{ "model": { "rank": 3709, "code": "thing-q192776", "model_id": "vr.tr.artificial-neural-network", "name": "artificial neural network", "purpose": "Let an agent explain artificial neural networks by architecture, training, evaluation and limitations, and discuss responsible use and regulation neutrally.", "family": "Thing Registry", "category": "Information and virtual systems", "status": "research-draft", "kind": "thing", "plane": "INF", "domain": "INF.KNW", "industry": "", "version": "", "url": "/models/thing/q192776/", "tier": 2, "score": 82, "payload": { "layer": "wikidata", "aliases": [ "autoregressive model", "Hopfield network", "artificial intelligence model", "artificial intelligence image scaling technology", "embedding model", "distilled AI model", "connectionist expert system", "physics-informed neural networks", "graph neural network", "modern Hopfield Network", "fuzzy neural network", "wavelet neural network", "dynamic neural network", "Elman neural network", "neural 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"research-draft", "adjudicationStatus": "unreviewed", "publishableCanonical": false, "generatedAt": "2026-09-11T13:41:12Z", "providers": [ "Claude" ], "breadth": "written by Claude from model knowledge without web access - no source was read, every claim is a lead to verify", "pass": 2, "wave": 1, "engine": "claude" }, "metaModel": { "id": "THING-Q192776", "registryId": "vr.tr.artificial-neural-network", "name": "artificial neural network", "version": "0.2.0-wave.1", "entryKind": "thing", "family": "Thing Registry", "domain": [ "INF.KNW" ], "status": "research-draft" }, "canonicalUrl": "https://ver.cy/models/thing/q192776/", "model": { "registry_id": "vr.tr.artificial-neural-network", "name": "artificial neural network", "purpose": "Let an agent explain artificial neural networks by architecture, training, evaluation and limitations, and discuss responsible use and regulation neutrally.", "definition": "A computational model made of interconnected units or neurons arranged in layers, whose connection weights are learned from data, used in machine learning for tasks such as classification, language modelling, image generation and embeddings; types include feedforward, convolutional, recurrent, Hopfield and transformer networks.", "what_it_is_for": "Machine learning and artificial intelligence.", "affordances": [ "explain how neural networks learn", "choose an architecture for a task", "evaluate models and their limits", "understand responsible AI rules" ], "distinguishing_features": [ "Learned weights", "Layered structure", "Trained with data and optimisation", "Can be opaque" ], "appearance": "Not physical; diagrams of layered nodes and software running on computers.", "visual_identification": [ "Layers of connected nodes", "Training curves and parameters", "Biological neural networks are living tissue" ], "physical_properties": [], "families_and_kinds": [ "feedforward networks", "convolutional networks", "recurrent and Hopfield networks", "transformers and autoregressive models", "embedding and distilled models" ], "related_models": [ { "relation": "is a kind of", "target": "machine learning model", "why": "category" }, { "relation": "is trained by", "target": "backpropagation", "why": "method" }, { "relation": "is inspired by", "target": "biological neural network", "why": "analogy" }, { "relation": "is used in", "target": "artificial intelligence", "why": "field" } ], "identifiers": [], "standards_and_regulation": [ "EU AI Act", "ISO/IEC 42001 AI management systems", "Data protection law for training data" ], "failure_modes_and_hazards": [ "Bias from training data", "Hallucinated outputs", "Overstated capability claims" ], "in_scope": [], "out_of_scope": [], "characteristics": [] }, "sources": [], "structure": { "bundles": [ { "id": "concept", "name": "Concept", "description": "How they work.", "rationale": "Mechanics matter.", "layers": [ { "id": "structure", "name": "Structure", "description": "Layers and weights.", "findings": [ { "id": "structure-finding", "name": "Structure", "description": "Structure.", "questions": [ { "text": "How are layers, weights and activation functions arranged in this network?", "kind": "definition" }, { "text": "How many parameters does it have?", "kind": "measurement" } ] } ] }, { "id": "training", "name": "Training", "description": "Learning.", "findings": [ { "id": "training-finding", "name": "Training", "description": "Training.", "questions": [ { "text": "How is the network trained, such as by gradient descent and backpropagation?", "kind": "definition" }, { "text": "What data was used?", "kind": "provenance" } ] } ] } ] }, { "id": "choose", "name": "Choose", "description": "Architecture choice.", "rationale": "Tasks drive choice.", "layers": [ { "id": "architecture", "name": "Architecture", "description": "Which type.", "findings": [ { "id": "architecture-finding", "name": "Architecture", "description": "Architecture.", "questions": [ { "text": "Which architecture suits this task, such as a CNN or transformer?", "kind": "action" }, { "text": "What are the trade-offs?", "kind": "boundary" } ] } ] }, { "id": "evaluate", "name": "Evaluate", "description": "Testing.", "findings": [ { "id": "evaluate-finding", "name": "Evaluate", "description": "Evaluation.", "questions": [ { "text": "How should the model be evaluated, and on which benchmarks?", "kind": "measurement" }, { "text": "How can overfitting be detected?", "kind": "boundary" } ] } ] } ] }, { "id": "limits", "name": "Limits", "description": "Risks.", "rationale": "Limits must be stated.", "layers": [ { "id": "bias", "name": "Bias", "description": "Fairness.", "findings": [ { "id": "bias-finding", "name": "Bias", "description": "Bias and fairness.", "questions": [ { "text": "Could the model produce biased or unfair outputs for some groups?", "kind": "boundary" }, { "text": "Which