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Plurality: The End of Singularity?

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The 21st Century Singularity and Global Futures

Part of the book series: World-Systems Evolution and Global Futures ((WSEGF))

Abstract

Singularity is a very intriguing future scenario to fantasticate upon, but it is even a quite controversial theoretical issue. Turning back to our previous (Plebe and Perconti 2013) skepticism about the singularity hypothesis, based on an alternative slowdown hypothesis for artificial intelligence (AI), we consider the new possibilities of the so-called AI Renaissance and the opportunities provided by the techniques collected under the name of deep learning in order to suggest a “pluralistic” view on singularity. Plurality refers to the two following AI features: the compositionality of its subdomains and the spreading of intelligence in the social environment. We can suppose a new kind of singularity in the case of intelligence, due to both the emergentist nature of compositionality of subdomains and the “bearer problem,” i.e., the detachment between intentional content and its producer in a future scenario characterized by a massive spreading of intelligence in smart devices and the Internet. This kind of singularity for intelligence, however, could lead toward a “broken intelligence,” that is, an intelligence without anyone owns or uses it, more than toward the usually supposed “superintelligence.”

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References

  • Abolafia DA, Norouzi M, Shen J, Zhao R, Le QV (2018) Neural program synthesis with priority queue training. arXiv preprint 1801.03526. https://arxiv.org/abs/1801.03526

  • Arel I, Rose DC, Karnowski TP (2010) Deep machine learning—a new frontier in artificial intelligence research. IEEE Comput Intell Mag 5:13–18

    Google Scholar 

  • Bartunov S, Santoro A, Richards BA, Marris L, Hinton GE, Lillicrap T (2018) Assessing the scalability of biologically-motivated deep learning algorithms and architectures. In: Systems Bassett DS, Zurn P, Gold JI (eds) Advances in neural information processing

    Google Scholar 

  • Batin M, Turchin A, Markov S, Zhila A, Denkenberger D (2017) Artificial intelligence in life extension: from deep learning to superintelligence. Informatica 41(4):401–417

    Google Scholar 

  • Bednar JA (2009) Topographica: building and analyzing map-level simulations from Python, C/C++, MATLAB, NEST, or NEURON components. Front Neuroinformatics 3(8):1–9. https://doi.org/10.3389/neuro.11.008.2009

    Article  Google Scholar 

  • Bednar JA (2014) Topographica. In: Jaeger D, Jung R (eds) Encyclopedia of computational neuroscience. Springer-Verlag, Berlin, pp 1–5

    Google Scholar 

  • Benschop JPH (2016) How lithography enables Moore’s law. In: Luryi S, Xu J, Zaslavsky A (eds) Future trends in microelectronics. Journey into the unknown. Wiley, New York, pp 23–34

    Google Scholar 

  • Bianchini M, Scarselli F (2014) On the complexity of shallow and deep neural network classifiers. Proc Eur Symp Artif Neural Netw 2014:371–376

    Google Scholar 

  • Booker L, Forrest S, Mitchell M, Riolo R (eds) (2005) Perspectives on adaptation in natural and artificial systems. Oxford University Press, Oxford

    Google Scholar 

  • Bostrom N (ed) (2014) Superintelligence: paths, dangers, strategies. Oxford University Press, Oxford

    Google Scholar 

  • Bower JM, Beeman D (1998) The book of GENESIS: exploring realistic neural models with the general neural simulation system. Springer-Verlag, New York. https://doi.org/10.1007/978-1-4612-1634-6

    Article  Google Scholar 

  • Brooks RA (1991) Intelligence without representation. Artif Intell 47(1–3):139–159. https://doi.org/10.1016/0004-3702(91)90053-M

    Article  Google Scholar 

  • Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186–198. https://doi.org/10.1038/nrn2575

    Article  Google Scholar 

  • Carter JA, Gordon EC (2017) Googled assertion. Philos Psychol 30:486–497. https://doi.org/10.1080/09515089.2017.1285395

    Article  Google Scholar 

  • Chalmers D (2010) The singularity: a philosophical analysis. J Conscious Stud 17:7–65

