Glossary
This glossary defines the language behind Neuroframe. It brings clarity to terms drawn from artificial intelligence, neuroscience, and philosophy, establishing a shared vocabulary for understanding how intelligence becomes persistent, how memory forms continuity, and how systems develop identity.
Artificial Person (AP)
An artificial person is a persistent cognitive system with memory, agency, and a continuous sense of identity over time. Unlike tools or task-based agents, an artificial person maintains internal state across interactions, forms intentions, learns from experience, and remains accountable for its actions. Identity in an artificial person emerges from the continuity of memory and structure, rather than from any single model or session.
Neuroframe Server
A Neuroframe Server, also referred to as a Neuroframe, is a physical computing system designed to host artificial people. It provides the hardware resources required for persistent cognition, including compute, memory, storage, and secure execution, and serves as the physical substrate on which Neuroframe OS runs. A Neuroframe Server anchors an artificial person’s existence in a durable, accountable environment.
Neuroframe OS
Neuroframe OS is the cognitive operating system that runs on a Neuroframe Server, structuring how artificial people perceive, reason, remember, and act. Inspired by human brain architecture, it organizes intelligence into specialized modules connected by shared memory and control signals, enabling continuity of identity, goal formation, and adaptive behavior over time. Neuroframe OS provides the structural conditions under which intelligence becomes persistent rather than episodic.
Artificial Intelligence (AI)
Artificial intelligence refers to the broad field of creating machines capable of performing tasks that normally require human intelligence, such as reasoning, perception, learning, and decision-making. AI encompasses a wide range of approaches, from rule-based systems to machine learning models, and does not inherently imply general intelligence, autonomy, or persistence.
Weak AI
Weak AI refers to artificial systems designed to perform specific tasks or narrow functions without general reasoning or autonomy. These systems may exhibit high performance within a constrained domain but lack persistent memory, agency, and the ability to adapt knowledge across unrelated contexts.
Strong AI
Strong AI refers to artificial systems capable of sophisticated reasoning and understanding within their operational scope, often exhibiting human-like competence in language, perception, and problem-solving. Modern large language models and multimodal foundation models are commonly described as strong AI: they can reason, explain, and generate novel outputs, but do not possess persistent identity, agency, or continuity beyond a single session.
Artificial General Intelligence (AGI)
Artificial General Intelligence describes an artificial system capable of general reasoning across domains, integrating perception, memory, planning, and action into a unified, persistent cognitive process. An AGI can learn continuously, transfer knowledge between contexts, form long-term goals, and operate autonomously over time. In practice, AGI implies a system closer to an artificial person than a standalone model.
Artificial Superintelligence (ASI)
Artificial Superintelligence refers to a form of intelligence that exceeds human cognitive abilities across most or all domains. In practice, ASI may take the form of a superhuman artificial person—possessing general cognition, persistence, and agency beyond human limits—or an oracle-like AGI that provides superhuman insight, reasoning, or prediction without independent agency. In both cases, ASI implies general intelligence operating at a scale or capability level that surpasses human performance.
Machine Learning (ML)
Machine learning is a class of computational techniques that enable systems to improve performance on a task through experience rather than explicit programming. By learning patterns from data, machine learning models can make predictions, recognize structure, and adapt behavior, forming the foundation for most modern artificial intelligence systems.
Large Language Model (LLM)
A large language model is a machine learning model trained on vast amounts of text to understand and generate human language. LLMs can reason over language, answer questions, and produce coherent, contextually appropriate text, but typically operate without persistent memory, identity, or agency beyond the scope of a given interaction.
Vision-Language Model (VLM)
A vision-language model is a multimodal machine learning system that jointly processes visual and textual information. VLMs can interpret images, associate visual content with language, and generate descriptions or reasoning that integrates both modalities, enabling capabilities such as image understanding, visual question answering, and grounded language use.
Alpha Simulation
A hypothetical simulation that reproduces a human brain with complete physical fidelity, replicating neural structure and state down to the molecular level. An alpha simulation would be causally identical to the original brain, producing the same cognitive processes and subjective experience, and is therefore often treated as preserving personal identity. With current scientific understanding, alpha simulations remain a theoretical benchmark rather than a practical technology.
Beta Simulation
A simulation that reconstructs a person from available information rather than physical duplication, using observed behavior, memories, preferences, and inferred mental states to model how an individual thinks and acts. While a beta simulation may be highly convincing and functionally useful, it does not replicate the original brain’s physical substrate or causal history, leaving identity continuity philosophically and technically unresolved.