audits or mitigations apply?", "kind": "provenance" } ] } ] }, { "id": "claims", "name": "Claims", "description": "Capability claims.", "findings": [ { "id": "claims-finding", "name": "Claims", "description": "Capability claims.", "questions": [ { "text": "Is a capability claim supported by independent evaluation?", "kind": "boundary" }, { "text": "Who makes and who disputes it?", "kind": "provenance" } ] } ] } ] }, { "id": "governance", "name": "Governance", "description": "Rules.", "rationale": "Regulation is developing.", "layers": [ { "id": "law", "name": "Law", "description": "AI regulation.", "findings": [ { "id": "law-finding", "name": "Law", "description": "AI regulation.", "questions": [ { "text": "Which obligations apply to this AI system under current law?", "kind": "provenance" }, { "text": "Is it classed as high-risk?", "kind": "provenance" } ] } ] }, { "id": "data", "name": "Data", "description": "Training data rights.", "findings": [ { "id": "data-finding", "name": "Data", "description": "Training data.", "questions": [ { "text": "What rules govern personal or copyrighted data used in training?", "kind": "provenance" }, { "text": "Is the user seeking legal advice?", "kind": "boundary" } ] } ] } ] } ] }, "openQuestions": [ "Should architectures be separate entries?", "How should model cards be linked?", "How should capability claims be attributed?" ], "statistics": { "bundles": 4, "layers": 8, "findings": 8, "questions": 16 } }, "draft": { "generator": "vr.draft.v3", "status": "draft-generated", "researched": false, "archetype": "discipline or field of knowledge", "method": "Written from the archetype playbook - what this kind of thing needs beyond identity and provenance - and from the structure that recurred across 6,333 models already researched by two engines. Applied to this entry by rule. No source was read for this thing and no claim here is researched. This entry carries no facets of its own, so they were inferred from its domain - a guess about a whole domain applied to one thing.", "facetsInferred": true, "nextPass": "A researcher replaces this draft with a sourced specification. Treat every sentence below as a proposal to argue with.", "purpose": "Give an agent a durable, checkable way to recognise a artificial neural network, record what state it is in, and decide what may be done with it.", "whatItIs": "Let an agent explain artificial neural networks by architecture, training, evaluation and limitations, and discuss responsible use and regulation neutrally.", "characteristics": { "substance": "information", "origin": "conceptual", "agency": "inert" }, "whatYouCanDoWithIt": [ "read and interpreted" ], "distinguishingFeatures": [ "Names folded into this entry, which a task may need to split apart again: autoregressive model, Hopfield network, artificial intelligence model, artificial intelligence image scaling technology, embedding model, distilled AI model, connectionist expert system, physics-informed neural networks, graph neural network, modern Hopfield Network, fuzzy neural network, wavelet neural network.", "79 finer distinctions are held as aliases rather than separate entries, because telling them apart needs a task that asks for it.", "Described in 82 Wikipedia languages, which is a measure of how widely the thing is known, not of how important it is." ], "openQuestionsForResearch": [ "Which of the bundles below does a real task actually need, and which are ceremony?", "What does this thing have that the facets do not capture at all?", "Which neighbouring kind is most often confused with a artificial neural network, and on what evidence are they told apart?" ], "whatItIsMadeOf": "content that has to be carried by something else", "physicalCharacter": [ "Does nothing on its own; everything it does, something else did to it.", "These come from the domain this entry sits in rather than from the entry itself, so treat them as a first guess about the whole domain applied to one thing." ], "whatCanBeDoneWithIt": [ "read it and act on what it says" ], "howItIsRecognised": [], "relatedModels": [ { "relation": "covers", "note": "Finer kinds folded into this entry because telling them apart needs a task that asks for it. Each is a model waiting to be split out when one does.", "targets": [ "autoregressive model", "Hopfield network", "artificial intelligence model", "artificial intelligence image scaling technology", "embedding model", "distilled AI model", "connectionist expert system", "physics-informed neural networks", "graph neural network", "modern Hopfield Network", "fuzzy neural network", "wavelet neural network" ] } ], "standing": "Described in 82 Wikipedia languages, which measures how widely it is written about rather than how important or how common it is. 79 finer distinctions are held inside this entry as names rather than as separate models.", "structure": { "bundles": [ { "id": "identity-and-classification", "name": "Identity, naming and classification", "description": "How an agent tells one artificial neural network from another, and a artificial neural network from things that resemble it.", "rationale": "Recognition comes before every other claim. Without stable identity nothing else in the model can be trusted to be about the same thing twice.", "layers": [ { "id": "naming-and-identifiers", "name": "Names and identifiers", "description": "The names this thing goes by and the identifiers that survive translation and time.", "findings": [ { "id": "preferred-name-and-aliases", "name": "Preferred name, aliases and local names", "description": "Which name to use, which names mean the same thing, and which merely sound similar.", "questions": [ { "id": "preferred-name-and-aliases-q01", "text": "What identifies and describes the name of a artificial neural network, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "preferred-name-and-aliases-q02", "text": "Who