<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Runtime on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/runtime/</link><description>Recent content in Runtime on English AI Terms Dictionary</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 18 Jul 2026 11:44:44 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/en/tags/runtime/index.xml" rel="self" type="application/rss+xml"/><item><title>Dynamic</title><link>https://terms-en.ai-term-hub.com/en/terms/dynamic/</link><pubDate>Sat, 18 Jul 2026 09:31:32 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/dynamic/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Unlike static systems with fixed architectures or predetermined execution paths, dynamic AI systems can modify their operations during runtime. In deep learning, dynamic computation graphs allow the network structure to change depending on the input, enabling variable-length sequence processing. In broader contexts, dynamic systems might adjust hyperparameters on the fly or alter their decision boundaries based on new data streams. This flexibility enhances robustness and efficiency in non-stationary environments where conditions evolve continuously.&lt;/p></description></item></channel></rss>