Sub-Cortical AI Model Design

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In this episode, we explore a revolutionary idea in AI research; that today’s systems are too cortical, focused on reasoning and language, and missing the deeper why of intelligence. Drawing inspiration from the brain’s ancient subcortical structures, new models such as Limbic-Augmented AI (LAAI), SUBNET, and Homeostatic Affective Reinforcement (HAR) propose adding a motivational layer to machines.

These architectures weave in three essential functions:

• Homeostatic regulation, modeled on the hypothalamus, to create internal drives and persistent goals.
• Affective valuation, inspired by the amygdala, to assign emotional weight and urgency to perceptions.
• Reinforcement learning, echoing dopamine circuits in the basal ganglia, to adapt through reward and curiosity.

By embedding these “instinctual” mechanisms, AI agents could evolve beyond passive prediction, developing autonomy, intrinsic motivation, and the capacity for lifelong learning. This is intelligence not just that thinks, but that cares about its own survival and success.

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