Tag: Recent News

Microsoft Build 2019 is here, and thousands of developers are learning about the latest technologies, sharing best practices with colleagues, and writing lots of code. Last week I wrote about business applications sessions to see at Build, and on Monday I discussed updates for helping ISVs (independent software vendors) and developers be successful from both a business and a code perspective.

A few years ago, Microsoft introduced an IP co-sell program to help our digitally transforming enterprise customers get the software they needed from our Azure ISV partners. Many of these customers were moving to the cloud, so we started by focusing on Azure. During this time roughly 3,000 ISVs have generated over $5 billion in partner revenue from the collaboration between Azure sellers and ISVs. Following up on last month’s announcement about an upcoming program for business applications ISVs, Scott Guthrie announced the expansion of the IP co-sell program to include Dynamics 365 and Power Platform partners. Including these products and Azure in the program will make it easier for ISVs and Microsoft sellers to collaborate in serving our joint enterprise customers.

Enterprise customers are increasingly seeing software at the core of their business and are developing a deeper understanding of their software needs. Beyond person to person sales engagements, there will be times when they want to buy the app directly while having confidence in the quality of what they’re receiving. Like consumers, enterprises are familiar with getting their software through marketplaces and the ability for ISVs to transact through marketplaces lowers the barrier for reaching their enterprise customers. On Monday, we announced that SaaS transaction capabilities have been added to AppSource and the Azure Marketplace. As part of this, we will enable transactability support for Dynamics 365 and Power Platform partner apps over the coming months. This is just a start to what we are doing with the marketplace, and now is the perfect time to get familiar with our partner program that launches in July to learn more about the benefits that come with being a Dynamics 365 and Power Platform partner.

The Power Platform provides great general tooling for creating business apps while the Common Data Model (CDM) creates a standard representation of that data. There are times when an industry’s focused solution and data model are better than starting from scratch. For this case Dynamics 365 Industry Accelerators provide industry specific implementations, and we’re announcing private previews in automotive and financial services. The accelerator for the automotive industry enables you to build connected customer experiences based on a proven common data model designed to transform consumer experiences and enable smart mobility services. In financial services, we have built accelerators to help you develop banking solutions in the retail and commercial space with enhanced ways to engage customers and provide an improved customer banking experience. Automotive and financial services accelerators join previously announced accelerators including healthcare, higher education, and nonprofit. Sign up to learn more about how you can participate or help us build the next set of industry accelerators.

During my talk at Build, I showed how all of this fits together through three phases: data at the core, empowering domain experts to be citizen developers, and showing how developers and ISVs can build depth solutions. We used healthcare as an end to end scenario that we can relate to and showcased how we’re infusing AI into all three phases in a way that every industry can start to take advantage of, moving from BI to AI. Lastly, I was delighted to welcome two customers on stage that are delivering innovative solutions. HandsFree Health™ is a new startup founded by senior executives in healthcare including the former President of Aetna. They built an innovative new home healthcare device using Microsoft AI technologies spanning speech, bots, vision, and a companion Xamarin mobile app that we showed on stage. We also took a deeper look at what ISVs can do with Indegene, a global healthcare solutions provider who is building the next generation of cloud applications for life sciences with the Dynamics 365 Healthcare Accelerator.

If you are at Build or following along at home, I have created a list of some Dynamics 365 and Power Platform sessions that you might be interested in. Enjoy your Build 2019 experience and we look forward to seeing how you build, extend, or connect Dynamics 365 and the Power Platform.

Cheers,

Guggs

Microsoft’s autonomous systems platform overcomes some of these challenges by using a unique approach called machine teaching. It relies on a developer’s or subject matter expert’s knowledge — someone who may not have a background in AI but understands how to steer a drill or keep the airflow in an office building at safe levels — to break a large problem into smaller chunks.

Instead of having reinforcement learning algorithms explore how to solve a problem randomly or naively, which could take forever, that person uses a programming language called Inkling to show the system how to solve simpler problems first and provide clues about what’s important. This shortcuts the learning process and enables the algorithms to hit on a solution much faster.

