A challenge in A(G)I, cybernetics revived in the Ouroboros Model as one algorithm for all thinking

Knud Thomsen

doi:10.55092/aias20240001

Article

Open Access

A challenge in A(G)I, cybernetics revived in the Ouroboros Model as one algorithm for all thinking

Knud Thomsen

Paul Scherrer Institute, Villigen and Würenlingen, Switzerland

E-mail: knud.thomsen@psi.ch

Volume
Volume 1 Issue 1, 2024
Citation
Thomsen K. A challenge in A(G)I, cybernetics revived in the Ouroboros Model as one algorithm for all thinking. Artif. Intell. Auton. Syst. 2024(1):0001, https://doi.org/10.55092/aias20240001.
DOI
10.55092/aias20240001
Copyright
Copyright2024 by the authors. Published by ELSP.

Abstract

A topical challenge for algorithms in general and for automatic image categorization and generation in particular is presented in the form of a drawing for AI to “understand”. In a second vein, AI is challenged to produce something similar from verbal description. The aim of the paper is to highlight strengths and deficiencies of current Artificial Intelligence approaches while coarsely sketching a way forward. A general lack of encompassing symbol-embedding and (not only) -grounding in some bodily basis is made responsible for current deficiencies. A concomitant dearth of hierarchical organization of concepts follows suite. As a remedy for these shortcomings, it is proposed to take a wide step back and to newly incorporate aspects of cybernetics and analog control processes. It is claimed that a promising overarching perspective is provided by the Ouroboros Model with a valid and versatile algorithmic backbone for general cognition at all accessible levels of abstraction and capabilities. Reality, rules, truth, and Free Will are all useful abstractions according to the Ouroboros Model. Logic deduction as well as intuitive guesses are claimed as produced on the basis of one compartmentalized memory for schemata and a pattern-matching, i.e., monitoring process termed consumption analysis. The latter directs attention on short (attention proper) and also on long times scales (emotional biases). In this cybernetic approach, discrepancies between expectations and actual activations (e.g., sensory precepts) drive the general process of cognition and at the same time steer the storage of new and adapted memory entries. Dedicated structures in the human brain work in concert according to this scheme.

Keywords

AI-challenge; large language models; cybernetics; synergetics; common sense; consciousness; Free Will

