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Bridging the Gap: The Integration of Sustainable Technologies in Artificial General Intelligence



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


    Google Scholar
     

  • IDC.: Worldwide Artificial Intelligence Spending Guide. International Data Corporation (2020)


    Google Scholar
     

  • Goertzel, B.: Artificial general intelligence: concept, state of the art, and future prospects. J. Artif. Intell. Res. 49, 1–48 (2014)


    Google Scholar
     

  • Sutskever, I.: The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence. https://doi.org/10.48550/arXiv.2002.06177 (2020)

  • Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Pearson (2016)


    Google Scholar
     

  • Vaswani, A., et al.: Attention Is All You Need. https://doi.org/10.48550/arXiv.1706.03762 (2017)

  • Brown, T.B., et al.: Language Models are Few-Shot Learners. https://doi.org/10.48550/arXiv.2005.14165 (2020)

  • Jones, N.: How to stop data centers from gobbling up the world’s electricity. Nature 561, 163–166 (2018)

    Article 

    Google Scholar
     

  • Andrae, A.S.: Global ICT energy use and greenhouse gas emissions 2010–2030. Environ. Sci. Technol. 54(22), 14025–14033 (2020)


    Google Scholar
     

  • Belkhir, L., Elmeligi, A.: Assessing ICT global emissions footprint: trends to 2040 & recommendations. J. Clean. Prod. 177, 448–463 (2018)

    Article 

    Google Scholar
     

  • OpenAI.: AI and Compute. OpenAI Blog (2019)


    Google Scholar
     

  • Sustainable, A.G.I.: Environmental impact of Artificial General Intelligence. J. Sustain. Res. 2(4), 255–267 (2019)


    Google Scholar
     

  • Greenpeace.: Clicking Clean: Who is Winning the Race to Build A Green Internet. Greenpeace Inc. (2017)


    Google Scholar
     

  • United Nations.: Transforming our World: The 2030 Agenda for Sustainable Development. United Nations (2015)


    Google Scholar
     

  • McCarthy, J., Minsky, M., Rochester, N., Shannon, C.E.: A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence (1955)


    Google Scholar
     

  • Newell, A., Simon, H.A.: Computer science as empirical inquiry: symbols and search. Commun. ACM 19(3), 113–126 (1976)

    Article 
    MathSciNet 

    Google Scholar
     

  • Turing, A.M.: Computing machinery and intelligence. Mind 59(236), 433–460 (1950)

    Article 
    MathSciNet 

    Google Scholar
     

  • Minsky, M.: Steps toward artificial intelligence. Proc. IRE 49(1), 8–30 (1961)

    Article 
    MathSciNet 

    Google Scholar
     

  • Mitchell, T.M.: Machine Learning. McGraw Hill (1997)


    Google Scholar
     

  • Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    Article 

    Google Scholar
     

  • Legg, S., Hutter, M.: A Collection of definitions of intelligence. In: Advances in Artificial General Intelligence, pp. 17–24 (2007)


    Google Scholar
     

  • Halevy, A., Norvig, P., Pereira, F.: The unreasonable effectiveness of data. IEEE Intell. Syst. 24(2), 8–12 (2009)

    Article 

    Google Scholar
     

  • Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)


    Google Scholar
     

  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)


    Google Scholar
     

  • Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016)


    Google Scholar
     

  • Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, pp. 4171–4186 (2019)


    Google Scholar
     

  • Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)

    Article 

    Google Scholar
     

  • Caruana, R.: Multitask learning. Mach. Learn. 28(1), 41–75 (1997)

    Article 
    MathSciNet 

    Google Scholar
     

  • Green, M.A., Emery, K., Hishikawa, Y., Warta, W., Dunlop, E.D.: Solar cell efficiency tables (version 45). Prog. Photovolt. Res. Appl. 23(1), 1–9 (2015)

    Article 

    Google Scholar
     

  • Archer, C.L., Jacobson, M.Z.: Evaluation of global wind power. J. Geophys. Res. 110(D12) (2005)


    Google Scholar
     

  • Paish, O.: Small hydro power: technology and current status. Renew. Sustain. Energy Rev. 6(6), 537–556 (2002)

    Article 

    Google Scholar
     

  • Zhou, Y., Zhang, C., Xia, L., Zhang, Z.: Current status of research on optimum sizing of stand-alone hybrid solar-wind power generation systems. Appl. Energy 155, 606–619 (2015)


    Google Scholar
     

  • Jacobson, M.Z., Delucchi, M.A., Cameron, M.A., Mathiesen, B.V.: Matching demand with supply at low cost in 139 countries among 20 World Regions With 100% Wind, Water, and Solar Power (WWSP). Renew. Energy 123, 236–248 (2017)

    Article 

    Google Scholar
     

  • Solomon, S., Qin, D., Manning, M., Marquis, M., Averyt, K., Tignor, M.M., Miller Jr., H.L., Chen, Z.: IPCC, 2007: climate change 2007: the physical science basis. In: Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (2009)


    Google Scholar
     

  • Dunn, B., Kamath, H., Tarascon, J.M.: Electrical energy storage for the grid: a battery of choices. Science 334(6058), 928–935 (2011)

