1. Henrich, J. The Secret of Our Success: How Culture Is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter (Princeton Univ. Press, 2016).

2. Heyes, C. Cognitive Gadgets: The Cultural Evolution of Thinking (Harvard Univ. Press, 2018).

3. Thompson, B., van Opheusden, B., Sumers, T. & Griffiths, T. L. Complex cognitive algorithms preserved by selective social learning in experimental populations. Science 376, 95–98 (2022).

   Article 
   CAS 
   PubMed 

   Google Scholar 

4. Whiten, A. Cultural evolution in animals. Annu. Rev. Ecol. Evol. Syst. 50, 27–48 (2019).

   Article 

   Google Scholar 

5. Gray, R. D. & Atkinson, Q. D. Language-tree divergence times support the Anatolian theory of Indo-European origin. Nature 426, 435–439 (2003).

   Article 
   CAS 
   PubMed 

   Google Scholar 

6. Kirby, S., Cornish, H. & Smith, K. Cumulative cultural evolution in the laboratory: an experimental approach to the origins of structure in human language. Proc. Natl Acad. Sci. USA 105, 10681–10686 (2008).

   Article 
   CAS 
   PubMed 
   PubMed Central 

   Google Scholar 

7. Shennan, S. Genes, Memes, and Human History: Darwinian Archaeology and Cultural Evolution (Thames & Hudson, 2002).

8. Kiley, K. & Vaisey, S. Measuring stability and change in personal culture using panel data. Am. Sociol. Rev. 85, 477–506 (2020).

   Article 

   Google Scholar 

9. Mokyr, J. A Culture of Growth: The Origins of the Modern Economy (Princeton Univ. Press, 2017).

10. Mesoudi, A., Whiten, A. & Laland, K. N. Perspective: is human cultural evolution Darwinian? Evidence reviewed from the perspective of the origin of species. Evolution 58, 1–11 (2004).

    PubMed 

    Google Scholar 

11. Needham, J. in Chemistry and Chemical Technology, Pt. 7: Military Technology—the Gunpowder Epic Vol. 5 (Cambridge Univ. Press, 1986).

12. Eisenstein, E. L. The Printing Press as an Agent of Change Vol. 1 (Cambridge Univ. Press, 1980).

13. Mesoudi, A. Culture and the Darwinian Renaissance in the social sciences and humanities: for a special issue of the Journal of Evolutionary Psychology, “The Darwinian Renaissance in the Social Sciences and Humanities”. J. Evol. Psychol. 9, 109–124 (2011).

    Article 

    Google Scholar 

14. Acerbi, A. Cultural Evolution in the Digital Age (Oxford Univ. Press, 2019).

15. Russell, S. & Norvig, P. Artificial Intelligence: A Modern Approach (Prentice Hall, 2009).

16. Kurzweil, R., Richter, R., Kurzweil, R. & Schneider, M. L. The Age of Intelligent Machines (MIT Press, 1990).

17. Goodfellow, I. et al. Generative adversarial networks. Commun. ACM 63, 139–144 (2020).

    Article 

    Google Scholar 

18. Epstein, Z., Boulais, O., Gordon, S. & Groh, M. Interpolating GANs to scaffold autotelic creativity. Preprint at arXiv https://doi.org/10.48550/arXiv.2007.11119 (2020).

19. Ramesh, A. et al. Zero-shot text-to-image generation. In International Conf. on Machine Learning 8821–8831 (PMLR, 2021).

20. Ramesh, A., Dhariwal, P., Nichol, A., Chu, C. & Chen, M. Hierarchical text-conditional image generation with CLIP latents. Preprint at arXiv https://doi.org/10.48550/arXiv.2204.06125 (2022).

21. Rombach, R. et al. High-resolution image synthesis with latent diffusion models. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recogn. 10684–10695 (2022).

22. Epstein, Z., Levine, S., Rand, D. G. & Rahwan, I. Who gets credit for AI-generated art? iScience 23, 101515 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

23. Thagard, P. & Stewart, T. C. The AHA! experience: creativity through emergent binding in neural networks. Cogn. Sci. 35, 1–33 (2011).

    Article 
    PubMed 

    Google Scholar 

24. Mikolov, T., Yih, W. & Zweig, G. Linguistic regularities in continuous space word representations. In Proc. 2013 Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 746–751 (Association for Computational Linguistics, 2013).

25. Colas, C., Karch, T., Moulin-Frier, C. & Oudeyer, P.-Y. Language and culture internalization for human-like autotelic AI. Nat. Mach. Intell. 4, 1068–1076 (2022).