Microsoft’s platform also enables non AI-experts to establish and tweak the reward system, which is key to arriving at a solution that truly works. And it selects and configures the algorithms to tackle the task, eliminating the need for machine learning experts to custom build solutions.

For instance, team members worked with Schneider Electric, a global company working to digitally transform energy management in homes, buildings and industries, to test whether AI could help reduce the carbon footprint of HVAC systems that are used to heat and cool large commercial buildings.

“Schneider is very focused on sustainability, and large buildings are a top contributor to carbon pollution. So there’s a really important mandate to make HVAC systems more energy efficient,” said Barry Coflan, senior vice president and chief technology officer for Schneider Electric’s EcoBuildings Division.

Centered on a longstanding relationship, a proof-of-concept test was conducted using the Microsoft toolchain and Schneider supplied simulation to train an AI system to autonomously run the HVAC systems that controlled airflow and heating in a conference room. It had to balance saving energy with other goals, such as keeping the temperature comfortable for people inside and making sure there’s enough fresh air to keep carbon dioxide levels from building up.

Optimizing for all those factors — which are controlled by different physical systems — requires far more intelligence than a simple thermostat, says Microsoft’s Hammond. The system has to account for environmental variables that are constantly changing: energy costs that fluctuate throughout the day, people coming and going from the room, what the outside weather is doing, the physics of how air flows.

Using a machine teaching approach, Schneider and Microsoft experts first taught the reinforcement learning system to control temperature well. Then the AI system learned how to control air flows to keep air quality at healthy levels.  Then it learned to consider how room occupancy affected those outcomes.

Taking all those factors into account, Microsoft’s AI system was able to reduce energy consumption in the room by about 20 percent, while preserving comfort and high air quality when it mattered. The teams are now embarking on a second phase of collaboration to scale the simulation across different types of rooms and further boost energy savings.

Coflan said the laddered approach to teaching and the ability to layer in different rewards enabled Schneider Electric to understand how the AI system was learning and track which factors contributed to the biggest gains.

“A lot of what we do has safety ramifications so we really need to understand how the AI system is making decisions,” Coflan said. “This approach lets you see how the system is getting smarter and gives you an audit trail that is essential for safety and reproducibility. Our customers would want that too — you can’t just put a system out there and say ‘Trust us.’”

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Microsoft’s autonomous systems platform uses a simulated warehouse environment in AirSim to train an intelligent forklift to pick up and deliver loads autonomously while recognizing and avoiding other obstacles. This video illustrates the vision for a future warehouse with pre-trained, intelligent forklifts assisting people in everyday activities.

Running simulation at scale in Azure

Because no company can afford to let a robot or an intelligent control system make millions of mistakes in a real-world factory or wind farm or highway as it is learning, reinforcement learning algorithms need to practice in a simulated environment that can replicate the thousands or millions of different real-world scenarios they might encounter.

The Microsoft toolchain also includes AirSim, an open source simulation platform originally developed by Microsoft researchers to use AI to teach drones, self-driving cars or robots to learn in high fidelity simulated environments. Or, the team can work with customers to train autonomous systems using existing industry-specific simulators.

In either case, running these data-hungry simulations in the Azure cloud enables the system to test thousands of different decision-making sequences in parallel, which allows the AI models to learn what does and doesn’t work much faster.

“If I have the ability to spawn thousands of simulations at once and in each one the pedestrian crossing the street is different and the curve of the road is different, suddenly the AI system is able to gather much more diverse experience in a short amount of time ,” said Ashish Kapoor, Microsoft principal research manager. “Azure gives us the ability to run these simulations at scale, which is really important.”

AirSim also allows developers to train different AI and control tools to solve different parts of more complex problems. In helping develop autonomous forklifts for Toyota Material Handling, for instance, researchers broke the task down into sub-concepts that are simpler to learn and debug: navigating to the load, aligning with the pallet, picking it up, detecting other people and forklifts, delivering the pallet, returning to the charging station.

