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  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.  17
    Word meaning in minds and machines.Brenden M. Lake & Gregory L. Murphy - 2023 - Psychological Review 130 (2):401-431.
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  3.  9
    The role of developmental change and linguistic experience in the mutual exclusivity effect.Molly Lewis, Veronica Cristiano, Brenden M. Lake, Tammy Kwan & Michael C. Frank - 2020 - Cognition 198 (C):104191.
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  4.  6
    Cross‐Situational Word Learning With Multimodal Neural Networks.Wai Keen Vong & Brenden M. Lake - 2022 - Cognitive Science 46 (4).
    Cognitive Science, Volume 46, Issue 4, April 2022.
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  5.  25
    The Emergence of Organizing Structure in Conceptual Representation.Brenden M. Lake, Neil D. Lawrence & Joshua B. Tenenbaum - 2018 - Cognitive Science 42 (S3):809-832.
    Both scientists and children make important structural discoveries, yet their computational underpinnings are not well understood. Structure discovery has previously been formalized as probabilistic inference about the right structural form—where form could be a tree, ring, chain, grid, etc.. Although this approach can learn intuitive organizations, including a tree for animals and a ring for the color circle, it assumes a strong inductive bias that considers only these particular forms, and each form is explicitly provided as initial knowledge. Here we (...)
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  6.  11
    Commonsense psychology in human infants and machines.Gala Stojnić, Kanishk Gandhi, Shannon Yasuda, Brenden M. Lake & Moira R. Dillon - 2023 - Cognition 235 (C):105406.
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  7.  6
    Finding Structure in One Child's Linguistic Experience.Wentao Wang, Wai Keen Vong, Najoung Kim & Brenden M. Lake - 2023 - Cognitive Science 47 (6):e13305.
    Neural network models have recently made striking progress in natural language processing, but they are typically trained on orders of magnitude more language input than children receive. What can these neural networks, which are primarily distributional learners, learn from a naturalistic subset of a single child's experience? We examine this question using a recent longitudinal dataset collected from a single child, consisting of egocentric visual data paired with text transcripts. We train both language-only and vision-and-language neural networks and analyze the (...)
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  8.  33
    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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    Spatial relation categorization in infants and deep neural networks.Guy Davidson, A. Emin Orhan & Brenden M. Lake - 2024 - Cognition 245 (C):105690.
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    Compositional diversity in visual concept learning.Yanli Zhou, Reuben Feinman & Brenden M. Lake - 2024 - Cognition 244 (C):105711.
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