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Distributed, decentralized, and democratized artificial intelligence

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  • Montes, Gabriel Axel
  • Goertzel, Ben

Abstract

The accelerating investment in artificial intelligence has vast implications for economic and cognitive development globally. However, AI is currently dominated by an oligopoly of centralized mega-corporations, who focus on the interests of their stakeholders. There is a now universal need for AI services by businesses who lack access to capital to develop their own AI services, and independent AI developers lack visibility and a source of revenue. This uneven playing field has a high potential to lead to inequitable circumstances with negative implications for humanity. Furthermore, the potential of AI is hindered by the lack of interoperability standards. The authors herein propose an alternative path for the development of AI: a distributed, decentralized, and democratized market for AIs run on distributed ledger technology. We describe the features and ethical advantages of such a system using SingularityNET, a watershed project being developed by Ben Goertzel and colleagues, as a case study. We argue that decentralizing AI opens the doors for a more equitable development of AI and AGI. It will also create the infrastructure for coordinated action between AIs that will significantly facilitate the evolution of AI into true AGI that is both highly capable and beneficial for humanity and beyond.

Suggested Citation

  • Montes, Gabriel Axel & Goertzel, Ben, 2019. "Distributed, decentralized, and democratized artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 354-358.
    • Handle: RePEc:eee:tefoso:v:141:y:2019:i:c:p:354-358
    DOI: 10.1016/j.techfore.2018.11.010
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    Cited by:

    1. Bennich, Amelie, 2024. "The digital imperative: Institutional pressures to digitalise," Technology in Society, Elsevier, vol. 76(C).
    2. Gupta, Shivam & Modgil, Sachin & Bhatt, Priyanka C. & Chiappetta Jabbour, Charbel Jose & Kamble, Sachin, 2023. "Quantum computing led innovation for achieving a more sustainable Covid-19 healthcare industry," Technovation, Elsevier, vol. 120(C).
    3. Braganza, Ashley & Chen, Weifeng & Canhoto, Ana & Sap, Serap, 2021. "Productive employment and decent work: The impact of AI adoption on psychological contracts, job engagement and employee trust," Journal of Business Research, Elsevier, vol. 131(C), pages 485-494.
    4. Gordon, Adam Vigdor & Ramic, Mirza & Rohrbeck, René & Spaniol, Matthew J., 2020. "50 Years of corporate and organizational foresight: Looking back and going forward," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    5. Chand Bhatt, Priyanka & Kumar, Vimal & Lu, Tzu-Chuen & Daim, Tugrul, 2021. "Technology convergence assessment: Case of blockchain within the IR 4.0 platform," Technology in Society, Elsevier, vol. 67(C).
    6. Shima Beigi, 2020. "How do the Covid-19 Prevention Measures Interact with Sustainable Development Goals?," Papers 2011.02290, arXiv.org.
    7. Liu, Jun & Chang, Huihong & Forrest, Jeffrey Yi-Lin & Yang, Baohua, 2020. "Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    8. Denicolai, Stefano & Zucchella, Antonella & Magnani, Giovanna, 2021. "Internationalization, digitalization, and sustainability: Are SMEs ready? A survey on synergies and substituting effects among growth paths," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    9. Walkowiak, Emmanuelle, 2021. "Neurodiversity of the workforce and digital transformation: The case of inclusion of autistic workers at the workplace," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    10. Fosso Wamba, Samuel & Bawack, Ransome Epie & Guthrie, Cameron & Queiroz, Maciel M. & Carillo, Kevin Daniel André, 2021. "Are we preparing for a good AI society? A bibliometric review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    11. Dominic Chalmers & Niall G. MacKenzie & Sara Carter, 2021. "Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution," Entrepreneurship Theory and Practice, , vol. 45(5), pages 1028-1053, September.
    12. Kanzola, Anna-Мaria & Papaioannou, Konstantina & Petrakis, Panagiotis, 2024. "Unlocking society's standings in artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    13. Fossen, Frank M. & McLemore, Trevor & Sorgner, Alina, 2024. "Artificial Intelligence and Entrepreneurship," IZA Discussion Papers 17055, Institute of Labor Economics (IZA).
    14. Daim, Tugrul & Lai, Kuei Kuei & Yalcin, Haydar & Alsoubie, Fayez & Kumar, Vimal, 2020. "Forecasting technological positioning through technology knowledge redundancy: Patent citation analysis of IoT, cybersecurity, and Blockchain," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    15. Balakrishnan, Janarthanan & Abed, Salma S. & Jones, Paul, 2022. "The role of meta-UTAUT factors, perceived anthropomorphism, perceived intelligence, and social self-efficacy in chatbot-based services?," Technological Forecasting and Social Change, Elsevier, vol. 180(C).

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