arXiv subjects

Patrick Connolly

Publications and source records attributed to Patrick Connolly.

3 recordsLinked to original sources

arXiv2024

Responsible AI in the Global Context: Maturity Model and Survey

Responsible AI (RAI) has emerged as a major focus across industry, policymaking, and academia, aiming to mitigate the risks and maximize the benefits of AI, both on an organizational and societal level. This study explores the global state of RAI through one of the most extensive surveys to date on the topic, surveying 1000 organizations across 20 industries and 19 geographical regions. We define a conceptual RAI maturity model for organizations to map how well they implement organizational and operational RAI measures. Based on this model, the survey assesses the adoption of system-level measures to mitigate identified risks related to, for example, discrimination, reliability, or privacy, and also covers key organizational processes pertaining to governance, risk management, and monitoring and control. The study highlights the expanding AI risk landscape, emphasizing the need for comprehensive risk mitigation strategies. The findings also reveal significant strides towards RAI maturity, but we also identify gaps in RAI implementation that could lead to increased (public) risks from AI systems. This research offers a structured approach to assess and improve RAI practices globally and underscores the critical need for bridging the gap between RAI planning and execution to ensure AI advancement aligns with human welfare and societal benefits.

cs.CY↗

arXiv2022

Designing a boron nitride polyethylene composite for shielding neutrons

Neutrons are encountered in many different fields, including condensed matter physics, space exploration, nuclear power, and healthcare. Neutrons interacting with a biological target produce secondary charged particles that are damaging to human health. The most effective way to shield neutrons is to slow them to thermal energies and then capture the thermalized neutrons. These factors lead us to consider potential materials solutions for neutron shields that maximize the protection of humans while minimizing the shield mass, and which adapt well to modern additive manufacturing techniques. Using hexagonal boron nitride (hBN) as a capture medium and high-density polyethylene (HDPE) as a thermalization medium, we aim to design the optimal internal structure of h$^{10}$BN/HDPE composites by minimizing the effective dose, which is a measure of the estimated radiation damage exposure for a human. Through Monte Carlo simulations in Geant4, we find that the optimal structure reduces the effective dose up to a factor of 72x over aluminum (Al) and 4x over HDPE; this is a significant improvement in shielding effectiveness that could dramatically reduce the radiation exposure of occupational workers.

physics.app-ph↗

arXiv2020

Uncertainty-based Modulation for Lifelong Learning

The creation of machine learning algorithms for intelligent agents capable of continuous, lifelong learning is a critical objective for algorithms being deployed on real-life systems in dynamic environments. Here we present an algorithm inspired by neuromodulatory mechanisms in the human brain that integrates and expands upon Stephen Grossberg\'s ground-breaking Adaptive Resonance Theory proposals. Specifically, it builds on the concept of uncertainty, and employs a series of neuromodulatory mechanisms to enable continuous learning, including self-supervised and one-shot learning. Algorithm components were evaluated in a series of benchmark experiments that demonstrate stable learning without catastrophic forgetting. We also demonstrate the critical role of developing these systems in a closed-loop manner where the environment and the agent\'s behaviors constrain and guide the learning process. To this end, we integrated the algorithm into an embodied simulated drone agent. The experiments show that the algorithm is capable of continuous learning of new tasks and under changed conditions with high classification accuracy (greater than 94 percent) in a virtual environment, without catastrophic forgetting. The algorithm accepts high dimensional inputs from any state-of-the-art detection and feature extraction algorithms, making it a flexible addition to existing systems. We also describe future development efforts focused on imbuing the algorithm with mechanisms to seek out new knowledge as well as employ a broader range of neuromodulatory processes.

cs.LG↗