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 …

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 …

Even after fine-tuning and reinforcement learning, large language models (LLMs) can be
difficult, if not impossible, to control reliably with prompts alone. We propose a new inference …

Questions combine our mastery of language with our remarkable facility for reasoning about
uncertainty. How do people navigate vast hypothesis spaces to pose informative questions …

When humans cooperate, they frequently coordinate their activity through both verbal
communication and non-verbal actions, using this information to infer a shared goal and …

Human beings are social creatures. We routinely reason about other agents, and a crucial
component of this social reasoning is inferring people's goals as we learn about their …

Predictive models often need to work with incomplete information in real-world tasks.
Consequently, they must provide reliable probability or confidence estimation, especially in …

Abstract Language is one of the hallmarks of intelligence, demanding explanation in a
theory of human cognition. However, language presents unique practical challenges for …

State of the art large language models (LLMs) have shown impressive performance on a
variety of benchmark tasks and are increasingly used as components in larger applications …

Common-sense reasoning is a key challenge in robot navigation and 3D scene
understanding. Humans tend to reason about their environments in abstract terms, with a …