skip to main content
opinion
Free Access

Artificial intelligence across company borders

Published:17 December 2021Publication History
Skip Abstract Section

Abstract

Enabling effective cross-company AI without data disclosure.

References

  1. Böhmann, T. et al. Service systems engineering. Business and Information Systems Engineering 6 (2014), 73--79.Google ScholarGoogle ScholarCross RefCross Ref
  2. Ganin, Y. and Lempitsky, V. Unsupervised domain adaptation by backpropagation. In Proceedings of the International Conference on Machine Learning (ICML) (2015).Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Hirt, R. et al. Service-oriented cognitive analytics for smart service systems: A research agenda. In Proceedings of the 51st Hawaii International Conference on System Sciences (HICSS) (2018).Google ScholarGoogle ScholarCross RefCross Ref
  4. Kairouz, P. et al. Advances and Open Problems in Federated Learning. arXiv (2019); http://arxiv.org/abs/1912.04977. 1912.04977.Google ScholarGoogle Scholar
  5. Lalitha, A. et al. Fully decentralized federated learning. In Proceedings of the 3rd Workshop on Bayesian Deep Learning. (NeurIPS) (2018).Google ScholarGoogle Scholar
  6. McKinsey Global Institute. Modeling the global economic impact of AI. (2018); https://mck.co/3kEz9tzGoogle ScholarGoogle Scholar
  7. McMahan, B. et al. Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics (AISTATS) (2017).Google ScholarGoogle Scholar
  8. Pan, S.J. and Yang, Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22 (2010), 1345--1359.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Wang, Q., Michau, G., and Fink, O. Missing-class-robust domain adaptation by unilateral alignment. IEEE Transactions on Industrial Electronics 68 (2020), 663--671.Google ScholarGoogle ScholarCross RefCross Ref
  10. World Economic Forum. Share to gain: Unlocking data value in manufacturing (2020); https://bit.ly/3CgQBdvGoogle ScholarGoogle Scholar
  11. Yang, Q., Liu, Y., Chen, T., and Tong, Y. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 10, Article 19 (2019).Google ScholarGoogle Scholar

Index Terms

  1. Artificial intelligence across company borders

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      Full Access

      • Published in

        cover image Communications of the ACM
        Communications of the ACM  Volume 65, Issue 1
        January 2022
        106 pages
        ISSN:0001-0782
        EISSN:1557-7317
        DOI:10.1145/3507640
        Issue’s Table of Contents

        Copyright © 2021 Owner/Author

        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 17 December 2021

        Check for updates

        Qualifiers

        • opinion
        • Popular
        • Un-reviewed

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format .

      View HTML Format