Machine-Learning

Yesterday I explored how proof trees and machine learning can work together. Today, let’s get practical: how do you actually build these hybrid declarative systems? Back in 2012, I built a proof of concept for medical software that tracked drug interactions, advised on dosage, and provided decision support that human doctors could review. It was a glimpse into what’s now becoming mainstream. Here’s how I’d build it now. The Four-Layer Architecture Think of a hybrid declarative system like a brain with specialized regions that collaborate: ...

In my previous article, I explored proof trees: how declarative languages like Prolog break down questions into smaller steps until they can be answered with known facts. It’s structured, logical, and explainable. But it also has a weakness: proof trees need perfect information. If a fact is missing or noisy, the tree collapses. Machine learning has the opposite strengths and weaknesses. ML thrives in messy environments (images, speech, incomplete data), but its reasoning is often a black box. You get an answer, but not much of a “why.” ...

The software landscape is undergoing a fundamental shift. Where we once built monolithic applications designed to handle every conceivable use case, AI is now enabling a new paradigm: just-in-time (JIT) applications that dynamically adapt, generate, and optimize themselves based on real-time context and user needs. What Are Just-in-Time Applications? Just-in-time applications represent a departure from traditional software architecture. Instead of pre-building every feature and workflow, these applications use AI to generate functionality, interfaces, and logic on-demand. Think of them as software that writes itself in response to specific situations, user requests, or environmental changes. ...