{"pk":29855,"title":"Leveraging Unstructured Statistical Knowledge in aProbabilistic Language of Thought","subtitle":null,"abstract":"One hallmark of human reasoning is that we can bring to beara diverse web of common-sense knowledge in any situation.The vastness of our knowledge poses a challenge for the prac-tical implementation of reasoning systems as well as for ourcognitive theories – how do people represent their common-sense knowledge? On the one hand, our best models of so-phisticated reasoning are top-down, making use primarily ofsymbolically-encoded knowledge. On the other, much of ourunderstanding of the statistical properties of our environmentmay arise in a bottom-up fashion, for example through asso-ciationist learning mechanisms. Indeed, recent advances in AIhave enabled the development of billion-parameter languagemodels that can scour for patterns in gigabytes of text from theweb, picking up a surprising amount of common-sense knowl-edge along the way—but they fail to learn the structure of co-herent reasoning. We propose combining these approaches, byem- bedding language-model-backed primitives into a state-of-the-art probabilistic programming language (PPL). On twoopen-ended reasoning tasks, we show that our PPL modelswith neural knowledge components characterize the distribu-tion of human responses more accurately than the neural lan-guage models alone, raising interesting questions about howpeople might use language as an interface to common-senseknowledge, and suggesting that building probabilistic modelswith neural language-model components may be a promisingapproach for more human-like AI.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"probabilistic language of thought; language mod-els; neurosymbolic reasoning; common sense"}],"section":"Poster Session 2","is_remote":true,"remote_url":"https://escholarship.org/uc/item/8qg6c08r","frozenauthors":[{"first_name":"Alexander","middle_name":"K.","last_name":"Lew","name_suffix":"","institution":"MIT","department":""},{"first_name":"Michael","middle_name":"Henry","last_name":"Tessler","name_suffix":"","institution":"MIT","department":""},{"first_name":"Vikash","middle_name":"K.","last_name":"Mansinghka","name_suffix":"","institution":"MIT","department":""},{"first_name":"Joshua","middle_name":"B.","last_name":"Tenenbaum","name_suffix":"","institution":"MIT","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2020-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/29855/galley/19709/download/"}]}