Building Machines That Learn and Think Like People Building Machines That Learn and Think Like People
Paper summary This paper performs a comparitive study of recent advances in deep learning with human-like learning from a cognitive science point of view. Since natural intelligence is still the best form of intelligence, the authors list a core set of ingredients required to build machines that reason like humans. - Cognitive capabilities present from childhood in humans. - Intuitive physics; for example, a sense of plausibility of object trajectories, affordances. - Intuitive psychology; for example, goals and beliefs. - Learning as rapid model-building (and not just pattern recognition). - Based on compositionality and learning-to-learn. - Humans learn by inferring a general schema to describe goals, object types and interactions. This enables learning from few examples. - Humans also learn richer conceptual models. - Indicator: variety of functions supported by these models: classification, prediction, explanation, communication, action, imagination and composition. - Models should hence have strong inductive biases and domain knowledge built into them; structural sharing of concepts by compositional reuse of primitives. - Use of both model-free and model-based learning. - Model-free, fast selection of actions in simple associative learning and discriminative tasks. - Model-based learning when a causal model has been built to plan future actions or maximize rewards. - Selective attention, augmented working memory, and experience replay are low-level promising trends in deep learning inspired from cognitive psychology. - Need for higher-level aforementioned ingredients.
Building Machines That Learn and Think Like People
Lake, Brenden M. and Ullman, Tomer D. and Tenenbaum, Joshua B. and Gershman, Samuel J.
arXiv e-Print archive - 2016 via Local Bibsonomy
Keywords: dblp

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