<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Program-Synthesis on Ryan Orban</title><link>https://ryanorban.com/categories/program-synthesis/</link><description>Recent content in Program-Synthesis on Ryan Orban</description><generator>Hugo</generator><language>en-us</language><managingEditor>me@ryanorban.com (Ryan Orban)</managingEditor><webMaster>me@ryanorban.com (Ryan Orban)</webMaster><copyright>Ryan Orban</copyright><lastBuildDate>Sun, 02 Oct 2022 00:00:00 +0000</lastBuildDate><atom:link href="https://ryanorban.com/categories/program-synthesis/index.xml" rel="self" type="application/rss+xml"/><item><title>Self-Programming Artificial Intelligence</title><link>https://ryanorban.com/notes/self-programming-artificial-intelligence/</link><pubDate>Sun, 02 Oct 2022 00:00:00 +0000</pubDate><author>me@ryanorban.com (Ryan Orban)</author><guid>https://ryanorban.com/notes/self-programming-artificial-intelligence/</guid><description>&lt;h3 id="summary" class="scroll-mt-8 group"&gt;
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&lt;p&gt;Self-programming AI refers to systems that can modify or generate their own code, algorithms, or learning procedures — a longstanding aspiration in artificial intelligence research that sits at the intersection of meta-learning, program synthesis, and automated machine learning. The central challenge is the bootstrap problem: a system modifying itself needs to reason about how its modifications will affect its future behavior, which requires a kind of self-model that is computationally and representationally difficult to maintain accurately.&lt;/p&gt;</description></item><item><title>GPT-3 Complete-Program Synthesis (Riley Goodside)</title><link>https://ryanorban.com/notes/goodside-program-synthesis/</link><pubDate>Sat, 20 Aug 2022 00:00:00 +0000</pubDate><author>me@ryanorban.com (Ryan Orban)</author><guid>https://ryanorban.com/notes/goodside-program-synthesis/</guid><description>&lt;h3 id="summary" class="scroll-mt-8 group"&gt;
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&lt;p&gt;Riley Goodside describes a &amp;ldquo;novel, powerful method of complete-program synthesis using GPT-3&amp;rdquo; that combines instruction prompts with what he calls &amp;ldquo;contextually informative templates — a generalization of the format trick&amp;rdquo; shown to him by Boris Power. The approach generates complete programs in a single call at temperature 0 (deterministic), not just code snippets.&lt;/p&gt;</description></item></channel></rss>