AutoAI: Autonomous AI

Cao, L

AutoAI: Autonomous AI

Cao, L

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Publisher:: IEEE COMPUTER SOC
Publication Type:: Journal Article
Citation:: IEEE Intelligent Systems, 2022, 37, (5), pp. 3-5
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429
dc.date.accessioned	2023-03-20T03:19:44Z
dc.date.available	2023-03-20T03:19:44Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Intelligent Systems, 2022, 37, (5), pp. 3-5
dc.identifier.issn	1541-1672
dc.identifier.issn	1941-1294
dc.identifier.uri	http://hdl.handle.net/10453/167735
dc.description.abstract	In the AI evolution, a significant and lasting vision and mission has been on designing autonomous AI systems (AutoAI). AutoAI differs significantly from another set of movements on automated machine learning (AutoML) and automated data science (AutoDS), which are often deemed interchangeable with automated AI. AutoML and AutoDS aim to automate some of the analytical and learning tasks, processes, and pipelines. This issue highlights the theme on AutoAI: Autonomous AI with six feature articles. My editorial further clarifies various misconceptions, myths, and pitfalls about the three related and often confused areas: AutoAI, AutoML, and AutoDS. This issue also includes an article on parallel population and human in the column AI Expert, expert-machine collaboration in the column AI Focus, and another article on intelligent mobile spaces and metaverses for the AI and Cyber-Physical-Social Systems (AI-CPSS) department.
dc.language	English
dc.publisher	IEEE COMPUTER SOC
dc.relation.ispartof	IEEE Intelligent Systems
dc.relation.isbasedon	10.1109/MIS.2022.3209046
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	AutoAI: Autonomous AI
dc.type	Journal Article
utslib.citation.volume	37
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
utslib.for	0906 Electrical and Electronic Engineering
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	closed_access	*
dc.date.updated	2023-03-20T03:19:43Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	37
utslib.citation.issue	5

Abstract:

In the AI evolution, a significant and lasting vision and mission has been on designing autonomous AI systems (AutoAI). AutoAI differs significantly from another set of movements on automated machine learning (AutoML) and automated data science (AutoDS), which are often deemed interchangeable with automated AI. AutoML and AutoDS aim to automate some of the analytical and learning tasks, processes, and pipelines. This issue highlights the theme on AutoAI: Autonomous AI with six feature articles. My editorial further clarifies various misconceptions, myths, and pitfalls about the three related and often confused areas: AutoAI, AutoML, and AutoDS. This issue also includes an article on parallel population and human in the column AI Expert, expert-machine collaboration in the column AI Focus, and another article on intelligent mobile spaces and metaverses for the AI and Cyber-Physical-Social Systems (AI-CPSS) department.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/167735