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  

LI LYNA ZHANG: Thank you for having me. HUIZINGA: So let’s start with a brief overview of your paper. Tell us about the issue your research addresses and why it matters. ZHANG: OK, so this paper is about how to effectively extend the context window of large language models beyond 2 million tokens. Why this is important? Because enabling longer input contexts can improve LLM capabilities. Right now, some LLMs can only handle a limited context window of 4K tokens, […]

In an era increasingly steered by data, machine learning is a pivotal force, transforming vast amounts of information into actionable intelligence with unprecedented speed and accuracy. For example, recent advances in machine learning have led to breakthroughs in precision health, helping doctors make more informed decisions about patient care. Similarly, in climate science, machine learning is improving scientists’ ability to predict and mitigate the impact of extreme weather events. These innovations illustrate that machine learning not only streamlines workflows, it […]

MITRA: So the post-training phase is very important for language models. You can really improve the model a lot by creating high-quality synthetic data. The problem is, however, though, high-quality synthetic data creation requires lots of human effort and expertise. The problem that we’re trying to tackle is, how do you reduce human effort? How can you create high-quality data with really low amount of human effort? When you have a language model and, let’s say, you want to apply […]

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 MG-TSD: Advancing time series analysis with multi-granularity guided diffusion model Diffusion probabilistic models have the capacity to generate high-fidelity samples for generative time series forecasting. However, they also present issues of instability due to their stochastic nature. In a recent article: MG-TSD: Advancing time series analysis with multi-granularity