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References

[1]Goertzel B. Is ChatGPT Real Progress Toward Human-Level AGI? 2023. Available: https://bengoertzel.substack.com/p/ischatgpt-real-progress-toward-human (accessed on 17 January 2023).
[2]Chomsky N. The False Promise of ChatGPT. New York Times, 8 March 2023. Available: https://www.nytimes.com/2023/03/08/opinion/noam-chomsky-chatgpt-ai.html (accessed on 10 March 2023).
[3]Senjowski TJ. Large Language Models and the Reverse Turing Test. Neural. Comput. 2023, 35:309–342.
[4]Entry page of ChatGPT, limitations. Available: https://chat.openai.com (accessed on 30 May 2023.
[5]Thomsen K. The Ouroboros model, selected facets. In From Brains to Systems: Brain-Inspired Cognitive Systems. New York: Springer, 2011, 239–250.
[6]Von Sydow M. Rational and Semi-Rational Explanations of the Conjunction Fallacy: A Polycausal Approach. In Proceedings of the Thirty-Ninth Annual Conference of the Cognitive Science Society, Austin, TX, USA, 26–29 July 2017, pp. 3472–3477.
[7]Riedl R. Evolution und Erkenntnis. München: Piper, 1984.
[8]Czégel D, Giaffar H, Tenenbaum JB, Szathmáry E. Bayes and Darwin: How replicator populations implement Bayesian computations. BioEssays 2022, 44(4):2100255. doi:10.1002/bies.202100255.
[9]Kant I. Critik der reinen Vernunft. Riga: Hartknoch, 1781.
[10]Thomsen K. The Ouroboros Model in the light of venerable criteria. Neurocomputing 2010, 74, 12–128. doi:1016/j.neucom.2009.031.
[11]Thomsen K. Concept formation in the Ouroboros Model. In Proceedings of the Third Conference on Artificial General Intelligence, Lugano, Switzerland, 5–8 March, 2010, pp. 66–67.
[12]Thomsen K. It Is Time to Dissolve Old Dichotomies in Order to Grasp the Whole Picture of Cognition. In Proceedings of Theory and Practice of Natural Computing: 7th International Conference, Dublin, Ireland, December 12–14, 2018, pp. 317–327.
[13]Nilsson NJ. The Quest for Artificial Intelligence: A History of Ideas and Achievements. New York: Cambridge University Press. 2010, pp. 52–55.
[14]Cybernetics. Available: https://en.wikipedia.org/wiki/Cybernetics (accessed on 16 March, 2023).
[15]Haken H. Synergetics: an introduction: nonequilibrium phase transitions and self-organization in physics, chemistry, and biology. Springer Series in Synergetics. Berlin: Springer, 1978.
[16]Hartmann N. Die Erkenntnis im Lichte der Ontologie, Philosophische Bibliothek. Felix Meiner: Hamburg, 1982.
[17]Wei J, Tay Y, Bommasani R, Raffel C, Zoph B, et al. Emergent abilities of large language models. arXiv 2022, arXiv:2206.07682.
[18]Proportional–integral–derivative controller (PID controller). Available: https://en.wikipedia.org/wiki/Proportional%E2%80%93integral%E2%80%93derivative_controller (accessed on 31 May 2023).
[19]Carl Sagan. Available: https://en.wikipedia.org/wiki/Carl_Sagan (accessed on 31 May 2023).
[20]Levy MG. Chatbots Don’t Know What Stuff Isn’t. Quanta Magazine 2023.
[21]Schulman J. RL and truthfulness. EECS Colloquium. Available: https://www.youtube.com/watch?v=hhiLw5Q_UFg (accessed on 19 April 2023).
[22]Thomsen K. The Ouroboros Model, Proposal for Self-Organizing General Cognition Substantiated. AI 2021, 2, 89–105.
[23]Selz O. Über die Gesetze des geordneten Denkverlaufs, erster Teil. Stuttgart: Spemann, 1913.
[24]Selz O. Über die Gesetze des geordneten Denkverlaufs, zweiter Teil, Zur Psychologie des produktiven Denkens und des Irrtums. Bonn: Cohen, 1922.
[25]Thomsen K. AI and We in the Future in the Light of the Ouroboros Model, A Plea for Plurality. AI 2022, 3, 778–788.
[26]Simulation hypothesis. Available: https://en.wikipedia.org/wiki/Simulation_hypothesis (accessed on 31 May 2023).
[27]Lakatos I. Falsification and the methodology of scientific research programmes. In Lakatos. Cambridge: Cambridge University Press, 1970, pp. 91–195.
[28]Schurz G. Tacking by conjunction, genuine confirmation and convergence to certainty. Eur. J. Philos. Sci. 2023, 12(3):46.
[29]Evans JS, Stanovich KE. Dual-process theories of higher cognition: Advancing the debate. Perspect|. Psychol. Sci. 2013, 8(3):223–241.
[30]Tschacher W, Haken H. A complexity science account of humor. Entropy 2023, 25(2):341.
[31]Narayanan A. Translation tutorial: 21 fairness definitions and their politics. In Proceedings of the 2018 ACM Conference on Fairness, Accountability, and Transparency. New York, USA, 23–24 February 2018, 1170, p. 3.
[32]Kleinberg I, Mullainathan S, Raghavan M. Inherent Trade-Offs in the Fair Determination of Risk Scores. In Proceedings of the 8th Conference on Innovations in Theoretical Computer Science (ITCS), Berkeley, USA, 9–11 January 2017, 43:1–43.
[33]Rawls J. A Theory of Justice. Cambridge: The Belknap Press of Harvard University Press, 1971.
[34]Thomsen K. Ethics for artificial intelligence, ethics for all. Paladyn. J. Behav. Robot. 2019, 10(1):359–363.
[35]Fiedler K. von Sydow M. Heuristics and biases: Beyond Tversky and Kahneman’s (1974) judgment under uncertainty. In Revisiting the Classical Studies. Eysenck MW, Groome, DA, Eds. Thousand Oaks: Sage Publications, 2015, pp.146–161.