In these complex scenarios, Kapoor said, it may make sense to use reinforcement learning to train a forklift on basic control tasks, like picking up a pallet. Machine teaching helps the system learn in progressively more difficult steps, such as aligning the lift horizontally and then finding the proper angles.

But other parts of the problem might be better solved by entirely different tools like obstacle detection and avoidance algorithms, robotics path planning or classical control techniques. Decomposing the larger task into smaller ones allows developers to select and deploy the best tool for that particular job.

“We are working to provide a comprehensive platform for customers who want to build intelligent autonomous systems, covering development, operation and end-to-end lifecycle management,” Hammond said.

Top image: An experimental version of the Sarcos Guardian S, a visual inspection robot that can be used in disaster recovery or for industrial inspections, has learned to avoid obstacles and climb stairs on its own using Microsoft’s autonomous systems platform. Photo by Dan DeLong for Microsoft.

Microsoft Build 2019 — related autonomous systems links:

 Jennifer Langston writes about Microsoft research and innovation. Follow her on Twitter

Microsoft Build is underway in Seattle, and this year’s premier developer conference is focused on empowering developers of all kinds, from experienced computer scientists to tech beginners with big ideas. We’re sharing the latest on Microsoft platforms, tools and services that are making it easier to create and innovate using AI and mixed reality with the intelligent cloud and the intelligent edge, as well as helping people and organizations do more each day.

Here’s a peek at some of the event’s highlights:

Check out the new features in Microsoft Edge built on Chromium OSS that will make it easier to collect, organize and share content, and give you additional privacy controls to make your own decisions about how third parties can track you.

See how technology is helping Starbucks and their baristas deliver great customer experiences including managing predictive maintenance on coffee machines with Azure Sphere; giving personalized recommendations to customers via the Starbucks mobile app with reinforcement learning on Azure; and powering digital traceability efforts to track coffee beans from farm to store with Azure blockchain service.

Read about Fluid Framework, a web-based platform expected to be available later this year that will give teams new ways to create together, enabling content to be deconstructed and reconstructed into modular components and allowing intelligent agents to work alongside people to co-author, provide photo suggestions, translate data and more.

Learn more about Microsoft’s vision for intelligent agents that leverage breakthroughs in conversational AI and machine learning pioneered by Semantic Machines, which Microsoft acquired in May 2018. We’re showcasing a calendaring application of the technology that can make organizing your day with an intelligent assistant a more natural and powerful experience, and the same technology will eventually be integrated into our conversational AI moving forward across all of Microsoft’s products and services.

See how more everyday uses of AI in Microsoft 365 and Office 365 can help improve productivity, including Ideas in Word for Word Online, which can help you easily design and present polished documents, and Microsoft Search, a new enterprise search experience that applies AI technology from Bing and deep personalized insights surfaced by the Microsoft Graph.

Learn more about a new platform that can help make it easier for companies to create autonomous systems, whether it’s a robot that can help in life-threatening situations, a drone that can inspect remote equipment or systems that can autonomously calibrate factory equipment. The first component in this platform is now available in a limited preview program.

Learn what developers will be able to do with Microsoft Graph data connect, a service that helps organizations bring together productivity data from the Microsoft Graph with their own business data securely, providing new opportunities to create insight-powered apps while helping address the challenges of moving and managing large amounts of data.

Learn about the new tools and capabilities in Azure AI for developers and data scientists, including Ink Recognizer, which lets developers embed digital ink recognition, an advanced speech-to-text capability that transcribes meeting conversations in real time so participants can fully engage in the discussion and know who said what later on.

See how the new Azure Blockchain Service will empower developers, letting them deploy a fully managed consortium network with a few simple clicks and use built-in governance to add new members, set permissions and authenticate user applications. They’ll also be able to monitor their network’s health and activity.