    Google Scholar 

  • Chollet F (2018) Deep learning with python. Manning, Shelter Island NY

    Google Scholar 

  • Chui M, Manyika J, Miremadi M, Henke N, Chung R, Nel P, Malhotra S (2018) Notes from the AI frontier: insights from hundreds of use cases. McKinsey Global Institute, New York

    Google Scholar 

  • Clark A (2008) Supersizing the mind. Oxford University Press, Oxford

    Book  Google Scholar 

  • Clark A, Chalmers D (1998) The extended mind. Analysis 58:7–19. https://doi.org/10.1111/1467-8284.00096

    Article  Google Scholar 

  • Coates A, Huval B, Wang T, Wu DJ, Ng AY, Catanzaro B (2013) Deep learning with COTS HPC systems. Int Conf Mach Learn 28:1337–1345

    Google Scholar 

  • David C, Kroening D (2017) Program synthesis: challenges and opportunities. Philosophical transactions of the royal society A 375(2104). https://doi.org/10.1098/rsta.2015.0403

  • Dayan P, Abbott LF (2001) Theoretical neuroscience. MIT Press, Cambridge MA

    Google Scholar 

  • Devlin J, Uesato J, Bhupatiraju S, Singh R, Mohamed A, Kohli P (2017) Robustfill: neural program learning under noisy I/O. Proc Mach Learn Res 70:990–998

    Google Scholar 

  • Dobrolyubov S (2020) The Transition to Global Society as a Singularity of Social Evolution. In: Korotayev AV, LePoire D (eds) The 21st century Singularity and global futures. A Big History perspective. Springer, Cham, pp 535–558. https://doi.org/10.1007/978-3-030-33730-8_24

  • Eliasmith C (2013) How to build a brain: a neural architecture for biological cognition. Oxford University Press, Oxford

    Book  Google Scholar 

  • Eliasmith C, Stewart TC, Choo X, Bekolay T, DeWolf T, Tang Y, Rasmussen D (2012) A large-scale model of the functioning brain. Science 338(6111):1202–1205. https://doi.org/10.1126/science.1225266

    Article  Google Scholar 

  • Fodor J (1978) Propositional attitudes. Monist 61:501–523. https://doi.org/10.5840/monist197861444

    Article  Google Scholar 

  • Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36:193–202. https://doi.org/10.1007/BF00344251

    Article  Google Scholar 

  • Gardner H (2006) Multiple intelligences: new horizons. Basic Books, New York

    Google Scholar 

  • Goleman D (1995) Emotional intelligence. Bantam Books, New York

    Google Scholar 

  • Good IJ (1965) Speculations concerning the first ultraintelligent machine. In: Alt FL, Rubinoff M (eds) Advances in computers. Academic Press, New York, pp 31–88

    Google Scholar 

  • Grinchenko S, Shchapova Y (2020) The deductive approach to Big History’s Singularity. In: Korotayev AV, LePoire D (eds) The 21st century Singularity and global futures. A Big History perspective. Springer, Cham, pp 201–210. https://doi.org/10.1007/978-3-030-33730-8_10

  • Grinin L, Grinin A (2020) The cybernetic revolution and the future of technologies. In: Korotayev AV, LePoire D (eds) The 21st century Singularity and global futures. A Big History perspective. Springer, Cham, pp 377–396. https://doi.org/10.1007/978-3-030-33730-8_17

  • Grinin L, Grinin A, Korotayev AV (2020) Dynamics of technological growth rate and the 21st century singularity. In: Korotayev AV, LePoire D (eds) The 21st century Singularity and global futures. A Big History perspective. Springer, Cham, pp 287–344. https://doi.org/10.1007/978-3-030-33730-8_14

