The Text Analytics Cognitive Service announces Public Preview of Named Entity Recognition. With a simple API call, apply robust machine learning models to your unstructured text and recognize more than 20 types of named entities such as people, places, organizations, quantities, dates, and more.
We are excited to release the preview of Open Neural Network Exchange (ONNX) Runtime, a high-performance inference engine for machine learning models in the ONNX format.
Combining biometric identification with artificial intelligence (AI) enables banks to take a new approach to verifying the digital identity of their prospects and customers.
In the past two years since PyTorch’s first release in October 2016, we’ve witnessed the rapid and organic adoption of the deep learning framework among academia, industry, and the AI community at large.
Insurance companies that sell life, health, and property and casualty insurance are using machine learning (ML) to drive improvements in customer service, fraud detection, and operational efficiency.
Intelligent experiences powered by machine learning can seem like magic to users. Developing them, however, can be anything but.
Today we are very happy to release the new capabilities for the Azure Machine Learning service. Since our initial public preview launch in September 2017, we have received an incredible amount of valuable and constructive feedback.
To build an effective and scalable solution, developers need technology that can be deployed around the world and still provide results with high confidence.
Conversational AI is the next user interface paradigm in computing, making human and computer interactions more natural. We’ve evolved from a world where humans have to learn and adapt to computers to one where computers are learning how to understand and interact with humans.
Microsoft has reached a milestone in text-to-speech synthesis with a production system that uses deep neural networks to make the voices of computers nearly indistinguishable from recordings of people.
