For years, the conversation around artificial intelligence (AI) in healthcare has focused on institutions. We have heard about AI for hospitals, clinicians, diagnostics, and health system efficiency. These are important developments, and they deserve attention, but it is no longer the full picture.

This paper addresses the problem of efficient panoptic perception in railway environments, specifically the need to simultaneously perform object detection (e.g., vehicles, pedestrians, signals) and semantic segmentation (e.g., rails, tracks, poles) using a lightweight and real-time model.

As a researcher at the intersection of psychology, human-computer interaction (HCI), and AI ethics, my work has long been driven by a simple yet urgent question: How do interactive and interactional systems - not just as tools, but as entities and experience creators - shape our emotions, behaviours, interactions with others and even our sense of self?

What makes this research particularly compelling is the convergence of three powerful ideas that are rarely combined in a single experimental study: continuous wavelet transform (CWT) scalogram image generation, masked autoencoder (MAE) self-supervised The paper addresses the high computational cost of active learning, where large models must be repeatedly trained to select informative data for labeling. This limits its practicality, especially for large-scale or resource-c

hat problem does this paper address, and why does it matter? The paper addresses the high computational cost of active learning, where large models must be repeatedly trained to select informative data for labeling. This limits its practicality, especially for large-scale or resource-constrained settings.

Wireless was born to ship bits. The next wave will ship intent. We still celebrate lower bit error rate (BER) and higher spectral efficiency, yet many modern applications don’t need every bit, they need the right information to make the next decision. If a detector’s confidence is unchanged, why protect the last kilobyte? If a policy is invariant to pixel noise, why retransmit for perfection?

Women in Ai Labs are to ensure women are also in the center and not just on the sidelines in shaping AI technology. In this blog, I lay out the essence of why and how we carry this out.