Herbert, A. S. The Sciences of the Artificial (MIT Press, 1969).
Brynjolfsson, E. & Mitchell, T. What can machine learning do? Workforce implications. Science 358, 1530–1534 (2017).
Google Scholar
Agrawal, A., Gans, J. & Goldfarb, A. The Economics of Artificial Intelligence: An Agenda (Univ. Chicago Press, 2019).
Autor, D., Mindell, D. A. & Reynolds, E. B. The Work of the Future: Shaping Technology and Institutions (MIT Task Force, 2019).
Acemoglu, D., Autor, D., Hazell, J. & Restrepo, P. Artificial intelligence and jobs: evidence from online vacancies. J. Labor Econ. 40, S293–S340 (2022).
Google Scholar
Aghion, P., Jones, B. F. & Jones, C. I. in The Economics of Artificial Intelligence: An Agenda Ch. 9, 237–290 (Univ. Chicago Press, 2019).
Cockburn, I. M., Henderson, R. & Stern, S. in The Economics of Artificial Intelligence: An Agenda Ch. 4, 115–148 (Univ. Chicago Press, 2019).
Tomasev, N. et al. AI for social good: unlocking the opportunity for positive impact. Nat. Commun. 11, 2468 (2020).
Google Scholar
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).
Google Scholar
Frey, C. B. & Osborne, M. A. The future of employment: how susceptible are jobs to computerisation? Technol. Forecast. Soc. Change 114, 254–280 (2017).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Eshraghian, J. K. Human ownership of artificial creativity. Nat. Mach. Intell. 2, 157–160 (2020).
Google Scholar
Marcus, G. & Davis, E. Rebooting AI: Building Artificial Intelligence We Can Trust (Pantheon Books, 2019).
Liang, W. et al. Advances, challenges and opportunities in creating data for trustworthy AI. Nat. Mach. Intell. 4, 669–677 (2022).
Google Scholar
Bengio, Y. et al. Managing extreme AI risks amid rapid progress. Science 384, 842–845 (2024).
Google Scholar
Frank, M. R. et al. Toward understanding the impact of artificial intelligence on labor. Proc. Natl Acad. Sci. USA 116, 6531–6539 (2019).
Google Scholar
Agrawal, A., Gans, J. S. & Goldfarb, A. Artificial intelligence: the ambiguous labor market impact of automating prediction. J. Econ. Perspect. 33, 31–50 (2019).
Google Scholar
Koebis, N., Starke, C. & Rahwan, I. The promise and perils of using artificial intelligence to fight corruption. Nat. Mach. Intell. 4, 418–424 (2022).
Google Scholar
Brynjolfsson, E., Li, D. & Raymond, L. R. Generative AI at Work NBER Working Paper No. w31161 (National Bureau of Economic Research, 2023).
Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023).
Google Scholar
Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016).
Google Scholar
Geirhos, R. et al. Generalisation in humans and deep neural networks. In Proc. Advances in Neural Information Processing Systems 7538–7550 (MIT Press, 2018).
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).
Google Scholar
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).
Google Scholar
Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nat. Mach. Intell. 1, 517–521 (2019).
Google Scholar
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).
Google Scholar
Wurman, P. R. et al. Outracing champion Gran Turismo drivers with deep reinforcement learning. Nature 602, 223–228 (2022).
Google Scholar
Maslej, N. et al. The AI Index 2024 Annual Report (AI Index Steering Committee, Institute for Human-Centered AI, Stanford Univ., 2024).
Gil, Y., Greaves, M., Hendler, J. & Hirsh, H. Amplify scientific discovery with artificial intelligence. Science 346, 171–172 (2014).
Google Scholar
Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 47–60 (2023).
Google Scholar
Carleo, G. et al. Machine learning and the physical sciences. Rev. Mod. Phys. 91, 045002 (2019).
Google Scholar
Rahwan, I. et al. Machine behaviour. Nature 568, 477–486 (2019).
Google Scholar
Jimenez-Luna, J., Grisoni, F. & Schneider, G. Drug discovery with explainable artificial intelligence. Nat. Mach. Intell. 2, 573–584 (2020).
Google Scholar
Xu, Y. et al. Artificial intelligence: a powerful paradigm for scientific research. Innovation 2, 100179 (2021).
Google Scholar
Davies, A. et al. Advancing mathematics by guiding human intuition with AI. Nature 600, 70–74 (2021).
Google Scholar
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).
Google Scholar
Krenn, M. et al. On scientific understanding with artificial intelligence. Nat. Rev. Phys. 4, 761–769 (2022).
Google Scholar
Belikov, A. V., Rzhetsky, A. & Evans, J. A. Prediction of robust scientific facts from literature. Nat. Mach. Intell. 4, 445–454 (2022).
