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From Seed AI to Technological Singularity via Recursively Self-Improving Software

paper

Author

Credibility Rating

3/5
Good(3)

Good quality. Reputable source with community review or editorial standards, but less rigorous than peer-reviewed venues.

Rating inherited from publication venue: arXiv

A 2015 theoretical paper by Yampolskiy offering one of the more structured academic treatments of recursive self-improvement; useful as a reference for RSI concepts, though its empirical predictions remain speculative and should be read alongside more recent capabilities literature.

Paper Details

Citations
16
0 influential
Year
2015
Methodology
survey

Metadata

Importance: 58/100arxiv preprintprimary source

Abstract

Software capable of improving itself has been a dream of computer scientists since the inception of the field. In this work we provide definitions for Recursively Self-Improving software, survey different types of self-improving software, review the relevant literature, analyze limits on computation restricting recursive self-improvement and introduce RSI Convergence Theory which aims to predict general behavior of RSI systems. Finally, we address security implications from self-improving intelligent software.

Summary

Yampolskiy provides a systematic survey and theoretical framework for Recursively Self-Improving (RSI) software, classifying existing self-improvement mechanisms and introducing RSI Convergence Theory to predict behavioral trajectories. The paper analyzes security risks and computational constraints limiting recursive self-improvement, situating these dynamics within broader discussions of intelligence explosion and technological singularity.

Key Points

  • Defines and taxonomizes RSI systems, surveying existing types of self-improving software from metaheuristics to autonomous code-rewriting agents.
  • Introduces RSI Convergence Theory, which attempts to predict the general long-run behavioral patterns of recursively self-improving systems.
  • Analyzes fundamental computational and physical limits (e.g., Gödelian constraints, hardware bounds) that constrain how far recursive self-improvement can proceed.
  • Examines security implications of RSI systems, including containment challenges and risks of uncontrolled capability gain.
  • Connects RSI dynamics to seed AI hypotheses and technological singularity scenarios, providing a structured conceptual bridge between current software and hypothetical superintelligence.

Cited by 2 pages

PageTypeQuality
Early Warnings EraHistorical31.0
SuperintelligenceConcept92.0

Cached Content Preview

HTTP 200Fetched Mar 15, 20264 KB
[1502.06512] From Seed AI to Technological Singularity via Recursively Self-Improving Software 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 
 

 
 
 
 
 
--> 

 
 
 Computer Science > Artificial Intelligence

 

 
 arXiv:1502.06512 (cs)
 
 
 
 
 
 [Submitted on 23 Feb 2015] 
 Title: From Seed AI to Technological Singularity via Recursively Self-Improving Software

 Authors: Roman V. Yampolskiy View a PDF of the paper titled From Seed AI to Technological Singularity via Recursively Self-Improving Software, by Roman V. Yampolskiy 
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 Abstract: Software capable of improving itself has been a dream of computer scientists since the inception of the field. In this work we provide definitions for Recursively Self-Improving software, survey different types of self-improving software, review the relevant literature, analyze limits on computation restricting recursive self-improvement and introduce RSI Convergence Theory which aims to predict general behavior of RSI systems. Finally, we address security implications from self-improving intelligent software.
 

 
 
 
 Subjects: 
 
 Artificial Intelligence (cs.AI) 
 
 Cite as: 
 arXiv:1502.06512 [cs.AI] 
 
 
 
 (or 
 arXiv:1502.06512v1 [cs.AI] for this version)
 
 
 
 
 https://doi.org/10.48550/arXiv.1502.06512 
 
 
 Focus to learn more 
 
 
 
 arXiv-issued DOI via DataCite 
 
 
 
 
 
 
 
 Submission history

 From: Roman Yampolskiy [ view email ] 
 [v1] 
 Mon, 23 Feb 2015 17:08:30 UTC (314 KB)

 
 
 
 
 
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Resource ID: fb817d8609639fc8 | Stable ID: Njk3NDViYm