<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Autonomy on Omanshu Thapliyal</title><link>https://omanshuthapliyal.github.io/tags/autonomy/</link><description>Recent content in Autonomy on Omanshu Thapliyal</description><generator>Hugo</generator><language>en</language><lastBuildDate>Tue, 15 Oct 2024 11:07:46 -0700</lastBuildDate><atom:link href="https://omanshuthapliyal.github.io/tags/autonomy/index.xml" rel="self" type="application/rss+xml"/><item><title>Technology Readiness Levels of AI methods</title><link>https://omanshuthapliyal.github.io/blog/readiness/</link><pubDate>Tue, 15 Oct 2024 11:07:46 -0700</pubDate><guid>https://omanshuthapliyal.github.io/blog/readiness/</guid><description>&lt;p>Recent news of the SpaceX catching its Starship Super Heavy booster&lt;sup id="fnref:1">&lt;a href="#fn:1" class="footnote-ref" role="doc-noteref">1&lt;/a>&lt;/sup> is quite discussed and marveled upon in the media, and rightly so.
Executing what was done can perhaps be explained as trying to catch a falling stick, then balancing it vertically, all while the stick is over 200 ft tall, weighs over 150 tonnes, spews fire, and costs over tens of millions of dollars.
One cannot but feel a little giddy as a controls engineering enthusiast over this massive success.
It makes me think why we are closer to automation in certain domains/problems than others? Why, for example, is your vacuum cleaning robot seemingly better at navigating in a cluttered environment than your possibly self-driving car? Why is the tremendous progress in machine learning &amp;amp; AI not permeating outside of our web lives as much as we expected in the early 2000&amp;rsquo;s?&lt;/p></description></item></channel></rss>