{ "schemaVersion": "1.4.0", "datasetVersion": "0.20.0", "evidenceAsOf": "2026-09-15", "bundleSHA256": "e74392d479c0e7da8636a7d6a0454d03510df86ffca9931eb86babd952665ca5", "snapshotUrl": "https://theaiatlas.org/editions/e74392d479c0e7da8636a7d6a0454d03510df86ffca9931eb86babd952665ca5/data.json", "id": "term-neural-network", "type": "term", "title": "Neural networks & deep learning", "url": "https://theaiatlas.org/evidence.html#idea-neural-network", "pageUrl": "https://theaiatlas.org/ideas/neural-network/", "jsonUrl": "https://theaiatlas.org/records/term-neural-network.json", "markdownUrl": "https://theaiatlas.org/records/term-neural-network.md", "bundlePointer": "/glossary/42", "reviewedOn": "2026-09-15", "sourceAge": { "asOf": "2026-09-15", "thresholdMonths": 18, "cutoff": "2025-03-15", "status": "within-window", "sourceCount": 5, "newestPublished": "2025-03-27", "newestSourceIds": [ "circuit-tracing" ], "undatedSourceIds": [ "google-neural-layers", "google-gradient-descent", "google-ml-glossary", "google-llm-intro" ] }, "interpretation": [ "This is a curated, AI-assisted editorial atlas, not a census, affiliation classifier or independently fact-checked authority.", "Coordinates and ranges summarize public positions. 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Used for the mathematical structure, not a claim that an artificial network reproduces a human brain." }, { "id": "google-gradient-descent", "title": "Linear regression: Gradient descent", "publisher": "Google for Developers", "url": "https://developers.google.com/machine-learning/crash-course/linear-regression/gradient-descent", "published": null, "updated": "2026-02-03", "checkedOn": "2026-09-15", "kind": "primary", "verification": "read", "notes": "Read the iterative prediction, loss and parameter-update explanation. The page's guarantees for convex linear regression are not extended here to neural-network training. Updated date follows the page; initial publication is unspecified." }, { "id": "google-ml-glossary", "title": "Machine Learning Glossary", "publisher": "Google for Developers", "url": "https://developers.google.com/machine-learning/glossary", "published": null, "updated": "2026-04-10", "checkedOn": "2026-09-15", "kind": "primary", "verification": "read", "notes": "Read the artificial intelligence, deep model, context window, inference and chat entries. Used for terminology, not product performance claims. Updated date follows the earlier displayed page date; initial publication is unspecified. The chat entry was reread during the same-day beginner-content review. Also read the generalization and compute entries." }, { "id": "google-llm-intro", "title": "LLMs: What's a large language model?", "publisher": "Google for Developers", "url": "https://developers.google.com/machine-learning/crash-course/llm/transformers", "published": null, "updated": "2026-01-02", "checkedOn": "2026-09-15", "kind": "primary", "verification": "read", "notes": "Read token prediction, encoder-only and decoder-only variants, and self-attention. Used for architecture and terminology; broad performance comparisons and claims about all LLMs on the teaching page are not adopted." }, { "id": "circuit-tracing", "title": "Circuit Tracing: Revealing Computational Graphs in Language Models", "publisher": "Emmanuel Ameisen and coauthors / Anthropic, Transformer Circuits", "url": "https://transformer-circuits.pub/2025/attribution-graphs/methods.html", "published": "2025-03-27", "checkedOn": "2026-09-15", "kind": "primary", "verification": "read", "notes": "Read introduction, method overview and limitations including reconstruction errors, graph complexity, global circuits and mechanistic faithfulness. The authors' replacement-model analyses reveal selected mechanisms; they do not provide a complete explanation of all behavior. Later attention-tracing work is cited alongside this paper to avoid treating its missing-attention limitation as a permanent field-wide result." } ] }