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Engineering >> 2020, Volume 6, Issue 3 doi: 10.1016/j.eng.2020.01.009

The Next Breakthroughs of Artificial Intelligence: The Interdisciplinary Nature of AI

a College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
b School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
c Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China
d Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China

Available online: 2020-01-28

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References

[ 1 ] Fluri J, Kacprzak T, Lucchi A, Refregier A, Amara A, Hofmann T, et al. Cosmological constraints with deep learning from KiDS-450 weak lensing maps. 2019. arXiv: 1906.03156.

[ 2 ] Chien S, Wagstaff KL. Robotic space exploration agents. Sci Robo 2017;2(7): eaan4831. link1

[ 3 ] Anthes G. Lifelong learning in artificial neural networks. Commun ACM 2019;62(6):13–5. link1

[ 4 ] Goues CL, Pradel M, Roychoudhury A. Automated program repair. Commun ACM 2019;62(12):56–65. link1

[ 5 ] Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med 2019;25(1):24–9. link1

[ 6 ] Washburn JD, Mejia-Guerra MK, Ramstein G, Kremling KA, Valluru R, Buckler ES, et al. Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence. Proc Natl Acad Sci 2019;116 (12):5542–9. link1

[ 7 ] Eraslan G, Avsec Zˇ, Gagneur J, Theis FJ. Deep learning: new computational modelling techniques for genomics. Nat Rev Genet 2019;20(7):389–403. link1

[ 8 ] Corea F. The convergence of AI and blockchain. In: Corea F, editor. Applied artificial intelligence: where AI can be used in business. Cham: Springer; 2018. p. 1–26. link1

[ 9 ] Zhou Q, Tang P, Liu S, Pan J, Yan Q, Zhang SC. Learning atoms for materials discovery. Proc Natl Acad Sci 2018;115(28):E6411–7. link1

[10] Segler MHS, Preuss M, Waller MP. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 2018;555(7698):604–10. link1

[11] Luo X. Subwavelength artificial structures: opening a new era for engineering optics. Adv Mater 2019;31(4):1804680. link1

[12] Lin X, Rivenson Y, Yardimci NT, Veli M, Luo Y, Jarrahi M, et al. All-optical machine learning using diffractive deep neural networks. Science 2018;361 (6406):1004–8. link1

[13] Yang Q, Liu Y, Chen T, Tong Y. Federated machine learning: concept and applications. ACM Trans Intellt Syst Technol TIST 2019;10(2):12. link1

[14] McMahan HB, Moore E, Ramage D, Hampson S, Arcas BAy. Communicationefficient learning of deep networks from decentralized data. 2017. arXiv:1602.05629. link1

[15] Liu Y, Chen T, Yang Q. Secure federated transfer learning. 2019. arXiv:1812.03337.

[16] Bonawitz K, Ivanov V, Kreuter B, Marcedone A, McMahan HB, Patel S, et al. Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS’17); 2017 Oct 30–Nov 3; Dallas, TX, USA; 2017. p. 1175–91.

[17] Yang Q, Liu Y, Cheng Y, Kang Y, Chen T, Yu H. Federated learning. Williston, VT, USA: Morgan & Claypool; 2019.

[18] Pan Y. Heading toward artificial intelligence 2.0. Engineering 2016;2 (4):409–13. link1

[19] Pan Y. Special issue on artificial intelligence 2.0. Front Inform Technol Electron Eng 2017;18(1):1–2. link1

[20] Pan Y. Special issue on artificial intelligence 2.0: theories and applications. Front Inform Technol Electron Eng 2018;19(1):1–2. link1

[21] Zhuang Y, Wu F, Chen C, Pan Y. Challenges and opportunities: from big data to knowledge in AI 2.0. Front Inform Technol Electron Eng 2017;18(1):3–14. link1

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