Beyond AutoML: mindful and actionable AI and AutoAI with mind and action

Longbing Cao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Automated machine learning (AutoML), in particular, neural architecture search (NAS) for deep learning, has ignited the fast-paced development of automating data science (AutoDS) and artificial intelligence. However, in the existing literature and practice, AutoML, AutoDS, and autonomous AI (AutoAI) are highly interchangeable and primarily centered on the automation engineering of data-driven analytics and learning pipelines. This challenges the realization of the full spectrum of AI paradigms and human-like to human-level intelligent and autonomous systems. Going beyond the state-of-the-art paradigm of AutoML and their automation engineering, there is an expectation that the new age of AI and autonomous AI (or AutoAI+) will incorporate mind-to-action intelligence and integrate them with autonomy. We pave the way for this new AI and AutoAI integrating mindful AI and AutoAI with AI mind and mindfulness and actionable AI and AutoAI with AI actions and actionability and translating AI mind to AI action for autonomous, all-around AI systems.

Original languageEnglish
Pages (from-to)6-18
Number of pages13
JournalIEEE Intelligent Systems
Volume37
Issue number5
DOIs
Publication statusPublished - 2022
Externally publishedYes

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