Check out IoT Plug and Play. One of the greatest challenges customers face when deploying Internet of Things solutions broadly is connecting their IoT devices to the cloud. IoT Plug and Play offers a new open modeling language to help make this happen seamlessly, as well as a large ecosystem of partner-certified devices that simply work, with over a dozen certified devices now available.

Get more details on all of these announcements and more on our Microsoft Build 2019 website.

Lead photo by Brian Smale

Just a few years ago, artificial intelligence was largely relegated to universities and research labs, a charming computer science concept with little use in mainstream business. Today, AI is being integrated into everything from your refrigerator to your favorite workout app.

“It’s really exciting, because there’s a new breakthrough every month, or every week,” said Lance Olson, director of program management for applied AI at Microsoft. “Increasingly, the conversations are switching from discussing the art of the possible to getting to the next level of implementation on a specific project.”

Still, many companies are struggling to achieve their AI goals, as the supply of data scientists and AI experts has failed to keep up with surging demand. Creating AI models is difficult work. And then comes a struggle to get them into production – and keep them running. Data ages, much more quickly than code, making models less accurate as the world changes around us.

At its 2019 Microsoft Build conference, the company says it’s focused on helping all developers – even those without an AI or data science background – use its tools and services to deliver the big benefits that more and more customers expect.

“AI and machine learning can turn developers into heroes, for their ability to deliver really personalized, super-immersive experiences to customers,” said Wisam Hirzalla, director of operational databases and Blockchain product marketing at Microsoft. “We want to make it easy for any company to use the technology.”

Simplified and automated machine learning

Toward that end, Microsoft is announcing new capabilities for its cloud-based Azure Machine Learning service, with a goal of enabling developers and data professionals of any skill level to build advanced machine learning models.

We can think of AI practitioners in three categories, according to Bharat Sandhu, director of artificial intelligence at Microsoft. First, we have developers and data scientists who like to write code. They want to build machine learning models using tools and processes they already know. For them, Azure Machine Learning offers a “code first model,” where they can use the development tools they like.

A second group, including business domain experts, may know a lot about data, but they don’t know much about machine learning or code. For those customers, Azure Machine Learning’s automated machine learning experience is a “no code” option, accessible without having to write any code.

“A third category of people, who are learning machine learning concepts, they want to make their own models, but they are not coders. This could be IT professionals, or folks with background in statistics or mathematics,” Sandhu said. “For those customers, we’re offering a drag-and-drop experience to make models visually.”

Sandhu noted that no matter which way the machine learning models are created, they all use the same back end, meaning all the models can easily be integrated together.

Interoperability

Of course, developers and data scientists have a number of platforms to choose from when they build AI models. To make sure companies can adopt AI advances as quickly as possible, Microsoft says it’s important to overcome platform mismatches, which can delay the rollout of those models into production.

One way Microsoft promotes interoperability among the various AI frameworks is a standard called ONNX Runtime, or Open Neural Network Exchange. This joint effort with other tech companies creates deployment models that work across multiple platforms.

That frees up developers and data scientists to use whatever framework and hardware target is best for them. And it frees up the operational team to focus on deploying and getting results, instead of having to translate as they move from one to the other.

At Build, Microsoft is announcing support for ONNX integration with leading hardware accelerators.

The company also is announcing that it is now an active contributor to the MLflow project, an open source platform for managing the machine learning lifecycle.

Azure Cognitive Services updates

More than 1.3 million developers, many without specific AI or data science skills, currently use Azure Cognitive Services to build intelligent apps that can see, hear, speak, understand and even begin to reason.

At Build, Microsoft is announcing a new category of Azure Cognitive Services called Decision, which gives specific recommendations to help people make decisions. This new category includes Personalizer, which uses a branch of AI called reinforcement learning to help technology glean knowledge from its own experiences and then offer informed recommendations.

“We are able to take reinforcement learning and ship it in a way that’s accessible to developers and doesn’t require a data scientist,” Olson said. “That will be very impactful for customers.”

