But it's still interesting to see the types of projects the company's researchers deem ready to show off at industry conferences and internal confabs, as this often provides clues as to what Microsoft may be ready to try to commercialize next.
One of these areas where the company is upping its focus is in machine reading, or the automatic understanding of text. At Microsoft's Faculty Research Summit in Redmond this week, officials shared a glimpse of what Microsoft's doing on this front.
On July 17, two Microsoft researchers presented on machine learning. One of these researchers, Jianfeng Gao, Partner Research Manager, also is one of the authors on a paper on a new neural network architecture in which Microsoft has been investing, called ReasoNet.
ReasoNet, short for the Reasoning Network, is targeted at machine comprehension. "ReasoNets make use of multiple turns to effectively exploit and then reason over the relation among queries, documents and answers," according to an abstract about the paper, which the researchers will present at the August SIGKDD Conference on Knowledge Discovery and Data Mining.
ReasoNet is a project from Microsoft Research's Deep Learning Group in Redmond. The Deep Learning for Machine Comprehension project, which Microsoft established in September 2016, has set its sights on teaching computers to read and answer general questions pertaining to a document.
"The long-term vision for this product is to apply MRC technology to all types of user manuals, such as cars, home appliances and more," according to a Channel 9 video of the demo that's part of the Research Faculty Summit collateral.
"When you go to look at communications you shouldn't have to just look at a timed order fashion, you should trust that it's understanding of you and the context and priorities are there. But only by reading that text will we do that, so there's a frontier here that's very exciting that Rajesh Jha (the head of Office applications), Harry Shum (the head of Microsoft's AI + Research group), a lot of the key people under Satya are grabbing onto that, and some particular opportunities around that are where the resources are being shifted," Gates said.
The biggest hurdle for machine reading, according to the researchers presenting this week at the Faculty Research Summit, is introducing the element of common sense into machine-reading situations. Humans, being multi-modal, have a variety of ways to filter and understand things that are inherent in written text, the researchers noted.
While that work continues, the next chapter in machine reading at Microsoft is being written... and read.