<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>The beauty of Machine Learning</title><link>https://ha2emnomer.github.io/thebeautyofml/</link><description>Recent content on The beauty of Machine Learning</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Wed, 19 Aug 2026 10:00:00 +0200</lastBuildDate><atom:link href="https://ha2emnomer.github.io/thebeautyofml/index.xml" rel="self" type="application/rss+xml"/><item><title>How to work on niche problems/areas/fields in AI/ML?</title><link>https://ha2emnomer.github.io/thebeautyofml/posts/how-to-work-on-niche-problems/</link><pubDate>Wed, 19 Aug 2026 10:00:00 +0200</pubDate><guid>https://ha2emnomer.github.io/thebeautyofml/posts/how-to-work-on-niche-problems/</guid><description>&lt;h2 id="introduction">Introduction&lt;/h2>
&lt;p>One would think probably that a paper on LLMs is more mainstream than a paper that applies deep learning to identify animal sounds or detect malfunction in manufacturing machines. Usually, applications of AI in any field -which does not directly deal with computer algorithms- are considered niche, in the sense that usually these types of publications don&amp;rsquo;t fundamentally contribute something to AI itself and usually go to conferences or journals that are not for AI/ML.&lt;/p></description></item></channel></rss>