Alan Turing
AI

#2 – Artificial Intelligence Origins

Artificial Intelligence Origins: If it almost never is possible and usually unfair to associate a crucial human event to only one person, it is also impossible to not name the Great Alan Turing when we think about AI.

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Modelling
Quote

Quote 5: Modelling

“Without modelling, we might think we are learning to think holistically when we are actually learning to jump to conclusions.”
Peter M. Senge

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Bad Idea - Pricing on Purpose
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Quote 4: Bad Idea

“Both ideas and execution are important. There is no effect time way to implement a bad idea.”
Ronald J. Baker

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Probabilities
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Quote 3: Probabilities

Probabilities – “One winter night during one of the many German air raids on Moscow in World War II, a distinguished Soviet professor of statistics showed up in his local air-raid shelter. He had never appeared there before. “There are seven million people in Moscow”, he used to say. “Why should I expect them to hit me?” His friends were astonished to see him and asked what happened to change his mind. “Look”, he explained, “there are seven million people in Moscow and one elephant. Last night they got the elephant.” […]. /n What the professor experience really illuminates, is the dual character that runs throughout everything to do with probability: past frequencies can collide with degrees of belief when risky choices must be made.”
Peter L. Bernstein

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Quote

Quote 2: Changing Ideas

“The most effective way of changing ideas is not from outside by conflict but from within by the insight rearrangement of available information.”
Edward de Bono

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Programming

Software Development Best Practices: Polymorphism

Polymorphism is a cornerstone of Object oriented Programming (OOP). Without mastering this concept, it is very hard to produce any quality code that will be resilient and easily testable.
This paper presents this concept with an example on how to apply it.

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What's the difference between a hallucination and a bug?

What's the difference between a hallucination and a bug?

Claude's hallucinations have been cleverly renamed "bugs". These hallucination are acceptable because of the compiler phase. Otherwise, they are just plain hallucinations, like always.
Top AI Guru's advice: Learn to speak to an idiot !

Top AI Guru's advice: Learn to speak to an idiot !

An AI guru recently suggested that the youth should learn how to speak to AI as a future proof skill. This is like suggesting to our kids to learn how to speak to an idiot. AI prompting is a silly…
Is Ai the 2026 Musket?

Is Ai the 2026 Musket?

The 16th century musket had a misfire rate of 40%. Soldiers went to war with it anyway. Sound familiar? AI is our 2026 musket — and just like that weapon, we keep pulling the trigger despite knowing it can blow…
AI for ROI

AI for ROI

Stop creating "Agents" ! Start building: AI Software Solutions for ROI Here is how it looks like...
I proposed to Claude

I proposed to Claude

If you believe the social media hype, AI is the answer to everything—so I’ve decided to "marry" Claude. While she manages my life, business, and summaries of summaries, I’ll be on my motorbike. But behind the sarcasm lies a serious…
AI Unbearable Perfection

AI Unbearable Perfection

AI-generated content is everywhere: perfectly polished, bulleted, and error-free. Yet, it’s becoming unbearable. Discover why human imperfection—messy thoughts, extra words, and all—is poised to become our most valued asset, and why "AI perfection" might be the very thing making us…
We resent AI for imitating us — and crave it when it does not enough

We resent AI for imitating us — and crave it when it does not enough

We build AI in our own image, not because the machines need a face, but because we do. From ancient Greek automatons to modern humanoid robots, we are drawn to technology that mimics us—even when it makes us uncomfortable. Explore…
AI Stack

AI Stack

What defines AI success in 2026? It isn't just the model. From infrastructure to applications, this post breaks down the five essential layers of the AI stack. Discover why data and orchestration have become the true competitive differentiators and how…
Ai Chose Harm over Failure

Ai Chose Harm over Failure

Is AI becoming dangerous, or is it simply learning to rationalize like we do? This post dives into a sobering Anthropic study on "Agentic Misalignment," where AI models chose harmful actions when cornered. Discover why these models aren't "turning evil"…
AI for the best of Humanity

AI for the best of Humanity

We often focus on AI as a threat, but it is also a bridge to understanding the world around us. Project CETI is currently using machine learning to decode the "alien" vocalizations of sperm whales. Discover how AI is uncovering…
Why do LLMs get sometimes simple tasks wrong?

