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 …

Recurrent Neural Networks are ubiquitous and pervasive in many artificial intelligence
applications such as speech recognition, predictive healthcare, creative art, and so on …

We observe that pre-trained large language models (LLMs) are capable of autoregressively
completing complex token sequences--from arbitrary ones procedurally generated by …

The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

How does language inform our downstream thinking? In particular, how do humans make
meaning from language--and how can we leverage a theory of linguistic meaning to build …

Scientific computing is increasingly incorporating the advancements in machine learning
and the ability to work with large amounts of data. At the same time, machine learning …

This book is a graduate-level introduction to probabilistic programming. It not only provides a
thorough background for anyone wishing to use a probabilistic programming system, but …

Changes in an animal's behavior and internal state are accompanied by widespread
changes in activity across its brain. However, how neurons across the brain encode …

Applying differentiable programming techniques and machine learning algorithms to foreign
programs requires developers to either rewrite their code in a machine learning framework …

We present 3DP3, a framework for inverse graphics that uses inference in a structured
generative model of objects, scenes, and images. 3DP3 uses (i) voxel models to represent …