At a glance Imitation learning becomes easier when an AI agent understands why an action is taken. Predictive Inverse Dynamics Models (PIDMs) predict plausible future states, clarifying the direction of behavior during imitation learning. Even imperfect predictions reduce ambiguity, making it clearer which action makes sense in the moment. This makes PIDMs far more data‑efficient  

At a glance AI-driven medical image report generation can help medical providers become more efficient and productive. Current models are difficult to train because reporting practices vary widely among providers. Universal Report Generation (UniRG) uses reinforcement learning to align model training with real-world radiology practice rather than proxy text-generation objectives. UniRG  

At a glance Today’s multimodal AI systems can give answers that sound right but may not be grounded in what they actually observe over time, leading to unpredictable errors and safety risks in real-world settings. Argos is a verification framework

AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks. Reinforcement learning (RL) is an approach where AI systems learn to make optimal decisions by receiving rewards or penalties for their actions, improving through  

Anyone who uses AI systems knows the frustration: a prompt is given, the response misses the mark, and the cycle repeats. This trial-and-error loop can feel unpredictable and discouraging. To address this, we are excited to introduce Promptions (prompt + options), a UI framework that helps developers build AI interfaces with more precise user control. Its simple design makes  

The convergence of digital transformation and the GenAI revolution creates an unprecedented opportunity for accelerating progress in precision health. Precision immunotherapy is a poster child for this transformation. Emerging technologies such as multiplex immunofluorescence (mIF) can assess internal states of individual cells along with their spatial locations, which is critical for deciphering how tumors interact  

HANNA WALLACH: Yeah, so I was a PhD student at the University of Cambridge, and I was working with the late David MacKay. I was focusing on machine learning for analyzing text, and at that point in time, I’d actually just begun working on Bayesian latent variable models for text analysis, and my research was really focusing on trying to combine ideas from n-gram language modeling with statistical topic modeling in order to come up with models that just did […]

Pushing the frontiers of computer-use agents with an open-weight, ultra-compact model, optimized for real-world web tasks In 2024, Microsoft introduced small language models (SLMs) to customers, starting with the release of Phi (opens in new tab) models on

Modern multimodal AI models can recognize objects, describe scenes, and answer questions about images and short video clips, but they struggle with long-form and large-scale visual data, where real-world reasoning requires moving beyond object recognition and short-clip analysis. Real-world reasoning increasingly involves analyzing long-form video content, where context spans minutes or hours, far beyond the context limits of most models. It