1. Herbert, A. S. The Sciences of the Artificial (MIT Press, 1969).

2. Brynjolfsson, E. & Mitchell, T. What can machine learning do? Workforce implications. Science 358, 1530–1534 (2017).

   Article 
   CAS 
   PubMed 

   Google Scholar 

3. Agrawal, A., Gans, J. & Goldfarb, A. The Economics of Artificial Intelligence: An Agenda (Univ. Chicago Press, 2019).

4. Autor, D., Mindell, D. A. & Reynolds, E. B. The Work of the Future: Shaping Technology and Institutions (MIT Task Force, 2019).

5. Acemoglu, D., Autor, D., Hazell, J. & Restrepo, P. Artificial intelligence and jobs: evidence from online vacancies. J. Labor Econ. 40, S293–S340 (2022).

   Article 

   Google Scholar 

6. Aghion, P., Jones, B. F. & Jones, C. I. in The Economics of Artificial Intelligence: An Agenda Ch. 9, 237–290 (Univ. Chicago Press, 2019).

7. Cockburn, I. M., Henderson, R. & Stern, S. in The Economics of Artificial Intelligence: An Agenda Ch. 4, 115–148 (Univ. Chicago Press, 2019).

8. Tomasev, N. et al. AI for social good: unlocking the opportunity for positive impact. Nat. Commun. 11, 2468 (2020).

   Article 
   CAS 
   PubMed 
   PubMed Central 

   Google Scholar 

9. Dwivedi, Y. K. et al. Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inf. Manag. 57, 101994 (2021).

   Article 

   Google Scholar 

10. Frey, C. B. & Osborne, M. A. The future of employment: how susceptible are jobs to computerisation? Technol. Forecast. Soc. Change 114, 254–280 (2017).

    Article 

    Google Scholar 

11. Acemoglu, D. & Restrepo, P. The race between man and machine: implications of technology for growth, factor shares, and employment. Am. Econ. Rev. 108, 1488–1542 (2018).

    Article 

    Google Scholar 

12. Khan, H. N., Hounshell, D. A. & Fuchs, E. R. H. Science and research policy at the end of Moore’s law. Nat. Electron. 1, 14–21 (2018).

    Article 

    Google Scholar 

13. Iansiti, M. & Lakhani, K. R. Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World (Harvard Business Press, 2020).

14. Eshraghian, J. K. Human ownership of artificial creativity. Nat. Mach. Intell. 2, 157–160 (2020).

    Article 

    Google Scholar 

15. Marcus, G. & Davis, E. Rebooting AI: Building Artificial Intelligence We Can Trust (Pantheon Books, 2019).

16. Liang, W. et al. Advances, challenges and opportunities in creating data for trustworthy AI. Nat. Mach. Intell. 4, 669–677 (2022).

    Article 

    Google Scholar 

17. Bengio, Y. et al. Managing extreme AI risks amid rapid progress. Science 384, 842–845 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar 

18. Frank, M. R. et al. Toward understanding the impact of artificial intelligence on labor. Proc. Natl Acad. Sci. USA 116, 6531–6539 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

19. Agrawal, A., Gans, J. S. & Goldfarb, A. Artificial intelligence: the ambiguous labor market impact of automating prediction. J. Econ. Perspect. 33, 31–50 (2019).

    Article 

    Google Scholar 

20. Koebis, N., Starke, C. & Rahwan, I. The promise and perils of using artificial intelligence to fight corruption. Nat. Mach. Intell. 4, 418–424 (2022).

    Article 

    Google Scholar 

21. Brynjolfsson, E., Li, D. & Raymond, L. R. Generative AI at Work NBER Working Paper No. w31161 (National Bureau of Economic Research, 2023).

22. Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

23. Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar 

24. Geirhos, R. et al. Generalisation in humans and deep neural networks. In Proc. Advances in Neural Information Processing Systems 7538–7550 (MIT Press, 2018).

25. Grace, K., Salvatier, J., Dafoe, A., Zhang, B. & Evans, O. When will AI exceed human performance? Evidence from AI experts. J. Artif. Intell. Res. 62, 729–754 (2018).

    Article 

    Google Scholar 

26. Liu, X. et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit. Health 1, e271–e297 (2019).

    Article 
    PubMed 

    Google Scholar 

27. Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nat. Mach. Intell. 1, 517–521 (2019).

