How AI Transforms Business – A New Microsoft Series

Microsoft is privileged to work with leading-edge customers and partners who are taking the power of the cloud and artificial intelligence and applying it to their businesses in novel ways. Our new series, How AI Transforms Business, features insights from selective such customers and partners. Join us in these conversations and see how your company and customers may be able to benefit from these solutions and insights.
All Episodes 1. How Can Autonomous Drones Help the Energy and Utilities Industry?
Headquartered in Norway, eSmart Systems develops digital intelligence for the energy industry and for smart communities. When it comes to next-generation grid management systems or efficiently running operations for the connected cities of the future, they are at forefront of digital transformation. In a conversation with Joseph Sirosh, CTO of AI in Microsoft’s Worldwide Commercial Business, Davide Roverso, Chief Analytics Officer at eSmart Systems, talks about interesting new AI-enabled scenarios in world of energy, utilities and physical infrastructure.
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How Can Autonomous Drones Help the Energy and Utilities Industry?

Welcome to How AI Transform Business, a new series featuring insights from conversations with Microsoft partners who are combining deep industry knowledge with AI in novel ways and, in doing so, creating leading-edge intelligent business solutions for our digital age.

Our first episode features eSmart Systems, which is in the business of creating solutions to accelerate global progress towards sustainable societies. Headquartered in the heart of Østfold county, Norway, eSmart Systems develops digital intelligence for the energy industry and for smart communities. The company is strategically co-located with the NCE Smart Energy Markets cluster and the Østfold University College and thrives in a very innovative environment. When it comes to next-generation grid management systems, or efficiently running operations for the connected cities of the future or driving citizen engagement, the company is at the forefront of digital transformation.

We recently caught up with Davide Roverso, Chief Analytics Officer at eSmart Systems. Davide has many interesting things to share about where and how AI is being applied in the infrastructure industry. Among other things, he talks about how utilities companies are forced to fly manned helicopters missions over live electrical power lines today, just to perform routine inspections, and how –…

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Machine Reading at Scale – Transfer Learning for Large Text Corpuses

This post is authored by Anusua Trivedi, Senior Data Scientist at Microsoft.

This post builds on the MRC Blog where we discussed how machine reading comprehension (MRC) can help us “transfer learn” any text. In this post, we introduce the notion of and the need for machine reading at scale, and for transfer learning on large text corpuses.


Machine reading for question answering has become an important testbed for evaluating how well computer systems understand human language. It is also proving to be a crucial technology for applications such as search engines and dialog systems. The research community has recently created a multitude of large-scale datasets over text sources including:

Wikipedia (WikiReading, SQuAD, WikiHop). News and newsworthy articles (CNN/Daily Mail, NewsQA, RACE). Fictional stories (MCTest, CBT, NarrativeQA). General web sources (MS MARCO, TriviaQA, SearchQA).

These new datasets have, in turn, inspired an even wider array of new question answering systems.

In the MRC blog post, we trained and tested different MRC algorithms on these large datasets. We were able to successfully transfer learn smaller text excepts using these pretrained MRC algorithms. However, when we tried creating a QA system for the Gutenberg book corpus (English only) using these…

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Power Bat – How Spektacom is Powering the Game of Cricket with Microsoft AI

A special guest post by cricket legend and founder of Spektacom Technologies, Anil Kumble. This post was co-authored by Tara Shankar Jana, Senior Technical Product Marketing Manager at Microsoft.

While cricket is an old sport with a dedicated following of fans across the globe, the game has been revolutionized in the 21st century with the advent of the Twenty20 format. This shorter format has proven to be very popular, resulting in a massive growth of interest in the game and a big fan following worldwide. This has, in turn, led to increased competitiveness and the desire on the part of both professionals and amateurs alike to take their game quality to the next level.

As the popularity of the game has increased, so have innovative methods of improving batting techniques. This has resulted in a need for data-driven assistance for players, information that will allow them to digitally assess their quality of game.

Spektacom was born from the idea of using non-intrusive sensor technology to harness data from “power bats” and using that data to power insights driven by the cloud and artificial intelligence.

Before we highlight how Spektacom built this solution using Microsoft AI, there are a couple…

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Deep Learning Without Labels

Announcing new open source contributions to the Apache Spark community for creating deep, distributed, object detectors – without a single human-generated label

This post is authored by members of the Microsoft ML for Apache Spark Team – Mark Hamilton, Minsoo Thigpen,
Abhiram Eswaran, Ari Green, Courtney Cochrane, Janhavi Suresh Mahajan, Karthik Rajendran, Sudarshan Raghunathan, and Anand Raman.

In today’s day and age, if data is the new oil, labelled data is the new gold.

Here at Microsoft, we often spend a lot of our time thinking about “Big Data” issues, because these are the easiest to solve with deep learning. However, we often overlook the much more ubiquitous and difficult problems that have little to no data to train with. In this work we will show how, even without any data, one can create an object detector for almost anything found on the web. This effectively bypasses the costly and resource intensive processes of curating datasets and hiring human labelers, allowing you to jump directly to intelligent models for classification and object detection completely in sillico.

We apply this technique to help monitor and protect the endangered population of snow leopards.

This week at the Spark + AI Summit in…

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“Snip Insights” – An Open Source Cross-Platform AI Tool for Intelligent Screen Capture

This post is authored by Tara Shankar Jana, Senior Technical Product Marketing Manager at Microsoft.

