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

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

  • 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
     

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

    Article 

    Google Scholar
     

  • 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
     

  • 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
     

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

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

    Article 

    Google Scholar
     

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

  • 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
     

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

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

  • 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
     

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

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

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

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

    Article 

    Google Scholar
     

  • 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).

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

  • 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).

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

  • 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
     

  • 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
     

  • 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).

  • 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
     

  • 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
     

  • 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).

  • 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
     

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

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

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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
     

  • 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).

  • 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
     

  • 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
     

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

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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
     

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

    Article 

    Google Scholar
     

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

  • 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).

  • 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).

  • 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).

  • 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
     

  • 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

  • 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).

  • 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

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

    Article 

    Google Scholar
     

  • 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).

  • 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
     

  • 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
     

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

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

  • 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
     

  • 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
     

  • 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
     

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

    Article 

    Google Scholar
     

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

    Article 

    Google Scholar
     

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

    Article 
    PubMed 

    Google Scholar
     

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

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

  • 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).

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

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

    Article 

    Google Scholar
     

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

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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).

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

    Article 
    CAS 
    PubMed 

    Google Scholar
     

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

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

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

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

  • 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).

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


    Google Scholar
     

  • 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
     

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

    Article 

    Google Scholar
     

  • 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
     

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

  • 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
     

  • 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).

  • 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
     

  • 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
     

  • 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).

  • 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
     

  • 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).

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

    Article 

    Google Scholar
     

  • 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).

  • 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).

  • 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
     

  • 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).

  • 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
     

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

  • 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
     

  • 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).

  • 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
     

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

    Article 

    Google Scholar
     

  • 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
     

  • 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).

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

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

    Article 

    Google Scholar
     

  • 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
     

  • 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
     

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

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

    Article 

    Google Scholar
     

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

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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
     

  • 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).

  • 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
     

  • 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
     

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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
     

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

    Article 
    PubMed 

    Google Scholar
     

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

    Article 
    PubMed 

    Google Scholar
     

  • 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
     

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

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

  • 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
     

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

    Article 
    PubMed 

    Google Scholar
     

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

    Article 

    Google Scholar
     

  • 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
     

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


    Google Scholar
     

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

  • 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).

  • 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

  • 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

  • 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).

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

    Article 

    Google Scholar
     

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

    Article 
    CAS 
    PubMed 

    Google Scholar
     

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

    Article 

    Google Scholar
     

  • 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
     

  • 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
     

  • 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).

  • 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).

  • 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).

  • 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
     

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


    Google Scholar
     

  • 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
     

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

    Article 

    Google Scholar
     

  • 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
     

  • 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).

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

    Article 

    Google Scholar
     

  • 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).

  • 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).

  • 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).

  • 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
     

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

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

  • 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).

  • 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).

  • 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).

  • 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).

  • 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).

  • 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).

  • 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).

  • 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
     

  • 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
     

  • 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
     

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

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


    Google Scholar
     

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

  • 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
     

  • 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
     

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

    Article 
    PubMed 

    Google Scholar
     

  • 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).

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

  • 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).

  • 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).

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

    Article 

    Google Scholar
     

  • 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
     

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

    Article 

    Google Scholar
     

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

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

  • 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
     

  • 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
     

  • 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
     

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

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

    Article 
    PubMed 

    Google Scholar
     

  • 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).

  • 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
     

  • 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
     

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

  • 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
     

  • 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
     

  • 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
     

  • 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).

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

    Article 
    CAS 
    PubMed 

    Google Scholar
     

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

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

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

    Article 

    Google Scholar
     

  • 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

  • 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
     

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

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

  • 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
     

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

  • 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
     

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



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