    Article 

    Google Scholar 

28. Yang, Y., Youyou, W. & Uzzi, B. Estimating the deep replicability of scientific findings using human and artificial intelligence. Proc. Natl Acad. Sci. USA 117, 10762–10768 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

29. Wurman, P. R. et al. Outracing champion Gran Turismo drivers with deep reinforcement learning. Nature 602, 223–228 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar 

30. Maslej, N. et al. The AI Index 2024 Annual Report (AI Index Steering Committee, Institute for Human-Centered AI, Stanford Univ., 2024).

31. Gil, Y., Greaves, M., Hendler, J. & Hirsh, H. Amplify scientific discovery with artificial intelligence. Science 346, 171–172 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar 

32. Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 47–60 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

33. Carleo, G. et al. Machine learning and the physical sciences. Rev. Mod. Phys. 91, 045002 (2019).

    Article 
    CAS 

    Google Scholar 

34. Rahwan, I. et al. Machine behaviour. Nature 568, 477–486 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar 

35. Jimenez-Luna, J., Grisoni, F. & Schneider, G. Drug discovery with explainable artificial intelligence. Nat. Mach. Intell. 2, 573–584 (2020).

    Article 

    Google Scholar 

36. Xu, Y. et al. Artificial intelligence: a powerful paradigm for scientific research. Innovation 2, 100179 (2021).

    PubMed 
    PubMed Central 

    Google Scholar 

37. Davies, A. et al. Advancing mathematics by guiding human intuition with AI. Nature 600, 70–74 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

38. Peng, H., Ke, Q., Budak, C., Romero, D. M. & Ahn, Y.-Y. Neural embeddings of scholarly periodicals reveal complex disciplinary organizations. Sci. Adv. 7, eabb9004 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

39. Krenn, M. et al. On scientific understanding with artificial intelligence. Nat. Rev. Phys. 4, 761–769 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

40. Belikov, A. V., Rzhetsky, A. & Evans, J. A. Prediction of robust scientific facts from literature. Nat. Mach. Intell. 4, 445–454 (2022).

    Article 

    Google Scholar 

41. Grossmann, I. et al. AI and the transformation of social science research. Science 380, 1108–1109 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

42. Groh, M. et al. Deep learning-aided decision support for diagnosis of skin disease across skin tones. Nat. Med. 30, 573–583 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

43. Bail, C. A. Can generative AI improve social science? Proc. Natl Acad. Sci. USA 121, e2314021121 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

44. Alvarez, A. et al. Science communication with generative AI. Nat. Hum. Behav. 8, 625–627 (2024).

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    CAS 
    PubMed 

    Google Scholar 

46. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

47. Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

48. Popova, M., Isayev, O. & Tropsha, A. Deep reinforcement learning for de novo drug design. Sci. Adv. 4, aap7885 (2018).

    Article 

    Google Scholar 

49. Zhavoronkov, A. et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat. Biotechnol. 37, 1038–1040 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar 

50. Schneider, P. et al. Rethinking drug design in the artificial intelligence era. Nat. Rev. Drug Discov. 19, 353–364 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar 

51. Sadybekov, A. V. & Katritch, V. Computational approaches streamlining drug discovery. Nature 616, 673–685 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

52. Iten, R., Metger, T., Wilming, H., Del Rio, L. D. & Renner, R. Discovering physical concepts with neural networks. Phys. Rev. Lett. 124, 010508 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar 

53. Seif, A., Hafezi, M. & Jarzynski, C. Machine learning the thermodynamic arrow of time. Nat. Phys. 17, 105–113 (2021).

    Article 
    CAS 

    Google Scholar 

54. Wu, T. & Tegmark, M. Toward an artificial intelligence physicist for unsupervised learning. Phys. Rev. E 100, 033311 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar 

55. Lu, L., Jin, P., Pang, G., Zhang, Z. & Karniadakis, G. E. Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nat. Mach. Intell. 3, 218–229 (2021).

