Wind energy is an important renewable energy source, and artificial intelligence (AI) plays an important role in improving its efficiency, reliability and cost-effectiveness while minimizing its environmental impact. Based on an analysis of the latest scientific literature, this article examines AI applications for the entire life cycle of wind turbines, including planning, operation and decommissioning. A key focus is on AI-driven maintenance, which reduces downtime, improves reliability and extends the lifetime of the turbines. AI also optimizes the design of wind turbines, particularly in the development of aerodynamically efficient blade shapes through rapid design iterations. In addition, AI helps to reduce the impact on the environment, e.g., by reducing bird collisions, and improves wind energy forecasting, which is essential for balancing energy flows in power systems. Despite its benefits, AI applications face challenges, including algorithmic errors, data accuracy, ethical concerns and cybersecurity risks. Further testing and validation of AI algorithms is needed to ensure their effectiveness in advancing wind energy systems.

Predicting innovation outcomes at the firm level continues to be an important but challenging goal for researchers and practitioners alike. In this study, multiple machine learning models, encompassing both ensemble-based and single-model approaches, were applied to data from the Community Innovation Survey. Methods included random forests, gradient boosting frameworks, support vector machines, neural networks, and logistic regression, each with hyperparameters optimized through Bayesian search routines and evaluated using corrected cross-validation techniques. The results showed that tree-based boosting algorithms consistently outperformed other models in accuracy, precision, F1-score, and ROC-AUC, while the kernel-based approach excelled in recall. Logistic regression proved to be the most computationally efficient model despite its weaker predictive power. The statistical analyses made it clear that the choice of an appropriate cross-validation protocol and accounting for overlapping data splits are crucial to reduce bias and ensure reliable comparisons. Overall, the results indicate that ensemble methods generally provide robust classification performance for innovation prediction tasks. However, individual models may still prove advantageous under certain metric-specific conditions or computational constraints. These observations emphasize the need to match model selection with data structure, performance objectives, and practical resource constraints when predicting and improving innovation outcomes at the firm level.

The paper aims to address large language models’ (LLMs) cultural bias using the World Value Survey Wave 7 (WVS) questionnaire on social values, attitudes, and stereotypes. Comparative analysis and LLMs interview methods measure the Euclidean distance of response vectors of four culturally diverse LLMs (USA, China, Russia, UAE) in a multidimensional vector space to contrast originated WVS research countries and population positions. The results confirmed the initial hypotheses reflecting culturally and linguistically biased LLM answers, considering specific socio-cultural contexts and English language and Latin script digital dominance in available training materials. USA-constructed LLMs showed the most liberal attitudes, followed by China, Russia, and the UAE. LLM interview results also show WVS results closest to the United States population, positioning the similarity of the responses in first place for China and Russia followed by the USA and the UAE. Mitigating initiatives in LLMs’ cultural and linguistic debiasing is required to preserve cultural and linguistic diversity in the digital space.