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Tomer D. Ullman [8]Tomer David Ullman [1]
  1. Building machines that learn and think like people.Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum & Samuel J. Gershman - 2017 - Behavioral and Brain Sciences 40.
    Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking (...)
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  2.  13
    Learning a theory of causality.Noah D. Goodman, Tomer D. Ullman & Joshua B. Tenenbaum - 2011 - Psychological Review 118 (1):110-119.
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  3.  18
    Lucky or clever? From expectations to responsibility judgments.Tobias Gerstenberg, Tomer D. Ullman, Jonas Nagel, Max Kleiman-Weiner, David A. Lagnado & Joshua B. Tenenbaum - 2018 - Cognition 177 (C):122-141.
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  4.  7
    Non-commitment in mental imagery.Eric J. Bigelow, John P. McCoy & Tomer D. Ullman - 2023 - Cognition 238 (C):105498.
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  5.  6
    What Could Go Wrong: Adults and Children Calibrate Predictions and Explanations of Others' Actions Based on Relative Reward and Danger.Nensi N. Gjata, Tomer D. Ullman, Elizabeth S. Spelke & Shari Liu - 2022 - Cognitive Science 46 (7):e13163.
    Cognitive Science, Volume 46, Issue 7, July 2022.
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  6.  10
    Sticking to the Evidence? A Behavioral and Computational Case Study of Micro‐Theory Change in the Domain of Magnetism.Elizabeth Bonawitz, Tomer D. Ullman, Sophie Bridgers, Alison Gopnik & Joshua B. Tenenbaum - 2019 - Cognitive Science 43 (8):e12765.
    Constructing an intuitive theory from data confronts learners with a “chicken‐and‐egg” problem: The laws can only be expressed in terms of the theory's core concepts, but these concepts are only meaningful in terms of the role they play in the theory's laws; how can a learner discover appropriate concepts and laws simultaneously, knowing neither to begin with? We explore how children can solve this chicken‐and‐egg problem in the domain of magnetism, drawing on perspectives from computational modeling and behavioral experiments. We (...)
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  7.  8
    Plans or Outcomes: How Do We Attribute Intelligence to Others?Marta Kryven, Tomer D. Ullman, William Cowan & Joshua B. Tenenbaum - 2021 - Cognitive Science 45 (9):e13041.
    Humans routinely make inferences about both the contents and the workings of other minds based on observed actions. People consider what others want or know, but also how intelligent, rational, or attentive they might be. Here, we introduce a new methodology for quantitatively studying the mechanisms people use to attribute intelligence to others based on their behavior. We focus on two key judgments previously proposed in the literature: judgments based on observed outcomes (you're smart if you won the game) and (...)
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  8.  28
    Ingredients of intelligence: From classic debates to an engineering roadmap.Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum & Samuel J. Gershman - 2017 - Behavioral and Brain Sciences 40:e281.
    We were encouraged by the broad enthusiasm for building machines that learn and think in more human-like ways. Many commentators saw our set of key ingredients as helpful, but there was disagreement regarding the origin and structure of those ingredients. Our response covers three main dimensions of this disagreement: nature versus nurture, coherent theories versus theory fragments, and symbolic versus sub-symbolic representations. These dimensions align with classic debates in artificial intelligence and cognitive science, although, rather than embracing these debates, we (...)
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  9.  11
    Heroes of our own story: Self-image and rationalizing in thought experiments.Tomer David Ullman - 2020 - Behavioral and Brain Sciences 43.
    Cushman's rationalization account can be extended to cover another part of his portrayal of representational exchange: thought experiments that lead to conclusions about the self. While Cushman's argument is compelling, a full account of rationalization as adaptive will need to account for the divergence in rationalizing one's actions compared to the actions of others.
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