Nat­u­ral Abstraction

TagLast edit: 10 Oct 2022 17:45 UTC by Raemon

The Natural Abstraction hypothesis says that:

Our physical world abstracts well: for most systems, the information relevant “far away” from the system (in various senses) is much lower-dimensional than the system itself. These low-dimensional summaries are exactly the high-level abstract objects/​concepts typically used by humans.

These abstractions are “natural”: a wide variety of cognitive architectures will learn to use approximately the same high-level abstract objects/​concepts to reason about the world.

(from “Testing the Natural Abstraction Hypothesis”)

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Align­ment By Default

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The Nat­u­ral Ab­strac­tion Hy­poth­e­sis: Im­pli­ca­tions and Evidence

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Test­ing The Nat­u­ral Ab­strac­tion Hy­poth­e­sis: Pro­ject Update

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Public Static: What is Ab­strac­tion?

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Agency As a Nat­u­ral Abstraction

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The Light­cone The­o­rem: A Bet­ter Foun­da­tion For Nat­u­ral Ab­strac­tion?

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Ab­strac­tion is Big­ger than Nat­u­ral Abstraction

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Why I’m not work­ing on {de­bate, RRM, ELK, nat­u­ral ab­strac­tions}

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AGI will be made of het­ero­ge­neous com­po­nents, Trans­former and Selec­tive SSM blocks will be among them

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The Plan − 2023 Version

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A rough and in­com­plete re­view of some of John Went­worth’s research

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Nat­u­ral Cat­e­gories Update

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Com­put­ing Nat­u­ral Ab­strac­tions: Lin­ear Approximation

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AXRP Epi­sode 15 - Nat­u­ral Ab­strac­tions with John Wentworth

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The Core of the Align­ment Prob­lem is...

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The Plan − 2022 Update

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Take 4: One prob­lem with nat­u­ral ab­strac­tions is there’s too many of them.

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Take 5: Another prob­lem for nat­u­ral ab­strac­tions is laz­i­ness.

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Rele­vant to nat­u­ral ab­strac­tions: Eu­clidean Sym­me­try Equiv­ar­i­ant Ma­chine Learn­ing—Overview, Ap­pli­ca­tions, and Open Questions

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[ASoT] Nat­u­ral ab­strac­tions and AlphaZero

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The “Min­i­mal La­tents” Ap­proach to Nat­u­ral Abstractions

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Causal ab­strac­tions vs infradistributions

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Si­mu­lacra are Things

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[talk] Os­bert Bas­tani—In­ter­pretable Ma­chine Learn­ing via Pro­gram Syn­the­sis—IPAM at UCLA

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World-Model In­ter­pretabil­ity Is All We Need

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Ideal­ized Agents Are Ap­prox­i­mate Causal Mir­rors (+ Rad­i­cal Op­ti­mism on Agent Foun­da­tions)

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[Ap­pendix] Nat­u­ral Ab­strac­tions: Key Claims, The­o­rems, and Critiques

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Wittgen­stein’s Lan­guage Games and the Cri­tique of the Nat­u­ral Ab­strac­tion Hypothesis

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[Heb­bian Nat­u­ral Ab­strac­tions] Math­e­mat­i­cal Foundations

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[Heb­bian Nat­u­ral Ab­strac­tions] Introduction

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Nat­u­ral ab­strac­tions are ob­server-de­pen­dent: a con­ver­sa­tion with John Wentworth

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Align­ment Tar­gets and The Nat­u­ral Ab­strac­tion Hypothesis

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If Went­worth is right about nat­u­ral ab­strac­tions, it would be bad for alignment

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[Question] Does the Tele­phone The­o­rem give us a free lunch?

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[Linkpost] Map­ping Brains with Lan­guage Models: A Survey

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[Linkpost] Rosetta Neu­rons: Min­ing the Com­mon Units in a Model Zoo

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[Linkpost] A shared lin­guis­tic space for trans­mit­ting our thoughts from brain to brain in nat­u­ral conversations

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[Linkpost] Large lan­guage mod­els con­verge to­ward hu­man-like con­cept organization

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[Linkpost] Gen­er­al­iza­tion in diffu­sion mod­els arises from ge­om­e­try-adap­tive har­monic representation

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Nat­u­ral Ab­strac­tion: Con­ver­gent Prefer­ences Over In­for­ma­tion Structures

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«Boundaries/​Mem­branes» and AI safety compilation

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Si­mu­la­tors In­crease the Like­li­hood of Align­ment by Default

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[Question] [DISC] Are Values Ro­bust?

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[Linkpost] Con­cept Align­ment as a Pr­ereq­ui­site for Value Alignment

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Select Agent Speci­fi­ca­tions as Nat­u­ral Abstractions

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Con­tra Steiner on Too Many Nat­u­ral Abstractions

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