With the advances in artificial intelligence (AI) as well as in cloud computing, the pace of research in AI has been on the rise. Some of the key ways AI is impact the world around us, include in healthcare, in food security, and in human communications. Whether it be helping scientists combat cancer more effectively, or enabling farmers to practice sustainable agriculture and increasing food production while reducing environmental impact, or whether it be in helping humans communicate across borders and language barriers seamlessly and in real time, AI is here to stay.
In 2016, Microsoft created the Microsoft Research AI, a new research group comprising of approximately 7,500 computer scientists, researchers and engineers from the company’s research labs as well as product groups such as Bing, Cortana and Azure Machine Learning.
In 2017, Microsoft made a significant shift in its vision statement, moving from a “mobile-first and cloud-first world” to “an intelligent cloud and an intelligent edge infused with AI”.
Some of the key goals of the Microsoft Research AI include making significant advance in AI, and integrating the same into Microsoft’s product and service suite. It also aimed to help create a cohesive research framework for AI and all its subdomains, whether it be machine learning, perception and natural language processing that have evolved over time into separate fields of research.
Across Microsoft’s product suite, including the MS Office suite, or its virtual assistant, Cortana, there has been a steady roll-out of AI-assisted features designed to offer help with everyday tasks. For instance, among others, Cortana can now sort and provide summaries of the most important emails from Outlook, Gmail and other accounts.
The governing frame for Microsoft’s foray in Artificial Intelligence is the Microsoft AI Platform that brings together many specialized packages, including Microsoft Cognitive Services, Microsoft Cognitive Toolkit and Microsoft Bot Framework. By allowing AI algorithms to be deployed on the powerful and popular Azure cloud computing platform, Microsoft made it convenient for users to simply pay for processing and storage as it is required.
Alongside, Microsoft actively focused on building industry specific AI applications, cutting across healthcare to autonomous vehicles. Microsoft is also building up its AI knowledgebase with through a steady stream of startup acquisitions, aimed to bring in specialized research talent and advance the AI research at Microsoft.
Here are the key AI acquisitions that Microsoft has made in the last two years:
Based in San Francisco, Lobe is working to make deep learning simple, understandable and accessible to everyone. Lobe’s simple visual interface empowers anyone to develop and apply deep learning and AI models quickly, without writing code.
Based in Berkeley, California, and an M12 portfolio company, Bonsai has developed a novel approach using machine teaching that abstracts the low-level mechanics of machine learning, so that subject matter experts, regardless of AI aptitude, can specify and train autonomous systems to accomplish tasks. The actual training takes place inside a simulated environment. The company is building a general-purpose, deep reinforcement learning platform especially suited for enterprises leveraging industrial control systems such as robotics, energy, HVAC, manufacturing and autonomous systems in general. This includes unique machine-teaching innovations, automated model generation and management, a host of APIs and SDKs for simulator integration, as well as pre-built support for leading simulations all packaged in one end-to-end platform.
Semantic Machines is a Berkeley, California-based company that has developed a revolutionary new approach to building conversational AI. Their work uses the power of machine learning to enable users to discover, access and interact with information and services in a much more natural way, and with significantly less effort. The company is led by many pioneers in conversational AI, including technology entrepreneur Dan Roth and two of the most prominent and innovative natural language AI researchers in the world, UC Berkeley professor Dan Klein and Stanford University professor Percy Liang, as well as former Apple chief speech scientist Larry Gillick.
Maluuba, a Montreal-based company with one of the world’s most impressive deep learning research labs for natural language understanding, aims to address some of the fundamental problems in language understanding by modeling some of the innate capabilities of the human brain, from memory and common sense reasoning to curiosity and decision making.