Recently an influx of studies claims emergent cognitive abilities in large language models
(LLMs). Yet, most rely on anecdotes, overlook contamination of training sets, or lack …

This review offers an accessible primer to social neuroscientists interested in neural
networks. It begins by providing an overview of key concepts in deep learning. It then …

Performance on a dataset is often regarded as the key criterion for assessing NLP models. I
argue for a broader perspective, which emphasizes scientific explanation. I draw on a long …

New approaches to 3D vision are enabling new advances in artificial intelligence and
autonomous vehicles, a better understanding of how animals navigate the 3D world, and …

Predictability is a prerequisite for effective human control of artificial intelligence (AI). For
example, the inability to predict the malfunctioning of AI impedes timely human intervention …

We introduce the fundamental ideas and challenges of Predictable AI, a nascent research
area that explores the ways in which we can anticipate key indicators of present and future …

Memory is inherently entangled with prediction and planning. Flexible behavior in biological
and artificial agents depends on the interplay of learning from the past and predicting the …

In this paper we give a narrative review of multi-modal video-language (VidL) models. We
introduce the current landscape of VidL models and benchmarks, and draw inspiration from …

In this survey, we examine algorithms for conducting credit assignment in artificial neural
networks that are inspired or motivated by neurobiology, unifying these various processes …

In this paper, we propose a new approach called Adaptive Behavioral Costs in
Reinforcement Learning (ABC-RL) for training a human-like agent with competitive strength …