Building Human-Like Artificial Intelligence: A Research Synthesis

The creation of Artificial General Intelligence (AGI), particularly systems exhibiting human-like cognitive capabilities, represents a long-standing and formidable scientific challenge. This paper synthesizes research across artificial intelligence, cognitive science, neuroscience, and philosophy to provide a comprehensive overview of the multifaceted endeavor to build human-like AI. We examine the cognitive foundations differentiating human and artificial intelligence, delving into fundamental challenges such as embodiment and symbol grounding , causality and commonsense reasoning , human-like memory systems , the potential for artificial consciousness , and the critical issues of alignment, ethics, and safety. The paper reviews major architectural paradigms, including cognitive architectures (symbolic, emergent, hybrid like Soar, ACT-R, LIDA) , Large Language Models (LLMs) as potential precursors , and neuro-symbolic approaches aiming to integrate learning and reasoning. We assess the state-of-the-art in key capabilities like multimodal perception , adaptive learning (lifelong, few-shot, transfer) , advanced reasoning, natural language understanding , affective computing , and computational creativity. Furthermore, we discuss the crucial need for operationalizing progress, examining frameworks like the "Levels of AGI" and the inherent challenges in benchmarking genuine human-like intelligence. The synthesis reveals that achieving human-like AI likely requires integrating diverse approaches, moving beyond current paradigms towards hybrid, neuro-symbolic, and embodied systems. Significant research gaps persist in foundational understanding, architectural innovation, robust capability development, evaluation methodologies, and ethical governance. This paper concludes by identifying critical future directions, emphasizing the necessity of interdisciplinary collaboration, foundational research, and a steadfast commitment to responsible development to navigate the complex path toward truly human-like artificial intelligence