<?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 Notes from the Rabbit Hole</title><link>https://magnus919.com/categories/machine-learning/</link><description>Recent content in Machine Learning on Notes from the Rabbit Hole</description><generator>Hugo</generator><language>en</language><copyright>© [Magnus Hedemark](https://github.com/magnus919)</copyright><lastBuildDate>Wed, 16 Jul 2025 23:37:00 -0400</lastBuildDate><atom:link href="https://magnus919.com/categories/machine-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>The Architecture Decision Point: Why Current AI May Need a Complete Rethink</title><link>https://magnus919.com/2025/07/the-architecture-decision-point-why-current-ai-may-need-a-complete-rethink/</link><pubDate>Wed, 16 Jul 2025 23:37:00 -0400</pubDate><guid>https://magnus919.com/2025/07/the-architecture-decision-point-why-current-ai-may-need-a-complete-rethink/</guid><description>&lt;h1 id="nyai-ai-breakthroughs--limitations-the-great-architecture-debatehttpswwwmeetupcommeetup-group-rtxnkeltevents309528596">NYAI AI &lt;a href="https://www.meetup.com/meetup-group-rtxnkelt/events/309528596/">Breakthroughs &amp;amp; Limitations: The Great Architecture Debate&lt;/a>&lt;/h1>
&lt;p>&lt;strong>Hosted by&lt;/strong>: &lt;a href="https://www.meetup.com/meetup-group-rtxnkelt/">New York AI (NYAI)&lt;/a>&lt;br>
&lt;strong>Date&lt;/strong>: July 17, 2025&lt;br>
&lt;strong>Duration&lt;/strong>: 180+ minutes&lt;br>
&lt;strong>Host&lt;/strong>: Tone Fonseca&lt;br>
&lt;strong>Speakers&lt;/strong>: Tone Fonseca, Andrea Jordan, Rose Kudlac, Jody Solomon, Phineas Samuel&lt;/p>
&lt;p>The conversation began with warm, collaborative energy as participants solved technical issues together (Magnus helping Tone pin comments), setting a tone of mutual support that would characterize the entire session. Over three intense hours, the NYAI community dove deep into one of AI&amp;rsquo;s most fundamental questions: are we simply scaling what works, or do we need entirely new architectures to achieve true artificial general intelligence?&lt;/p></description></item><item><title>The Complete Guide to Google AI/ML Interviews: What It Takes to Land Your Dream Job</title><link>https://magnus919.com/2025/06/the-complete-guide-to-google-ai/ml-interviews-what-it-takes-to-land-your-dream-job/</link><pubDate>Sat, 07 Jun 2025 09:30:00 -0400</pubDate><guid>https://magnus919.com/2025/06/the-complete-guide-to-google-ai/ml-interviews-what-it-takes-to-land-your-dream-job/</guid><description>&lt;p>Getting hired for an AI/ML role at Google is extraordinarily competitive—with millions of applications and extremely selective acceptance rates, Google&amp;rsquo;s AI/ML interviews represent one of the most challenging selection processes in technology. Recent data suggests Google processes approximately &lt;a href="https://www.cnbc.com/2019/04/17/how-google-screens-resumes-and-interviews-candidates.html">3.8 million applications annually&lt;/a>, with technical roles having particularly low acceptance rates.&lt;/p>
&lt;p>But here&amp;rsquo;s the thing: people don&amp;rsquo;t just apply to Google for the prestige. They apply because working there offers something genuinely unique in the AI landscape. Let me show you what makes Google so appealing, what their interview process really looks like, and how you can prepare yourself to succeed.&lt;/p></description></item></channel></rss>