AI for Domains (AID)

Agents for Productivity (A4P) is a M365 Research initiative to enable Microsoft to deliver reliable, highly capable, and scalable agentic solutions that drive measurable productivity impact. The strategy addresses two core challenges: technological gaps (tool integration/selection, memory & context management,…

Training transformer models with differential privacy Transformer models have recently taken the field of Natural Language Processing (NLP) by storm as large language models based on the transformer architecture have shown impressive performance across a wide range of applications. However,…

Large machine learning model can memorize the training data, which poses privacy risk. To preserve privacy,  it requires to control the data access and measure the privacy loss. Differential privacy (DP) is widely recognized as a gold standard of privacy…

Our goal is to make Azure the most trustworthy cloud platform for AI. The platform we envisage offers confidentiality and integrity against privileged attackers including attacks on the code, data and hardware supply chains, performance close to that offered by…