One of the primary challenges when building any kind of chatbot is producing or obtaining high-quality, diversified training data. The training data that you use across your model’s intents will determine how readily your model picks up on a real user’s true intent when exposed to queries it’s never seen before. So no matter what chatbot framework you’re using (e.g. Microsoft LUIS, IBM Watson, etc.), having high-quality training data is a must..

Microsoft AI & Research today shared what it calls the largest Transformer-based language generation model ever and open-sourced a deep learning library named DeepSpeed to make distributed training of large models easier.

Microsoft’s mission is to empower every person and organization to achieve more. So, we are constantly looking for opportunities to simplify workflows and save people time and effort. Sending replies to email or chat messages is a common activity and people spend considerable amount of time on it.

Earlier in the year, Microsoft detailed the ways Bing has benefited from AI at Scale, an initiative to apply large-scale AI and supercomputing to language processing across Microsoft’s apps, services, and managed products. AI at Scale chiefly bolstered the search engine’s ability to directly answer questions and generate image captions, but in a blog post today, Microsoft says it has led to Bing improvements in things like autocomplete suggestions.

Knowledge workers spend close to 20% of their time searching for and gathering information. When using document management systems such as Microsoft OneDrive and SharePoint people find themselves looking at directories full of documents. Interacting with such a list of documents can be time-consuming without a mechanism for previewing the documents.

Microsoft is continuing to evolve its unified Microsoft Search service. The latest pieces it is integrating into Microsoft Search involve its 'Project Turing' deep-learning work, as well as advances it is making around semantic meaning and intent.