With its various available frameworks and possible devices, augmented reality is a proven useful tool in various industrial processes such as maintenance, repairing, training, reconfiguration, and even monitoring tasks of production lines in large factories. Despite its advantages, augmented reality still does not usually give meaning to the elements it complements, staying in a physical or geometric layer of its environment and without providing information that may be of great interest to industrial operators in carrying out their work. An expert’s remote human assistance is becoming an exciting complement in these environments, but this is expensive or even impossible in many cases. This paper shows how a machine learning semantic layer can complement augmented reality solutions in the industry by providing an intelligent layer, sometimes even beyond some expert’s skills. This layer, using state-of-the-art models, can provide visual validation and new inputs, natural language interaction, and automatic anomaly detection. All this new level of semantic context can be integrated into almost any current augmented reality system, improving the operator’s job with additional contextual information, new multimodal interaction and validation, increasing their work comfort, operational times, and security.

Generative AI presents a profound challenge to traditional notions of human uniqueness, particularly in creativity. Fueled by neural network based foundation models, these systems demonstrate remarkable content generation capabilities, sparking intense debates about authorship, copyright, and intelligence itself. This paper argues that generative AI represents an alternative form of intelligence and creativity, operating through mathematical pattern synthesis rather than biological understanding or verbatim replication. The fundamental differences between artificial and biological neural networks reveal AI learning as primarily statistical pattern extraction from vast datasets crystallized forms of collective human knowledge scraped from the internet. This perspective complicates copyright theft narratives and highlights practical challenges in attributing AI outputs to individual sources. Rather than pursuing potentially futile legal restrictions, we advocate for human AI synergy. By embracing generative AI as a complementary tool alongside human intuition, context, and ethical judgment, society can unlock unprecedented innovation, democratize creative expression, and address complex challenges. This collaborative approach, grounded in realistic understanding of AIs capabilities and limitations, offers the most promising path forward. Additionally, recognizing these models as products of collective human knowledge raises ethical questions about accessibility ensuring equitable access to these tools could prevent widening societal divides and leverage their full potential for collective benefit.

Industrial environments frequently rely on analog control instruments due to their reliability and robustness; however, automating the interpretation of these controls remains challenging due to variability in design, lighting conditions, and scale precision requirements. This research investigates the effectiveness of Vision–Language Models (VLMs) for automated interpretation of industrial controls through analysis of three distinct approaches: general-purpose VLMs, fine-tuned specialized models, and lightweight models optimized for edge computing. Each approach was evaluated using two prompting strategies, Holistic-Thought Protocol (HTP) and sequential Chain-of-Thought (CoT), across a representative dataset of continuous and discrete industrial controls. The results demonstrate that the fine-tuned Generative Pre-trained Transformer 4 omni (GPT-4o) significantly outperformed other approaches, achieving low Mean Absolute Error (MAE) for continuous controls and the highest accuracy and Matthews Correlation Coefficient (MCC) for discrete controls. Fine-tuned models demonstrated less sensitivity to prompt variations, enhancing their reliability. In contrast, although general-purpose VLMs showed acceptable zero-shot performance, edge-optimized models exhibited severe limitations. This work highlights the capability of fine-tuned VLMs for practical deployment in industrial scenarios, balancing precision, computational efficiency, and data annotation requirements.

This work describes the implementation of a flip teaching alternative in an introductory game development course, using resources from a MOOC (Massive Open On-Line Course). The results proved to achieve better grades and higher satisfaction to previous and similar lecture-based courses.