    Article 

    Google Scholar 

26. Lisi, E., Malekzadeh, M., Haddadi, H., Lau, F.D.-H. & Flaxman, S. Modelling and forecasting art movements with CGANs. R. Soc. Open Sci. 7, 191569 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

27. Elgammal, A., Liu, B., Elhoseiny, M. & Mazzone, M. CAN: Creative Adversarial Networks, generating ‘art’ by learning about styles and deviating from style norms. Preprint at arXiv https://doi.org/10.48550/arXiv.1706.07068 (2017).

28. Wang, Y., Shimada, K. & Barati Farimani, A. Airfoil GAN: encoding and synthesizing airfoils for aerodynamic shape optimization. J. Comput. Des. Eng. 10, 1350–1362 (2023).

    Google Scholar 

29. Metz, C. In two moves, AlphaGo and Lee Sedol redefined the future. Wired (16 March 2016).

30. Silver, D. et al. Mastering the game of Go without human knowledge. Nature 550, 354–359 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar 

31. Shin, M., Kim, J., van Opheusden, B. & Griffiths, T. L. Superhuman artificial intelligence can improve human decision-making by increasing novelty. Proc. Natl Acad. Sci. USA 120, e2214840120 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

32. Choi, S., Kim, N., Kim, J. & Kang, H. How does AI improve human decision-making? Evidence from the AI-powered Go program. Preprint at SSRN https://doi.org/10.2139/ssrn.3893835 (2022).

33. Shin, M., Kim, J. & Kim, M. Human learning from artificial intelligence: evidence from human Go players’ decisions after AlphaGo. Proc. Annu. Meet. Cogn. Sci. Soc. 43, 43 (2021).

    Google Scholar 

34. Schrittwieser, J. et al. Mastering Atari, Go, chess and shogi by planning with a learned model. Nature 588, 604–609 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar 

35. Fawzi, A. et al. Discovering faster matrix multiplication algorithms with reinforcement learning. Nature 610, 47–53 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

36. Kasneci, E. et al. ChatGPT for good? On opportunities and challenges of large language models for education. Learn. Individ. Differ. 103, 102274 (2023).

    Article 

    Google Scholar 

37. Wagner, G., Lukyanenko, R. & Paré, G. Artificial intelligence and the conduct of literature reviews. J. Inf. Technol. 37, 209–226 (2022).

    Article 

    Google Scholar 

38. Chen, M. et al. Evaluating large language models trained on code. Preprint at arXiv https://doi.org/10.48550/arXiv.2107.03374 (2021).

39. Eloundou, T., Manning, S., Mishkin, P. & Rock, D. GPTs are GPTs: an early look at the labor market impact potential of large language models. Preprint at arXiv https://doi.org/10.48550/arXiv.2303.10130 (2023).

40. Stevenson, C., Smal, I., Baas, M., Grasman, R. & van der Maas, H. Putting GPT-3’s creativity to the (alternative uses) test. Preprint at arXiv https://doi.org/10.48550/arXiv.2206.08932 (2022).

41. Popli, N. How to get a six-figure job as an AI prompt engineer. Time https://time.com/6272103/ai-prompt-engineer-job/ (14 April 2023).

42. Epstein, Z., Hertzmann, A. & the Investigators of Human Creativity. Art and the science of generative AI. Science 380, 1110–1111 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

43. Oppenlaender, J. The creativity of text-to-image generation. In Proc. 25th International Academic Mindtrek Conference 192–202 (Association for Computing Machinery, 2022); https://doi.org/10.1145/3569219.3569352

44. Li, Z. (L.), Fang, X. & Sheng, O. R. L. A survey of link recommendation for social networks: methods, theoretical foundations, and future research directions. ACM Trans. Manage. Inf. Syst. 9, 1–26 (2018).

45. Lops, P., de Gemmis, M. & Semeraro, G. in Recommender Systems Handbook (eds Ricci, F. et al.) 73–105 (Springer US, 2011); https://doi.org/10.1007/978-0-387-85820-3_3

46. Su, X. & Khoshgoftaar, T. M. A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 421425 (2009).

    Article 

    Google Scholar 

47. Anderson, A., Maystre, L., Anderson, I., Mehrotra, R. & Lalmas, M. Algorithmic effects on the diversity of consumption on Spotify. In Proc. Web Conference 2020 2155–2165 (Association for Computing Machinery, 2020).

48. Krumme, C., Cebrian, M., Pickard, G. & Pentland, S. Quantifying social influence in an online cultural market. PLoS ONE 7, e33785 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

49. Salganik, M. J., Dodds, P. S. & Watts, D. J. Experimental study of inequality and unpredictability in an artificial cultural market. Science 311, 854–856 (2006).

    Article 
    CAS 
    PubMed 

    Google Scholar 

50. Richerson, P. J. & Boyd, R. Not by Genes Alone: How Culture Transformed Human Evolution (Univ. of Chicago Press, 2005).

