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

Cao, L

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

Cao, L

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Publisher:: SPRINGERNATURE
Publication Type:: Journal Article
Citation:: International Journal of Data Science and Analytics, 2023, 15, (2), pp. 119-132
Issue Date:: 2023-03-01

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Field	Value	Language
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429
dc.date.accessioned	2024-04-12T04:03:44Z
dc.date.available	2024-04-12T04:03:44Z
dc.date.issued	2023-03-01
dc.identifier.citation	International Journal of Data Science and Analytics, 2023, 15, (2), pp. 119-132
dc.identifier.issn	2364-415X
dc.identifier.issn	2364-4168
dc.identifier.uri	http://hdl.handle.net/10453/177820
dc.description.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.
dc.language	English
dc.publisher	SPRINGERNATURE
dc.relation	http://purl.org/au-research/grants/arc/DP190101079
dc.relation	http://purl.org/au-research/grants/arc/FT190100734
dc.relation.ispartof	International Journal of Data Science and Analytics
dc.relation.isbasedon	10.1007/s41060-023-00383-y
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0801 Artificial Intelligence and Image Processing
dc.subject.classification	4605 Data management and data science
dc.title	Trans-AI/DS: transformative, transdisciplinary and translational artificial intelligence and data science
dc.type	Journal Article
utslib.citation.volume	15
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	open_access	*
dc.date.updated	2024-04-12T04:03:43Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	15
utslib.citation.issue	2

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.

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http://hdl.handle.net/10453/177820