TY - GEN
T1 - Towards Autonomous Developmental Artificial Intelligence
T2 - 19th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2023
AU - Starkey, Andrew
AU - Ezenkwu, Chinedu Pascal
PY - 2023/6/1
Y1 - 2023/6/1
N2 - 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.
AB - 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.
KW - Autonomous AI
KW - Green AI
KW - Unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=85173556288&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-34107-6_8
DO - 10.1007/978-3-031-34107-6_8
M3 - Published conference contribution
AN - SCOPUS:85173556288
SN - 9783031341069
T3 - IFIP Advances in Information and Communication Technology
SP - 94
EP - 105
BT - Artificial Intelligence Applications and Innovations
A2 - Maglogiannis, Ilias
A2 - Iliadis, Lazaros
A2 - MacIntyre, John
A2 - Dominguez, Manuel
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 14 June 2023 through 17 June 2023
ER -