51. Cavalli-Sforza, L. L. & Feldman, M. W. Cultural Transmission and Evolution: A Quantitative Approach (Princeton Univ. Press, 1981).

52. Mesoudi, A. Pursuing Darwin’s curious parallel: prospects for a science of cultural evolution. Proc. Natl Acad. Sci. USA 114, 7853–7860 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

53. Enquist, M. & Ghirlanda, S. Evolution of social learning does not explain the origin of human cumulative culture. J. Theor. Biol. 246, 129–135 (2007).

    Article 
    PubMed 

    Google Scholar 

54. Acerbi, A. & Mesoudi, A. If we are all cultural Darwinians what’s the fuss about? Clarifying recent disagreements in the field of cultural evolution. Biol. Phil. 30, 481–503 (2015).

    Article 

    Google Scholar 

55. Morin, O. Reasons to be fussy about cultural evolution. Biol. Phil. 31, 447–458 (2016).

    Article 

    Google Scholar 

56. Weitzman, M. L. Recombinant growth. Q. J. Econ. 113, 331–360 (1998).

    Article 

    Google Scholar 

57. Griffiths, T. L. Understanding human intelligence through human limitations. Trends Cogn. Sci. 24, 873–883 (2020).

    Article 
    PubMed 

    Google Scholar 

58. Boyd, R. & Richerson, P. J. Culture and the Evolutionary Process (Univ. of Chicago Press, 1985).

59. Mesoudi, A. Cultural Evolution: How Darwinian Theory Can Explain Human Culture and Synthesize the Social Sciences (Univ. of Chicago Press, 2011).

60. Leibo, J. Z., Hughes, E., Lanctot, M. & Graepel, T. Autocurricula and the emergence of innovation from social interaction: a manifesto for multi-agent intelligence research. Preprint at arXiv https://doi.org/10.48550/arXiv.1903.00742 (2019).

61. Aveni, A. F. Skywatchers: A Revised and Updated Version of Skywatchers of Ancient Mexico (Univ. of Texas Press, 2001).

62. Hornik, K. Approximation capabilities of multilayer feedforward networks. Neural Netw. 4, 251–257 (1991).

    Article 

    Google Scholar 

63. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar 

64. Zenil, H. et al. The future of fundamental science led by generative closed-loop artificial intelligence. Preprint at arXiv https://doi.org/10.48550/arXiv.2307.07522 (2023).

65. Senior, A. W. et al. Improved protein structure prediction using potentials from deep learning. Nature 577, 706–710 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar 

66. Cooper, S. et al. Predicting protein structures with a multiplayer online game. Nature 466, 756–760 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

67. Bommasani, R. et al. On the opportunities and risks of foundation models. Preprint at arXiv https://doi.org/10.48550/arXiv.2108.07258 (2022).

68. Hoffmann, J. et al. Training compute-optimal large language models. Preprint at arXiv https://doi.org/10.48550/arXiv.2203.15556 (2022).

69. Bender, E. M., Gebru, T., McMillan-Major, A. & Shmitchell, S. On the dangers of stochastic parrots: can language models be too big? In Proc. 2021 ACM Conference on Fairness, Accountability, and Transparency 610–623 (Association for Computing Machinery, 2021).

70. Brown, T. et al. Language models are few-shot learners. Adv. Neural Inf. Process. Syst. 33, 1877–1901 (2020).

    Google Scholar 

71. Mitchell, M. & Krakauer, D. C. The debate over understanding in AI’s large language models. Proc. Natl Acad. Sci. USA 120, e2215907120 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

72. Charbonneau, M. Modularity and recombination in technological evolution. Phil. Technol. 29, 373–392 (2016).

    Article 

    Google Scholar 

73. Henrich, J. Demography and cultural evolution: how adaptive cultural processes can produce maladaptive losses—the Tasmanian case. Am. Antiq. 69, 197–214 (2004).

    Article 

    Google Scholar 

74. Henrich, J. & Muthukrishna, M. What makes us smart? Top. Cogn. Sci. https://doi.org/10.1111/tops.12656 (2023).

75. Youn, H., Strumsky, D., Bettencourt, L. M. A. & Lobo, J. Invention as a combinatorial process: evidence from US patents. J. R. Soc. Interface 12, 20150272 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

76. Sourati, J. & Evans, J. A. Accelerating science with human-aware artificial intelligence. Nat. Hum. Behav. https://doi.org/10.1038/s41562-023-01648-z (2023).

77. Tinits, P. & Sobchuk, O. Open-ended cumulative cultural evolution of Hollywood film crews. Evol. Hum. Sci. 2, e26 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

78. Grizou, J., Points, L. J., Sharma, A. & Cronin, L. A curious formulation robot enables the discovery of a novel protocell behavior. Sci. Adv. 6, eaay4237 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

79. Kramer, S., Cerrato, M., Džeroski, S. & King, R. Automated scientific discovery: from equation discovery to autonomous discovery systems. Preprint at arXiv https://doi.org/10.48550/arXiv.2305.02251 (2023).