or what asserted this about the name of a artificial neural network, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "preferred-name-and-aliases-q03", "text": "What may an agent decide or do once the name of a artificial neural network is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] }, { "id": "stable-identifiers", "name": "Stable identifiers and external keys", "description": "Identifiers that keep pointing at this kind of thing across systems and languages.", "questions": [ { "id": "stable-identifiers-q01", "text": "What identifies and describes an identifier for a artificial neural network, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "stable-identifiers-q02", "text": "Who or what asserted this about an identifier for a artificial neural network, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "stable-identifiers-q03", "text": "What may an agent decide or do once an identifier for a artificial neural network is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "classification-and-granularity", "name": "Classification and granularity", "description": "Where a artificial neural network sits among kinds, and how finely a task needs to cut it.", "findings": [ { "id": "kind-and-parents", "name": "Kind, parents and neighbouring kinds", "description": "The classes this thing belongs to and the ones it is next to.", "questions": [ { "id": "kind-and-parents-q01", "text": "What identifies and describes the kind of a artificial neural network, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "kind-and-parents-q02", "text": "Who or what asserted this about the kind of a artificial neural network, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "kind-and-parents-q03", "text": "What may an agent decide or do once the kind of a artificial neural network is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] }, { "id": "distinguishing-features", "name": "Distinguishing features", "description": "What separates a artificial neural network from the things most often confused with it.", "questions": [ { "id": "distinguishing-features-q01", "text": "What identifies and describes what distinguishes a artificial neural network, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "distinguishing-features-q02", "text": "Who or what asserted this about what distinguishes a artificial neural network, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "distinguishing-features-q03", "text": "What may an agent decide or do once what distinguishes a artificial neural network is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] }, { "id": "state-and-lifecycle", "name": "State and lifecycle", "description": "The states a artificial neural network passes through and the events that move it between them.", "rationale": "Most decisions about a thing depend on what state it is in now, which is a claim with a time on it, not a property.", "layers": [ { "id": "lifecycle-stages", "name": "Lifecycle stages", "description": "From coming into existence to ceasing to be one of these.", "findings": [ { "id": "stages-and-transitions", "name": "Stages and transitions", "description": "The stages worth naming and what moves a artificial neural network between them.", "questions": [ { "id": "stages-and-transitions-q01", "text": "What identifies and describes the lifecycle of a artificial neural network, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "stages-and-transitions-q02", "text": "Who or what asserted this about the lifecycle of a artificial neural network, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "stages-and-transitions-q03", "text": "What may an agent decide or do once the lifecycle of a artificial neural network is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "observations-and-status", "name": "Observations and current status", "description": "What is observed about a artificial neural network, how often and by whom.", "findings": [ { "id": "observation-record", "name": "Observation record", "description": "How an observation of a artificial neural network is recorded so that it can be superseded rather than overwritten.", "questions": [ { "id": "observation-record-q01", "text": "What identifies and describes an observation of a artificial neural network, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "observation-record-q02", "text": "Who or what asserted this about an observation of a artificial neural network, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "observation-record-q03", "text": "What may an agent decide or do once an observation of a artificial neural network is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] }, { "id": "content-and-access", "name": "Content, encoding and access", "description": "What a artificial neural network says, how it is encoded and who may read it.", "rationale": "An informational thing carries content that can be copied, versioned and withheld, none of which its physical carrier explains.", "layers": [ { "id": "content-and-encoding", "name": "Content and encoding", "description": "The content itself, its format and its language.", "findings": [ { "id": "content-and-format", "name": "Content, format and language", "description": "What a artificial neural network contains and in what form it is held.", "questions": [ { "id": "content-and-format-q01", "text": "What identifies and describes the content of a artificial neural network, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "content-and-format-q02", "text": "Who or what asserted this about the content of a artificial neural network, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "content-and-format-q03", "text": "What may an agent decide or do once the content of a artificial neural network is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "access-and-rights", "name": "Access, rights and retention", "description": "Who