Publication Library
WIPO TREATY ON INTELLECTUAL PROPERTY, GENETIC RESOURCES AND ASSOCIATED TRADITIONAL KNOWLEDGE 2024
Description: WIPO TREATY ON INTELLECTUAL PROPERTY, GENETIC RESOURCES AND ASSOCIATED TRADITIONAL KNOWLEDGE
Created At: 29 January 2025
Updated At: 29 January 2025
BetaExplainer - A Probabilistic Method to Explain Graph Neural Networks
Description: Graph neural networks (GNNs) are powerful tools for conducting inference on graph data but are often seen as "black boxes" due to difficulty in extracting meaningful subnetworks driving predictive performance. Many interpretable GNN methods exist, but they cannot quantify uncertainty in edge weights and suffer in predictive accuracy when applied to challenging graph structures. In this work, we proposed BetaExplainer which addresses these issues by using a sparsity-inducing prior to mask unimportant edges during model training. To evaluate our approach, we examine various simulated data sets with diverse real-world characteristics. Not only does this implementation provide a notion of edge importance uncertainty, it also improves upon evaluation metrics for challenging datasets compared to state-of-the art explainer methods.
Created At: 26 January 2025
Updated At: 26 January 2025
Comprehensive Insights into Drones - Challenges, and Future Trends
Description: Unmanned Aerial Vehicles (UAVs), commonly known as Drones, are one of 21st century most transformative technologies. Emerging first for military use, advancements in materials, electronics, and software have catapulted drones into multipurpose tools for a wide range of industries. In this paper, we have covered the history, taxonomy, architecture, navigation systems and branched activities for the same. It explores important future trends like autonomous navigation, AI integration, and obstacle avoidance systems, emphasizing how they contribute to improving the efficiency and versatility of drones. It also looks at the major challenges like technical, environmental, economic, regulatory and ethical, that limit the actual take-up of drones, as well as trends that are likely to mitigate these obstacles in the future. This work offers a structured synthesis of existing studies and perspectives that enable insights about how drones will transform agriculture, logistics, healthcare, disaster management, and other areas, while also identifying new opportunities for innovation and development.
Created At: 26 January 2025
Updated At: 26 January 2025
As Confidence Aligns - Exploring the Effect of AI Confidence on Human Self-confidence in Human-AI Decision Making
Description: Complementary collaboration between humans and AI is essential for human-AI decision making. One feasible approach to achieving it involves accounting for the calibrated confidence levels of both AI and users. However, this process would likely be made more difficult by the fact that AI confidence may influence users' self-confidence and its calibration. To explore these dynamics, we conducted a randomized behavioral experiment. Our results indicate that in human-AI decision-making, users' self-confidence aligns with AI confidence and such alignment can persist even after AI ceases to be involved. This alignment then affects users' self-confidence calibration. We also found the presence of real-time correctness feedback of decisions reduced the degree of alignment. These findings suggest that users' self-confidence is not independent of AI confidence, which practitioners aiming to achieve better human-AI collaboration need to be aware of. We call for research focusing on the alignment of human cognition and behavior with AI.
Created At: 26 January 2025
Updated At: 26 January 2025
Improving robot understanding using conversational AI - demonstration and feasibility study
Description: Explanations constitute an important aspect of successful human robot interactions and can enhance robot understanding. To improve the understanding of the robot, we have developed four levels of explanation (LOE) based on two questions: what needs to be explained, and why the robot has made a particular decision. The understandable robot requires a communicative action when there is disparity between the human s mental model of the robot and the robots state of mind. This communicative action was generated by utilizing a conversational AI platform to generate explanations. An adaptive dialog was implemented for transition from one LOE to another. Here, we demonstrate the adaptive dialog in a collaborative task with errors and provide results of a feasibility study with users.
Created At: 26 January 2025
Updated At: 26 January 2025