80. Lucas, A. J. et al. The value of teaching increases with tool complexity in cumulative cultural evolution. Proc. R. Soc. B 287, 20201885 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

81. Borsa, D., Piot, B., Munos, R. & Pietquin, O. Observational learning by reinforcement learning. Preprint at arXiv https://doi.org/10.48550/arXiv.1706.06617 (2017).

82. Kohnke, L., Moorhouse, B. L. & Zou, D. ChatGPT for language teaching and learning. RELC J. 54, 537–550 (2023).

    Article 

    Google Scholar 

83. Haller, E. & Rebedea, T. Designing a chat-bot that simulates an historical figure. In 2013 19th International Conference on Control Systems and Computer Science 582–589 (IEEE, 2013).

84. Zhang, S., Frey, B. & Bansal, M. How can NLP help revitalize endangered languages? A case study and roadmap for the Cherokee language. Preprint at arXiv https://doi.org/10.48550/arXiv.2204.11909 (2022).

85. Ijaz, K., Bogdanovych, A. & Trescak, T. Virtual worlds vs books and videos in history education. Interact. Learn. Environ. 25, 904–929 (2017).

    Article 

    Google Scholar 

86. Buolamwini, J. & Gebru, T. Gender shades: intersectional accuracy disparities in commercial gender classification. In Proc. 1st Conference on Fairness, Accountability and Transparency 77–91 (PMLR, 2018).

87. Caliskan, A., Bryson, J. J. & Narayanan, A. Semantics derived automatically from language corpora contain human-like biases. Science 356, 183–186 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar 

88. O’Neil, C. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (Crown, 2016).

89. Prates, M. O., Avelar, P. H. & Lamb, L. C. Assessing gender bias in machine translation: a case study with google translate. Neural Comput. Appl. 32, 6363–6381 (2020).

    Article 

    Google Scholar 

90. Acerbi, A. & Stubbersfield, J. Large language models show human-like content biases in transmission chain experiments. Preprint at OSF https://doi.org/10.31219/osf.io/8zg4d (2023).

91. Vig, J. et al. Investigating gender bias in language models using causal mediation analysis. Adv. Neural Inf. Process. Syst. 33, 12388–12401 (2020).

    Google Scholar 

92. Pessach, D. & Shmueli, E. A review on fairness in machine learning. ACM Comput. Surv. 55, 1–51 (2022). 44.

    Article 

    Google Scholar 

93. Argyle, L. P. et al. Out of one, many: using language models to simulate human samples. Political Anal. 31, 337–351 (2023).

    Article 

    Google Scholar 

94. Hendy, A. et al. How good are GPT models at machine translation? A comprehensive evaluation. Preprint at arXiv https://doi.org/10.48550/arXiv.2302.09210 (2023).

95. Bartlett, F. C. Remembering: A Study in Experimental and Social Psychology xix, 317 (Cambridge Univ. Press, 1932).

96. Kashima, Y. Maintaining cultural stereotypes in the serial reproduction of narratives. Pers. Soc. Psychol. Bull. 26, 594–604 (2000).

    Article 

    Google Scholar 

97. Griffiths, T. L., Christian, B. R. & Kalish, M. L. Using category structures to test iterated learning as a method for identifying inductive biases. Cogn. Sci. 32, 68–107 (2008).

    Article 
    PubMed 

    Google Scholar 

98. Lieder, F. & Griffiths, T. L. Resource-rational analysis: understanding human cognition as the optimal use of limited computational resources. Behav. Brain Sci. 43, e1 (2020).

    Article 

    Google Scholar 

99. Simon, H. A. in Utility and Probability (eds Eatwell, J. et al.) 15–18 (Palgrave Macmillan UK, 1990).

100. Todd, P. M. & Gigerenzer, G. Environments that make us smart: ecological rationality. Curr. Dir. Psychol. Sci. 16, 167–171 (2007).

     Article 

     Google Scholar 

101. Tversky, A. & Kahneman, D. Judgment under uncertainty: heuristics and biases. Science 185, 1124–1131 (1974).

     Article 
     CAS 
     PubMed 

     Google Scholar 

102. Gershman, S. J., Horvitz, E. J. & Tenenbaum, J. B. Computational rationality: a converging paradigm for intelligence in brains, minds, and machines. Science 349, 273–278 (2015).

     Article 
     CAS 
     PubMed 

     Google Scholar 

103. Malle, B. F., Scheutz, M., Arnold, T., Voiklis, J. & Cusimano, C. Sacrifice one for the good of many? People apply different moral norms to human and robot agents. In Proc. Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction 117–124 (Association for Computing Machinery, 2015).

