AI Consciousness is Inevitable: A Theoretical Computer Science Perspective

[2403.17101] AI Consciousness is Inevitable: A Theoretical Computer Science Perspective Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2403.17101 (cs) [Submitted on 25 Mar 2024 ( v1 ), last revised 1 May 2026 (this version, v15)] Title: AI Consciousness is Inevitable: A Theoretical Computer Science Perspective Authors: Lenore Blum , Manuel Blum View a PDF of the paper titled AI Consciousness is Inevitable: A Theoretical Computer Science Perspective, by Lenore Blum and Manuel Blum View PDF Abstract: We look at consciousness through the lens of Theoretical Computer Science, a branch of mathematics that studies computation under resource limitations, distinguishing functions that are efficiently computable from those that are not. From this perspective, we develop a formal machine model for consciousness. The model is inspired by Alan Turing's simple yet powerful model of computation and Bernard Baars' theater model of consciousness. Though extremely simple, the model (1) aligns at a high level with many of the major scientific theories of human and animal consciousness, (2) provides explanations at a high level for many phenomena associated with consciousness, (3) gives insight into how a machine can have subjective consciousness, and (4) is clearly buildable. This combination supports our claim that machine consciousness is not only plausible but inevitable. Subjects: Artificial Intelligence (cs.AI) MSC classes: 68T01 ACM classes: F.1; I.2 Cite as: arXiv:2403.17101 [cs.AI] (or arXiv:2403.17101v15 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2403.17101 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Lenore Blum [ view email ] [v1] Mon, 25 Mar 2024 18:38:54 UTC (1,010 KB) [v2] Fri, 19 Apr 2024 17:28:44 UTC (1,023 KB) [v3] Thu, 16 May 2024 23:07:04 UTC (866 KB) [v4] Mon, 10 Jun 2024 22:29:49 UTC (887 KB) [v5] Sat, 27 Jul 2024 20:52:55 UTC (941 KB) [v6] Thu, 29 Aug 2024 20:14:14 UTC (1,013 KB) [v7] Mon, 16 Sep 2024 20:58:36 UTC (1,101 KB) [v8] Wed, 13 Nov 2024 17:14:29 UTC (1,167 KB) [v9] Wed, 15 Jan 2025 02:23:44 UTC (1,538 KB) [v10] Tue, 18 Feb 2025 17:58:13 UTC (1,488 KB) [v11] Sun, 6 Apr 2025 21:09:23 UTC (974 KB) [v12] Sat, 28 Jun 2025 23:32:11 UTC (1,406 KB) [v13] Fri, 31 Oct 2025 13:32:59 UTC (1,397 KB) [v14] Sat, 15 Nov 2025 13:28:21 UTC (1,425 KB) [v15] Fri, 1 May 2026 02:05:42 UTC (1,481 KB) Full-text links: Access Paper: View a PDF of the paper titled AI Consciousness is Inevitable: A Theoretical Computer Science Perspective, by Lenore Blum and Manuel Blum View PDF view license Current browse context: cs.AI < prev | next > new | recent | 2024-03 Change to browse by: cs 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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