Human-Centric AI: The Symbiosis of Human and Artificial Intelligence

Abstract

Well-evidenced advances of data-driven complex machine learning approaches emerging within the so-called second wave of artificial intelligence (AI) fostered the exploration of possible AI applications in various domains and aspects of human life, practices, and society. Most of the recent success in AI comes from the utilization of representation learning with end-to-end trained deep neural network models in tasks such as image, text,and speech recognition or strategic board and video games. By enabling automatic feature engineering, deep learning models significantly reduce the reliance on domain-expert knowledge, outperforming traditional methods based on handcrafted feature engineering and achieving performance that equals or even supersedes humans in some respects.

Investigating the Causal Relationships among Enablers of the Construction 5.0 Paradigm: Integration of Operator 5.0 and Society 5.0 with Human-Centricity, Sustainability, and Resilience

“…The findings of assessing the causal relationships between the enablers of Society 5.0 and human-centricity highlighted in H2 are coherent with the previous studies [13,30,31,[34][35][36][37][38][39][40][41][42][43][44][57][58][59][60][61][62][63][64]83,84]. The indicators associated with Society 5.0 emphasize the importance of a human-centric society, merging cyberspace with physical space, a knowledgeintensive society, a data-driven society, and the encouragement of new social value, all of which contribute to initiating human-centricity in the Construction 5.0 framework.…”

“…The findings of the assessment of the causal relationships between the enablers of human-centricity and sustainability highlighted in H5 are coherent with the previous studies [4,6,13,31,[34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49]. Human-centricity and sustainability are deeply interconnected in the context of Construction 5.0.…”

“…When applied to industrial work systems, critical humancentric thinking combines human factors and ergonomics to boost system efficiency and employee satisfaction, all while accommodating the obstacles posed by social technologies [43]. Human-centric explainable AI (HC-XAI) [44] is based on the idea that human-centricity necessitates intelligent robots that can grasp the interrelated interactions between humans and machines in unstructured situations. Trust is the foundation of every human-centered society.…”

Data-Centric and Model-Centric AI: Twin Drivers of Compact and Robust Industry 4.0 Solutions

“…He defined it as the "science and engineering of making intelligent machines, especially intelligent computer programmes" [22]. Today, much of the hype around AI is attributed to a particular technique called deep learning (DL) [23] which is a modified version of artificial neural networks (ANNs) (Figure 2). In an accelerated pace of enhancing AI/ML prowess, DL was repeatedly reported to have accomplished human-level performance in a variety of applications.…”

Reinforcement Learning Approaches to Optimal Market Making

“…As acknowledged by [76], the outstanding advancements and potential benefits of data-driven complex machine learning approaches in finance emerged within the so-called second wave of artificial intelligence (AI). However, in order to foster their further applications in various domains, especially in finance, there is an emerging need for both transparent approaches behind the behavior of deep learning-based AI solutions and understandable interpretations for specific algorithmic behavior and outcomes.…”