to a single chapter. Throughout AI Foundations, readers are encouraged to examine the broader human impact of artificial intelligence and the responsibilities that come with designing, deploying, and governing AI systems. Rather than treating ethics as an afterthought, the book integrates discussions of fairness, transparency, accountability, privacy, safety, and societal impact into every stage of the AI journey.
✓ Algorithmic Bias & Fairness✓ Explainability & Transparency✓ Privacy & Data Protection✓ AI Safety & Reliability✓ Regulation & Governance✓ Societal & Human Impact

Key Ethical Questions Explored

Fairness How can we prevent AI systems from reinforcing existing biases?Transparency Should AI systems be able to explain their decisions?Responsibility Who is accountable when AI systems make mistakes?Privacy How should organizations balance innovation with data protection?Societal Impact What role should AI play in shaping the future of work, education, healthcare, and public life?

“AI is not magic. It is not beyond your comprehension. The only prerequisite is curiosity.”

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