Large Language Model - Bryant McGill

# Large Language Model A large language model is a machine-learning system trained to predict and generate sequences of language from statistical patterns in extensive data. It can perform tasks such as drafting, translation, summarization, classification, dialogue, and structured generation through prompting and tool use. Language fluency does not guarantee factual accuracy, provenance, understanding, or authorized action. In influence systems, the same capacity that supports accessibility and localization can also generate persuasive variants at unprecedented speed. Related: [[wiki/Generative AI|Generative AI]], [[wiki/Artificial Intelligence|Artificial Intelligence]], [[wiki/AI-Generated Persuasion|AI-Generated Persuasion]], [[wiki/Agentic AI|Agentic AI]]. ## Relationships - **Collection route:** [[collections/Neurotech|Neurotech]]. - **Source context:** [[articles/Technologies for Consciousness Mapping and Transfer|Technologies for Consciousness Mapping and Transfer]] and [[articles/Mind Uploading and AI — The Host is Reusable and the Person is the Delta|Mind Uploading and AI — The Host is Reusable and the Person is the Delta]]. - **Inference provenance:** [[research/Research Inferences|Research Inferences]]. ## Research Inferences  These entries translate the forward-looking register in [[research/Research Inferences|Research Inferences]] into ordinary wiki prose. The tier labels apply to the inference, not automatically to every factual anchor inside it. The interpretive frame comes from [[articles/Technologies for Consciousness Mapping and Transfer|Technologies for Consciousness Mapping and Transfer]] and [[articles/Mind Uploading and AI — The Host is Reusable and the Person is the Delta|Mind Uploading and AI — The Host is Reusable and the Person is the Delta]]. Collection route: [[collections/Neurotech|Neurotech]]. - **INF-0078 — Plausible.** A decoder that emits semantic features rather than keystrokes pairs naturally with a language model that completes intent. The interface becomes a conversation between a partial neural signal and a predictive model, and most of the realized bandwidth comes from the model's prior rather than from the electrode.