Barriers to Complexity-Theoretic Proofs that "AGI" Using Machine Learning is Impossible

[2411.06498] Barriers to Complexity-Theoretic Proofs that "AGI" Using Machine Learning is Impossible Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2411.06498 (cs) [Submitted on 10 Nov 2024 ( v1 ), last revised 4 Apr 2026 (this version, v2)] Title: Barriers to Complexity-Theoretic Proofs that "AGI" Using Machine Learning is Impossible Authors: Michael Guerzhoy View a PDF of the paper titled Barriers to Complexity-Theoretic Proofs that "AGI" Using Machine Learning is Impossible, by Michael Guerzhoy View PDF HTML (experimental) Abstract: A recent paper (van Rooij et al. 2024) claims to have proved that achieving human-like intelligence using learning from data is intractable in a complexity-theoretic sense. We point out that the proof relies on an unjustified assumption about the distribution of (input, output) tuples in the data. We briefly discuss that assumption in the context of two fundamental barriers to repairing the proof: the need to precisely define ``human-like," and the need to account for the fact that a particular machine learning system will have particular inductive biases that are key to the analysis. Another attempt to repair the proof, by focusing on subsets of the data, faces barriers in terms of defining the subsets. Subjects: Artificial Intelligence (cs.AI) ; Computational Complexity (cs.CC) Cite as: arXiv:2411.06498 [cs.AI] (or arXiv:2411.06498v2 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2411.06498 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Comput. Brain Behav., 2026 Related DOI : https://doi.org/10.1007/s42113-026-00284-w Focus to learn more DOI(s) linking to related resources Submission history From: Michael Guerzhoy [ view email ] [v1] Sun, 10 Nov 2024 15:47:30 UTC (103 KB) [v2] Sat, 4 Apr 2026 15:17:43 UTC (9 KB) Full-text links: Access Paper: View a PDF of the paper titled Barriers to Complexity-Theoretic Proofs that "AGI" Using Machine Learning is Impossible, by Michael Guerzhoy View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2024-11 Change to browse by: cs cs.CC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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