Angelov, Plamen Parvanov and Gu, Xiaowei (2018) Towards Anthropomorphic Machine Learning. IEEE Computer, 51 (9). pp. 18-27. ISSN 0018-9162
Abstract
In this paper, we introduce and discuss the concept of anthropomorphic machine learning as an emerging direction for the future development in the area of artificial intelligence (AI) and data science. We start with outlining research challenges and opportunities, which the contemporary landscape offers. We focus on machine learning, statistical learning, deep learning and computational intelligence as theoretical and methodological areas of greater promise for breakthrough results and underpinning the future revolutionary changes in technology development as well as in our everyday life and societies. Our critical analysis brings us to the open problems and we formulate the paradigm shift in the understanding of machine learning. In a nutshell, our vision for the next generational machine learning methods and algorithms is anthropomorphic, which resembles the way people/humans learn from data. This concept brings machine learning from the statistics to the area of computational intelligence and AI.