Bringing AI supercomputing to customers

The trend toward the use of massive AI models to power a large number of tasks is changing how AI is built. At Microsoft Build 2020, we shared our vision for AI at Scale utilizing state-of-the-art AI supercomputing in Azure and a new class of large-scale AI models enabling next-generation AI. The advantage of large scale models is that they only need to be trained once with massive amounts of data using AI supercomputing, enabling them to then be "fine-tuned" for different tasks and domains with much smaller datasets and resources. The more parameters that a model has, the better it can capture the difficult nuances of the data, as demonstrated by our 17-billion-parameter Turing Natural Language Generation (T-NLG) model and its ability to understand language to answer questions from or summarize documents seen for the first time. Natural language models like this, significantly larger than the state-of-the-art models a year ago, and many orders of magnitude the size of earlier image-centric models, are now powering a variety of tasks throughout Bing, Word, Outlook, and Dynamics.