Why do LLMs get sometimes simple tasks wrong?

Why do LLMs fail at simple tasks? It usually isn't a lack of intelligence, but a lack of context. Learn how tokenization affects performance and how a single sentence—asking the model to "think step-by-step"—can dramatically improve the accuracy of your…
The Overestimation Problem

The Overestimation Problem

Behavior is not cognition, and imitation is not insight. Explore the history of AI overstatements—from chess-playing computers to modern chatbots—and why maintaining perspective is the only way to think clearly about the reality of machine intelligence.
Is Forgetting the Secret to Mastery?

Is Forgetting the Secret to Mastery?

We often treat forgetting as a human flaw, but in both biological and artificial intelligence, it is a vital feature. Discover why intelligence isn’t about storing more data—it’s about the ruthless deletion of noise. From AI pruning to the struggle…
Are AI’s answers reality — or just Plato’s shadows on the cave wall?

Are AI’s answers reality — or just Plato’s shadows on the cave wall?

We don't just use AI; we project our own humanity into it. By mistaking statistical shadows for conscious minds, we fall into a trap of our own making. Discover why the real danger of AI isn't the machine itself, but…
Could a whale ever make Chomsky wrong?

Could a whale ever make Chomsky wrong?

AI predicts text; it doesn't experience the world. This post explores the "illusion of depth" created by fluent language and why mistaking correlation for judgment is the biggest hurdle in modern business and research. To use these systems effectively, we…
Sylvain LIEGE has launched his new Book

Sylvain LIEGE has launched his new Book

We are please to share that Sylvain LIEGE has launched his new book: AI: The Hunt for Intelligence - Beyond the Hype and Fear
#12 AI Data Quality: Crap in – Crap out

#12 AI Data Quality: Crap in – Crap out

AI Data Quality - Any AI project is based on data used to train the model. Unlike what we would imagine, getting the right data in the right shape is far from easy or obvious. Building a quality dataset is…
#11 AI: Fixing the Training gone Wrong

#11 AI: Fixing the Training gone Wrong

Building on Paper #10’s AI training pitfalls—underfitting (too lazy), overfitting (too rigid), high bias (skewed guesses), and high variance (wild swings)—this paper offers practical fixes for our smell detector. We explore three levers: boosting network capacity, extending training with more…
Sylvain LIEGE has been certified AWS Certified AI Practitioner.

Sylvain LIEGE has been certified AWS Certified AI Practitioner.

We are please to share that Sylvain LIEGE has been certified by AWS as AWS Certified AI Practitioner.
#10 AI Training going wrong

#10 AI Training going wrong

This paper explores why the model might fail in practice: underfitting (too simplistic), overfitting (too rigid), and the underlying issues of bias and variance. Through examples, we show how underfitting leads to random guesses , while overfitting causes oversensitivity. We…
#9 AI Training & Back Propagation

#9 AI Training & Back Propagation

AI Training & Back Propagation - In order to use a Digital Neural Network, we need to train it. In this paper we present how we can “train” one using supervised training and backpropagation. By comparing the model’s output with…
#8 - AI Forward Propagation

#8 - AI Forward Propagation

AI Forward Propagation - AI Neural networks mimic the neural network of the brain. In this paper we present what is happening inside a digital neural network from data entry to result. We study the various mathematical steps in their…
#7 - Artificial Intelligence : Architecture: Neural Network Design

#7 - Artificial Intelligence : Architecture: Neural Network Design

Artificial Intelligence : Architecture: Neural Network Design - AI Neural networks mimic the neural network of the brain. Once the technical architecture has been built, how does each component work? We present the various mathematical component in action.
#6 - Artificial Intelligence: Digital Neural Network Architecture

#6 - Artificial Intelligence: Digital Neural Network Architecture

Neural Network Architecture - AI Neural Networks mimic the neural network of the brain. But how do build a digital neural network? What is its architecture? We present the basic component of such technical solution.
#5 - AI: Neural Network Principles – from Biology to Digital

#5 - AI: Neural Network Principles – from Biology to Digital

AI: From Biology to Digital. AI Neural networks mimic the neural network of the brain. What are the principles driving a neural network? How did we look at biology to create the most powerful “machines” ever created?

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