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{
    "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/"
        }
    ]
}