Biomimetic cognitive architecture for ai systems

A computer system for cognitive processing, comprising:

• a compute device having a processor and in communication with a memory device, where in the memory device stores computer instructions that, when executed by the one or more processors, cause the computer system to implement:
• a top-level cognitive architecture comprising a plurality of cognitive modules including an integration hub, a hierarchical memory module, an attention control module, a learning and feedback module, a planning and risk assessment module, an execution and response module, and an intelligence processing module;
• wherein at least one cognitive module instantiates a subordinate cognitive architecture sharing a common structural blueprint with the top-level cognitive architecture, the subordinate cognitive architecture comprising its own integration hub and its own instances of at least a subset of the plurality of cognitive modules, and specializing through configuration parameters determined by the parent cognitive module's purpose;
• wherein the subordinate cognitive architecture operates as a self-contained cognitive processing unit that autonomously plans, executes, monitors, and learns within its specialized domain while coordinating with the top-level cognitive architecture through message passing via the top-level integration hub;
• and
• wherein the planning and risk assessment module at each level of the architecture evaluates whether to instantiate further subordinate cognitive architectures based on whether computational overhead of further instantiation exceeds a cognitive benefit of additional specialization.

A computer system for capturing tacit knowledge, comprising:

• a server having computer readable instructions that adapt the server to:
• receive observational data streams from a user's professional information system, the observational data streams comprising at least two of textual inputs, auditory inputs, visual inputs, and behavioral pattern inputs;
• detect, by a bottom-up attention mechanism comprising a stimulus processing component and a novelty processing component, a deviation between an observed expert behavior pattern within the observational data streams and a predicted behavior pattern derived from a hierarchical memory module;
• classify, by a top-down attention mechanism comprising a goal-driven processing component and a task management component, the deviation as a potential tacit knowledge signal when the deviation aligns with configured knowledge acquisition objectives, and suppress the deviation when the deviation falls outside the configured knowledge acquisition objectives;
• route, by an integration hub, a classified tacit knowledge signal to a plurality of specialized intelligence sub-modules selected based on characteristics of the classified tacit knowledge signal;
• generate, by the selected specialized intelligence sub-modules operating in parallel with dynamically assigned weights, a structured interpretation of the tacit knowledge signal comprising interpreted knowledge content, contributing intelligence types, and confidence levels;
• validate, by an intelligence integration system, the structured interpretation through cross-validation against existing knowledge stored in the hierarchical memory module and error prevention evaluation that distinguishes the observed expert behavior pattern from expert errors;
• and
• store validated tacit knowledge that meets a minimum confidence threshold in a long-term memory component of the hierarchical memory module, and maintain tacit knowledge that does not meet the minimum confidence threshold in working memory as a provisional observation pending confirmation through subsequent consistent observations.

A computer system for operating a knowledge infrastructure, comprising:

• a server;
• a monitoring channel included in the server and configured to receive data streams from one or more input sources associated with a domain, the one or more input sources comprising at least one of textual, visual, auditory, behavioral, and sensory data streams, and to detect deviations between observed behavior patterns within the data streams and predicted behavior patterns derived from knowledge stored in a hierarchical memory module;
• an autonomous acquisition channel included in the server comprising a curiosity architecture configured to identify knowledge gaps based on information stored in the hierarchical memory module, generate retrieval queries targeting the identified knowledge gaps, and retrieve information from at least one of an internal data source, a third-party data source, and an external data source without human intervention;
• an elicitation channel included in the server comprising a question generation component configured to generate context-driven queries directed to a user to capture the user's knowledge when the curiosity architecture determines that a knowledge gap cannot be resolved through the autonomous acquisition channel;
• an integration hub included in the server configured to coordinate the monitoring channel, the autonomous acquisition channel, and the elicitation channel, wherein the integration hub routes unresolved knowledge gaps identified by one channel to another channel based on gap characteristics;
• and
• a hierarchical memory module in electronic communications with the server configured to retain knowledge obtained from at least one of the monitoring channel, the autonomous acquisition channel, and the elicitation channel in a form accessible for subsequent retrieval.

A computer system for cognitive processing, comprising:

• a computer device having a processor and in communication with a memory device, wherein the memory device stores computer instructions that, when executed by the processor, cause the computer system to implement:
• a plurality of cognitive modules, each configured to perform a specialized cognitive function;
• and
• an integration hub configured to coordinate operations across the plurality of cognitive modules, the integration hub comprising:
• a module synchronization unit configured to generate coordination signals at a plurality of frequencies, wherein the module synchronization unit implements cross-frequency coupling wherein an amplitude envelope of a higher-frequency coordination signal is modulated according to a phase of a lower-frequency coordination signal, creating nested timing windows for inter-module communications;
• and
• a dynamic routing processor configured to route information between the cognitive modules based on task requirements.