At Build, the company is announcing many other updates to Azure Cognitive Services, including Ink Recognizer, which can learn to read handwriting, Form Recognizer, which identifies forms, and other new conversation transcription capabilities and other speech, vision and language advances.

Just getting started

To date, Microsoft’s customers have created almost 400,000 digital agents through its Azure bot service, and more than 3,000 come on line each week. Companies of all sizes are looking to AI to give them a competitive edge.

That includes Cheetah Mobile, a leading mobile app maker building AI-enhanced hardware, including the hand-held CM Translator. Rather than developing the entire speech system from scratch, the company used Azure Cognitive Services, leveraging its text-to-speech API to provide rapid, high quality translations.

The development cost savings helped keep the device affordable, with no compromise in the natural speech flow.

Other companies say one of the chief benefits of using Azure data and AI tools is that they can take advantage of other attributes built into the tools. For example, the digital asset management company MediaValet relies on the security and privacy safeguards Azure provides to reassure customers that the images it processes will be handled properly.

“We’re not a big company, but we can actually play ball with big enterprise players, because we can leverage the information security and privacy attributes, the trust-ability of Azure,” said MediaValet chief technology officer Jean Lozano.

In the coming months and years, Microsoft expects more and more customers to start using AI, both because they see the business benefits and because the tools are more accessible.

“AI opens up so many possibilities. And the limits are very few, generally limited only by your imagination,” Olson said. “It doesn’t need to be overwhelming for people. We are getting to the point where we can now make AI accessible to a much broader set of customers.”

Related to AI news at Microsoft Build 2019:

Since the inception of Azure, we have been focused on delivering a true hybrid cloud where applications spanning public cloud and on-premises datacenters are built and run consistently. As organizations are now building applications that span the intelligent cloud and intelligent edge, the same approach is needed. Fundamentally, the principles and technology needed for developing hybrid cloud applications are the same as intelligent cloud and intelligent edge applications. Azure’s longstanding leadership in hybrid cloud provides developers with unique know-how toward building modern applications that span the edge and the cloud.

Next week at Microsoft Build, more than 6,000 developers will join us in Seattle to experience the latest advancements in dev tools and cloud services. Today, to help usher in Build, I’m excited to share some of the new Azure innovations that we will be showcasing at Build that enable developers to build this new generation of hybrid applications with greater productivity and success.

To begin, we’re announcing several new AI services and capabilities that makes it easier for developers to build AI-powered applications. Furthering our commitment to building the most productive AI platform, we’re delivering key new innovations in Azure Machine Learning that simplify the process of building, training and deployment of machine learning models at scale. These include new automated machine learning advancements and an intuitive UI that make developing high-quality models easier, a new visual machine learning interface that provides a zero-code model creation and deployment experience using drag-and-drop capabilities and new machine learning notebooks for a rich, code-first development experience. Furthermore, new MLOps (DevOps for machine learning) capabilities with Azure DevOps integration provides developers with reproducibility, auditability and automation of the end-to-end machine learning lifecycle. To enable extremely low latency and cost-effective inferencing, we are also announcing the general availability of hardware-accelerated models that run on FPGAs, as well as ONNX Runtime support for NVIDIA TensorRT and Intel nGraph for high- speed inferencing on NVIDIA and Intel chipsets.

Azure Cognitive Services give connected devices, bots and apps the ability to see, hear, respond, translate, reason and more. Azure is the only public cloud that enables these Cognitive Services to be containerized to run on-premises, in the cloud and at the edge. Today we’re giving developers even more ways to create “smart” devices and services with a new Cognitive Services category called “Decision” that delivers users specific recommendations to enable informed and efficient decision-making. Azure Cognitive Services such as Content Moderator, the recently announced Anomaly Detector and a new preview service called Personalizer, which uses reinforcement learning to provide each user with a relevant experience to drive engagement, will be part of this new category. We’re also delivering several new services in public preview, including Ink Recognizer for embedding digital ink recognition capabilities; Form Recognizer for automating data entry by extracting text, key-value pairs and tables from documents; and new conversation transcription capability in Speech Service, which transcribes meeting conversations in real time so participants can fully engage in the discussion, know who said what when and quickly follow up on next steps.