104. Griffiths, T. L., Kalish, M. L. & Lewandowsky, S. Theoretical and empirical evidence for the impact of inductive biases on cultural evolution. Phil. Trans. R. Soc. B 363, 3503–3514 (2008).

     Article 
     PubMed 
     PubMed Central 

     Google Scholar 

105. Kirby, S., Dowman, M. & Griffiths, T. L. Innateness and culture in the evolution of language. Proc. Natl Acad. Sci. USA 104, 5241–5245 (2007).

     Article 
     CAS 
     PubMed 
     PubMed Central 

     Google Scholar 

106. Thompson, B. & Griffiths, T. L. Human biases limit cumulative innovation. Proc. R. Soc. B 288, 20202752 (2021).

     Article 
     PubMed 
     PubMed Central 

     Google Scholar 

107. Brinkmann, L. et al. Hybrid social learning in human-algorithm cultural transmission. Phil. Trans. R. Soc. A 380, 20200426 (2022).

     Article 
     CAS 
     PubMed 
     PubMed Central 

     Google Scholar 

108. Tamariz, M. & Kirby, S. Culture: copying, compression, and conventionality. Cogn. Sci. 39, 171–183 (2015).

     Article 
     PubMed 

     Google Scholar 

109. Chater, N. & Vitányi, P. Simplicity: a unifying principle in cognitive science? Trends Cogn. Sci. 7, 19–22 (2003).

     Article 
     PubMed 

     Google Scholar 

110. Kirby, S., Tamariz, M., Cornish, H. & Smith, K. Compression and communication in the cultural evolution of linguistic structure. Cognition 141, 87–102 (2015).

     Article 
     PubMed 

     Google Scholar 

111. Anderson, C. The end of theory: the data deluge makes the scientific method obsolete. Wired (23 June 2018).

112. Spinney, L. Are we witnessing the dawn of post-theory science? Guardian (9 January 2022).

113. Liu, Z., Madhavan, V. & Tegmark, M. AI Poincaré 2.0: machine learning conservation laws from differential equations. Phys. Rev. E 106, 045307 (2022).

     Article 
     CAS 
     PubMed 

     Google Scholar 

114. Kendal, R. L. et al. Social learning strategies: bridge-building between fields. Trends Cogn. Sci. 22, 651–665 (2018).

     Article 
     PubMed 

     Google Scholar 

115. Henrich, J. & McElreath, R. The evolution of cultural evolution. Evol. Anthropol. 12, 123–135 (2003).

     Article 

     Google Scholar 

116. Mesoudi, A., Whiten, A. & Dunbar, R. A bias for social information in human cultural transmission. Br. J. Psychol. 97, 405–423 (2006).

     Article 
     PubMed 

     Google Scholar 

117. Sharma, D. K. & Sharma, A. A comparative analysis of web page ranking algorithms. Int. J. Comput. Sci. Eng. 2, 2670–2676 (2010).

     Google Scholar 

118. Duhan, N., Sharma, A. K. & Bhatia, K. K. Page ranking algorithms: a survey. In 2009 IEEE International Advance Computing Conference 1530–1537 (IEEE, 2009).

119. Koren, Y., Rendle, S. & Bell, R. Advances in collaborative filtering. In Recommender Systems Handbook (eds Ricci, F., Rokach, L. & Shapira, B.) 91–142 (Springer US, Boston, MA, 2021).

120. Banihashemi, S. & Abhari, A. Effects of different recommendation algorithms on structure of social networks. In 2021 International Conference on Computational Science and Computational Intelligence (CSCI) 1395–1400 (IEEE, 2021); https://doi.org/10.1109/CSCI54926.2021.00279

121. Ferrara, A., Espín-Noboa, L., Karimi, F. & Wagner, C. Link recommendations: their impact on network structure and minorities. In 14th ACM Web Science Conference 2022. 228–238 (Association for Computing Machinery, 2022); https://doi.org/10.1145/3501247.3531583

122. Su, J., Sharma, A. & Goel, S. The effect of recommendations on network structure. In Proc. 25th International Conference on World Wide Web 1157–1167 (International World Wide Web Conferences Steering Committee, 2016).

123. Lazer, D. & Friedman, A. The network structure of exploration and exploitation. Adm. Sci. Q. 52, 667–694 (2007).

     Article 

     Google Scholar 

124. Mason, W. & Watts, D. J. Collaborative learning in networks. Proc. Natl Acad. Sci. USA 109, 764–769 (2012).

     Article 
     CAS 
     PubMed 

     Google Scholar 

125. Woolley, A. W., Aggarwal, I. & Malone, T. W. Collective intelligence and group performance. Curr. Dir. Psychol. Sci. 24, 420–424 (2015).

