The AI Economy Institute (AIEI) was founded on a bold premise: the future of the economy should be shaped—not simply observed. To realize this vision, we’ve built a think tank with a deliberate rhythm of inquiry, collaboration, publication, and dissemination—turning ideas into impact. Twice a year, AIEI issues…
Microsoft launched the AI Economy Institute (AIEI) in 2025 as a corporate “think tank” to ensure that AI’s rapid advancements translate into broad societal benefits and a people-first economy. The Institute’s strategic rationale is to bridge the gap between technological innovation and societal impact, providing deep research…
Artificial intelligence (AI) is diffusing globally at unprecedented speed, but adoption remains uneven. Frontier Large Language Models (LLMs) are known to perform poorly on low-resource languages due to data scarcity. We hypothesize that this performance deficit reduces the utility of AI, thereby slowing adoption in…
While Large Language Models (LLMs) are increasingly used in agentic frameworks to assist individual users, there is a growing need for agents that can proactively manage complex, multi-party collaboration. Systematic evaluation methods for such proactive agents remain scarce, limiting progress in developing AI that can…
Measuring global AI diffusion remains challenging due to a lack of population-normalized, crosscountry usage data. We introduce AI User Share, a novel indicator that estimates the share of each country’s working-age population actively using AI tools. Built from anonymized Microsoft telemetry and adjusted for device…
Light is edging into roles once reserved for electricity in computing. As researchers race to ease the growing energy and performance strain that AI puts on data centers, some are experimenting with using photons instead of electrons to process information, an approach that could make computations…
AI-driven video analytics has become increasingly important across diverse domains. However, existing systems are often constrained to specific, predefined tasks, limiting their adaptability in open-ended analytical scenarios. The recent emergence of Vision Language Models (VLMs) as transformative technologies offers significant potential for enabling open-ended video…
Large vision-language models are improving at describing images, yet hallucinations still erode trust by introducing contradictions and fabricated details that limit practical applications. In response, Microsoft Research Asia has developed On-Policy Alignment DPO (OPA-DPO), a new algorithm that aligns expert feedback with the model’s own…
In the new era of AI, this role within Azure CXP Data & Applied Sciences team will provide you the opportunity to work on cutting-edge GenAI and ML (Machine Learning) solutions that drive specific, measurable, and impactful improvements to key areas of Azure customer experience.…
While large transformer models excel in predictive performance, their lack of interpretability restricts their usefulness in high-stakes domains. To remedy this, we propose the Generalized Induction-Head Model (GIM), an interpretable model for next-token prediction inspired by the observation of"induction heads"in LLMs. GIM is a retrieval-based…
Open Source fuels the AI for Good Lab’s mission by providing research tools, data, and code that drive innovation, foster collaboration, and enhance accessibility for the global community. Explore our full library of resources to start building and collaborating today.
We consider the problem of maximizing a submodular function with access to a noisy value oracle for the function instead of an exact value oracle. Similar to prior work, we assume that the noisy oracle is persistent in that multiple calls to the oracle for…