  • Harari YN (2017) Homo deus: a brief history of tomorrow. Harper, New York

    Book  Google Scholar 

  • Hazelwood K, Bird S, Brooks D, Chintala S, Diril U, Dzhulgakov D, Fawzy M, Jia B, Jia Y, Kalro A, Law J, Lee K, Lu J, Noordhuis P, Smelyanskiy M, Xiong L, Wang X (2018) Applied machine learning at Facebook: a datacenter infrastructure perspective. In: Gschwind M (ed) 2018 IEEE international symposium on high performance computer architecture. IEEE, Los Alamitos, pp 620–629. https://doi.org/10.1109/HPCA.2018.00059

  • Herrera C, Sanz R (2016) Heideggerian AI and the being of robots. In: Müller VC (ed) Fundamental issues of artificial intelligence. Springer-Verlag, Berlin, pp 497–513. https://doi.org/10.1007/978-3-319-26485-1_29

  • Higginbotham J (1991) Belief and logical form. Minds Lang 6:344–369. https://doi.org/10.1111/j.1468-0017.1991.tb00261.x

    Article  Google Scholar 

  • Hines M, Carnevale N (1997) The NEURON simulation environment. Neural Comput 9:1179–1209. https://doi.org/10.1162/neco.1997.9.6.1179

    Article  Google Scholar 

  • Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507. https://doi.org/10.1126/science.1127647

    Article  Google Scholar 

  • Hinton GE, McClelland JL, Rumelhart DE (1986) Distributed representations. In: Rumelhart DE, McClelland JL (eds) Parallel distributed processing: explorations in the microstructure of cognition, vol 2. Psychological and biological models. MIT Press, Cambridge MA, pp 77–109

    Google Scholar 

  • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  • Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Pres, Ann Arbor

    Google Scholar 

  • Khan HN, Hounshell DA, Fuchs ERH (2018) Science and research policy at the end of Moore’s law. Nature Electronics 1:14–21. https://doi.org/10.1038/s41928-018-0031-2

    Article  Google Scholar 

  • Korotayev AV (2020) The 21st century Singularity in the Big History perspective. A re-analysis. In: Korotayev AV, LePoire D (eds) The 21st century Singularity and global futures. A Big History perspective. Springer, Cham, pp 19–75. https://doi.org/10.1007/978-3-030-33730-8_2

  • Kotseruba I, Tsotsos JK (2018) 40 years of cognitive architectures: core cognitive abilities and practical applications. Artificial intelligence review 50. https://doi.org/10.1007/s10462-018-9646-y

  • Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105

    Google Scholar 

  • Kurzweil R (2005) The singularity is near: when humans transcend biology. Viking, New York

    Google Scholar 

  • Kurzweil R (2012) How to create a mind: the secret of human thought revealed. Viking, New York

    Google Scholar 

  • Kuszyk A, Hammoudeh M (2018) Contemporary alternatives to traditional processor design in the post Moore’s law era. In: Proceedings of the 2nd International Conference on Future Networks and Distributed Systems. ACM, New York, pp 46–51

    Google Scholar 

  • Last C (2020) Global brain: foundations of a distributed singularity. In: Korotayev AV, LePoire D (eds) The 21st century Singularity and global futures. A Big History perspective. Springer, Cham, pp 363–375. https://doi.org/10.1007/978-3-030-33730-8_16

  • LePoire DJ (2005) Application of logistic analysis to the history of physics. Technol Forecast Soc Chang 72(4):471–479. https://doi.org/10.1016/S0040-1625(03)00044-1

    Article  Google Scholar 

  • LePoire DJ (2015) Potential nested accelerating returns logistic growth in big history. Evolution 4:46–60

    Google Scholar 

  • LePoire DJ (2020) Exploring the singularity concept within Big History. In: Korotayev AV, LePoire D (eds) The 21st century Singularity and global futures. A Big History perspective. Springer, Cham, pp 77–97. https://doi.org/10.1007/978-3-030-33730-8_3

  • Lorente R (1938) Architectonics and structure of the cerebral cortex. In: Fulton J (ed) Physiology of the nervous system. Oxford University Press, Oxford, pp 291–330

    Google Scholar 

  • Mackintosh NJ (2011) History of theories and measurement of intelligence. In: Sternberg RJ, Kaufman SB (eds) The cambridge handbook of intelligence. Cambridge University Press, Cambridge, pp 3–19