     Article 

     Google Scholar 

126. Derex, M. & Boyd, R. Partial connectivity increases cultural accumulation within groups. Proc. Natl Acad. Sci. USA 113, 2982–2987 (2016).

     Article 
     CAS 
     PubMed 
     PubMed Central 

     Google Scholar 

127. Kant, V., Jhalani, T. & Dwivedi, P. Enhanced multi-criteria recommender system based on fuzzy Bayesian approach. Multimed. Tools Appl. 77, 12935–12953 (2018).

     Article 

     Google Scholar 

128. Bollen, D., Knijnenburg, B. P., Willemsen, M. C. & Graus, M. Understanding choice overload in recommender systems. In Proc. Fourth ACM Conference on Recommender Systems 63–70 (Association for Computing Machinery, 2010).

129. Tkalcic, M., Kosir, A. & Tasic, J. Affective recommender systems: the role of emotions in recommender systems. In The RecSys 2011 Workshops-Decisions@ RecSys 2011 and UCERSTI-2: Human Decision Making in Recommender Systems; User-Centric Evaluation of Recommender Systems and Their Interfaces-2 Vol. 811, 9–13 (CEUR-WS.org, 2011).

130. Gonzalez, G., de la Rosa, J. L., Montaner, M. & Delfin, S. Embedding emotional context in recommender systems. In 2007 IEEE 23rd International Conference on Data Engineering Workshop 845–852 (IEEE, 2007).

131. Osman, N. A., Mohd Noah, S. A., Darwich, M. & Mohd, M. Integrating contextual sentiment analysis in collaborative recommender systems. PLoS ONE 16, e0248695 (2021).

     Article 
     CAS 
     PubMed 
     PubMed Central 

     Google Scholar 

132. Zheng, Y., Mobasher, B. & Burke, R. D. The role of emotions in context-aware recommendation. Decis. RecSys 2013, 21–28 (2013).

     Google Scholar 

133. Zhang, X., Ferreira, P., Godinho De Matos, M. & Belo, R. Welfare properties of profit maximizing recommender systems: theory and results from a randomized experiment. MIS Q. 45, 1 (2021).

     Article 

     Google Scholar 

134. Levy, R. Social media, news consumption, and polarization: evidence from a field experiment. Am. Econ. Rev. 111, 831–870 (2021).

     Article 

     Google Scholar 

135. Brady, W. J., Gantman, A. P. & Van Bavel, J. J. Attentional capture helps explain why moral and emotional content go viral. J. Exp. Psychol. Gen. 149, 746–756 (2020).

     Article 
     PubMed 

     Google Scholar 

136. Brady, W. J., Jackson, J. C., Lindström, B. & Crockett, M. J. Algorithm-mediated social learning in online social networks. Trends Cogn. Sci. (in the press).

137. Acerbi, A. Cognitive attraction and online misinformation. Palgrave Commun. 5, 1–7 (2019).

     Article 

     Google Scholar 

138. Brady, W. J. et al. Overperception of moral outrage in online social networks inflates beliefs about intergroup hostility. Nat. Hum. Behav. https://doi.org/10.1038/s41562-023-01582-0 (2023).

139. Brady, W. J. & Crockett, M. J. Norm psychology in the digital age: how social media shapes the cultural evolution of normativity. Perspect. Psychol. Sci. https://doi.org/10.1177/17456916231187395 (2023).

140. Milli, S., Carroll, M., Pandey, S., Wang, Y. & Dragan, A. D. Engagement, user satisfaction, and the amplification of divisive content on social media. Preprint at arXiv https://doi.org/10.48550/arXiv.2305.16941 (2023).

141. Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W. & Starnini, M. The echo chamber effect on social media. Proc. Natl Acad. Sci. USA 118, e2023301118 (2021).

     Article 
     CAS 
     PubMed 
     PubMed Central 

     Google Scholar 

142. Pariser, E. The Filter Bubble: What the Internet Is Hiding from You (Penguin, 2011).

143. Sunstein, C. R. Republic.com 2.0 (Princeton Univ. Press, 2007).

144. Jiang, R., Chiappa, S., Lattimore, T., György, A. & Kohli, P. Degenerate feedback loops in recommender systems. In Proc. 2019 AAAI/ACM Conference on AI, Ethics, and Society 383–390 (ACM, 2019).

145. Pagan, N. et al. A classification of feedback loops and their relation to biases in automated decision-making systems. Preprint at arXiv https://doi.org/10.48550/arXiv.2305.06055 (2023).

146. Stray, J. et al. Building human values into recommender systems: an interdisciplinary synthesis. Preprint at arXiv https://doi.org/10.48550/arXiv.2207.10192 (2022).

147. Kleinberg, J., Mullainathan, S. & Raghavan, M. The challenge of understanding what users want: inconsistent preferences and engagement optimization. Preprint at arXiv https://doi.org/10.48550/arXiv.2202.11776 (2022).

