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 of retaking a driving test, this post explores how what we discard defines the quality of our expertise.
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 our irresistible urge to attribute intention and reasoning to mere probability.
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 must first learn to see the shadows for what they are.
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
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 an engineering work. This paper covers the various steps of this job.
#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 epochs, and enriching data for smarter learning.
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
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 introduce bias (consistent errors) and variance (prediction variability).
#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 the value that we know to be correct, we can tune the parameters and make it solve the problem at hand.
#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 simplest format to allow global understanding of the inside mechanisms. The end-to-end process is called Forward Propagation.
#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
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.