<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Machine Learning on VinhMDev</title><link>https://vinhmdev.com/topics/machine-learning/</link><description>Recent content in Machine Learning on VinhMDev</description><generator>Hugo</generator><language>en</language><lastBuildDate>Wed, 25 Feb 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://vinhmdev.com/topics/machine-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Paper 01: The Illusion of Control: System Design in the Era of AI</title><link>https://vinhmdev.com/posts/paper-01-the-illusion-of-control-system-design-in-the-era-of-ai/</link><pubDate>Wed, 25 Feb 2026 00:00:00 +0000</pubDate><guid>https://vinhmdev.com/posts/paper-01-the-illusion-of-control-system-design-in-the-era-of-ai/</guid><description>&lt;h2 id="i-the-limits-of-traditional-programming" class="relative group"&gt;I. The Limits of Traditional Programming &lt;span class="absolute top-0 w-6 transition-opacity opacity-0 -start-6 not-prose group-hover:opacity-100"&gt;&lt;a class="group-hover:text-primary-300 dark:group-hover:text-neutral-700" style="text-decoration-line: none !important;" href="#i-the-limits-of-traditional-programming" aria-label="Anchor"&gt;#&lt;/a&gt;&lt;/span&gt;&lt;/h2&gt;&lt;p&gt;Software engineering has long relied on absolute control. System architects design software with the core principle that explicitly written logic will always return a predictable result.&lt;/p&gt;
&lt;p&gt;However, this model falls short when integrating Large Language Models (LLMs) directly into core features. We are moving from managing strict if-else statements to orchestrating probability distributions. Applying the old control-based mindset to AI will inevitably cause cascading failures when the system encounters unfamiliar data.&lt;/p&gt;</description></item></channel></rss>