A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions

A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology

The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% (n = 21/30). The second category, recurrent neural network (RNN), accounts for 10% (n = 3/30). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% (n = 30). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% (n = 1/30). The literature's findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided.

The Empowerment of Artificial Intelligence in Post-Digital Organizations: Exploring Human Interactions with Supervisory AI

Technology evolves together with humans. Across industrial revolutions, its role has evolved from that of a simple tool used by humans to that of intelligent decision-maker and teammate. In the post-digital era where ongoing advances in artificial intelligence are widely visible, the question arises regarding the extent to which technology will be “upgraded” into roles previously filled by human supervisors, thereby replacing persons in managerial positions. This text aims to delineate how the organizational role of technology has been transformed across decades and the forms that it currently takes within companies, with an eye to the future. We draw on posthuman managerial literature and known cases of organizations where some forms of supervisory artificial intelligence are already used. The text is conceptual-reflective by nature; it seeks to initiate a discussion on the many challenges that humanity will face in connection with the deployment of empowered posthuman agents in companies.