    Chapter  Google Scholar 

  • Makridakis S (2017) The forthcoming artificial intelligence (AI) revolution: its impact on society and firms. Futures 90:46–60. https://doi.org/10.1016/j.futures.2017.03.006

    Article  Google Scholar 

  • Malkov S (2020) About the singularity in biological and social evolution. In: Korotayev AV, LePoire D (eds) The 21st century Singularity and global futures. A Big History perspective. Springer, Cham, pp 517–534. https://doi.org/10.1007/978-3-030-33730-8_23

  • Marcus G (2018) Deep learning: a critical appraisal. Marcus, G. (2018). Deep learning: a critical appraisal. arXiv preprint 1801.00631. https://arxiv.org/abs/1801.00631

  • Markram H, Muller E, Ramaswamy S (2015) Reconstruction and simulation of neocortical microcircuitry. Cell 163(2):456–492. https://doi.org/10.1016/j.cell.2015.09.029

    Article  Google Scholar 

  • Martınez-Plumed F, Loe BS, Flach P, hEigeartaigh SO, Vold K, Hern‘andez-Orallo J (2018) The facets of artificial intelligence: a framework to track the evolution of AI. In: International Joint Conferences on Artificial Intelligence, pp 5180–5187

    Google Scholar 

  • Mehta P, Schwab DJ (2014) An exact mapping between the variational renormalization group and deep learning. arXiv preprint 1410.3831. https://arxiv.org/abs/1410.3831

  • Mei S, Montanari A, Nguyen PM (2018) A mean field view of the landscape of two-layer neural networks. Proc Nat Acad Sci 115:7665–7671. https://doi.org/10.1073/pnas.1806579115

    Article  Google Scholar 

  • Messé A, Hütt MT, Hilgetag CC (2018) Toward a theory of co-activation patterns in excitable neural networks. PLoS Comput Biol 14(4):e1006084. https://doi.org/10.1371/journal.pcbi.1006084

    Article  Google Scholar 

  • Miller J, Bower JM (2013) Introduction: origins and history of the CNS meetings. In: Bower JM (ed) 20 Years of computational neuroscience. Springer-Verlag, Berlin, pp 1–13. https://doi.org/10.1007/978-1-4614-1424-7_1

  • Minsky ML (1954) Neural nets and the brain-model problem. Ph.D thesis, Princeton University, Princeton

    Google Scholar 

  • Minsky M, Papert S (1969) Perceptrons. MIT Press, Cambridge MA

    Google Scholar 

  • Moore G (1965) Cramming more components onto integrated circuits. Electronics 38:114–117

    Google Scholar 

  • Moore G (1975) Progress in digital integrated electronics. IEEE Int Electron Devices Meet 21:11–13

    Google Scholar 

  • Moran K, Wallace BC, Brodley CE (2014) Discovering better AAAI keywords via clustering with community-sourced constraints. In: AAAI Conference on Artificial Intelligence, pp 1265–1271

    Google Scholar 

  • Murdoch S (2007) IQ: the brilliant idea that failed. Wiley, Hoboken

    Google Scholar 

  • Nazaretyan A (2020) The 21st century’s “mysterious singularity” in the light of the Big History. In: Korotayev AV, LePoire D (eds) The 21st century Singularity and global futures. A Big History perspective. Springer, Cham, pp 345–362. https://doi.org/10.1007/978-3-030-33730-8_15

  • Newell A, Simon HA (1972) Human problem solving. Prentice Hall, Englewood Cliffs, New Jersey

    Google Scholar 

  • Newell A, Shaw C, Simon HA (1957) Empirical explorations of the logic theory machine: a case study in heuristic. In: Western Joint Computer Conference Proceedings, ACM. New York, pp 218–230

    Google Scholar 

  • Newell A, Shaw C, Simon HA (1959) Report on a general problem-solving program. Scientific Report P-1584, RAND Corporation, Santa Monica, California

    Google Scholar 

  • Niu J, Tang W, Xu F, Zhou X, Song Y (2016) Global research on artificial intelligence from 19902014: spatially-explicit bibliometric analysis. Int J Geo-Inf 5(5):66. https://doi.org/10.3390/ijgi5050066

