Trans-AI/DS: transformative, transdisciplinary and translational artificial intelligence and data science

Longbing Cao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
50 Downloads (Pure)

Abstract

After the many ups and downs over the past 70 years of AI and 50 years of data science (DS), AI/DS have migrated into their new age. This new-generation AI/DS build on the consilience and universology of science, technology and engineering. In particular, it synergizes AI and data science, inspiring Trans-AI/DS (i.e., Trans-AI, Trans-DS and their hybridization) thinking, vision, paradigms, approaches and practices. Trans-AI/DS feature their transformative (or transformational), transdisciplinary, and translational AI/DS in terms of thinking, paradigms, methodologies, technologies, engineering, and practices. Here, we discuss these important paradigm shifts and directions. Trans-AI/DS encourage big and outside-the-box thinking beyond the classic AI, data-driven, model-based, statistical, shallow and deep learning hypotheses, methodologies and developments. They pursue foundational and original AI/DS thinking, theories and practices from the essence of intelligences and complexities inherent in humans, nature, society, and their creations.

Original languageEnglish
Pages (from-to)119-132
Number of pages14
JournalInternational Journal of Data Science and Analytics
Volume15
Issue number2
DOIs
Publication statusPublished - Mar 2023
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2023. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Trans-AI
  • Trans-DS
  • Trans-AI/DS
  • Transformative AI
  • Transformative data science
  • Transdisciplinary AI
  • Transdisciplinary data science
  • Translational AI
  • Translational data science

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