Microsoft Azure delivers game-changing performance for generative AI Inference

Microsoft Azure has delivered industry-leading results for AI inference workloads among cloud service providers in the most recent MLPerf Inference results published publicly by MLCommons. The Azure results were achieved using the new NC H100 v5 series virtual machines (VMs) powered by NVIDIA H100 NVL Tensor Core GPUs and reinforced the commitment from Azure to designing AI infrastructure that is optimized for training and inferencing in the cloud.

The evolution of generative AI models

Models for generative AI are rapidly expanding in size and complexity, reflecting a prevailing trend in the industry toward ever-larger architectures. Industry-standard benchmarks and cloud-native workloads consistently push the boundaries, with models now reaching billions and even trillions of parameters. A prime example of this trend is the recent unveiling of Llama2, which boasts a staggering 70 billion parameters, marking it as MLPerf’s most significant test of generative AI to date (figure 1). This monumental leap in model size is evident when comparing it to previous industry standards such as the Large Language Model GPT-J, which pales in comparison with 10x fewer parameters. Such exponential growth underscores the evolving demands and ambitions within the AI industry, as customers strive to tackle increasingly complex tasks and generate more sophisticated outputs.

Tailored specifically to address the dense or generative inferencing needs that models like Llama 2 require, the Azure NC H100 v5 VMs marks a significant leap forward in performance for generative AI applications. Its purpose-driven design ensures optimized performance, making it an ideal choice for organizations seeking to harness the power of AI with reliability and efficiency. With the NC H100 v5-series, customers can expect enhanced capabilities with these new standards for their AI infrastructure, empowering them to tackle complex tasks with ease and efficiency. 

Figure 1: Evolution of the size of the models in the MLPerf Inference benchmarking suite. 

However, the transition to larger model sizes necessitates a shift toward a different class of hardware that is capable of accommodating the large models on fewer GPUs. This paradigm shift presents a unique opportunity for high-end systems, highlighting the capabilities of advanced solutions like the NC H100 v5 series. As the industry continues to embrace the era of mega-models, the NC H100 v5 series stands ready to meet the challenges of tomorrow’s AI workloads, offering unparalleled performance and scalability in the face of ever-expanding model sizes.

Enhanced performance with purpose-built AI infrastructure

The NC H100 v5-series shines with purpose-built infrastructure, featuring a superior hardware configuration that yields remarkable performance gains compared to its predecessors. Each GPU within this series is equipped with 94GB of HBM3 memory. This substantial increase in memory capacity and bandwidth translates in a 17.5% boost in memory size and a 64% boost in memory bandwidth over the previous generations. . Powered by NVIDIA H100 NVL PCIe GPUs and 4th-generation AMD EPYC™ Genoa processors, these virtual machines feature up to 2 GPUs, alongside up to 96 non-multithreaded AMD EPYC Genoa processor cores and 640 GiB of system memory.

In today’s announcement from MLCommons, the NC H100 v5 series premiered performance results in the MLPerf Inference v4.0 benchmark suite. Noteworthy among these achievements is a 46% performance gain over competing products equipped with GPUs of 80GB of memory (figure 2), solely based on the impressive 17.5% increase in memory size (94 GB) of the NC H100 v5-series. This leap in performance is attributed to the series’ ability to fit the large models into fewer GPUs efficiently. For smaller models like GPT-J with 6 billion parameters, there is a notable 1.6x speedup from the previous generation (NC A100 v4) to the new NC H100 v5. This enhancement is particularly advantageous for customers with dense Inferencing jobs, as it enables them to run multiple tasks in parallel with greater speed and efficiency while utilizing fewer resources.

Figure 2: Azure results on the model Llama2 (70 billion parameters) from MLPerf Inference v4.0 in March 2024 (4.0-0004) and (4.0-0068). 

