Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

TLDR

Deep Learning has become a driving force in AI, excelling at extracting high‑level features and achieving human‑level performance across domains such as clinical diagnostics, robotics, genomics, neuroimaging, computer vision, and industrial automation. The paper aims to present and review the most relevant recent advances in AI and their applications to neuroscience, neuroimaging, computer vision, and robotics. The authors summarize state‑of‑the‑art AI methods, models, and applications from works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works highlighted in the paper exemplify scientific discoveries that have successfully transitioned from laboratory settings to real‑world applications.

Abstract

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.