Abstract: A computer-based neurosymbolic system for deterministic policy compliance verification combines neural language models for flexible natural language understanding with symbolic reasoning for deterministic, auditable rule evaluation. The system operates in two phases: (1) Configuration Time, where policy documents 20 are processed to automatically extract formal rules 10 and generate a minimum viable extraction schema 11, 23 containing only information required by the rules; (2) Inference Time, where input documents 24 are processed to extract only policy-relevant information 12, 17, which is evaluated against the rules 10 using deterministic symbolic reasoning. In an embodiment, information extraction 25 uses automatically generated questions 23 with explicit UNKNOWN handling, and compliance is determined using three-valued logic (compliant/non-compliant/cannot-determine), providing epistemic clarity and thwarting hallucinations.

Abstract: Apparati and methods for designing and evaluating machine learning (ML) and other computer systems at the metadata level. A unique graphical meta-level formalism for representing these systems is employed, which supports both human and machine evaluation, simulation, and evolution of alternate architectures and designs. Each graph comprises a plurality of nodes and a plurality of edges connecting the nodes. Each node represents an operation that produces at least one outbound feature, while each edge represents a set of features. The graph (or a subgraph within the graph) can be reconfigured by applying a transform operation to the graph or subgraph.

Abstract: Domain-specific computer languages (DSL's) 22, such as DSAIL (Domain-Specific Artificial Intelligence Language) are used as bridges for combining the best attributes of generative AI models 21 such as Large Language Models (LLM's) with formal reasoning systems such as logical solvers 25, to combat hallucinations that can be introduced by the LLM's 21 and to automate the process of discovering alternative solutions to problems posed by human users 2. The creativity of the LLM's 21 and the rigorous validation provided by the logical solver(s) 25 are thus both present in the solutions produced by the generative AI. The DSL 22 and a Model of Computation module 24 function as the primary means of communication between the AI model 21 and the solver(s) 25.

Abstract: Methods and apparati for continuous growth, re-use, and application of automated labelers 4, 7 for machine learning algorithms into ensembles 10. A method embodiment of the present invention comprises an iterative cycle (steps 11 through 15) in which data 2 is collected, indexed, and then used to create labelers 4 to generate training data for supervised and semi-supervised machine learning algorithms. A new set of unlabeled training data 5 is then similarly indexed and combined with the most similar, relevant, or useful previous labelers 4 by means of index 6, 3 comparisons in order to create an optimized ensemble 10 of labelers 4, 7, thus maximizing the training value of the labels generated from the labelers 4, 7.