Angelov, Plamen P., Gu, Xiaowei (2018) Toward Anthropomorphic Machine Learning. Computer, 51 (9). pp. 18-27. ISSN 0018-9162. (doi:10.1109/MC.2018.3620973) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90114)
| The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication) | |
| Official URL: https://doi.org/10.1109/MC.2018.3620973 |
Abstract
Future intelligent machines will be more human-friendly and human-like, while offering much higher throughput and automation, thus augmenting our (human) capabilities. Anthropomorphic machine learning is an emerging direction for future development in artificial intelligence (AI) and data science. This revolutionary shift offers human-like abilities to the next generation of machine learning with greater potential for underpinning breakthroughs in technology development as well as in various aspects of everyday life.
| Item Type: | Article |
|---|---|
| DOI/Identification number: | 10.1109/MC.2018.3620973 |
| Uncontrolled keywords: | Anthropomorphism; Machine learning; Artificial intelligence; Learning systems; Data science; AI; deep learning; anthropomorphic machine learning; future of AI |
| Subjects: | Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science |
| Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
| Depositing User: | Amy Boaler |
| Date Deposited: | 09 Sep 2021 15:16 UTC |
| Last Modified: | 05 Nov 2024 12:55 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/90114 (The current URI for this page, for reference purposes) |
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