148. Ovadya, A. & Thorburn, L. Bridging systems: open problems for countering destructive divisiveness across ranking, recommenders, and governance. Preprint at arXiv https://doi.org/10.48550/arXiv.2301.09976 (2023).

149. Yao, B., Jiang, M., Yang, D. & Hu, J. Empowering LLM-based machine translation with cultural awareness. Preprint at arXiv https://doi.org/10.48550/arXiv.2305.14328 (2023).

150. Garimella, K., De Francisci Morales, G., Gionis, A. & Mathioudakis, M. Reducing controversy by connecting opposing views. In Proc. Tenth ACM International Conference on Web Search and Data Mining 81–90 (Association for Computing Machinery, 2017).

151. Santos, F. P., Lelkes, Y. & Levin, S. A. Link recommendation algorithms and dynamics of polarization in online social networks. Proc. Natl Acad. Sci. USA 118, e2102141118 (2021).

     Article 
     CAS 
     PubMed 
     PubMed Central 

     Google Scholar 

152. Möller, J., Trilling, D., Helberger, N. & Van Es, B. Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity. Inf. Commun. Soc. 21, 959–977 (2018).

     Article 

     Google Scholar 

153. Bakker, M. et al. Fine-tuning language models to find agreement among humans with diverse preferences. Adv. Neural Inf. Process. Syst. 35, 38176–38189 (2022).

     Google Scholar 

154. Christiano, P. F. et al. Deep reinforcement learning from human preferences. Adv. Neural Inf. Process. Syst. 30 (2017).

155. Ouyang, L. et al. Training language models to follow instructions with human feedback. Adv. Neural Inf. Process. Syst. 35, 27730–27744 (2022).

     Google Scholar 

156. Perez, E. et al. Discovering language model behaviors with model-written evaluations. Preprint at arXiv https://doi.org/10.48550/arXiv.2212.09251 (2022).

157. Claidière, N., Scott-Phillips, T. C. & Sperber, D. How Darwinian is cultural evolution? Phil. Trans. R. Soc. B 369, 20130368 (2014).

     Article 
     PubMed 
     PubMed Central 

     Google Scholar 

158. Blancke, S., Van Breusegem, F., De Jaeger, G., Braeckman, J. & Van Montagu, M. Fatal attraction: the intuitive appeal of GMO opposition. Trends Plant Sci. 20, 414–418 (2015).

     Article 
     CAS 
     PubMed 

     Google Scholar 

159. Miton, H. & Mercier, H. Cognitive obstacles to pro-vaccination beliefs. Trends Cogn. Sci. 19, 633–636 (2015).

     Article 
     PubMed 

     Google Scholar 

160. Poulsen, V. & DeDeo, S. Cognitive attractors and the cultural evolution of religion. In Proc. of the Annual Meeting of the Cognitive Science Society 45, 45 (2023).

161. Kirchenbauer, J. et al. A watermark for large language models. Preprint at arXiv https://doi.org/10.48550/arXiv.2301.10226 (2023).

162. Shumailov, I. et al. The curse of recursion: training on generated data makes models forget. Preprint at arXiv https://doi.org/10.48550/arXiv.2305.17493 (2023).

163. Veselovsky, V., Ribeiro, M. H. & West, R. Artificial artificial artificial intelligence: crowd workers widely use large language models for text production tasks. Preprint at arXiv https://doi.org/10.48550/arXiv.2306.07899 (2023).

164. Japkowicz, N. & Stephen, S. The class imbalance problem: a systematic study. Intell. Data Anal. 6, 429–449 (2002).

     Article 

     Google Scholar 

165. Kalish, M. L., Griffiths, T. L. & Lewandowsky, S. Iterated learning: intergenerational knowledge transmission reveals inductive biases. Psychon. Bull. Rev. 14, 288–294 (2007).

     Article 

     Google Scholar 

166. Axelrod, R. The dissemination of culture: a model with local convergence and global polarization. J. Confl. Resolut. 41, 203–226 (1997).

     Article 

     Google Scholar 

167. Touvron, H. et al. LLaMA: open and efficient foundation language models. Preprint at arXiv https://doi.org/10.48550/arXiv.2302.13971 (2023).

168. West, S. M., Whittaker, M. & Crawford, K. Discriminating Systems: Gender, Race and Power in AI (AI Now Institute, 2019).

169. Autor, D. H. Why are there still so many jobs? The history and future of workplace automation. J. Econ. Perspect. 29, 3–30 (2015).

     Article 

     Google Scholar 

170. Ayers, J. W. et al. Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Intern. Med. 183, 589–596 (2023).

     Article 
     PubMed 

     Google Scholar 

171. Sharma, A., Lin, I. W., Miner, A. S., Atkins, D. C. & Althoff, T. Human–AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support. Nat. Mach. Intell. 5, 46–57 (2023).