    Article 

    Google Scholar 

56. Han, J., Jentzen, A. & Weinan, E. Solving high-dimensional partial differential equations using deep learning. Proc. Natl Acad. Sci. USA 115, 8505–8510 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

57. Raayoni, G. et al. Generating conjectures on fundamental constants with the Ramanujan Machine. Nature 590, 67–73 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar 

58. Degrave, J. et al. Magnetic control of tokamak plasmas through deep reinforcement learning. Nature 602, 414–419 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

59. Tshitoyan, V. et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571, 95–98 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar 

60. Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: generative models for matter engineering. Science 361, 360–365 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar 

61. Chen, C. et al. A critical review of machine learning of energy materials. Adv. Energy Mater. 10, 1903242 (2020).

    Article 
    CAS 

    Google Scholar 

62. Merchant, A. et al. Scaling deep learning for materials discovery. Nature 624, 80–85 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

63. Zheng, S. et al. The AI Economist: taxation policy design via two-level deep multiagent reinforcement learning. Sci. Adv. 8, eabk2607 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

65. Dunjko, V. & Briegel, H. J. Machine learning & artificial intelligence in the quantum domain: a review of recent progress. Rep. Prog. Phys. 81, 074001 (2018).

    Article 
    PubMed 

    Google Scholar 

66. Sturm, B. L. et al. Machine learning research that matters for music creation: a case study. J. N. Music Res. 48, 36–55 (2019).

    Article 

    Google Scholar 

67. Rajkomar, A., Dean, J. & Kohane, I. Machine learning in medicine. N. Engl. J. Med. 380, 1347–1358 (2019).

    Article 
    PubMed 

    Google Scholar 

68. Ramesh, A. et al. Zero-shot text-to-image generation. In Proc. 38th International Conference on Machine Learning 8821–8831 (ICML, 2021).

69. 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 

70. Swanson, K. et al. Generative AI for designing and validating easily synthesizable and structurally novel antibiotics. Nat. Mach. Intell. 6, 338–353 (2024).

    Article 

    Google Scholar 

71. Crafts, N. Artificial intelligence as a general-purpose technology: an historical perspective. Oxf. Rev. Econ. Policy 37, 521–536 (2021).

    Article 

    Google Scholar 

72. Bloom, N., Hassan, T. A., Kalyani, A., Lerner, J. & Tahoun, A. The Diffusion of New Technologies NBER Working Paper No. w28999 (National Bureau of Economic Research, 2021).

73. Caselli, F. & Coleman, W. J. Cross-country technology diffusion: the case of computers. Am. Econ. Rev. 91, 328–335 (2001).

    Article 

    Google Scholar 

74. Comin, D. & Hobijn, B. An exploration of technology diffusion. Am. Econ. Rev. 100, 2031–2059 (2010).

    Article 

    Google Scholar 

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

76. Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar 

77. Hidalgo, C. A., Orghian, D., Albo Canals, J., de Almeida, F. & Martín Cantero, N. How Humans Judge Machines (MIT Press, 2021).

78. Raisch, S. & Krakowski, S. Artificial intelligence and management: the automation–augmentation paradox. Acad. Manag. Rev. 46, 192–210 (2021).

    Article 

    Google Scholar 

79. Fjelland, R. Why general artificial intelligence will not be realized. Humanit. Soc. Sci. Commun. 7, 10 (2020).

    Article 

    Google Scholar 

80. Messeri, L. & Crockett, M. J. Artificial intelligence and illusions of understanding in scientific research. Nature 627, 49–58 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar 

81. Kanitscheider, I. & Fiete, I. Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems. In Proc. Advances in Neural Information Processing Systems 4529–4538 (MIT Press, 2017).

82. Webb, M. The Impact of Artificial Intelligence on the Labor Market SSRN 3482150 (Social Science Research Network, 2019).

83. Kogan, L., Papanikolaou, D., Schmidt, L. D. & Seegmiller, B. Technology, Vintage-Specific Human Capital, and Labor Displacement: Evidence from Linking Patents with Occupations NBER Working Paper No. w29552 (National Bureau of Economic Research, 2022).

84. Atalay, E., Phongthiengtham, P., Sotelo, S. & Tannenbaum, D. The evolution of work in the United States. Am. Econ. J. Appl. Econ. 12, 1–34 (2020).

    Article 

    Google Scholar 

85. Felten, E. W., Raj, M. & Seamans, R. A method to link advances in artificial intelligence to occupational abilities. AEA Pap. Proc. 108, 54–57 (2018).

    Article 

    Google Scholar 

86. Wu, L., Hitt, L. & Lou, B. Data analytics, innovation, and firm productivity. Manag. Sci. 66, 2017–2039 (2020).

    Article 

    Google Scholar 

87. Brynjolfsson, E., Mitchell, T. & Rock, D. What can machines learn, and what does it mean for occupations and the economy? AEA Pap. Proc. 108, 43–47 (2018).