What if we could infuse AI into the everyday tools we use, to delight everyday users? With just a little bit of creativity – and the power of the Microsoft AI platform behind us – it’s now become easier than ever to create AI-enabled apps that can wow users.

Introducing Snip Insights!

An open source cross-platform AI tool for intelligent screen capture, Snip Insights is a step change in terms of how users can generate insights from their screen captures. The initial prototype of Snip Insights, built for Windows OS and released at Microsoft Build 2018 in May, was created by Microsoft Garage interns based out of Vancouver, Canada.

Our team at Microsoft AI Lab, in collaboration with the Microsoft AI CTO team, took Snip Insights to the next level by giving the tool an intuitive new user interface, adding cross-platform support (for MacOS, Linux, and Windows), and offering free download and usage under the MSA license.

Snip Insights taps into Microsoft Azure Cognitive Services APIs and helps increase user productivity by automatically providing them with intelligent insights on their screen captures.

Solution Overview



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Can AI-Generated Code Automate Tedious Elements of Knowledge Work?

By Joseph Sirosh, Corporate Vice President and CTO of AI, and Sumit Gulwani, Partner Research Manager, at Microsoft.

There are an estimated 250 million “knowledge workers” in the world, a term that encompasses anybody engaged in professional, technical or managerial occupations. These are individuals who, for most part, perform non-routine work that requires the handling of information and exercising the intellect and judgement. We, the authors of this blog post, count ourselves among them. So are a majority of you reading this post, regardless of whether you’re a developer, data scientist, business analyst or manager.

Although a majority of knowledge work tends to be non-routine, there are, nevertheless, many situations in which knowledge workers find ourselves doing tedious repetitive tasks as part of our day jobs, especially around tasks that involve manipulating data.

In this blog post, we take a look at Microsoft PROSE, an AI technology that can automatically produce software code snippets at just the right time and in just the right situations to help knowledge workers automate routine tasks that involve data manipulation. These are generally tasks that most users would otherwise find exceedingly tedious or too time consuming to even contemplate.

Details of Microsoft PROSE can…

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This New [AI] Software Constantly Improves – and that Makes all the Difference

Based on a recent conversation between Joseph Sirosh, CTO for AI at Microsoft, and Roger Magoulas, VP of Radar at O’Reilly Media. Link to video recording below.

Joseph and Roger had an interesting conversation at the recently concluded O’Reilly AI Conference in San Francisco where Joseph delivered a keynote talk on Connected Arms.

Their discussion initially focused on a new low-cost 3D-printed prosthetic arm that can “see” and which connects to cloud AI services to generate customized behaviors, such as different types of grips needed to grasp nearby objects. But the conversation soon pivoted into a discussion about the unlimited set of possibilities that open up when devices such as this are embedded with low-cost sensors, take advantage of cloud connectivity, sophisticated cloud services such as AI, link to other datasets and other things in the world around them.

True digital transformation is not about running a neural network or just about AI, as Joseph observes. It is about this ability to tap into software running as a service in the cloud, with the connectivity and global access that it brings. That can endow unexpected and almost magical new powers to ordinary everyday things.

Joseph draws the parallel between…

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AI-Based Virtual Tutors – The Future of Education?

This post is co-authored by Chun Ming Chin, Technical Program Manager, and Max Kaznady, Senior Data Scientist, of Microsoft, with Luyi Huang, Nicholas Kao and James Tayali, students at University of California at Berkeley.

This blog post is about the UC Berkeley Virtual Tutor project and the speech recognition technologies that were tested as part of that effort. We share best practices for machine learning and artificial intelligence techniques in selecting models and engineering training data for speech and image recognition. These speech recognition models, which are integrated with immersive games, are currently being tested at middle schools in California.


The University of California, Berkeley has a new program founded by alum and philanthropist Coleman Fung called the Fung Fellowship. In this program, students develop technology solutions to address education challenges such as enabling underserved children to help themselves in their learning. The solution involves building a Virtual Tutor that listens to what children say and interacts with them when playing educational games. The games were developed by a technology company founded by Coleman named Blue Goji. This work is being done in collaboration with the Partnership for a Healthier America, a nonprofit organization chaired by Michelle Obama.



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AI Adoption in Industry – New Report from MIT Sloan and BCG

Earlier this week, MIT, in collaboration with Boston Consulting Group, released their second global study looking at AI adoption in industry.

A top finding is that the leading companies in AI adoption are now convinced of the value of AI and are facing the challenge of moving beyond individual point solutions toward broad, systematic use of AI across the company and at-scale.

In the report, Joseph Sirosh, CTO of AI at Microsoft, discusses how Microsoft is building a complete AI platform that empowers enterprises to implement these AI-first business models and do so at scale. Scaling AI across an entire business requires companies to look far beyond just building that initial model.

As Joseph says, companies need an “AI Oriented Architecture capable of constantly running AI experiments reliably, with continuous integration and development, and then learning from those experiments and continuing to improve its operations.

For those of you who are attending Microsoft Ignite at Orlando next week, you can hear Joseph talk about AI Oriented Architectures first hand, and get guidance on how enterprises can build successful AI solutions at scale.

Adopting Microsoft AI is super easy – you can get started here.

AI / ML Blog Team&

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