Building machines that learn and think with people.

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 trustworthy systems that think with us. Current artificial intelligence systems satisfy some of these criteria, some of the time. In this Perspective, we show how the science of collaborative cognition can be put to work to engineer systems that really can be called 'thought partners', systems built to meet our expectations and complement our limitations.

Evaluating language models for mathematics through interactions.

There is much excitement about the opportunity to harness the power of large language models (LLMs) when building problem-solving assistants. However, the standard methodology of evaluating LLMs relies on static pairs of inputs and outputs; this is insufficient for making an informed decision about which LLMs are best to use in an interactive setting, and how that varies by setting. Static assessment therefore limits how we understand language model capabilities.

Perspectives on incorporating expert feedback into model updates.

Machine learning (ML) practitioners are increasingly tasked with developing models that are aligned with non-technical experts' values and goals. However, there has been insufficient consideration of how practitioners should translate domain expertise into ML updates. In this review, we consider how to capture interactions between practitioners and experts systematically.

How transparency modulates trust in artificial intelligence.

The study of human-machine systems is central to a variety of behavioral and engineering disciplines, including management science, human factors, robotics, and human-computer interaction. Recent advances in artificial intelligence (AI) and machine learning have brought the study of human-AI teams into sharper focus. An important set of questions for those designing human-AI interfaces concerns trust, transparency, and error tolerance.