    Article 

    Google Scholar
     

  • Simon, P., Gogotsi, Y., Dunn, B.: Where do batteries end and supercapacitors begin? Science 343(6176), 1210–1211 (2014)

    Article 

    Google Scholar
     

  • Barroso, L.A., Clidaras, J., Hölzle, U.: The datacenter as a computer: an introduction to the design of warehouse-scale machines. Synth. Lect. Comput. Arch. 8(3), 1–154 (2013)


    Google Scholar
     

  • Orgerie, A.C., de Assuncao, M.D., Lefeuvre, J.: A survey on techniques for improving the energy efficiency of large-scale distributed systems. ACM Comput. Surv. (CSUR) 46(4), 1–31 (2014)

    Article 

    Google Scholar
     

  • Beloglazov, A., Buyya, R.: Energy efficient resource management in virtualized cloud data centers. In: Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud, and Grid Computing, pp. 826–831 (2010)


    Google Scholar
     

  • Bolla, R., Bruschi, R., Davoli, F., Cucchietti, F.: Energy efficiency in the future internet: a survey of existing approaches and trends in energy-aware fixed network infrastructures. IEEE Commun. Surv. & Tutor. 13(2), 223–244 (2011)

    Article 

    Google Scholar
     

  • Schwartz, R., Dodge, J., Smith, N., Etzioni, O.: Green AI. https://doi.org/10.48550/arXiv.1907.10597 (2019)

  • Jobin, A., Ienca, M., Vayena, E.: The global landscape of AI ethics guidelines. Nat. Mach. Intell. 1(9), 389–399 (2019)

    Article 

    Google Scholar
     

  • Floridi, L.: Soft ethics and the governance of the digital. Philos. & Technol. 31(1), 1–8 (2018)

    Article 
    MathSciNet 

    Google Scholar
     

  • Veitas, V., Weinbaum, D.: Open-ended intelligence: the individuation of intelligent agents. J. Exp. Theor. Artif. Intell. 29(2), 371–396 (2017)

    Article 

    Google Scholar
     

  • Dignum, V., Dignum, F., Davidsson, P.: Sustainability and artificial intelligence: from intra-generational to inter-generational optimization. Artif. Intell. 274, 1–18 (2019)


    Google Scholar
     

  • Koomey, J., Berard, S.: Implications of historical trends in the electrical efficiency of computing. IEEE Ann. Hist. Comput. 37(3), 46–54 (2014)

    Article 
    MathSciNet 

    Google Scholar
     

  • Naess, A.: The shallow and the deep, long-range ecology movement. Inquiry 16(1–4), 95–100 (1973)

    Article 

    Google Scholar
     

  • Leopold, A.: A Sand County Almanac. Oxford University Press (1949)


    Google Scholar
     

  • Bookchin, M.: The Ecology of Freedom: The Emergence and Dissolution of Hierarchy. Cheshire Books (1982)


    Google Scholar
     

  • Capra, F.: The Web of Life: A New Scientific Understanding of Living Systems. Anchor Books (1996)


    Google Scholar
     

  • Petrovskii, S., Petrovskaya, N.: Computational ecology as an emerging science. Interface Focus 2(2), 241–254 (2012)


    Google Scholar
     

  • Levin, S.A.: Ecosystems and the biosphere as complex adaptive systems. Ecosystems 1(5), 431–436 (1998)

    Article 

    Google Scholar
     

  • Finnveden, G., Hauschild, M.Z., Ekvall, T., Guinée, J., Heijungs, R., Hellweg, S., Koehler, A., Pennington, D., Suh, S.: Recent developments in life cycle assessment. J. Environ. Manage. 91(1), 1–21 (2009)

    Article 

    Google Scholar
     

  • Reap, J., Roman, F., Duncan, S., Bras, B.: A survey of unresolved problems in life cycle assessment. Int. J. Life Cycle Assess. 13(4), 290–300 (2008)

    Article 

    Google Scholar
     

  • Guha, R.: Environmentalism: A Global History. Longman (2000)


    Google Scholar
     

  • Shiva, V.: Earth Democracy: Justice, Sustainability, and Peace. South End Press (2008)


    Google Scholar
     

  • Shehabi, A., Smith, S., Sartor, D., Brown, R., Herrlin, M., Koomey, J., Masanet, E., Horner, N., Azevedo, I., Lintner, W.: United States Data Center Energy Usage Report. Lawrence Berkeley National Laboratory (2016)


    Google Scholar
     

  • Koomey, J.: Worldwide electricity used in data centers. Environ. Res. Lett. 3(3), 034008 (2008)

    Article 

    Google Scholar
     

  • Nisbet, M.C.: Public opinion about stem cell research and human cloning. Public Opin. Q. 68(1), 131–154 (2004)

    Article 
    MathSciNet 

    Google Scholar
     

  • Bimber, B.: Three Faces of Technological Determinism. In Does Technology Drive History? The Dilemma of Technological Determinism, pp. 79–100. MIT Press (1994)