[36]Laird JE, Lebiere C, Rosenbloom PS. A Standard Model of the Mind: Toward a Common Computational Framework across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics. AI Mag. 2017, 38(4):13–26.
[37]John Searle. Available: https://en.wikipedia.org/wiki/John_Searle (accessed on 3 June 2023).
[38]Searle J. Minds, brains and programs. Behav. Brain. Sci. 1980, 3:417–457.
[39]Cohen SM, Reeve CDC. Aristotle’s Metaphysics. In Stanford Encyclopedia of Philosophy, Winter 2020 ed. Stanford: Metaphysics Research Lab, Stanford University, 2020.
[40]Thomsen K. The Hippocampus According to the Ouroboros Model, the “Expanding Memory Index Hypothesis”. In Proceedings of the Ninth International Conference on Advanced Cognitive Technologies and Applications, Athens, Greece, 19–23 February 2017.
[41]Thomsen K. The Cerebellum according to the Ouroboros Model, the “Interpolator Hypothesis”. J. Comput. Commun. 2014, 11:239–254.
[42]Thomsen K. ONE Function for the Anterior Cingulate Cortex and General AI: Consistency Curation. Med. Res. Arch. 2018, 6:1–23.
[43]Adam EM, Johns T, Sur M. Dynamic control of visually guided locomotion through corticosubthalamic projections. Cell Rep. 2022, 40:1–15.
[44]Roman A, Palanski K, Nemenman I, Ryu WS. A dynamical model of C. elegans thermal preference reveals independent excitatory and inhibitory learning pathways. PNAS 2023, 120(13):e22151911290.
[45]Yttri EA, Dudman JT. Opponent and bidirectional control of movement velocity in the basal ganglia. Nature 2016, 533: 402–406.
[46]Camm JP, Messenger JB, Tansey EM. New pathways to the “cerebellum” in Octopus, Studies by using a modified Fink-Heimer technique. Cell Tissue Res. 1985, 242:649–656.
[47]Thomsen K. Consciousness for the Ouroboros Model. J. Mach. Conscious. 2010, 3:163–175.
[48]Sanz R, López I, Rodríguez M, Hernandéz C. Principles for consciousness in integrated cognitive control. Neural. Netw. 2007, 20(9), 938–946.
[49]Chella A, Pipitone A, Morin A, Racy F. Developing Self-Awareness in Robots via Inner Speech. Front. Robot. AI 2020, 7:16.
[50]Pipitone A. Chella A. What robots want? Hearing the inner voice of a robot. iScience 2021, 24(4):10371.
[51]Jozwik KM, Kletzmann TC, Cichy RM, Kriegeskorte N, Mur M. Deep neural networks and visuosemantic models explain complementary components of human ventral-stream representational dynamics. J. Neurosci. 2023, 43:1731–1741.
[52]Drew L. Your brain on psychedelics. Nature 2022, 609:S92–S94
[53]Dangarembga,Tsitsi in an interview with Achermann, Barbara. Ich bin, weil du bist. Das Magazin, 20 January 2023. Available: https://www.tagesanzeiger.ch/die-westliche-gesellschaft-ist-der-ansicht-sie-sei-die-einzige-spezies-die-denkt-851868195418 (accessed on 5 June 2023).
[54]Uncanny valley. Available: https://en.wikipedia.org/wiki/Uncanny_valley (accessed on 5 June 2023).
[55]Thomsen K. Efficient Cognition needs Sleep. J. Sleep Med. Disord. 2021, 7:1120.
[56]Ungurean G, Behroozi M, Böger L, Helluy X, Libourel PA, et al. Wide-spread activation and reduced CSF flow during avian REM sleep. Nat. Commun. 2023,14:3259.
[57]Pophale A, Shimizu K, Mano T, Iglesias TL, Martin K, et al. Wake-like skin patterning and neural activity during octopus sleep. Nature 2023, 619(7968):129–134.
[58]Cummins DD, Lubart T, Alksnis O, Rist R. Conditional reasoning and causation, Mem. Cognit. 1991, 19:274–282.
[59]Deutscher Ethikrat. Mensch und Maschine – Herausforderungen durch Künstliche Intelligenz. 20 March 2023. pp. 1–71. Available: https://www.ethikrat.org/fileadmin/Publikationen/Stellungnahmen/deutsch/stellungnahme-mensch-und-maschine-kurzfassung.pdf (accessed on 5 June 2023)
[60]Seto E, Hicks JA. Dissociating the Agent From the Self: Undermining Belief in Free Will Diminishes True Self-Knowledge. Soc. Psychol. Personal. Sci. 2016, 7(7):726–734.
[61]Thomsen K. Timelessness Strictly inside the Quantum Realm. Entropy 2021, 23(6):772.
[62]Jones C, Bergen B. Does GPT-4 Pass the Turing Test? arXiv 2023, arXiv:2310.20216.
[63]Intelligence. Available: https://en.wikipedia.org/wiki/Intelligence (accessed on 3 November 2023).
[64]Wei J, Wang X, Schuurmans D, Bosma M, Xia F, et al. Chain-of-thought prompting elecits reasoning in large language models. Adv. Neural Inf. Process. Syst. 2022, 35:24824–24837.
[65]Ganguli D, Askell A, Schiefer N, Liao TI, Lukošiūtė K, et al. The Capacity for Moral Self-Correction in the Large Language Models. arXiv 2023, arXiv:2302.07459v2.
[66]Goggle lens via Pixel 7 Pro. 1 February 2023 and 6 June 2023.
[67]Google API demo. Available: https://cloud.google.com/vision#section-2 (accessed on 1 February 2023 and 6 June 2023).
[68]DALL·E. Available: https://openai.com/blog/dall-e-now-available-without-waitlist (accessed on 3 March, 6 June, 2 November, and 5 November 2023).
[69]ChatGPT. Available: https://chat.openai.com (accessed on 7 June 2023 and 2 November 2023).