Abstract
Classical AI research is oriented towards the performance capabilities of a program-controlled computer, which, according to Church’s thesis, is in principle equivalent to a Turing machine. According to Moore’s Law, gigantic computing and storage capacities have been achieved, which made AI performance possible in the first place. But the performance of supercomputers have a price that can be equivalent to the energy of a small town. Human brains are all the more impressive, that can compare the performance of a computer (e.g. speaking and understanding a natural language) with the energy consumption of a light bulb. At the latest, one is impressed by the efficiency of neuromorphic systems, that have emerged in evolution. Is there a common principle underlying these evolutionary systems that we can make use of in AI.
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Notes
- 1.
Ironically, when students hand in essays written by ChatGPT they can easily be convicted if the essays do not contain the “usual” orthographic mistakes of contemporary students. However, it should only be question of time that they ask ChatGPT for essays “written with the usual mistakes of an average student”.
- 2.
Alan Turing had already pointed out from the beginning that there are specific areas in which the comparison of computers and humans is not meaningful [18, p. 435]: “We do not wish to penalise the machine for its inability to shine in beauty competitions, nor to penalise a man for losing in a race against an aeroplane.“.
References
Mainzer, K.; Chua, L. (2013), Local Activity Principle, London.
Mainzer, K. (2019), Artificial Intelligence. When do machines take over? Springer, 2nd edition.
Siegelmann, H.T.; Sontag, E.D. (1995), On the computational power of neural nets, in: Journal of Computer and Systems Science 50, 132–150.
Siegelmann, H.T.; Sontag, E.D. (1994), Analog computation via neural networks, in: Theoretical Computer Science 131, 331–360.
Blum, L; Shub, M.; Smale, S. (1989), On a theory of computation and complexity over the real numbers: NP-completeness, recursive functions and universal Machines, in: Bulletin of the American Mathematical Society 21 1, 1–46.
Mainzer, K. (2018), The Digital and the Real World. Computational Foundations of Mathematics, Science, Technology, and Philosophy, World Scientific Singapore.
Bennett, C.H. (1995), Logical Depth and Physical Complexity, in: R. Herken (Hrsg.), The Universal Turing Machine. A Half-Century Survey, Wien, 2007–235.
Feynman, R.P. (1982), Simulating Physics with computers, in: Intern. J. Theor. Physics 21: 467–488.
Deutsch, D.; Eckert, A. (2000) Concepts of Quantum Computation, In: Bouwmeester, D.; Ekert, A.; Zeilinger, A. (Eds.), The Physics of Quantum Information, Quantum Cryptography, Quantum Teleportation, Quantum Computation. Berlin, chapt. 4.
Mainzer, K. (2020), Quantencomputer. Von der Quantenwelt zur Künstlichen Intelligenz, Springer.
Deutsch, D. (1985) Quantum theory, the Church-Turing principle and the universal quantum computer, in: Proc. R. Soc. London A 400: 97–117.
Keyl, M. (2002) Fundamentals of quantum information theory, in: Physics Reports. A Review Section of Physics Letters 369: 431–454.
S. Frieder et al. (2023), Mathematical Capabilities of ChatGPT, in: arXiv:2301.13867v1 [cs.LG] 32 Jan 2023.
L. Ouyang et al. (2022), Training language models to follow instructions with human feedback, in: arXiv:2203,02155vl [cs.CL] 4 Mar 2022.
J. Gogoll, D. Heckmann, A. Pretscher (2023), Endlich neue Prüfungen dank ChatGPT, in: FAZ 20.3.2023 Nr. 67, p. 18.
Leibniz, Gottfried Wilhelm (1760). Essai de Theodicée oder Betrachtung der Gütigkeit GOttes, der Freyheit des Menschen und des Ursprungs des Bösen. Amsterdam.
Voltaire (1752/1877). Micromégas, CEuvres complètes de Voltaire, Garnier, 1877, tome 21.
Alan Turing (1950). Computing machinery and intelligence. Mind 59, 433–460.
High-Level Expert Group on Artificial Intelligence. A Definition of AI: Main Capabilities and Scientific Disciplines. European Commission, Directorate-General for Communicatioin, 2018.
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Mainzer, K., Kahle, R. (2024). Prospects for Hybrid AI. In: Limits of AI - theoretical, practical, ethical . Technik im Fokus. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-68290-6_5
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