As developers, we face a relentless stream of emerging tech, with Artificial Intelligence sitting squarely at the center of the noise. It’s easy to read high-level summaries and feel like you understand a concept—until you try to build it yourself.
To move past the hype, I built three distinct side projects targeting three core pillars of modern AI: Machine Learning (ML), Large Language Models (LLMs), and Artificial Super Intelligence (ASI). Here is what each concept actually looks like under the hood, and what building them taught me about the limits of today's systems.
Let’s start at the foundation. What actually happens when a machine 'learns'?
A classic entry point is the MNIST handwritten digit classification challenge: given 60,000 28x28 grayscale images of handwritten numbers (0–9), write a program that learns to recognize them.
Strip away the marketing terminology, and the solution isn't magic—it's high-dimensional curve fitting via matrix multiplication. The process breaks down into two core phases:
Seeing a Convolutional Neural Network (CNN) classify digits with over 98% accuracy demonstrates how linear algebra can mimic pattern recognition.
Code & Deep Dive: Check out my implementation on GitHub: github.com/p-dirac/python-keras-cnn Scaling up linear algebra brings us to Large Language Models (LLMs). While LLMs rely on the same fundamental matrix operations as simple classifiers, the scale and architecture (Transformers) change what they can accomplish.
A helpful mental model for an LLM is a massive digital library compressed into a non-deterministic matrix. When an LLM is trained on millions of documents:
Beyond drafting articles, LLMs can model formal structured logic—like synthesizing financial filings, validating regulatory schemas, and authoring domain-specific code.
Code & Deep Dive: Explore my SEC compliance and document generation prototype: [github.com/p-dirac/disclosure-pilot-ai](https://github.com/p-dirac/disclosure-pilot-ai)
While ML classifies and LLMs synthesize, Artificial Super Intelligence (ASI) is hypothesized to invent entirely new conceptual models. Most discussion around ASI remains abstract, treating it as simply a 'smarter LLM.' But can today's architectures genuinely perform creative discovery?
Consider the history of physics: Kepler’s planetary laws, Newton’s gravity, Maxwell’s electromagnetism, the Bohr atom, Einstein’s relativity, and Schrödinger’s quantum mechanics. Could an AI discover these breakthroughs without human guidance?
To put this to the test, I focused on a specific 19th-century anomaly: the unexplained precession shift in Mercury's orbit. Newtonian gravity failed to account for the observed measurements, leading historical physicists to propose modified force laws (such as Hall, Gerber, and Yukawa potentials) before Einstein transformed the framework with General Relativity.
When evaluating whether current AI models can discover novel physical laws to resolve such anomalies, the answer is clear: current pattern-matching engines cannot bridge conceptual gaps that require discovering fundamental paradigm shifts.
Code & Deep Dive: Read my analysis on alternative gravity models and ASI boundaries: github.com/p-dirac/physics-asi-concepts Building across these three tiers highlights both the capabilities and limits of modern AI:
If you want to dive deeper into the code, mathematical derivations, or project documentation, feel free to explore the GitHub repositories linked above. "What are your thoughts on current AI limitations in scientific discovery? Let’s discuss in the comments below!"