Towards autonomous developmental artificial intelligence: case study for explainable AI.

Starkey, Andrew; Ezenkwu, Chinedu Pascal

Abstract

State-of-the-art autonomous AI algorithms such as reinforcement learning and deep learning techniques suffer from high computational complexity, poor explainability ability, and a limited capacity for incremental adaptive learning. In response to these challenges, this paper highlights the TMGWR-based algorithm, developed by the present authors, as a case study towards self-adaptive unsupervised learning in autonomous developmental AI, and makes the following contributions: it presents and reviews essential requirements for today's autonomous AI and includes analysis for their potential for Green AI; it demonstrates that, unlike these state-of-the-art algorithms, TMGWR possesses explainability potentials that can be further developed and exploited for autonomous learning applications. In addition to shaping researchers' choice of metrics for selecting autonomous learning strategies, this paper will help to motivate further innovative research in autonomous AI.

Citation

STARKEY, A. and EZENKWU, C.P. 2023. Towards autonomous developmental artificial intelligence: case study for explainable AI. In Maglogiannis, I., Iliadis, L., MacIntyre, J. and Dominguez, M. (eds.) Artificial intelligence applications and innovations: proceedings of the 19th IFIP (International Federation for Information Processing) WG 12.5 Artificial intelligence applications and innovations international conference (AIAI 2023), 14-17 June 2023, León, Spain. IFIP advances in information and communication technology, 676. Cham: Springer [online], pages 94-105. Available from: https://doi.org/10.1007/978-3-031-34107-6_8

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 19th IFIP (International Federation for Information Processing) Artificial intelligence applications and innovations international conference 2023 (AIAI 2023)
• Start Date: Jun 14, 2023
• End Date: Jun 16, 2023
• Acceptance Date: Mar 28, 2023
• Online Publication Date: Jun 1, 2023
• Publication Date: Dec 31, 2023
• Deposit Date: Jun 9, 2023
• Publicly Available Date: Jun 2, 2024
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 94-105
• Series Title: IFIP advances in information and communication technology (IFIPAICT)
• Series Number: 676
• Series ISSN: 1868-4238; 1868-422X
• Book Title: Artificial intelligence applications and innovations: proceedings of the 19th IFIP (International Federation for Information Processing) WG 12.5 Artificial intelligence applications and innovations international conference (AIAI 2023), 14-17 June 2023, Le
• ISBN: 9783031341069
• DOI: https://doi.org/10.1007/978-3-031-34107-6_8
• Keywords: Autonomous AI; Green AI; Unsupervised learning
• Public URL: https://rgu-repository.worktribe.com/output/1982490
• Related Public URLs: https://rgu-repository.worktribe.com/output/2293505 (Paper summary)
• Additional Information: A summary of this article has been published with the following citation: STARKEY, A. and EZENKWU, C.P. 2024. Advancing AI with green practices and adaptable solutions for the future. [Article summary]. Posted on The Academic [online], 28 March 2024. Available from: https://theacademic.com/ai-green-practices-adaptable-solutions/