Learning is using past experiences to inform new behaviors and actions. Because all
experiences are unique, learning always requires some generalization. An effective way of …

Deep learning (DL) is being successfully applied across multiple domains, yet these models
learn in a most artificial way: they require large quantities of labeled data to grasp even …

What do we want from machine intelligence? We envision machines that are not just tools
for thought but partners in thought: reasonable, insightful, knowledgeable, reliable and …

In this article, we integrate two constructionist approaches—the theory of constructed
emotion and rational constructivism—to introduce several novel hypotheses for …

Adult semantic memory has been traditionally conceptualized as a relatively static memory
system that consists of knowledge about the world, concepts, and symbols. Considerable …

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 …

Achieving a widely accepted definition of human intelligence has been challenging, a
situation mirrored by the diverse definitions of artificial intelligence in computer science. By …

Deep neural networks are powerful tools in learning sophisticated but fixed map** rules
between inputs and outputs, thereby limiting their application in more complex and dynamic …

People learning new concepts can often generalize successfully from just a single example,
yet machine learning algorithms typically require tens or hundreds of examples to perform …

A scientist integrates evolutionary biology, genetics, psychology, economics, and more to
explore the development and workings of human societies.“There is no good reason why …