Performance delivering a competitive edge

The increase in performance is important not just compared to previous generations of comparable infrastructure solutions In the MLPerf benchmarks results, Azure’s NC H100 v5 series virtual machines results are standout compared to other cloud computing submissions made. Notably, when compared to cloud offerings with smaller memory capacities per accelerator, such as those with 16GB memory per accelerator, the NC H100 v5 series VMs exhibit a substantial performance boost. With nearly six times the memory per accelerator, Azure’s purpose-built AI infrastructure series demonstrates a performance speedup of 8.6x to 11.6x (figure 3). This represents a performance increase of 50% to 100% for every byte of GPU memory, showcasing the unparalleled capacity of the NC H100 v5 series. These results underscore the series’ capacity to lead the performance standards in cloud computing, offering organizations a robust solution to address their evolving computational requirements.

Figure 3: Performance results on the model GPT-J (6 billion parameters) from MLPerf Inference v4.0 in March 2024 on Azure NC H100 v5 (4.0-0004) and an offering with 16GB of memory per accelerator (4.0-0045) – with one accelerator each.

In conclusion, the launch of the NC H100 v5 series marks a significant milestone in Azure’s relentless pursuit of innovation in cloud computing. With its outstanding performance, advanced hardware capabilities, and seamless integration with Azure’s ecosystem, the NC H100 v5 series is revolutionizing the landscape of AI infrastructure, enabling organizations to fully leverage the potential of generative AI Inference workloads. The latest MLPerf Inference v4.0 results underscore the NC H100 v5 series’ unparalleled capacity to excel in the most demanding AI workloads, setting a new standard for performance in the industry. With its exceptional performance metrics and enhanced efficiency, the NC H100 v5 series reaffirms its position as a frontrunner in the realm of AI infrastructure, empowering organizations to unlock new possibilities and achieve greater success in their AI initiatives. Furthermore, Microsoft’s commitment, as announced during the NVIDIA GPU Technology Conference (GTC), to continue innovating by introducing even more powerful GPUs to the cloud, such as the NVIDIA Grace Blackwell GB200 Tensor Core GPUs, further enhances the prospects for advancing AI capabilities and driving transformative change in the cloud computing landscape.

Source: microsoft.com

Continue reading

Unleashing the Power of Microsoft AI Copilot: Revolutionizing Productivity

In today's dynamic digital landscape, harnessing the capabilities of cutting-edge technologies is imperative for staying ahead of the competition. Microsoft AI Copilot stands at the forefront of innovation, redefining the way we approach productivity and collaboration. In this article, we delve into the intricacies of this groundbreaking tool, exploring its features and highlighting how it can elevate your workflow to unprecedented heights.

Understanding the Essence of Microsoft AI Copilot

Revolutionizing Coding with Intelligent Assistance

Gone are the days of solitary coding marathons. With Microsoft AI Copilot, developers now have an intelligent coding companion that understands context, anticipates code snippets, and significantly accelerates the coding process. This not only enhances efficiency but also fosters a collaborative coding environment, where ideas seamlessly transform into lines of code.

Enhanced Content Creation with Natural Language Understanding

In the realm of content creation, precision and creativity often go hand in hand. Microsoft AI Copilot, equipped with advanced natural language understanding, facilitates a seamless content creation process. It suggests relevant phrases, refines language, and transforms vague ideas into eloquent prose, empowering writers to express themselves with unparalleled clarity.

Elevating Productivity Across Industries

Streamlining Project Management

Efficient project management is the cornerstone of successful ventures. Microsoft AI Copilot integrates seamlessly with project management tools, offering real-time suggestions and insights to optimize task allocation, timeline management, and resource utilization. This not only ensures timely project completion but also enhances overall team collaboration.

Empowering Design Thinking in Graphic Design

For graphic designers, creativity is paramount. Microsoft AI Copilot becomes an invaluable ally by offering design suggestions, generating graphical elements, and providing a fresh perspective on visual compositions. This accelerates the design process and opens new avenues for creative exploration.

Overcoming Challenges with Microsoft AI Copilot

Addressing Privacy Concerns

As with any advanced AI technology, concerns about privacy and data security inevitably arise. Microsoft has implemented robust security measures in AI Copilot to protect user data. Understanding these security features ensures a secure and trustworthy user experience.