    Article  Google Scholar 

  • Novaes CD (2012) Formal languages in logic: a philosophical and cognitive analysis. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Nunn C (2016) More splodge than singularity? In: Awret U (ed) The singularity: could artificial intelligence really out-think us (and would we want it to)? Imprint Academic, New York, pp 408–412

    Google Scholar 

  • Özkural E (2018) The foundations of deep learning with a path towards general intelligence. In: Iklé M, Franz A, Rzepka R, Goertzel B (eds) Artificial general intelligence. Springer, Cham, pp 162–173. https://doi.org/10.1007/978-3-319-97676-1_16

  • Panov A (2020) Singularity of evolution and post-singular development in the Big History perspective. In: Korotayev AV, LePoire D (eds) The 21st century Singularity and global futures. A Big History perspective. Springer, Cham, pp 439–465. https://doi.org/10.1007/978-3-030-33730-8_20

  • Parloff R (2016) Why deep learning is suddenly changing your life. Fortune September

    Google Scholar 

  • Petke J, Haraldsson SO, Harman M, Langdon WB, White DR, Woodward JR (2018) Genetic improvement of software: a comprehensive survey. IEEE Trans Evol Comput 22(3):415–432. https://doi.org/10.1109/TEVC.2017.2693219

    Article  Google Scholar 

  • Plebe A (2018) The search of “canonical” explanations for the cerebral cortex. Hist Philos Life Sci 40:40–76. https://doi.org/10.1007/s40656-018-0205-2

    Article  Google Scholar 

  • Plebe A, Grasso G (2016) The brain in silicon: history, and skepticism. In: Gadducci F, Tavosanis M (eds) History and philosophy of computing. Springer, Berlin, pp 273–286. https://doi.org/10.1007/978-3-319-47286-7_19

  • Plebe A, Perconti P (2013) The slowdown hypothesis. In: Eden AH, Moor JH, Søraker JH, Steinhart E (eds) Singularity hypotheses. Springer, Berlin. https://doi.org/10.1007/978-3-642-32560-1_17

  • Ramón Y, Cajal S (1917) Recuerdos de mi vida. Imprenta y Librerıa de Nicolás Moya, Madrid

    Google Scholar 

  • Reichenbach H (1938) Experience and prediction: an analysis of the foundations and the structure of knowledge. Chicago University Press, Chicago

    Google Scholar 

  • Reimann MW, Nolte M, Scolamiero M, Turner K, Perin R, Chindemi G, Dlotko P, Levi R, Hess K, Markram H (2017) Cliques of neurons bound into cavities provide a missing link between structure and function. Frontiers in computational neuroscience 11. https://doi.org/10.3389/fncom.2017.00048

  • Rice HG (1953) Classes of recursively enumerable sets and their decision problems. Trans Am Math Soc 74(2):358–366

    Article  Google Scholar 

  • Richard M (1990) Propositional attitudes: an essay on thoughts and how we ascribe them. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organisation in the brain. Psychol Rev 65:386–408. https://psycnet.apa.org/doi/10.1037/h0042519

  • Rosenblatt F (1962) Principles of neurodynamics: perceptron and the theory of brain mechanisms. Spartan WA

    Google Scholar 

  • Rumelhart DE, McClelland JL (eds) (1986) Parallel distributed processing: explorations in the microstructure of cognition. MIT Press, Cambridge MA

    Google Scholar 

  • Sanders J, Kandrot E (2014) CUDA by example: an introduction to general-purpose GPU programming. Addison Wesley, Reading MA

    Google Scholar 

  • Schickore J, Steinle F (eds) (2006) Revisiting discovery and justification—historical and philosophical perspectives on the context distinction. Springer-Verlag, Berlin. https://doi.org/10.1007/1-4020-4251-5

  • Schmidhuber J (2006) Gödel machines: Fully self-referential optimal universal self–improvers. In: Goertzel B, Pennachin C (eds) Artificial general intelligence. Springer-Verlag, Berlin, pp 199–226. https://doi.org/10.1007/978-3-540-68677-4_7

  • Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117. https://doi.org/10.1016/j.neunet.2014.09.003

    Article  Google Scholar 

  • Schwab K (2016) Fourth industrial revolution. World Economic Forum, Geneva

    Google Scholar 

  • Shanahan M (ed) (2015) The technological singularity. MIT Press, Cambridge, Massachusetts

    Google Scholar 

  • Solis K, LePoire DJ (2020) Big History trends in information processes. In: Korotayev AV, LePoire D (eds) The 21st century Singularity and global futures. A Big History perspective. Springer, Cham, pp 145–161. https://doi.org/10.1007/978-3-030-33730-8_7

  • Stueckelberg E, Petermann A (1953) La normalisation des constantes dans la théorie des quanta. Helv Phys Acta 26:499–520

    Google Scholar 

  • Tan KH, Lim BP (2018) The artificial intelligence renaissance: deep learning and the road to human-level machine intelligence. APSIPA Trans Signal Inf Process 7(e6):1–19. https://doi.org/10.1017/ATSIP.2018.6

    Article  Google Scholar 

  • Theis TN, Wong P (2016) The end of Moore’s law: a new beginning for information technology. Comput Sci Eng 2371:41–50

    Google Scholar 

  • Tsirel S (2020) Big History and Singularity as metaphors, hypotheses and prediction. In: Korotayev AV, LePoire D (eds) The 21st century Singularity and global futures. A Big History perspective. Springer, Cham, pp 119–144. https://doi.org/10.1007/978-3-030-33730-8_6

  • Turing A (1948) Intelligent machinery. National Physical Laboratory, London

    Google Scholar 

  • VanRullen R (2017) Perception science in the age of deep neural networks. Front Psychol 8:142

    Google Scholar 

  • Vinge V (1993) The coming technological singularity: how to survive in the post-human era. In: Interdisciplinary Science and Engineering in the Era of Cyberspace. NASA, Lewis Research Center, pp 11–22

    Google Scholar 

  • Von Economo C, Koskinas GN (1925) Die Cytoarchitektonik der Hirnrinde des erwachsenen Menschen. Springer-Verlag, Berlin

    Google Scholar 

  • Wang X, Dillig I, Singh R (2018) Program synthesis using abstraction refinement. Proc ACM Program Lang 2(63):1–30. https://doi.org/10.1145/3158151

    Article  Google Scholar 

  • Widdowson M (2020) The 21st century Singularity: the role of perspective and perception. In: Korotayev AV, LePoire D (eds) The 21st century Singularity and global futures. A Big History perspective. Springer, Cham, pp 489–516. https://doi.org/10.1007/978-3-030-33730-8_22

  • Wolfgang C (1994) Frege’s theory of sense and reference: its origin and scope. Cambridge University Press, Cambridge

    Google Scholar 

  • Yampolskiy RV (2016) Artificial superintelligence: a futuristic approach. CRC Press, Boca Raton

    Google Scholar 

  • Yin P, Neubig G (2017) A syntactic neural model for general-purpose code generation. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp 440–450

    Google Scholar 

  • Zhang Q, Yang LT, Chen Z, Li P (2018) A survey on deep learning for big data. Inf Fusion 42:146–157. https://doi.org/10.1016/j.inffus.2017.10.006

    Article  Google Scholar 

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  1. Department of Cognitive Science, University of Messina, Messina, Italy

    Alessio Plebe & Pietro Perconti

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  1. Alessio Plebe
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  1. Higher School of Economics, National Research University, Moscow, Russia

    Andrey V. Korotayev

  2. Argonne National Laboratory, Argonne, IL, USA

    David J. LePoire

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Plebe, A., Perconti, P. (2020). Plurality: The End of Singularity?. In: Korotayev, A., LePoire, D. (eds) The 21st Century Singularity and Global Futures. World-Systems Evolution and Global Futures. Springer, Cham. https://doi.org/10.1007/978-3-030-33730-8_8

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