We propose a new version of the “theory theory” grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a …
The two fields of machine learning and graphical causality arose and are developed separately. However, there is, now, cross-pollination and increasing interest in both fields to …
Philosopher Ned Block argues in this book that there is a" joint in nature" between perception and cognition and that by exploring the nature of that joint, one can solve …
Do visual tasks have a relationship, or are they unrelated? For instance, could having surface normals simplify estimating the depth of an image? Intuition answers these …
Deep learning for AI Page 1 58 COMMUNICATIONS OF THE ACM | JULY 2021 | VOL. 64 | NO. 7 turing lecture RESEARCH ON ARTIFICIAL neural networks was motivated by the …
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 …
A data-driven exploration of how children's language learning varies across different languages, providing both a theoretical framework and reference. The Wordbank Project …
Cognitive Development and Cognitive Neuroscience: The Learning Brain is a thoroughly revised edition of the bestselling Cognitive Development. The new edition of this full-colour …
A Gopnik, S O'Grady, CG Lucas… - Proceedings of the …, 2017 - National Acad Sciences
How was the evolution of our unique biological life history related to distinctive human developments in cognition and culture? We suggest that the extended human childhood and …