Optimizing Customization for Diverse Workflows

Acknowledging the diverse needs of users, Microsoft AI Copilot offers extensive customization options. Tailoring the tool to align with specific workflows and industry requirements ensures maximum utility and a personalized user experience.

Embracing the Future of Productivity

In conclusion, Microsoft AI Copilot emerges as a game-changer in the realm of productivity tools. Its ability to enhance coding, streamline content creation, and elevate collaboration across various industries positions it as a must-have for forward-thinking professionals. Embrace the future of productivity with Microsoft AI Copilot and witness a paradigm shift in the way you work.

Continue reading

What’s new in Azure Data, AI, & Digital Applications: Modernize your data estate, build intelligent apps, and apply AI solutions

I write this blog each month to help navigate through the intense pace of news and innovation we’re releasing for customers. And what a year we’ve had! We witnessed incredible advances at breathtaking speed as AI reshaped what’s possible across industries, organizations, and our day-to-day lives.

Personally, I’m loving the features and capabilities of Copilot for Microsoft 365, especially meeting summaries after a Teams call so I can quickly access the most important points and any actions. We’re still experimenting with AI to craft marketing narratives across my team and it’s exciting to see how AI enhances our own creativity.

AI technology is not brand new. It’s been improving experiences across applications and business processes for some time but, the broad availability of generative AI models and tools this year was a watershed moment.  

Customers and partners made quick pivots to bring AI into their transformation roadmaps, and within months were deploying applications and services powered by AI. I have never seen a new technology generate such profound change at this pace. It shows how ready many organizations were for this moment; how investments made in the cloud, data, DevOps, and creating transformation cultures set the stage for AI adoption. This year Microsoft introduced hundreds of resources, models, services, and tools to help customers and partners maximize AI.

This month, I’m pleased to expand this blog’s scope and bring in what’s new for digital applications for a holistic look at everything we’re delivering to help customers modernize their data estate, build intelligent applications, and apply AI technologies to help achieve their business goals. Let’s go.

New models and multimodal capabilities available in Azure AI

Our focus is to deliver the most cutting-edge open and frontier models available so developers can build with confidence and unlock immediate value across their organization.  

Last month we announced a significant expansion of Azure OpenAI Service and introduced Models as a Service (MaaS). This is a way for model providers to offer their latest open and frontier LLMs on Azure for generative AI developers to integrate into their applications.

And just last week, we announced the availability of MaaS for Llama 2. With MaaS for Llama 2, developers can integrate with their favorite LLM tools like Prompt Flow, Semantic Kernel, and LangChain with a ready-to-use API and pay-as-you go billing based on tokens for LLMs. This allows generative AI developers to access Llama 2 via hosted fine-tuning without provisioning GPUs, greatly simplifying the model set up and deployment process. Then they can offer their custom applications utilizing Llama 2, purchased through and hosted on the Azure Marketplace.

Model catalog in Azure AI Studio Preview

We continue to advance our Azure OpenAI Service and recently launched several multimodal AI capabilities that empower businesses to build generative AI experiences with image, text and video, including:

• DALL·E 3, in preview: Generate images from text descriptions. DALL·E 3 is a remarkable AI model that does just that. Users describe an image, and DALL·E 3 will be able to create it.
• GPT-3.5 Turbo model with a 16k token prompt length, generally available, and GPT-4 Turbo, in preview: The latest models in Azure OpenAI Service enable customers to extend prompt length and bring more control and efficiency to their generative AI applications.
• GPT-4 Turbo with Vision (GPT-4V), in preview: When integrated with Azure AI Vision, GPT-4V enhances experiences by allowing the inclusion of images or videos along with text for generating text output, benefiting from Azure AI Vision enhancement like video analysis.
• Fine-tuning of Azure OpenAI Service models: Fine-tuning is now generally available for Azure OpenAI Service models including Babbage-002, Davinci-002, and GPT-35-Turbo. Developers and data scientists can customize these Azure OpenAI Service models for specific tasks.
• GPT-4 updates: Azure OpenAI Service has also rolled out updates to GPT-4, including the ability for fine-tuning. Fine-tuning will allow organizations to customize the AI model to better suit their specific needs. It’s akin to tailoring a suit to fit perfectly, but in the world of AI. Updates to GPT-4 are in preview.