    Article 

    Google Scholar 

88. Wang, D. & Barabási, A.-L. The Science of Science (Cambridge Univ. Press, 2021).

89. Fortunato, S. et al. Science of science. Science 359, eaao0185 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

90. Zeng, A. et al. The science of science: from the perspective of complex systems. Phys. Rep. 714, 1–73 (2017).

    Article 

    Google Scholar 

91. Frank, M. R., Wang, D., Cebrian, M. & Rahwan, I. The evolution of citation graphs in artificial intelligence research. Nat. Mach. Intell. 1, 79–85 (2019).

    Article 

    Google Scholar 

92. Miao, L. et al. The latent structure of global scientific development. Nat. Hum. Behav. 6, 1206–1217 (2022).

    Article 
    PubMed 

    Google Scholar 

93. Liu, L., Jones, B. F., Uzzi, B. & Wang, D. Data, measurement and empirical methods in the science of science. Nat. Hum. Behav. 7, 1046–1058 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

94. Sourati, J. & Evans, J. A. Accelerating science with human-aware artificial intelligence. Nat. Hum. Behav. 7, 1682–1696 (2023).

    Article 
    PubMed 

    Google Scholar 

95. Murray, D. et al. Unsupervised embedding of trajectories captures the latent structure of scientific migration. Proc. Natl Acad. Sci. USA 120, e2305414120 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

96. Krenn, M. et al. Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network. Nat. Mach. Intell. 5, 1326–1335 (2023).

    Article 

    Google Scholar 

97. Sinha, A. et al. An overview of Microsoft Academic Service (MAS) and applications. In Proc. 24th International Conference on World Wide Web 243–246 (WWW, 2015).

98. World Intellectual Property Organization (WIPO). WIPO Technology Trends 2019—Artificial Intelligence (WIPO, 2019).

99. Nivre, J. & Nilsson, J. Pseudo-projective dependency parsing. In Proc. 43rd Annual Meeting of the Association for Computational Linguistics 99–106 (ACL, 2005).

100. Honnibal, M. & Johnson, M. An improved non-monotonic transition system for dependency parsing. In Proc. 2015 Conference on Empirical Methods in Natural Language Processing 1373–1378 (ACL, 2015).

101. Benetka, J. R., Krumm, J. & Bennett, P. N. Understanding context for tasks and activities. In Proc. 2019 Conference on Human Information Interaction and Retrieval 133–142 (ACM, 2019).

102. Service, R. Science’s 2021 Breakthrough of the Year: protein structures for all. Science https://www.science.org/content/article/breakthrough-2021 (2021).

103. Börner, K. et al. Skill discrepancies between research, education, and jobs reveal the critical need to supply soft skills for the data economy. Proc. Natl Acad. Sci. USA 115, 12630–12637 (2018).

     Article 
     PubMed 
     PubMed Central 

     Google Scholar 

104. Wuchty, S., Jones, B. F. & Uzzi, B. The increasing dominance of teams in production of knowledge. Science 316, 1036–1039 (2007).

     Article 
     CAS 
     PubMed 

     Google Scholar 

105. Wu, L., Wang, D. & Evans, J. A. Large teams develop and small teams disrupt science and technology. Nature 566, 378–382 (2019).

     Article 
     CAS 
     PubMed 

     Google Scholar 

106. Littmann, M. et al. Validity of machine learning in biology and medicine increased through collaborations across fields of expertise. Nat. Mach. Intell. 2, 18–24 (2020).

     Article 

     Google Scholar 

107. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K. & Galstyan, A. A survey on bias and fairness in machine learning. ACM Comput. Surv. 54, 1–35 (2021).

     Article 

     Google Scholar 

108. Young, E., Wajcman, J. & Sprejer, L. Where Are the Women? Mapping the Gender Job Gap in AI (The Alan Turing Institute, 2021).

109. Xie, Y. & Shauman, K. A. Women in Science: Career Processes and Outcomes (Harvard Univ. Press, 2003).

110. Hoppe, T. A. et al. Topic choice contributes to the lower rate of NIH awards to African-American/black scientists. Sci. Adv. 5, eaaw7238 (2019).

