Posts

The Thinking Machine, Part 2: The Perceptron's Spark

In our last technical deep-dive , we built our first digital neuron, the McCulloch-Pitts (MP) model. It was a clever little switch, capable of basic logic. But it had two profound flaws: it treated all inputs as equally important, and worse, it couldn't learn . We had to set its rules by hand. It was a machine, but a dumb one. To get from a simple switch to true artificial intelligence, we needed a spark. We needed a model that could weigh evidence and, most critically, learn from its mistakes. That spark arrived in 1957, and its name was The Perceptron,  introduced by Frank Rosenblatt . A Step Up: Introducing Weights and Bias The Perceptron model took the simple elegance of the MP neuron and gave it two crucial upgrades, moving it much closer to its biological inspiration. Weights: Unlike its predecessor, the Perceptron understood that not all inputs are created equal. In making a decision, some factors are more important than others. It assigned a "weight" to each inpu...
Post a Comment
Read more

The Thinking Machine, Part 1: Building a Digital Neuron

Image
We have talked about the grand history of AI and the complex ethical questions it raises. But now, it's time to set aside the philosophy, roll up our sleeves, and look at the engine that drives it all. We are going to zoom in from the scale of decades to the scale of a single, microscopic cell. We are going to build a neuron. Before a single line of code was ever written for AI, nature had already perfected the ultimate thinking machine. The inspiration for everything we do in deep learning comes from the elegant and efficient design of the biological neuron . The Biological Blueprint Deep inside your brain, billions of these tiny cells are firing away. While the biology is incredibly complex, the core concept is beautifully simple. A typical neuron consists of three main parts: Dendrites: These are like the neuron's antennas. They receive incoming signals from thousands of other connected neurons. Soma (The Cell Body): This is the central processor. It gathers all the sign...
Post a Comment
Read more

The Conscience of the Machine: A Call for Sanity in AI

For the last decade, the story of Artificial Intelligence has been one of relentless, breathtaking progress. We've witnessed machines master ancient games, generate stunning works of art, and translate languages in the blink of an eye. We've been living through a gold rush, where the single-minded goal has been to push one metric ever higher: accuracy . But as the dust settles, a new, more sober conversation is emerging. We are beginning to grapple with the profound paradox at the heart of modern AI. Our models are incredibly powerful, yet they are also strangely fragile. They solve problems we once thought impossible, yet they create new challenges that are deeply human. The gold rush is evolving into an age of responsibility, with a collective call for sanity. The "Clever Hans" Problem In the early 1900s, a horse named Clever Hans became a celebrity for his apparent ability to perform arithmetic. He would tap his hoof to answer questions. It was, of course, a trick...
Post a Comment
Read more

A Brief History of Almost Everything (in Deep Learning)

Image
Before we can hope to build the future, it pays to understand the past. The story of Artificial Intelligence isn’t a neat, linear progression. It is a saga filled with flashes of brilliance, intense rivalries, long winters of crushing disappointment, and a sudden, explosive resurgence that is reshaping our world. It is the story of how we tried to build a brain, failed, and then, by learning from biology, cats, and our own mistakes, finally began to succeed. The First Age: The Promise and the Prophecy Long before the first silicon chip, the blueprint for AI was being mapped in the messy, wonderful world of neuroscience. For years, scientists debated: was the brain a single, continuous fabric, or a network of individual cells? The eventual victory of the neuron doctrine (the idea of discrete, interconnected cells) laid the conceptual foundation for everything to follow. This biological blueprint gave birth to the first artificial neuron: the Perceptron . Created in 1957, it was a s...
Post a Comment
Read more

From Curiosity to Commitment: Stepping into AI

Artificial Intelligence has quietly seeped into the fabric of our lives. It’s the ghost in the machine that recommends our next song, the silent navigator that guides our commute, and the unseen force shaping conversations about our future. To the outside eye, it often operates like a form of modern magic . For a long time, I've been a spectator, fascinated by its power but mystified by its methods. But I am convinced that behind this curtain of magic lies not spells, but science ; not incantations, but engineering . And I have decided it’s time to trade my spectator’s seat for an apprentice’s workbench. This post marks that commitment. It’s the start of a public journey to learn Deep Learning from the ground up, documenting the process step by step. The Method My initial roadmap will be charted by the acclaimed Deep Learning lecture series from Professor Mitesh Khapra. His work comes highly recommended, and I'm ready to dive in. My plan is to learn actively , guided by the s...
Post a Comment
Read more