Steering at the frontier: Extending the power of prompting

The power of prompting in GPT-4 continues to amaze! A recent Microsoft Research blog discusses promptbase, an approach to prompting GPT-4 that harnesses its powerful reasoning abilities. Across a wide variety of test sets (including the ones used to benchmark the recently announced Gemini Ultra), GPT-4 gets better results than other AI models (including Gemini Ultra). And it does this with zero-shot chain-of-thought prompting.

Infuse Responsible AI (RAI) tools and practices in your LLMOps

As AI adoption matures and companies put their AI apps into production, it’s important to consider the safety boundaries supporting the short- and long-term ROI of those applications. This month, we continued our series on LLMOps for business leaders with an article focused on how to infuse responsible AI into your AI development lifecycle. We highlight best practices and tools in Azure AI Studio to help development teams put their principles into practice.

Azure AI Advantage offer

Azure Cosmos DB is the cloud database for the Era of AI. It supports built-in AI, including natural language queries, AI vector search capabilities, and simple AI integration with Azure AI Search. We’re investing in helping customers discover these benefits with our new Azure AI Advantage offer, which helps new and existing Azure AI and GitHub Copilot customers save when using Azure Cosmos DB by providing 40,000 Request Units per second (RU/s) of Azure Cosmos DB for 90 days.

What’s new in the Azure data platform—because AI is only as good as your data

Every intelligent app starts with data so a modern data and analytics platform is essential for any AI transformation.

One example of this how multinational law firm Clifford Chance leverages new technologies to benefit their clients. The firm built a solid data platform on Azure to innovate with new technologies, including the new generation of large language models, Azure OpenAI and Microsoft 365 Copilot. Early innovations are already delivering value with cognitive translation proving to be one of the fastest growing products their IT team has ever released.

And Belfius, a Belgian insurance company, built on the Microsoft intelligent data platform using services like Azure Machine Learning and Azure Databricks to reduce development time, increase efficiency, and gain reliability. As a result, their data scientists can focus on creating and transforming features and the company can better detect fraud and money laundering. 

Azure and Databricks: Co-innovation for powerful AI experiences

Microsoft’s Scott Guthrie (left) and Arun Ulagaratchagan (right) with Databrick’s Co-Founder and CEO Ali Ghodsi (center) at Ignite

At Microsoft Ignite 2023 in November the benefits of maturing AI tools and services were in full view as customers and partners shared how Microsoft is empowering them to achieve more. 

Databricks is of one of our most strategic partners with some of the fastest growing data services on Azure. We recently showcased our co-innovation, including Azure Databricks interoperability with Microsoft Fabric, and how Azure Databricks is taking advantage of Azure OpenAI to deliver AI experiences for Azure Databricks’ customers. This means customers can take advantage of LLMs in Azure OpenAI as they build AI capabilities like retrieval-augmented generation (RAG) applications on Azure Databricks, and then use Power BI in Fabric to analyze the output.

If you missed Databricks’ CEO Ali Ghodsi on stage with Scott Guthrie at Ignite, I encourage you to check out the replay of Scott’s full segment Microsoft Cloud in the era of AI. If you only have a few minutes, his wrap up on LinkedIn is a great option, too. It’s a helpful overview of how the Microsoft Cloud is uniquely positioned to empower customers to transform by building AI solutions and unlocking data insights using the same platform and services that power all of Microsoft’s comprehensive solutions.

And for a closer look at our latest work with Databricks, check out the click-thru version of the Modern Analytics with Microsoft Fabric and Azure Databricks DREAM Lab from Ignite.

Now in preview: Azure AI extension for Azure Database for PostgreSQL 

The new Azure AI extension allows developers to leverage large language models (LLMs) in Azure OpenAI to generate vector embeddings and build rich, PostgreSQL generative AI applications. These powerful new capabilities combined with existing support for the pgvector extension, make Azure Database for PostgreSQL another great destination for building AI-powered apps. 