    Google Scholar
     

  • Pinker, S.: Enlightenment Now: The Case for Reason, Science, Humanism, and Progress. Viking (2018)


    Google Scholar
     

  • Walsh, T.: Machines That Think: The Future of Artificial Intelligence. Prometheus (2018)


    Google Scholar
     

  • Mokiy, V.: The digital age, artificial intelligence, and unemployment. Technol. Forecast. Soc. Chang. 151, 119777 (2020)


    Google Scholar
     

  • Schwab, K.: The Fourth Industrial Revolution. Crown Business (2016)


    Google Scholar
     

  • Winner, L.: Autonomous Technology: Technics-out-of-Control as a Theme in Political Thought. MIT Press (1977)


    Google Scholar
     

  • Sclove, R.E.: Democracy and Technology. Guilford Press (1995)


    Google Scholar
     

  • Juma, C.: Innovation and Its Enemies: Why People Resist New Technologies. Oxford University Press (2016)


    Google Scholar
     

  • van den Hoven, J.: Value sensitive design and responsible innovation. In: Responsible Innovation, pp. 75–83. Springer (2013)


    Google Scholar
     

  • Friedman, B., Hendry, D.G.: Value Sensitive Design: Shaping Technology with Moral Imagination. MIT Press (2019)


    Google Scholar
     

  • Boddington, P.: Towards a Code of Ethics for Artificial Intelligence. Springer (2017)

    Book 

    Google Scholar
     

  • Hagendorff, T.: The ethics of AI ethics: an evaluation of guidelines. Mind. Mach. 30(1), 99–120 (2020)

    Article 

    Google Scholar
     

  • Shelby, A., Darnall, N.: Why industrial symbiosis research should address social and environmental justice. J. Ind. Ecol. 18(2), 155–166 (1994)


    Google Scholar
     

  • Schlosberg, D.: Defining Environmental Justice: Theories, Movements, and Nature. Oxford University Press (2007)

    Book 

    Google Scholar
     

  • Agyeman, J., Bullard, R.D., Evans, B.: Just Sustainabilities: Development in an Unequal World. MIT Press (2003)


    Google Scholar
     

  • Creswell, J.W.: Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Sage Publications (2014)


    Google Scholar
     

  • Hart, C.: Doing a Literature Review: Releasing the Social Science Research Imagination. Sage Publications (1998)


    Google Scholar
     

  • Yin, R.K.: Case Study Research and Applications: Design and Methods. Sage Publications (2018)


    Google Scholar
     

  • Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3(2), 77–101 (2006)

    Article 

    Google Scholar
     

  • Siano, P., Piccolo, A., Sarno, D., Pietrosanto, A.: Real-time monitoring of distribution networks using machine learning techniques. Electr. Power Syst. Res. 127, 1–8 (2020)


    Google Scholar
     

  • Chicco, G.: Overview and performance assessment of the clustering methods for electrical load pattern grouping. Energy 42(1), 68–80 (2012)

    Article 

    Google Scholar
     

  • Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2019)

    Article 

    Google Scholar
     

  • Fraser, H., Coiera, E., Wong, D.: Safety of patient-facing digital symptom checkers. Lancet 392(10161), 2263–2270 (2020)

    Article 

    Google Scholar
     

  • Fagnant, D.J., Kockelman, K.M.: Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transp. Res. Part A Policy Pract. 77, 167–181 (2015)

    Article 

    Google Scholar
     

  • Greenblatt, J.B., Saxena, S.: Autonomous taxis could greatly reduce greenhouse-gas emissions of US light-duty vehicles. Nat. Clim. Chang. 5(9), 860–863 (2015)

    Article 

    Google Scholar
     

  • Alam, A., Gattami, A., Johansson, K.H.: An experimental study on the fuel reduction potential of heavy duty vehicle platooning. In: 13th International IEEE Conference on Intelligent Transportation Systems (2015)


    Google Scholar
     

  • Edenhofer, O., et al.: IPCC, 2014: climate change 2014: mitigation of climate change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (2014)


    Google Scholar
     

  • Victor, D.G.: Global Warming Gridlock: Creating More Effective Strategies for Protecting the Planet. Cambridge University Press (2015)


    Google Scholar
     

  • Mittelstadt, B., Allo, P., Taddeo, M., Wachter, S., Floridi, L.: The ethics of algorithms: mapping the debate. Big Data Soc. 3(2), 205395171667967 (2016)

    Article 

    Google Scholar
     

  • Steffen, W., et al.: Planetary boundaries: guiding human development on a changing planet. Science 347(6223), 1259855 (2015)

    Article 

    Google Scholar
     

  • Porter, M.E., van der Linde, C.: Toward a new conception of the environment-competitiveness relationship. J. Econ. Perspect. 9(4), 97–118 (1995)

    Article 

    Google Scholar
     

  • Kuzma, J., Kuzhabekova, A.: Corporate social responsibility for nanotechnology oversight. Med. Health Care Philos. 14, 407–419 (2011)

    Article 

    Google Scholar
     

  • Sarewitz, D.: How science makes environmental controversies worse. Environ. Sci. Policy 7(5), 385–403 (2004)

    Article 

    Google Scholar
     



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