Hybrid Intelligence is an emerging concept that emphasizes the complementary nature of
human intelligence and artificial intelligence (AI). One key requirement for collaboration …

Artificial intelligence (AI) researchers have been developing and refining large language
models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks …

Large language models (LLMs) have demonstrated remarkable capabilities in natural
language understanding and generation across various domains, including medicine. We …

Predictive artificial intelligence (AI) systems based on deep learning have been shown to
achieve expert-level identification of diseases in multiple medical imaging settings, but can …

Many researchers motivate explainable AI with studies showing that human-AI team
performance on decision-making tasks improves when the AI explains its recommendations …

In AI-assisted decision-making, it is critical for human decision-makers to know when to trust
AI and when to trust themselves. However, prior studies calibrated human trust only based …

Learning the distribution of a continuous or categorical response variable y given its
covariates x is a fundamental problem in statistics and machine learning. Deep neural …

We study a two-stage scenario for learning to defer with multiple experts, which is crucial in
practice for many applications. In this scenario, a predictor is derived in a first stage by …

Despite impressive performance in many benchmark datasets, AI models can still make
mistakes, especially among out-of-distribution examples. It remains an open question how …

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 …