What is it about?
The article advances AutoAI design with (1) using new and emerging sources of data for teaching and training AI algorithms and (2) using automated tools for self-training new and improved algorithms. This approach suggests a design that enables autonomous algorithms to self-optimise and self-adapt, and on a higher level, be capable to self-procreate.
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Why is it important?
The topic of artificial intelligence (AI) becoming autonomous has been discussed since the 1960s. This research study is targeted at designing automation that is autonomous (data preparation, feature engineering, hyperparameter optimisation, and model selection for pipeline optimisation) and self-procreating.
Perspectives
- The proposed AutoAI is superior to current AutoML, because it is based on new and emerging forms of big data to derive transferable artificial automation that resembles a self-procreating AI. - The algorithm can be used to establish the baseline for trust enhancing mechanisms in autonomous digital systems. Including the AI migration to the edge for enhancing the resilience of modern networks, such as 5G and IoT systems. - The article presents a new design of a self-optimising and self-adaptive AutoAI.
Dr Petar Radanliev
University of Oxford
Read the Original
This page is a summary of: Review of the state of the art in autonomous artificial intelligence, AI and Ethics, June 2022, Springer Science + Business Media,
DOI: 10.1007/s43681-022-00176-2.
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