IDEAS home Printed from https://ideas.repec.org/p/hal/wpaper/hal-02502178.html
   My bibliography  Save this paper

Artificial Intelligence, Historical Materialism, and close enough to a jobless society

Author

Listed:
  • Rogerio Silva Mattos

    (Universidade Federal de Juiz de Fora)

Abstract

Advancing artificial Intelligence draws most of its power from the artificial neural network, a software technique that has successfully replicated some information processing functions of the human brain and the unconscious mind. Jobs are at risk to disappear because even the tacit knowledge typically used by humans to perform complex tasks is now amenable to computerization. The paper discusses implications of this technology for capitalism and jobs, concluding that a very long run transition to a jobless economy should not be discarded. Rising business models and new collaborative schemes provide clues for how things may unfold. A scenario in which society is close enough to full unemployment is analyzed and strategic paths to tackle the challenges involved are discussed. The analysis follows an eclectic approach, based on the Marxist theory of historical materialism and the job task model created by mainstream economists.

Suggested Citation

  • Rogerio Silva Mattos, 2019. "Artificial Intelligence, Historical Materialism, and close enough to a jobless society," Working Papers hal-02502178, HAL.
    • Handle: RePEc:hal:wpaper:hal-02502178
    Note: View the original document on HAL open archive server: https://hal.science/hal-02502178
    as

    Download full text from publisher

    File URL: https://hal.science/hal-02502178/document
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The skill content of recent technological change: an empirical exploration," Proceedings, Federal Reserve Bank of San Francisco, issue Nov.
    2. Frey, Carl Benedikt & Osborne, Michael A., 2017. "The future of employment: How susceptible are jobs to computerisation?," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 254-280.
    3. Claude Diebolt & Michael Haupert (ed.), 2016. "Handbook of Cliometrics," Springer Books, Springer, edition 1, number 978-3-642-40406-1, January.
    4. Moses Abramovitz, 1956. "Resource and Output Trends in the United States since 1870," NBER Books, National Bureau of Economic Research, Inc, number abra56-1.
    5. Moses Abramovitz, 1956. "Resource and Output Trends in the United States since 1870," NBER Chapters, in: Resource and Output Trends in the United States since 1870, pages 1-23, National Bureau of Economic Research, Inc.
    6. Yona Rubinstein & James J. Heckman, 2001. "The Importance of Noncognitive Skills: Lessons from the GED Testing Program," American Economic Review, American Economic Association, vol. 91(2), pages 145-149, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matthias Firgo & Peter Mayerhofer, 2015. "Wissens-Spillovers und regionale Entwicklung - welche strukturpolitische Ausrichtung optimiert des Wachstum?," Working Paper Reihe der AK Wien - Materialien zu Wirtschaft und Gesellschaft 144, Kammer für Arbeiter und Angestellte für Wien, Abteilung Wirtschaftswissenschaft und Statistik.
    2. Matthias Firgo & Peter Mayerhofer, 2015. "Wissensintensive Unternehmensdienste, Wissens-Spillovers und regionales Wachstum. Teilprojekt 1: Wissens-Spillovers und regionale Entwicklung – Welche strukturpolitische Ausrichtung optimiert das Wach," WIFO Studies, WIFO, number 58342.
    3. Jones, C.I., 2016. "The Facts of Economic Growth," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 3-69, Elsevier.
    4. Eckhardt Bode & Stephan Brunow & Ingrid Ott & Alina Sorgner, 2019. "Worker Personality: Another Skill Bias beyond Education in the Digital Age," German Economic Review, Verein für Socialpolitik, vol. 20(4), pages 254-294, November.
    5. Charles R. Hulten & Leonard I. Nakamura, 2017. "Accounting for Growth in the Age of the Internet The Importance of Output-Saving Technical Change," Working Papers 17-24, Federal Reserve Bank of Philadelphia.
    6. Daron Acemoglu, 2002. "Technical Change, Inequality, and the Labor Market," Journal of Economic Literature, American Economic Association, vol. 40(1), pages 7-72, March.
    7. Feder, Christophe, 2018. "The effects of disruptive innovations on productivity," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 186-193.
    8. Jože P. Damijan & Mark Knell, 2005. "How Important Is Trade and Foreign Ownership in Closing the Technology Gap? Evidence from Estonia and Slovenia," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 141(2), pages 271-295, July.
    9. Jan Fagerberg & Bengt-Åke Lundvall & Martin Srholec, 2018. "Global Value Chains, National Innovation Systems and Economic Development," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 30(3), pages 533-556, July.
    10. Loebbing, Jonas, 2018. "An Elementary Theory of Endogenous Technical Change and Wage Inequality," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181603, Verein für Socialpolitik / German Economic Association.
    11. Noel Uri, 2001. "Telecommunications in the United States and Changing Productive Efficiency," Journal of Industry, Competition and Trade, Springer, vol. 1(3), pages 321-335, September.
    12. Kumar, Sanjesh & Singh, Baljeet, 2019. "Barriers to the international diffusion of technological innovations," Economic Modelling, Elsevier, vol. 82(C), pages 74-86.
    13. Iftekhairul Islam & Fahad Shaon, 2020. "If the Prospect of Some Occupations Are Stagnating With Technological Advancement? A Task Attribute Approach to Detect Employment Vulnerability," Papers 2001.02783, arXiv.org.
    14. Kox, Henk L.M. & Leeuwen, George van & Wiel, Henry van der, 2010. "Competitive, but too small - productivity and entry-exit determinants in European business services," MPRA Paper 24389, University Library of Munich, Germany.
    15. Noel Uri, 2003. "The Effect of Incentive Regulation in Telecommunications in the United States," Quality & Quantity: International Journal of Methodology, Springer, vol. 37(2), pages 169-191, May.
    16. Daniele Angelini, 2023. "Aging Population and Technology Adoption," Working Paper Series of the Department of Economics, University of Konstanz 2023-01, Department of Economics, University of Konstanz.
    17. Caitlin Allen Whitehead & Haroon Bhorat & Robert Hill & Tim Köhler & François Steenkamp, 2021. "The Potential Employment Implications of the Fourth Industrial Revolution Technologies: The Case of the Manufacturing, Engineering and Related Services Sector," Working Papers 202106, University of Cape Town, Development Policy Research Unit.
    18. Jens J. Krüger, 2020. "Long‐run productivity trends: A global update with a global index," Review of Development Economics, Wiley Blackwell, vol. 24(4), pages 1393-1412, November.
    19. Dutrénit, Gabriela & Natera, José Miguel & Puchet Anyul, Martín & Vera-Cruz, Alexandre O., 2019. "Development profiles and accumulation of technological capabilities in Latin America," Technological Forecasting and Social Change, Elsevier, vol. 145(C), pages 396-412.
    20. Benjamin Cole & Preeta Banerjee, 2013. "Morally Contentious Technology-Field Intersections: The Case of Biotechnology in the United States," Journal of Business Ethics, Springer, vol. 115(3), pages 555-574, July.

    More about this item

    Keywords

    artificial intelligence; historical materialism; task model; neural networks; jobless society;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:wpaper:hal-02502178. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.