  The intersection of Artificial Intelligence (AI) and neuroscience in
Explainable AI (XAI) is pivotal for enhancing transparency and interpretability
in complex decision-making processes. This paper explores the evolution of XAI
methodologies, ranging from feature-based to human-centric approaches, and
delves into their applications in diverse domains, including healthcare and
finance. The challenges in achieving explainability in generative models,
ensuring responsible AI practices, and addressing ethical implications are
discussed. The paper further investigates the potential convergence of XAI with
cognitive sciences, the development of emotionally intelligent AI, and the
quest for Human-Like Intelligence (HLI) in AI systems. As AI progresses towards
Artificial General Intelligence (AGI), considerations of consciousness, ethics,
and societal impact become paramount. The ongoing pursuit of deciphering the
mysteries of the brain with AI and the quest for HLI represent transformative
endeavors, bridging technical advancements with multidisciplinary explorations
of human cognition.