may read, copy or change it, and for how long it is kept.", "findings": [ { "id": "access-rules", "name": "Access rules and retention", "description": "The permissions attached to a artificial neural network and the period it survives.", "questions": [ { "id": "access-rules-q01", "text": "What identifies and describes access to a artificial neural network, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "access-rules-q02", "text": "Who or what asserted this about access to a artificial neural network, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "access-rules-q03", "text": "What may an agent decide or do once access to a artificial neural network is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] }, { "id": "subject-and-method", "name": "Subject matter and method", "description": "What artificial neural network studies and how it establishes anything.", "rationale": "A field is identified by its questions and its methods, and those are what distinguish it from the fields next to it.", "layers": [ { "id": "subject", "name": "Subject matter", "description": "The questions it takes as its own.", "findings": [ { "id": "subject-record", "name": "Questions and objects of study", "description": "What it is about, stated so a neighbouring field can be told apart.", "questions": [ { "id": "subject-record-q01", "text": "What questions and objects does artificial neural network take as its own?", "kind": "definition" }, { "id": "subject-record-q02", "text": "Which questions does it share with a neighbouring field, and who claims them?", "kind": "boundary" } ] } ] }, { "id": "method", "name": "Method and evidence", "description": "How claims are established and what counts as evidence.", "findings": [ { "id": "method-record", "name": "Methods and standards of evidence", "description": "The methods used and what they are taken to establish.", "questions": [ { "id": "method-record-q01", "text": "By what methods does artificial neural network establish claims, and what counts as sufficient evidence?", "kind": "provenance" }, { "id": "method-record-q02", "text": "What kind of claim can this field not settle, and where should an agent look instead?", "kind": "action" } ] } ] } ] }, { "id": "institutions-and-classification", "name": "Institutions and classification", "description": "Where artificial neural network is practised and how catalogues place it.", "rationale": "The institutional footprint is the evidence that a field exists as a field rather than as a topic.", "layers": [ { "id": "institutions", "name": "Institutional markers", "description": "Societies, journals, degrees and departments.", "findings": [ { "id": "institution-record", "name": "Bodies and venues", "description": "Where the field organises itself.", "questions": [ { "id": "institution-record-q01", "text": "Which societies, journals or degrees mark artificial neural network as an established field?", "kind": "provenance" }, { "id": "institution-record-q02", "text": "What would show that it is emerging or dissolving rather than established?", "kind": "boundary" } ] } ] }, { "id": "classification", "name": "Classification schemes", "description": "Where library and research classifications put it.", "findings": [ { "id": "classification-record", "name": "Codes and their disagreements", "description": "The classification codes that place it, and where they differ.", "questions": [ { "id": "classification-record-q01", "text": "Which classification schemes place artificial neural network, under what codes?", "kind": "definition" }, { "id": "classification-record-q02", "text": "Where do those schemes disagree about its scope?", "kind": "boundary" } ] } ] } ] }, { "id": "provenance-and-evidence", "name": "Provenance, evidence and time", "description": "Where every claim about a artificial neural network came from and when it held.", "rationale": "A claim without a source and a time cannot be superseded, only overwritten, and an agent that overwrites loses the ability to explain itself.", "layers": [ { "id": "source-and-authority", "name": "Source and authority", "description": "Who said it, on what evidence, and how strongly.", "findings": [ { "id": "claim-provenance", "name": "Claim provenance and confidence", "description": "The authority behind each claim about a artificial neural network and how confident it is.", "questions": [ { "id": "claim-provenance-q01", "text": "What identifies and describes a claim about a artificial neural network, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "claim-provenance-q02", "text": "Who or what asserted this about a claim about a artificial neural network, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "claim-provenance-q03", "text": "What may an agent decide or do once a claim about a artificial neural network is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "time-and-versions", "name": "Time, versions and supersession", "description": "When a claim was true, when it was learnt, and what replaced it.", "findings": [ { "id": "validity-and-supersession", "name": "Validity period and supersession", "description": "How an old claim about a artificial neural network is retired without being erased.", "questions": [ { "id": "validity-and-supersession-q01", "text": "What identifies and describes the validity of a claim about a artificial neural network, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "validity-and-supersession-q02", "text": "Who or what asserted this about the validity of a claim about a artificial neural network, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "validity-and-supersession-q03", "text": "What may an agent decide or do once the validity of a claim about a artificial neural network is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] } ] }, "statistics": { "bundles": 6, "layers": 12, "findings": 14, "questions": 38 } } } }