Software Architecture

Pretend play architectures utilize rule-bound simulations of non-literal situations to train AI systems by creating controlled environments where abstract concepts gain physical form through interactive narratives. Scenario fidelity refers to the degree to which a simulated environment preserves causal relationships relevant to real-world domains, ensuring that an action taken within the simulation yields a result consistent with physical laws or social dynamics found outside

Initial artificial intelligence research prioritized general problem-solving capabilities that inherently included embedded agency, allowing systems to interact with and modify their environments to achieve specified goals through feedback loops and environmental manipulation. This method relied on the assumption that intelligence necessitated action, leading to architectures where the system pursued objectives autonomously using internal models of the world to plan sequences

Complexity science and systems theory, originating in the 1980s, provide the foundational basis for this field by establishing that non-linear dynamics govern the evolution of knowledge within closed and open systems alike. These early theoretical frameworks moved researchers away from linear cause-and-effect models toward an understanding that simple rules can generate complex behaviors through iterative feedback loops. Innovation management literature subsequently adopted p

The relentless pursuit of miniaturization in semiconductor manufacturing has encountered formidable physical barriers as transistor dimensions approach the scale of individual atoms, causing quantum tunneling effects that disrupt electron containment and lead to significant leakage currents. This scaling limit implies that traditional silicon-based architectures can no longer sustain the exponential growth in computational power required by modern artificial intelligence mode

Recursive self-improvement constitutes a theoretical framework wherein an artificial intelligence system autonomously designs and implements a successor system possessing enhanced capabilities, thereby establishing a continuous chain of increasingly intelligent entities that iteratively surpass their predecessors. This framework marks a definitive departure from traditional human-directed AI development cycles, shifting the locus of innovation from external engineering teams

Monolithic end-to-end AI models present systemic safety risks due to opaque decision pathways and a lack of internal boundaries within their computational graphs. These architectures typically utilize deep neural networks trained via gradient descent to map raw inputs directly to outputs, creating a dense mesh of weighted connections where information flows through millions of non-linear transformations without distinct checkpoints or semantic delineations. This structural ho

Goal hierarchies structure objectives into layered formats where high-level aims decompose into subordinate subgoals to facilitate systematic execution and verification within complex computational systems. A goal is a measurable objective with defined success criteria that allow an autonomous agent to determine when a specific state has been achieved or when a particular condition has been satisfied within the operational environment. A subgoal functions as a necessary step

Defining agenthood requires a rigorous understanding of system dynamics where the property of agency exists exclusively at the system level rather than within individual sub-components. A system achieves agenthood when it possesses a unified objective function that directs its behavior toward a specific set of goals, whereas any internal process that begins to exhibit goal-directed behavior independent of this top-level objective is a core architectural failure. Such internal