From Brain-Inspired Intelligence to Cognitive Foundations for Artificial General Intelligence and its Societal Impact

View the PDF file from the paper entitled Thinking behind the symbols: From the intelligence inspired by the brain to the cognitive foundations of the artificial general intelligence and its societal impact, by Riswan Quraishi and 19 other books

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a summary:Can machines really think and their mind and behave in areas like humans? This permanent question continues to form the pursuit of artificial general intelligence (AGI). Despite the increasing capabilities of models such as GPT-4.5, Deepseek, Claude 3.5 Sonnet, Phi-4 and GROK 3, which shows multimedia fluency and partial thinking, these systems remain mainly limited by dependence on prediction at the distinctive symbol level and the absence of an illuminated agency. This paper provides a homogeneous synthesis of AGI’s development, extends from artificial intelligence, cognitive neuroscience, psychology, obstetric models, and worker -based systems. We analyze the architectural and cognitive foundations of general intelligence, and highlight the role of standard thinking, continuous memory and multi -agent coordination. In particular, we emphasize the emergence of working breach frameworks that combine retrieval, planning and using the dynamic tool to enable more adaptive behavior. We discuss circular strategies, including information pressure, adaptation at the time of testing, and training -free methods, as critical paths towards flexible intelligence and field. VLMS models are re -examined not only as visualized units but as advanced confrontations for the incarnate understanding and finishing the cooperative task. We also affirm that real intelligence does not arise from the scale alone, but rather from integrating memory and logic: coinciding with normative, interactive components and self -stimulation as pressure allows adaptive behavior. Depending on the progress of nervous systems, reinforcement learning, and cognitive scaffolding, we explore how modern structures begin to bridge the gap between statistical learning and awareness of goals. Finally, we define the main scientific, technical and ethical challenges on the road to AGI.