Fourth Industrial Revolution

Summary : Five surprising truths about how AI language models really work — from tokens and sudden, scale-driven abilities to why they sometimes "hallucinate", how you can program them with plain language, and how retrieval systems make them more reliable. Introduction If you've used tools like ChatGPT, you know how effortlessly they can write an email, generate code, or explain a concept. That ease feels close to magic. Under the surface, however, these systems run on patterns, probabilities, and careful engineering. Understanding a few core ideas will help you use them smarter and more safely. View my  LLM Concepts video below and then read on. 1. They Don’t See Words, They See Tokens When you type a sentence, you see words and spaces. A large language model (LLM) processes a sequence of tokens. Tokens are the smallest pieces the model works with — sometimes a whole word, sometimes a subword fragment. For example, “unbelievable” might be broken into subword parts...

Summary : Machine learning algorithms let computers learn from data to make predictions and discover patterns. This post explains the main algorithm types, the typical workflow, and how to choose the right approach for your problem. What Are Machine Learning Algorithms? Machine learning algorithms are sets of procedures a computer follows to learn from data. Instead of being explicitly programmed for every scenario, these algorithms identify patterns, make predictions, and improve as they see more data. The goal is to build models that generalize from past examples to new, unseen situations. 1. Supervised Learning In supervised learning, the training data includes inputs and the correct outputs, known as labels. The algorithm learns a mapping from inputs to outputs so it can predict labels for new examples. Examples : Linear regression — predicts continuous values, such as house prices. Logistic regression and support vector machines — common for classification task...

Summary : An end-to-end AI/ML lifecycle transforms data into production-ready models. This post explains development, fine-tuning, deployment, and continuous optimization with practical steps to keep models accurate, efficient, and reliable. The End-to-End AI/ML Model Lifecycle: From Concept to Continuous Improvement Building useful AI and machine learning systems means moving through a clear lifecycle: development, fine-tuning, deployment, and optimization. Each stage matters, and the lessons learned at the end feed back into the beginning. Below is a practical, readable walkthrough of each stage and the practices that help models succeed in production. Development: Problem, Data, and Baselines Development starts with a clear problem statement and the right data. Define the business objective, determine what success looks like, and gather representative data. Data preparation often takes the most time: clean the data, handle missing values, engineer features, and split the dat...