Large language models (LLMs) have come closest among all models to date to mastering
human language, yet opinions about their linguistic and cognitive capabilities remain split …

A fascinating hypothesis is that human and animal intelligence could be explained by a few
principles (rather than an encyclopaedic list of heuristics). If that hypothesis was correct, we …

Transformers have revolutionized vision and natural language processing with their ability to
scale with large datasets. But in robotic manipulation, data is both limited and expensive …

A central goal of machine learning is the development of systems that can solve many
problems in as many data domains as possible. Current architectures, however, cannot be …

Biological systems understand the world by simultaneously processing high-dimensional
inputs from modalities as diverse as vision, audition, touch, proprioception, etc. The …

Whether current or near-term AI systems could be conscious is a topic of scientific interest
and increasing public concern. This report argues for, and exemplifies, a rigorous and …

Natural data is redundant yet predominant architectures tile computation uniformly across
their input and output space. We propose the Recurrent Interface Networks (RINs), an …

The quadratic computational and memory complexities of the Transformer's attention
mechanism have limited its scalability for modeling long sequences. In this paper, we …

Multimodal learning is a framework for building models that make predictions based on
different types of modalities. Important challenges in multimodal learning are the inference of …

Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within
the field of NLP, aimed at addressing limitations in existing frameworks while aligning with …