Google Scholar
Grossmann, I. et al. AI and the transformation of social science research. Science 380, 1108–1109 (2023).
Google Scholar
Groh, M. et al. Deep learning-aided decision support for diagnosis of skin disease across skin tones. Nat. Med. 30, 573–583 (2024).
Google Scholar
Bail, C. A. Can generative AI improve social science? Proc. Natl Acad. Sci. USA 121, e2314021121 (2024).
Google Scholar
Alvarez, A. et al. Science communication with generative AI. Nat. Hum. Behav. 8, 625–627 (2024).
Google Scholar
Senior, A. W. et al. Improved protein structure prediction using potentials from deep learning. Nature 577, 706–710 (2020).
Google Scholar
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Google Scholar
Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).
Google Scholar
Popova, M., Isayev, O. & Tropsha, A. Deep reinforcement learning for de novo drug design. Sci. Adv. 4, aap7885 (2018).
Google Scholar
Zhavoronkov, A. et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat. Biotechnol. 37, 1038–1040 (2019).
Google Scholar
Schneider, P. et al. Rethinking drug design in the artificial intelligence era. Nat. Rev. Drug Discov. 19, 353–364 (2020).
Google Scholar
Sadybekov, A. V. & Katritch, V. Computational approaches streamlining drug discovery. Nature 616, 673–685 (2023).
Google Scholar
Iten, R., Metger, T., Wilming, H., Del Rio, L. D. & Renner, R. Discovering physical concepts with neural networks. Phys. Rev. Lett. 124, 010508 (2020).
Google Scholar
Seif, A., Hafezi, M. & Jarzynski, C. Machine learning the thermodynamic arrow of time. Nat. Phys. 17, 105–113 (2021).
Google Scholar
Wu, T. & Tegmark, M. Toward an artificial intelligence physicist for unsupervised learning. Phys. Rev. E 100, 033311 (2019).
Google Scholar
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).
Google Scholar
Han, J., Jentzen, A. & Weinan, E. Solving high-dimensional partial differential equations using deep learning. Proc. Natl Acad. Sci. USA 115, 8505–8510 (2018).
Google Scholar
Raayoni, G. et al. Generating conjectures on fundamental constants with the Ramanujan Machine. Nature 590, 67–73 (2021).
Google Scholar
Degrave, J. et al. Magnetic control of tokamak plasmas through deep reinforcement learning. Nature 602, 414–419 (2022).
Google Scholar
Tshitoyan, V. et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571, 95–98 (2019).
Google Scholar
Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: generative models for matter engineering. Science 361, 360–365 (2018).
Google Scholar
Chen, C. et al. A critical review of machine learning of energy materials. Adv. Energy Mater. 10, 1903242 (2020).
Google Scholar
Merchant, A. et al. Scaling deep learning for materials discovery. Nature 624, 80–85 (2023).
Google Scholar
Zheng, S. et al. The AI Economist: taxation policy design via two-level deep multiagent reinforcement learning. Sci. Adv. 8, eabk2607 (2022).
Google Scholar
Koster, R. et al. Human-centred mechanism design with Democratic AI. Nat. Hum. Behav. 6, 1398–1407 (2022).
Google Scholar
Dunjko, V. & Briegel, H. J. Machine learning & artificial intelligence in the quantum domain: a review of recent progress. Rep. Prog. Phys. 81, 074001 (2018).
Google Scholar
Sturm, B. L. et al. Machine learning research that matters for music creation: a case study. J. N. Music Res. 48, 36–55 (2019).
Google Scholar
Rajkomar, A., Dean, J. & Kohane, I. Machine learning in medicine. N. Engl. J. Med. 380, 1347–1358 (2019).
Google Scholar
Ramesh, A. et al. Zero-shot text-to-image generation. In Proc. 38th International Conference on Machine Learning 8821–8831 (ICML, 2021).
Epstein, Z., Hertzmann, A. & the Investigators of Human Creativity. Art and the science of generative AI. Science 380, 1110–1111 (2023).
Google Scholar
Swanson, K. et al. Generative AI for designing and validating easily synthesizable and structurally novel antibiotics. Nat. Mach. Intell. 6, 338–353 (2024).
Google Scholar
Crafts, N. Artificial intelligence as a general-purpose technology: an historical perspective. Oxf. Rev. Econ. Policy 37, 521–536 (2021).
Google Scholar
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).
Caselli, F. & Coleman, W. J. Cross-country technology diffusion: the case of computers. Am. Econ. Rev. 91, 328–335 (2001).
Google Scholar
Comin, D. & Hobijn, B. An exploration of technology diffusion. Am. Econ. Rev. 100, 2031–2059 (2010).
