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These sources explore the pursuit of advanced artificial intelligence by mimicking the biological structure and learning processes of the human brain. One text outlines a theoretical cognitive architecture designed to help machines gain common sense and planning abilities through internal world models and self-supervised learning. Complementing this vision, the other articles examine the hardware innovations necessary to support such systems, specifically focusing on neuromorphic computing and electro-photonic chips. These technological shifts aim to overcome the massive energy demands of current AI by using light-based data transmission and artificial neurons. Together, the materials present a roadmap for creating autonomous agents that are both intellectually sophisticated and physically efficient. This interdisciplinary effort bridges neuroscience, engineering, and computer science to redefine how machines interact with the world.