     Article 

     Google Scholar 

172. Perry, A. AI will never convey the essence of human empathy. Nat. Hum. Behav. https://doi.org/10.1038/s41562-023-01675-w (2023).

173. Weisz, E. & Zaki, J. Motivated empathy: a social neuroscience perspective. Curr. Opin. Psychol. 24, 67–71 (2018).

     Article 
     PubMed 

     Google Scholar 

174. Carroll, M., Hadfield-Menell, D., Russell, S. & Dragan, A. Estimating and penalizing preference shift in recommender systems. In Proc. 15th ACM Conference on Recommender Systems 661–667 (Association for Computing Machinery, 2021).

175. Bakshy, E., Messing, S. & Adamic, L. A. Exposure to ideologically diverse news and opinion on Facebook. Science 348, 1130–1132 (2015).

     Article 
     CAS 
     PubMed 

     Google Scholar 

176. Robertson, R. E. et al. Users choose to engage with more partisan news than they are exposed to on Google Search. Nature 618, 342–348 (2023).

     Article 
     CAS 
     PubMed 

     Google Scholar 

177. Art made by artificial intelligence is developing a style of its own. Economist (24 May 2023).

178. Obradovich, N. et al. Expanding the measurement of culture with a sample of two billion humans. J. R. Soc. Interface 19, 20220085 (2022).

     Article 
     PubMed 
     PubMed Central 

     Google Scholar 

179. Garg, N., Schiebinger, L., Jurafsky, D. & Zou, J. Word embeddings quantify 100 years of gender and ethnic stereotypes. Proc. Natl Acad. Sci. USA 115, E3635–E3644 (2018).

     Article 
     CAS 
     PubMed 
     PubMed Central 

     Google Scholar 

180. Karjus, A., Solà, M. C., Ohm, T., Ahnert, S. E. & Schich, M. Compression ensembles quantify aesthetic complexity and the evolution of visual art. EPJ Data Sci. 12, 21 (2023).

     Article 

     Google Scholar 

181. Santy, S., Liang, J. T., Bras, R. L., Reinecke, K. & Sap, M. NLPositionality: characterizing design biases of datasets and models. Preprint at arXiv https://doi.org/10.48550/arXiv.2306.01943 (2023).

182. Awad, E. et al. The Moral Machine experiment. Nature 563, 59–64 (2018).

     Article 
     CAS 
     PubMed 

     Google Scholar 

183. Brandt, F., Conitzer, V. & Endriss, U. in Multiagent Systems (ed. Weiss, G.) 213–284 (MIT Press, 2012).

184. Koster, R. et al. Human-centred mechanism design with Democratic AI. Nat. Hum. Behav. 6, 1398–1407 (2022).

     Article 
     PubMed 
     PubMed Central 

     Google Scholar 

185. Small, C. T. et al. Opportunities and risks of LLMs for scalable deliberation with Polis. Preprint at arXiv https://doi.org/10.48550/arXiv.2306.11932 (2023).

186. Rahwan, I. Society-in-the-loop: programming the algorithmic social contract. Ethics Inf. Technol. 20, 5–14 (2018).

     Article 

     Google Scholar 

187. Jernite, Y. et al. Data governance in the age of large-scale data-driven language technology. In 2022 ACM Conference on Fairness, Accountability, and Transparency 2206–2222 (Association for Computing Machinery, 2022); https://doi.org/10.1145/3531146.3534637

188. Laurençon, H. et al. The bigscience roots corpus: a 1.6 tb composite multilingual dataset. Adv. Neural Inf. Process. Syst. 35, 31809–31826 (2022).

     Google Scholar 

189. Ziegler, D. M. et al. Fine-tuning language models from human preferences. Preprint at arXiv https://doi.org/10.48550/arXiv.1909.08593 (2020).

190. Bai, Y. et al. Constitutional AI: harmlessness from AI feedback. Preprint at arXiv https://doi.org/10.48550/arXiv.2212.08073 (2022).

191. Bergstrom, C. T. & Lachmann, M. The Red King effect: when the slowest runner wins the coevolutionary race. Proc. Natl Acad. Sci. USA 100, 593–598 (2003).

     Article 
     CAS 
     PubMed 
     PubMed Central 

     Google Scholar 

192. Bostrom, N. Superintelligence: Paths, Dangers, Strategies (Oxford Univ. Press, 2014).

193. Wilson, D. S. et al. Multilevel cultural evolution: from new theory to practical applications. Proc. Natl Acad. Sci. USA 120, e2218222120 (2023).

     Article 
     CAS 
     PubMed 
     PubMed Central 

     Google Scholar 

194. DALL·E: Creating Images from Text, https://openai.com/research/dall-e (OpenAI, 2021).

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