Google Scholar
Zenil, H. et al. The future of fundamental science led by generative closed-loop artificial intelligence. Preprint at (2023).
Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019).
Google Scholar
Hidalgo, C. A., Orghian, D., Albo Canals, J., de Almeida, F. & Martín Cantero, N. How Humans Judge Machines (MIT Press, 2021).
Raisch, S. & Krakowski, S. Artificial intelligence and management: the automation–augmentation paradox. Acad. Manag. Rev. 46, 192–210 (2021).
Google Scholar
Fjelland, R. Why general artificial intelligence will not be realized. Humanit. Soc. Sci. Commun. 7, 10 (2020).
Google Scholar
Messeri, L. & Crockett, M. J. Artificial intelligence and illusions of understanding in scientific research. Nature 627, 49–58 (2024).
Google Scholar
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).
Webb, M. The Impact of Artificial Intelligence on the Labor Market SSRN 3482150 (Social Science Research Network, 2019).
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).
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).
Google Scholar
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).
Google Scholar
Wu, L., Hitt, L. & Lou, B. Data analytics, innovation, and firm productivity. Manag. Sci. 66, 2017–2039 (2020).
Google Scholar
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).
Google Scholar
Wang, D. & Barabási, A.-L. The Science of Science (Cambridge Univ. Press, 2021).
Fortunato, S. et al. Science of science. Science 359, eaao0185 (2018).
Google Scholar
Zeng, A. et al. The science of science: from the perspective of complex systems. Phys. Rep. 714, 1–73 (2017).
Google Scholar
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).
Google Scholar
Miao, L. et al. The latent structure of global scientific development. Nat. Hum. Behav. 6, 1206–1217 (2022).
Google Scholar
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).
Google Scholar
Sourati, J. & Evans, J. A. Accelerating science with human-aware artificial intelligence. Nat. Hum. Behav. 7, 1682–1696 (2023).
Google Scholar
Murray, D. et al. Unsupervised embedding of trajectories captures the latent structure of scientific migration. Proc. Natl Acad. Sci. USA 120, e2305414120 (2023).
Google Scholar
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).
Google Scholar
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).
World Intellectual Property Organization (WIPO). WIPO Technology Trends 2019—Artificial Intelligence (WIPO, 2019).
Nivre, J. & Nilsson, J. Pseudo-projective dependency parsing. In Proc. 43rd Annual Meeting of the Association for Computational Linguistics 99–106 (ACL, 2005).
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).
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).
Service, R. Science’s 2021 Breakthrough of the Year: protein structures for all. Science (2021).
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).
Google Scholar
Wuchty, S., Jones, B. F. & Uzzi, B. The increasing dominance of teams in production of knowledge. Science 316, 1036–1039 (2007).
Google Scholar
Wu, L., Wang, D. & Evans, J. A. Large teams develop and small teams disrupt science and technology. Nature 566, 378–382 (2019).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Young, E., Wajcman, J. & Sprejer, L. Where Are the Women? Mapping the Gender Job Gap in AI (The Alan Turing Institute, 2021).
Xie, Y. & Shauman, K. A. Women in Science: Career Processes and Outcomes (Harvard Univ. Press, 2003).
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).
Google Scholar
Ginther, D. K. et al. Race, ethnicity, and NIH research awards. Science 333, 1015–1019 (2011).
Google Scholar
Larivière, V., Ni, C., Gingras, Y., Cronin, B. & Sugimoto, C. R. Bibliometrics: global gender disparities in science. Nature 504, 211–213 (2013).
Google Scholar
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).
Google Scholar
The National Network for Critical Technology Assessment (NNCTA). Securing America’s Future: A Framework for Critical Technology Assessment (NNCTA, 2023).
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).
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).
Bommasani, R. et al. On the opportunities and risks of foundation models. Preprint at (2021).
Wei, J. et al. Emergent abilities of large language models. Preprint at (2022).
Moor, M. et al. Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Fawzi, A. et al. Discovering faster matrix multiplication algorithms with reinforcement learning. Nature 610, 47–53 (2022).
Google Scholar
Jobin, A., Ienca, M. & Vayena, E. The global landscape of AI ethics guidelines. Nat. Mach. Intell. 1, 389–399 (2019).
Google Scholar
Arrieta, A. B. et al. Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020).
Google Scholar
Lenharo, M. An AI revolution is brewing in medicine. What will it look like? Nature 622, 686–688 (2023).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Ahmadpoor, M. & Jones, B. F. The dual frontier: patented inventions and prior scientific advance. Science 357, 583–587 (2017).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Microsoft Academic. Microsoft Academic Graph. Zenodo (2022).
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).
Google Scholar
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