Large language models (LLMs) have achieved unprecedented accuracy on medical question-answering benchmarks, showcasing their potential to revolutionize healthcare by supporting clinicians and patients. However, these benchmarks often fail to capture the full complexity of real-world medical scenarios. To truly harness the power of LLMs in healthcare, we must go beyond these benchmarks by introducing challenges that bring us closer to the nuanced realities of clinical practice. Introducing MedFuzz Benchmarks like MedQA rely on simplifying assumptions to gauge accuracy. These assumptions […]

GraphRAG uses large language models (LLMs) to create a comprehensive knowledge graph that details entities and their relationships from any collection of text documents. This graph enables GraphRAG to leverage the semantic structure of the data and generate responses to complex queries that require a broad understanding of the entire text. In previous blog posts, we introduced GraphRAG and demonstrated how it could be applied to news articles. In this blog post, we show that it can also  

As AI research and technology development continue to advance, there is also a need to account for the energy and infrastructure resources required to manage large datasets and execute difficult computations. When we look to nature for models of efficiency, the human brain stands out, resourcefully handling complex tasks. Inspired by this, researchers at Microsoft are seeking to understand the brain’s efficient processes and replicate  

SHREY JAIN: Thank you. We’re happy to be back. ZOË HITZIG: Thanks so much. TINGLE: Shrey, let’s start with a brief overview of your paper. Why is this research important, and why do you think this is something we should all know about? JAIN: Malicious actors have been exploiting anonymity as a way to deceive others online. And historically, deception has been viewed as this unfortunate but necessary cost as a way to preserve the internet’s commitment to privacy and […]

Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. EVENT Register now for Research Forum on September 3 Discover what’s next in the world of AI at Microsoft Research Forum (opens in new tab), an event series that explores recent research advances, bold new ideas, and important discussions with  

Imagine if pathologists had tools that could help predict therapeutic responses just by analyzing images of cancer tissue. This vision may someday become a reality through the revolutionary field of computational pathology. By leveraging AI and machine learning, researchers are now able to analyze digitized tissue samples with unprecedented accuracy and scale, potentially transforming how we understand and treat cancer. When a patient is suspected of having cancer, a tissue specimen is sometimes removed, stained, affixed to a glass slide, […]

This paper was presented at the IEEE 2024 Conference on Games (opens in new tab) (IEEE CoG 2024), the leading forum on innovation in and through games. Mastering the art of storytelling, a highly valued skill across films, novels, games, and more, requires creating rich narratives with compelling plots and characters. In recent years, the rise of AI has prompted inquiries into whether large language models (LLMs) can effectively generate and sustain detailed, coherent storylines that engage audiences.  

This paper was presented at the IEEE 2024 Conference on Games (opens in new tab) (IEEE CoG 2024), the leading forum on innovation in and through games. In the fast-evolving landscape of video game development, crafting dialogues and narratives is a labor-intensive endeavor. Traditionally, creating these elements involved meticulous hand-coding, resulting in static interactions that limit player agency. However, the rise of large language models (LLMs) is introducing possibilities for richer, more dynamic narrative  

In this episode, I’m talking with Emre Kiciman, the senior principal research manager leading the AI for Industry research team at Microsoft Research Redmond. After completing a PhD in systems and networking in 2005, Emre began his career with Microsoft Research in the same area, studying reliability in large-scale internet services. Exposure to social data inspired him to refocus his research pursuits: his recent work in causal analysis—including DoWhy, a Python library for causal inference—is helping to connect the whats […]

Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. NEW RESEARCH Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior Differentiable causal discovery has made significant advancements in the learning of directed acyclic graphs. However, its application to real-world datasets remains restricted due to the ubiquity of latent confounders and the requirement to learn maximal ancestral