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.

Older adults often need guidance when visiting new buildings for the first time. However, indoor navigation remains challenging due to the lack of Global Positioning System (GPS) availability, visually repetitive corridors, and frequent location failures. This article presents a multimodal indoor navigation assistant that combines graph-based route planning with visual landmark verification to provide step-by-step guidance. The environment is modelled as a directed graph whose nodes are annotated with semantic landmarks, and the graph is constructed primarily from a video of the building, reducing the need for 3D scanners, beacons, or other specialised instruments. Routes are calculated using Dijkstra’s shortest-path algorithm over the semantic graph. During navigation, camera frames are analysed using a restricted vision-language recognition strategy that only considers candidate landmarks from the current and next nodes, reducing false detections and improving interpretability. To increase robustness, a temporary voting mechanism was introduced to confirm node transitions, as well as a hierarchical redirection strategy with local and global recovery. The system is implemented in two modes: handheld mode with visual cues using augmented reality arrows, mini map and voice instructions, and hands-free mode with front camera using voice instructions and keywords. Evaluation involved preliminary technical testing in the United Kingdom followed by formal user validation in Spain. During these trials, participants reported high usability, strong confidence and safety, and increased perceived independence.

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.

Aphasia, often resulting from brain injuries, significantly impairs individuals’ language abilities, creating substantial challenges for verbal communication. Existing assistive technologies frequently fall short in addressing these specialised communication needs, underscoring the urgent demand for adaptive, intelligent support systems. This research proposes a dual approach: an Automatic Speech Recognition (ASR) module fine-tuned on aphasic speech, and a multimodal component that integrates visual context to infer the speaker’s intended meaning. The ASR system leverages fine-tuned versions of Whisper and Wav2Vec 2.0 on data from the AphasiaBank corpus. Results show a notable reduction in Word Error Rate (WER) when comparing base pre-trained ASR models with their finetuned versions, decreasing from 70.36% to 31.53% in a contextindependent setting, and from 61.25% to 35.60% in a speaker-independent evaluation, demonstrating robustness across different scenarios. In contrast to the ASR module, the goal of the multimodal component is not to produce a literal word-by-word transcription, but rather to reconstruct the speaker’s communicative intent using contextual information. To evaluate this capability, we conducted a human study assessing the system’s ability to interpret what the speaker truly meant. The results confirmed that outputs combining visual cues with language model reasoning more reliably captured communicative intent than audio-only transcriptions.