The AI Story

Episodes

• Why AI, Why Now - AI already runs through your day and every industry. Why it matters now, and why it is worth understanding.
• Can Machines Think? - The birth of AI as an idea, and the Turing question that started it all.
• The First Learning Machine - The perceptron, and the first machine that learned from examples.
• Rules Hit a Wall - Why hand-written rules alone could not capture intelligence.
• The Long Winter - Overpromised and underdelivered: the funding freezes, the expert-systems boom and bust, and the lesson that carried the field forward.
• Data, Features, Labels - How raw data becomes something a model can actually learn from.
• Wrong by How Much? - Measuring error so a model knows exactly what to fix.
• Rolling Downhill - Following the slope of the loss to find the best weights.
• The Honest Test - Why a good model performs well on data it has never seen.
• Twenty Questions - How a tree of yes/no questions turns data into decisions.
• Wisdom of the Crowd - Why hundreds of trees voting beat any single one.
• The Widest Street - The max-margin boundary, support vectors, and the kernel trick.
• Birds of a Feather - Distance-based learning: k-nearest neighbors and k-means clustering.
• Choosing Your Tool - A guide to the classical toolbox, and the feature-engineering catch they all share.
• Stacking Layers - Hidden layers break the XOR wall that stopped the single perceptron.
• Learning Backwards - How backpropagation sends the error backward to train every weight.
• Layers Learn Features - How deep layers build up pixels into edges, parts, and whole objects.
• Eyes for the Machine - Convolution, sliding filters, weight sharing, and pooling: CNNs for images.
• The Breakthrough - AlexNet, GPUs, and ImageNet: the 2012 moment the deep-learning era began.
• Learning by Reward - Reinforcement learning: an agent learns from reward, from Atari pixels to beating the best at Go.
• Machines That Imagine - From the GAN forger-and-detective duel to diffusion and text-to-image, machines that create.
• AI Does Science - AlphaFold cracks the fifty-year protein-folding problem and hands the tool to all of biology.
• One Word at a Time - Why reading a sequence step by step forgets the start and cannot be parallelized.
• Pay Attention - Score every input word, weigh them by relevance, and blend them into a context vector.
• Attention Is All You Need - Drop recurrence: self-attention lets every word attend to every word, in parallel.
• Scaling Up - Predict the next word on raw text, self-supervised, then scale data, parameters, and compute.
• Machines That Talk - Emergent abilities, in-context learning, human feedback, and the ChatGPT moment.
• Breaking Text Into Tokens - Text becomes tokens, and each token becomes an ID number the model can process.
• Words Become Vectors - Each token ID becomes a vector in a meaning space where similar words sit close.
• The Stack of Blocks - Attention mixes context, feed-forward thinks, and a deep stack refines every vector.
• Predicting the Next Token - Logits over the vocabulary, softmax, sampling and temperature, then append and repeat.
• The Context Window - The fixed token window, why long chats forget, no memory between chats, and hallucination.
• Give It a Library - Retrieve relevant documents by meaning and put them in the prompt for grounded, cited answers.
• Let It Use Tools - The model emits a structured tool call, the system runs the real tool, and the result comes back.
• From Answering to Acting - The think, act, observe loop that lets a model plan and act over many steps toward a goal.
• Giving It Memory - Save durable facts to an external store and recall them across sessions, so it remembers you.
• Putting It Together - Assemble the model, retrieval, tools, the agent loop, and memory into one real AI system.
• From Demo to Production - Why a working demo is easy but a reliable, fast, affordable product for millions is hard.
• Testing the Untestable - How to evaluate a non-deterministic model with eval sets and judges, and guard it at runtime.
• The Cost of Intelligence - Every token is compute, so serving millions needs smaller models, caching, batching, and routing.
• Keeping It Safe - The alignment problem, human feedback, the real risks, and why safety is still unsolved.
• The Frontier - Multimodal, reasoning, and agents at the edge, then the whole 1950-to-now story in one breath.