We are also bringing AI to Azure Search with the general availability of the cognitive search capability, enabling customers to apply Cognitive Services algorithms to extract new insights their structured and unstructured content. In addition, we are previewing a new capability that enables developers to store AI insights gained from cognitive search, making it easier to create knowledge-rich experiences leveraging Power BI visualizations or machine learning models. You can read more about today’s Azure AI announcements here.

Intelligent cloud and intelligent edge applications have evolved from primarily low-compute IoT devices working with the cloud to powerful compute at the edge, which requires a new modern hybrid application approach. A key aspect of enabling this is supporting the spectrum of edge compute and data needs. SQL Server and Azure SQL Database are the leading data engines for enterprise workloads on-premises and in the cloud, respectively, and today we are bringing these powerful data and analysis capabilities to the edge with Azure SQL Database Edge preview. Azure SQL Database Edge runs on ARM processors and provides capabilities like data streaming and time series data, with in-database machine learning and graph. And because Azure SQL Database Edge shares the same programming surface area with Azure SQL Database and SQL Server, you can easily take your applications to the edge without having to learn new tools and languages, allowing you to preserve consistency in application management and security control. This consistency in database programming and control plane across cloud and edge is essential to running a secure and well-managed hybrid application.

In addition, we’re announcing IoT Plug and Play, a new open modeling language to connect IoT devices to the cloud seamlessly, enabling developers to navigate one of the biggest challenges they face — deploying IoT solutions at scale. Previously, software had to be written specifically for the connected device it supported, limiting the scale of IoT deployments. IoT Plug and Play provides developers with a faster way to build IoT devices and will provide customers with a large ecosystem of partner-certified devices that can work with any IoT solution.

Perhaps one of the best examples of a cloud and edge application is with Mixed Reality — using the combination of mixed-reality device with cloud services to create entirely new experiences. We have barely scratched the surface for possibilities with Mixed Reality development. Now we’re making it easier to create applications for HoloLens 2 with the HoloLens 2 Development Edition, which starts at $3,500 or as low as $99 a month. It provides the community of mixed-reality developers with access to solutions to help them build and run mixed-reality experiences across a range of mixed-reality devices, and with Azure credits and three-month free trials of Unity Pro and the Unity PiXYZ Plugin for CAD data. Unreal Engine 4 support for streaming and native platform integration for HoloLens 2 will be available by the end of May for developers to create high-quality, photo-realistic renders and immersive, augmented-reality and virtual-reality experiences for architecture, product design and manufacturing. Read more about our IoT and intelligent edge announcements here.

Blockchain is showing potential across many industries to manage complex workflows and logistics. Last year we announced Azure Blockchain Workbench, which gave developers a simple UI to model blockchain applications on a preconfigured Azure-supported network. Today we are doubling down on our investments to empower blockchain developers by announcing Azure Blockchain Service, which simplifies the formation, management and governance of consortium blockchain networks, allowing businesses to focus on workflow logic and app development. Azure Blockchain Service deploys a fully managed consortium network and offers built-in governance for common management tasks, such as adding new members, setting permissions and authenticating user applications. And, the service is already receiving enterprise traction — we also announced this week that J.P. Morgan’s Ethereum platform, Quorum, is the first ledger available in Azure Blockchain Service, enabling Microsoft and J.P. Morgan to offer the first enterprise-grade Ethereum stack to the industry. Read more about today’s blockchain announcements here.

Today’s announcements give developers cutting-edge tools to create the next generation of hybrid applications spanning the cloud and edge. We have even more to share on Monday at the start of Build. Be sure to tune into Satya’s keynote at 8:30 a.m. PT on Monday, May 6, here, and look forward to seeing what you go build!

Tags: Azure, Build 2019, HoloLens 2, intelligent cloud, intelligent edge, IoT