Microsoft Research Focus – Edition #97 features host Sowbhagya Suresh discussing recent developments across Microsoft. Topics include challenges faced by coding agents, insights into how people use AI at work, and advancements in extended‑range weather forecasting. The episode highlights cutting‑edge research and practical applications within the company.
Microsoft Research Asia – Singapore, launched a year ago, has built a solid foundation and strengthened collaboration with government, academia, and industry. The lab is exploring how frontier AI research can generate real‑world value. It continues to advance research, partnership, and talent for practical impact.
Computer-use AI agents struggle with multi-step workflows like email and customer support. Echoverse trains agents in realistic environments rather than simply providing more training tasks, helping them improve as the tasks, tests, and environments evolve.
Orchard is an open-source framework for the research community to train and evaluate AI agents across task types. It reduces complexity while supporting strong performance from smaller models by enabling researchers to reuse the same infrastructure.
Towards Data Science has released a video showcase titled "Introducing ShipAI," which highlights real‑world AI work. The post announces this new visual resource and its focus on practical AI applications. It is positioned as a first look into the platform’s capabilities.
What our over-dependence on external consulting teaches us about delegating our minds to machines The post The Big Con of Agentic AI appeared first on Towards Data Science .
Flint‑interactive is a language developed by Microsoft Research that enables AI agents to generate interactive data visualizations. It allows users to click on charts, filter data, highlight related content, and explore beyond the initially chosen view. The system is designed to enhance user interaction with visual data representations.
Jennifer Neville, who initially had no interest in computer science, found herself in the field and has since focused on uncovering the unexpected failures that challenge AI’s ability to manage complexity. In her article, she reflects on the career path that led her to this niche, highlighting the moments of both progress and setbacks that shaped her work. The piece emphasizes how these surprising failures reveal the limits of current AI systems.
The article "How to Work with AI Coding Agents" offers a practical guide aimed at improving code quality rather than merely increasing quantity. It focuses on strategies and best practices for effectively collaborating with AI coding tools to produce better code. The post was originally published on Towards Data Science.
AFDBench is a new benchmark that evaluates how well large language models can generate professional Area Forecast Discussions (AFDs) for the National Weather Service by reasoning through structured AI weather forecast data. It contains 7,732 expert-written discussions paired with real forecast inputs and introduces three metrics—Met-Align, Style-Align, and Input-Grounding—to assess numerical accuracy, professional dialect adherence, and fidelity to source data. Zero-shot tests show open-source LLMs perform poorly on style and grounding, but reinforcement learning with Group Relative Policy Optimization nearly doubles style alignment and improves grounding, enabling a 7B-parameter model to write like a professional meteorologist.
How AI has massively changed my day-to-day workflow The post A Day in the Life of a Data Scientist in 2026 appeared first on Towards Data Science .
The article titled "5 AI Skills That Will Keep Data Scientists Relevant in 2027" outlines five specific AI competencies, explaining what each skill addresses and providing runnable code snippets that readers can directly paste into a notebook. It serves as a practical guide for data scientists aiming to stay current with emerging AI technologies.
Understanding ow LLMs interact with the world around them, from returning data to taking action The post Tool Calling, Explained: How AI Agents Decide What to Do Next appeared first on Towards Data Science .