Azure Arc brings cloud innovation to SQL Server anywhere

This month, we’re introducing a new set of enhanced manageability and security capabilities from SQL Server enabled by Azure Arc. With Monitoring for SQL Server, customers can gain critical insights into their entire SQL Server estate and optimize for database performance. Customers can also view and manage Always On availability groups, failover cluster instances, and backups directly from the Azure portal, with better visibility and simplicity. Lastly, with Extended Security Updates as a service and automated patching, customers can always keep their apps secure, compliant, and up to date at all times. 

Lower pricing for Azure SQL Database Hyperscale compute

New pricing on Azure SQL Database Hyperscale offers cloud-native workloads the performance and security of Azure SQL at the price of commercial open-source databases. Hyperscale customers can save up to 35% on the compute resources they need to build scalable, AI-ready cloud applications of any size and I/O requirement. The new pricing is now available.

Digital applications deliver transformational operations and experiences

This era of AI is brought to life through digital applications developed and deployed by companies putting AI to work to enhance their operations and experiences—like a personalized app experience for employees or a customized chatbot for end customers. Here are some recent updates that help make all this innovation possible.

The seven pillars of modern AI development: Leaning into the era of custom copilots

Copilots are generating a lot of excitement and with Azure AI Studio in public preview, developers have a platform purpose-built for generative AI application development. As we lean into this new era, it’s important for businesses to carefully consider how to design a durable, adaptable, and effective approach. How can AI developers ensure their solutions enhance customer engagement? Here are seven pillars to think through when building your custom copilot.

AKS is a leading platform for modern cloud native and intelligent applications

The future of app development is at the intersection of AI and cloud-native technologies like Kubernetes. Cloud-native and AI are deeply rooted together in fueling innovation at scale and Azure Kubernetes Service (AKS) provides the scale customers need to run their compute intensive workloads like AI and machine learning. Check out Brendan Burn’s recent blog from KubeCon to learn how Microsoft is building and servicing open-source communities that benefit our customers.

There are no limits to your innovation with Azure

It has been amazing to see the tech community and customer response to our recent news, resources, and features across the portfolio, particularly digital applications. 

Resources like the Platform Engineering Guide launched in with Ignite have been incredibly popular, demonstrating the demand and appetite for this kind of training material.  

Seeing how organizations innovate with the technology is what it’s all about.   

Here are a couple of customer stories that caught my attention recently.

Modernizing interactive experiences across LEGO House with Azure Kubernetes Service

We’re collaborating with The LEGO House in Denmark—the ultimate LEGO experience center for children and adults—to migrate custom-built interactive digital experiences from an aging on-prem data center to Microsoft Azure Kubernetes Service (AKS) to improve stability, security, and the ability to iterate and collaborate on new guest experiences. This shift to the cloud enables LEGO House to more quickly update these experiences as they learn from guests. As the destination modernizes it hopes to share learnings and technologies with the broader LEGO Group ecosystem, like LEGOLAND and brand retail stores. 

Gluwa chose Azure for a reliable, scalable cloud solution to bring banking to emerging, underserved markets and close the financial gap

An estimated 1.4 billion people lack access to basic financial services because they live in a country with limited financial infrastructure, making it difficult to get credit or personal and business loans. Gluwa with Creditcoin uses blockchain technology to differentiate their business through borderless financial technology and chose Azure as the foundation to support it. Using a wide combination of our services and solutions—.NET framework, Azure Container Instances, AKS, Azure SQL, Azure Cosmos DB, and more—Gluwa has a strong platform to support their offerings. The business has also boosted operational efficiency with reliable uptime, stable services, and rich product offerings. 

CARIAD creates a service platform for Volkswagen Group vehicles with Azure and AKS

With the automotive industry shifting to software defined vehicles, CARIAD, the Volkswagen Group software subsidiary, collaborated with Microsoft using Azure and AKS to create the CARIAD Service Platform for providing automotive applications to brands like Audi, Porsche, Volkswagen, Seat and Skoda. This platform powers and accelerates the development and service of vehicle software by CARIAD’s developers, helping software become an advantage for the Volkswagen Group in the next generation of automotive mobility.

