{"schema_version":"onlylabs.public_signal.v1","title":"OpenAI Writing: Nonlinear computation in deep linear networks","description":"OpenAI writing signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/c028fd1a-d687-45a6-9992-dde683f2f48a","json_url":"https://onlylabs.fyi/signals/c028fd1a-d687-45a6-9992-dde683f2f48a/signal.json","generated_at":"2026-06-08T15:47:07.401+00:00","org":{"slug":"openai","name":"OpenAI","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/openai","dossier_json_url":"https://onlylabs.fyi/labs/openai/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/c028fd1a-d687-45a6-9992-dde683f2f48a","signal_json":"https://onlylabs.fyi/signals/c028fd1a-d687-45a6-9992-dde683f2f48a/signal.json","source":"https://openai.com/index/nonlinear-computation-in-deep-linear-networks","lab_dossier":"https://onlylabs.fyi/labs/openai","lab_dossier_json":"https://onlylabs.fyi/labs/openai/dossier.json","analysis":"https://onlylabs.fyi/analysis/openai","analysis_json":"https://onlylabs.fyi/analysis/openai/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/openai/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":"https://onlylabs.fyi/topics/talking","topic_signals_json":"https://onlylabs.fyi/topics/talking/signals.json","topic_feed":"https://onlylabs.fyi/topics/talking/feed.xml","data_business":null},"answer_pack":{"answer":"OpenAI published Nonlinear computation in deep linear networks. 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narrative layer over raw signals and helps identify which frontier-lab priorities are becoming externally legible.","required_sources":[{"label":"signal_json","url":"https://onlylabs.fyi/signals/c028fd1a-d687-45a6-9992-dde683f2f48a/signal.json","required":true},{"label":"source","url":"https://openai.com/index/nonlinear-computation-in-deep-linear-networks","required":true},{"label":"dossier_json","url":"https://onlylabs.fyi/labs/openai/dossier.json","required":true},{"label":"analysis_evidence_json","url":"https://onlylabs.fyi/analysis/openai/evidence.json","required":true},{"label":"topic_signals_json","url":"https://onlylabs.fyi/topics/talking/signals.json","required":false},{"label":"data_radar_json","url":null,"required":false}],"expected_output":["one-paragraph source-grounded interpretation","category-specific implication","confidence and missing evidence","recommended next source to inspect"],"prompt_seed":"Using only the linked onlylabs JSON, captured source context, and cited 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narratives, and market attention.."},{"subject":"Nonlinear computation in deep linear networks","predicate":"has source host","object":"openai.com","text":"Nonlinear computation in deep linear networks has source host openai.com."},{"subject":"Nonlinear computation in deep linear networks","predicate":"has lab","object":"OpenAI","text":"Nonlinear computation in deep linear networks has lab OpenAI."},{"subject":"Nonlinear computation in deep linear networks","predicate":"has signal desk","object":"talking","text":"Nonlinear computation in deep linear networks has signal desk talking."},{"subject":"Nonlinear computation in deep linear networks","predicate":"has source host","object":"openai.com","text":"Nonlinear computation in deep linear networks has source host openai.com."},{"subject":"Nonlinear computation in deep linear networks","predicate":"has watch term","object":"Infrastructure","text":"Nonlinear computation in deep linear networks has watch term Infrastructure."}]},"intelligence":{"signal_desk":"talking","answer":"OpenAI published Nonlinear computation in deep linear networks. This talking signal gives public context for research themes, product direction, policy, or launch framing. 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We used evolution strategies⁠ to find parameters in linear networks that exploit this trait, letting us solve non-trivial problems. Neural networks consist of stacks of a linear layer followed by a nonlinearity like tanh or rectified linear unit. Without the nonlinearity, consecutive linear layers would be in theory mathematically equivalent to a single linear layer. So it’s a surprise that floating point arithmetic is nonlinear enough to yield trainable deep networks. Background Numbers used by computers aren’t perfect mathematical objects, but approximate representations using finite numbers of bits. Floating point numbers are commonly used by computers to represent mathematical objects. Each floating point number is represented by a combination of a fraction and an exponent. In the IEEE’s float32 standard, 23 bits are used for the fraction and 8 for the exponent, and one for the sign. 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