Search Results (4)

Artificial intelligence (AI) soft skills have become increasingly vital in today’s technology-driven world, as they support decision-making systems, strengthen collaboration among stakeholders, and enable individuals to adapt to rapidly changing environments—factors that are fundamental for achieving the sustainability goals of smart cities. Even though AI soft skills are becoming more important, no scale specifically designed to identify and evaluate individuals’ AI soft skills has been found in the existing literature. Therefore, this paper aimed to develop a reliable and valid scale to identify the AI soft skills of individuals. A sample of 685 individuals who were employed in AI-active sectors, with a minimum of a bachelor’s degree, and at least one year of AI-related work experience, participated in the study. A sequential exploratory mixed-methods research design was utilized. Exploratory factor analysis (EFA) identified a five-factor structure that accounted for 67.37% of the total variation, including persuasion, collaboration, adaptability, emotional intelligence, and creativity. Factor loadings ranged from 0.621 to 0.893, and communalities ranged from 0.587 to 0.875. Confirmatory factor analysis (CFA) supported this structure, with strong model fit indices (GFI = 0.940, AGFI = 0.947, NFI = 0.949, PNFI = 0.833, PGFI = 0.823, TLI = 0.972, IFI = 0.975, CFI = 0.975, RMSEA = 0.052, SRMR = 0.035). Internal consistency for each factor was high, with Cronbach’s alpha values of dimensions ranging from 0.804 to 0.875, with a value of 0.921 for the overall scale. Convergent and discriminant validity analyses further confirmed the construct’s robustness. The finalized AI soft skills (AISS) scale, consisting of 24 items, offers a psychometrically valid and reliable tool for assessing essential AI soft skills in professional contexts. Ultimately, this developed scale enables the determination of the social and cognitive skills needed in the human-centered and participatory governance structures of smart cities, supporting the achievement of specific Sustainable Development Goals such as SDG 4, SDG 8, and SDG 11, and contributes to the design of policies and training programs to eliminate the deficiencies of individuals in these areas. Thus, it becomes possible to create qualified human resources that support sustainable development in smart cities, and for these individuals to take an active part in the labor market. Full article

The application of Artificial Intelligence (AI) facilitates medical activities by automating routine tasks for healthcare professionals. AI augments but does not replace human decision-making, thus complicating the process of addressing legal responsibility. This study investigates the legal challenges associated with the medical use of AI in radiology, analyzing relevant case law and literature, with a specific focus on professional liability attribution. In the case of an error, the primary responsibility remains with the physician, with possible shared liability with developers according to the framework of medical device liability. If there is disagreement with the AI’s findings, the physician must not only pursue but also justify their choices according to prevailing professional standards. Regulations must balance the autonomy of AI systems with the need for responsible clinical practice. Effective use of AI-generated evaluations requires knowledge of data dynamics and metrics like sensitivity and specificity, even without a clear understanding of the underlying algorithms: the opacity (referred to as the “black box phenomenon”) of certain systems raises concerns about the interpretation and actual usability of results for both physicians and patients. AI is redefining healthcare, underscoring the imperative for robust liability frameworks, meticulous updates of systems, and transparent patient communication regarding AI involvement. Full article

Computational intelligence (CI) uses applied computational methods for problem-solving inspired by the behavior of humans and animals. Biological systems are used to construct software to solve complex problems, and one type of such system is an artificial immune system (AIS), which imitates the immune system of a living body. AISs have been used to solve problems that require identification and learning, such as computer virus identification and removal, image identification, and function optimization problems. In the body’s immune system, a wide variety of cells work together to distinguish between the self and non-self and to eliminate the non-self. AISs enable learning and discrimination by imitating part or all of the mechanisms of a living body’s immune system. Certainly, some deep neural networks have exceptional performance that far surpasses that of humans in certain tasks, but to build such a network, a huge amount of data is first required. These networks are used in a wide range of applications, such as extracting knowledge from a large amount of data, learning from past actions, and creating the optimal solution (the optimization problem). A new technique for pre-training natural language processing (NLP) software ver.9.1by using transformers called Bidirectional Encoder Representations (BERT) builds on recent research in pre-training contextual representations, including Semi-Supervised Sequence Learning, Generative Pre-Training, ELMo (Embeddings from Language Models), which is a method for obtaining distributed representations that consider context, and ULMFit (Universal Language Model Fine-Tuning). BERT is a method that can address the issue of the need for large amounts of data, which is inherent in large-scale models, by using pre-learning with unlabeled data. An optimization problem involves “finding a solution that maximizes or minimizes an objective function under given constraints”. In recent years, machine learning approaches that consider pattern recognition as an optimization problem have become popular. This pattern recognition is an operation that associates patterns observed as spatial and temporal changes in signals with classes to which they belong. It involves identifying and retrieving predetermined features and rules from data; however, the features and rules here are not logical information, but are found in images, sounds, etc. Therefore, pattern recognition is generally conducted by supervised learning. Based on a new theory that deals with the process by which the immune system learns from past infection experiences, the clonal selection of immune cells can be viewed as a learning rule of reinforcement learning. Full article

Artificial intelligence systems (AISs) have become a part of our lives, with many even allowing themselves to be “programmed” by AI-based applications. However, AI can also aid people in carrying out various activities. The third hype of AI has focused on the exploration of an exponentially growing amount of data, most of which are not managed. What might the fourth hype be? The pursuit of the dream of AI initiators of building a machine more intelligent than humans and the race to achieve computer power raise some questions: is this compatible with human and planetary sustainability? How far can AI research and applications go? What future directions could AI research and businesses take? This paper will present perspectives on the synergy between humans and AI systems. Two aspects are discussed: the empowerment of humans through AI, and the use of AI to protect the planet, with the aim of trying to answer the difficult question of how we can balance researchers’ ambitions, greedy businesses, and sustainable development with protecting the planet. Full article