     Article 
     CAS 
     PubMed 
     PubMed Central 

     Google Scholar 

111. Ginther, D. K. et al. Race, ethnicity, and NIH research awards. Science 333, 1015–1019 (2011).

     Article 
     CAS 
     PubMed 
     PubMed Central 

     Google Scholar 

112. Larivière, V., Ni, C., Gingras, Y., Cronin, B. & Sugimoto, C. R. Bibliometrics: global gender disparities in science. Nature 504, 211–213 (2013).

     Article 
     PubMed 

     Google Scholar 

113. Huang, J., Gates, A. J., Sinatra, R. & Barabási, A.-L. Historical comparison of gender inequality in scientific careers across countries and disciplines. Proc. Natl Acad. Sci. USA 117, 4609–4616 (2020).

     Article 
     CAS 
     PubMed 
     PubMed Central 

     Google Scholar 

114. The National Network for Critical Technology Assessment (NNCTA). Securing America’s Future: A Framework for Critical Technology Assessment (NNCTA, 2023).

115. Cachola, I., Lo, K., Cohan, A. & Weld, D. S. TLDR: extreme summarization of scientific documents. In Proc. 2020 Conference on Empirical Methods in Natural Language Processing 4766–4777 (ACL, 2020).

116. Lew, A., Agrawal, M., Sontag, D. & Mansinghka, V. PClean: Bayesian data cleaning at scale with domain-specific probabilistic programming. In Proc. 24th International Conference on Artificial Intelligence and Statistics 1927–1935 (JMLR, 2021).

117. Bommasani, R. et al. On the opportunities and risks of foundation models. Preprint at https://arxiv.org/abs/2108.07258 (2021).

118. Wei, J. et al. Emergent abilities of large language models. Preprint at https://arxiv.org/abs/2206.07682 (2022).

119. Moor, M. et al. Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023).

     Article 
     CAS 
     PubMed 

     Google Scholar 

120. Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual-language foundation model for pathology image analysis using medical Twitter. Nat. Med. 29, 2307–2316 (2023).

     Article 
     CAS 
     PubMed 

     Google Scholar 

121. Goldfarb, A., Taska, B. & Teodoridis, F. Could machine learning be a general purpose technology? A comparison of emerging technologies using data from online job postings. Res. Policy 52, 104653 (2023).

     Article 

     Google Scholar 

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

     Article 
     CAS 
     PubMed 
     PubMed Central 

     Google Scholar 

123. Jobin, A., Ienca, M. & Vayena, E. The global landscape of AI ethics guidelines. Nat. Mach. Intell. 1, 389–399 (2019).

     Article 

     Google Scholar 

124. Arrieta, A. B. et al. Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020).

     Article 

     Google Scholar 

125. Lenharo, M. An AI revolution is brewing in medicine. What will it look like? Nature 622, 686–688 (2023).

     Article 
     CAS 
     PubMed 

     Google Scholar 

126. Bockting, C. L., van Dis, E. A. M., van Rooij, R., Zuidema, W. & Bollen, J. Living guidelines for generative AI—why scientists must oversee its use. Nature 622, 693–696 (2023).

     Article 
     CAS 
     PubMed 

     Google Scholar 

127. Schwartz, I. S., Link, K. E., Daneshjou, R. & Cortes-Penfield, N. Black box warning: large language models and the future of infectious diseases consultation. Clin. Infect. Dis. 78, 860–866 (2024).

     Article 
     PubMed 

     Google Scholar 

128. Ahmadpoor, M. & Jones, B. F. The dual frontier: patented inventions and prior scientific advance. Science 357, 583–587 (2017).

     Article 
     CAS 
     PubMed 

     Google Scholar 

129. Mukherjee, S., Romero, D. M., Jones, B. & Uzzi, B. The nearly universal link between the age of past knowledge and tomorrow’s breakthroughs in science and technology: the hotspot. Sci. Adv. 3, e1601315 (2017).

     Article 
     PubMed 
     PubMed Central 

     Google Scholar 

130. Yin, Y., Dong, Y., Wang, K., Wang, D. & Jones, B. F. Public use and public funding of science. Nat. Hum. Behav. 6, 1344–1350 (2022).

     Article 
     PubMed 

     Google Scholar 

131. Microsoft Academic. Microsoft Academic Graph. Zenodo https://doi.org/10.5281/zenodo.6511057 (2022).

132. Lin, Z., Yin, Y., Liu, L. & Wang, D. SciSciNet: a large-scale open data lake for the science of science research. Sci. Data 10, 315 (2023).

     Article 
     PubMed 
     PubMed Central 

     Google Scholar 

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