DICK’S Sporting Goods creates an omnichannel customer experience using Azure Arc and AKS

To create a more consistent, personalized experience for customers across its 850 stores and its online retail experience, DICK’S Sporting Goods envisioned a “one store” technology strategy with the ability to write, deploy, manage, and monitor its store software across all locations nationwide—and reflect those same experiences through its eCommerce site. DICK’S needed a new level of modularity, integration, and simplicity to seamlessly connect its public cloud environment with its computing systems at the edge. With the help of Microsoft, DICK’s Sporting Goods is migrating its on-premise infrastructure to Microsoft Azure and creating an adaptive cloud environment comprised of Azure Arc and Azure Kubernetes Service (AKS). Now the retailer can easily deploy new applications to every store to support a ubiquitous experience. 

Azure Cobalt delivers performance and efficiency for intelligent applications

Azure provides hundreds of services supporting the performance demands of cloud native and intelligent applications. Our work to maximize performance and efficiency now extends to the silicon powering Azure. We recently introduced Azure Maia, our first custom AI accelerator series to run cloud-based training and inferencing for AI workloads, and our custom in-house CPU series, Azure Cobalt, the first CPU designed by us, specifically for the Microsoft Cloud. 

Cobalt 100, the first generation in the series, is a 64-bit 128-core chip that delivers up to 40% performance improvement over current generations of Azure Arm chips and can power services such as Microsoft Teams and Azure SQL.

With Cobalt, we will deliver performance and economics to meet the demands of resource-intensive workloads like intelligent applications using generative AI for at-scale workloads. Our customized silicon and system features include dynamic power capabilities that can be tuned per-core based on the workload, leveraging the co-optimization of hardware with software to deliver best-in-class performance efficiency. 

Don’t just take it from me and Omar. Rani Borkar, CVP, Azure Hardware Systems shares how Cobalt delivers differentiated performance—and can even help drive toward achieving sustainability goals:

“We’re building sustainability into every part of our hardware for the cloud, from our silicon to the servers. This starts in the design phase, and on Cobalt we made those intentional design choices to be able to control performance and power consumption per core and on every single VM. We’re pleased with the performance we’ve seen testing Cobalt on internal workloads like Teams and Azure SQL, and looking forward to rolling this out more widely to customers as a VM offering next year.”

Grow your data, AI, and intelligent applications understanding and skillset

I often use December downtime to do a deep dive into a new technology or learn a new skill. I’m a data geek, and I have fond memories from a few years ago when I worked through more than a dozen Power BI trainings and loved every minute of it. Demystifying AI and helping customers build the right skillsets is just one way Microsoft is empowering AI transformation. Here are some new learning resources if you are like me and plan to end the year learning something new!

AI in a Minute: Looking for help ramping your teams on generative AI? Or maybe you want to go to end of year gatherings prepared to discuss AI and its possibilities? Our new “AI in a Minute” video series explains generative AI basics in short, snackable bites anyone can digest, regardless of job tile, level, or industry.

GenAI for beginners: This free 12-lesson course is designed to teach beginners everything they need to know to start building with generative AI. 

Microsoft Learn Cloud Skills Challenge—Microsoft Ignite Edition: Skill up for in-demand Data & AI tech scenarios and enter to win a VIP pass to the next Microsoft Ignite or Microsoft Build by completing a challenge by January 15, 2024. 

Cloud Workshop for the SQL Professional—If you missed this workshop at the PASS Data Summit, the labs are available to go through at your own pace. Our next workshop will be in March at the Microsoft Fabric Community Conference, so be sure to register and come see us in Las Vegas.

Accelerate your AI journey with key solution accelerators—Get started building intelligent apps on Azure with newly-published demos, GitHub repos, and Hackathon content to create AI-powered, intelligent apps. Customers and partners can “Build Your Own Copilot” using a click-through demo, sellers and partners can run a Hackathon with customers, and developers can leverage a code repo to quickly build solutions in their Azure account.

