New Microsoft AI and Research Group consolidates all AI research

Microsoft has formed the Microsoft AI and Research Group, bringing together Microsoft’s research organisation with more than 5,000 computer scientists and engineers focused on the company’s artificial intelligence (AI) product efforts. The Microsoft AI and Research Group will encompass AI product engineering, basic and applied research labs, and New Experiences and Technologies (NExT). The move is expected to accelerate the delivery of new capabilities to customers across agents, apps, services and infrastructure.

The new group will be led by computer vision luminary Harry Shum, a 20-year Microsoft veteran whose career has spanned leadership roles across Microsoft Research and Bing engineering. In addition to Shum’s existing leadership team, several of the company’s engineering leaders and teams will join the newly formed group including Information Platform, Cortana and Bing, and Ambient Computing and Robotics teams led by David Ku, Derrick Connell and Vijay Mital respectively.

“We live in a time when digital technology is transforming our lives, businesses and the world, but also generating an exponential growth in data and information,” said Satya Nadella, CEO, Microsoft. “At Microsoft, we are focused on empowering both people and organisations, by democratising access to intelligence to help solve our most pressing challenges. To do this, we are infusing AI into everything we deliver across our computing platforms and experiences.”

“Microsoft has been working in artificial intelligence since the beginning of Microsoft Research, and yet we’ve only begun to scratch the surface of what’s possible,” said Shum, Executive VP of the Microsoft AI and Research Group. “Today’s move signifies Microsoft’s commitment to deploying intelligent technology and democratising AI in a way that changes our lives and the world around us for the better. We will significantly expand our efforts to empower people and organisations to achieve more with our tools, our software and services, and our powerful, global-scale cloud computing capabilities.”

Microsoft is taking a four-pronged approach towards democratising AI:

Agents 

Harness AI to fundamentally change human and computer interaction through agents such as Microsoft’s digital personal assistant Cortana

Applications

Infuse every application, from the photo app on people’s phones to Skype and Office 365, with intelligence

Services

Make these same intelligent capabilities that are infused in Microsoft’s apps —cognitive capabilities such as vision and speech, and machine analytics — available to every application developer in the world

Infrastructure

Build the world’s most powerful AI supercomputer with Azure and make it available to anyone, to enable people and organisations to harness its power

For 25 years, Microsoft Research has contributed to advancing computing through basic and applied research that has been shared openly with the industry and academic communities, and with product groups within Microsoft. The organisation has contributed innovative technologies to nearly every product and service Microsoft has produced in this timeframe, from Office and Xbox to HoloLens and Windows. More recently, Shum has expanded the organisation’s mission to include the incubation of disruptive technologies and new businesses.

“My job has been to take Microsoft Research, an amazing asset for the company, and make it even more of a value-creation engine for Microsoft and our industry,” Shum said. “Today’s move to bring research and engineering even closer will accelerate our ability to deliver more personal and intelligent computing experiences to people and organisations worldwide.”

Interested?

The Microsoft AI and Research Group is hiring for positions in its labs and offices worldwide. Browse positions available at the Microsoft AI and Research Group. At the time of writing Microsoft supports research internships in Asia, and was looking for researchers in Beijing for computer vision and video analytics as well as large-scale machine learning.