What will you build in 2024? Transform your business with a trusted partner

The ability of AI to accelerate transformation across industries, organizations, and daily life will certainly continue at an intense pace in 2024. Microsoft is proud to be your trusted partner in this era of AI and we’re committed to helping you achieve more for your business. I am excited to see how data, AI, and digital applications innovation unfolds for your business in the new year!

Source: microsoft.com

Continue reading

Removing barriers to autonomous vehicle adoption with Microsoft Azure

Autonomous vehicles, also known as self-driving cars, have the potential to truly revolutionize the transportation industry, with its impact anticipated across many industries. Several stubborn obstacles, however, stand in the way of mass adoption.

In the over 150 years since the automotive industry was founded, it has never experienced such rapid innovation and transformational change as it is currently experiencing. Since the advent of the horseless carriage in the 1860s, vehicle manufacturers have continued to improve the quality, safety, speed, and comfort of millions of automotive models sold around the world, each year.

Today, however, all eyes are on autonomous vehicles as a cornerstone of future human mobility.

Exponential market growth expected

Over the past decade, the impact of emerging technologies such as AI, machine vision, and high-performance computing (HPC) has changed the face of the automotive industry. Today, nearly every car manufacturer in the world is exploring the potential and power of these technologies to usher in a new age of self-driving vehicles. Microsoft Azure HPC and Azure AI infrastructure are tools to help accomplish that.

Data suggests that the global autonomous vehicle market, with level two autonomous features present in cars, was worth USD76 billion in 2020, but is expected to grow exponentially over the coming years to reach over USD2.1 trillion by 2030, as levels of autonomy features in cars continue to increase.

The platformization of autonomous taxis also holds enormous potential for the broader adoption and usage of autonomous vehicles. Companies like Tesla, Waymo, NVIDIA, and Zoox are all investing in the emerging category of driverless transportation that leverages powerful AI and HPC capabilities to transform the concept of human mobility. However, several challenges still need to be overcome for autonomous vehicles to reach their potential and become the de facto option for car buyers, passengers, and commuters.

Common challenges persist

One of the most important challenges with autonomous vehicles is ethics. If the vehicle determines what action to take during a trip, how does it decide what holds the most value during an emergency? To illustrate, if an autonomous vehicle is traveling down a road and two pedestrians suddenly run across the road from opposite directions, what are the ethics underpinning whether the vehicle swerves to collide with one pedestrian instead of another?

Another of the top challenges with autonomous vehicles is that the AI algorithms underpinning the technology are continuously learning and evolving. Autonomous vehicle AI software relies heavily on deep neural networks, with a machine learning algorithm tracking on-road objects as well as road signs and traffic signals, allowing the vehicle to ‘see’ and respond to—for example, a red traffic light.

Where the tech still needs some refinement is with the more subtle cues that motorists are instinctually aware of. For example, a slightly raised hand by a pedestrian may indicate they are about to cross the road. A human will see and understand the cue far better than an AI algorithm does, at least for now.

Another challenge is whether there is sufficient technology and connectivity infrastructure for autonomous vehicles to offer the optimal benefit of their value proposition to passengers, especially in developing countries. With car journeys from A to B evolving into experiences, people will likely want to interact with their cars based on their personal technology preferences, linked to tools from leading technology providers. In addition, autonomous vehicles will also need to connect to the world around them to guarantee safety and comfort to their passengers.

As such, connectivity will be integral to the mass adoption of autonomous vehicles. And with the advent and growing adoption of 5G, it may improve connectivity and enable communication between autonomous vehicles—which could enhance autonomous vehicles’ safety and functioning.

Road safety is not the only concern with autonomous vehicles. Autonomous vehicles will be designed to be hyper-connected, almost like an ultra-high-tech network of smartphones on wheels. However, an autonomous vehicle must be precisely that—standalone autonomous. If connectivity is lost, the autonomous vehicle must still be able to operate fully autonomously.

That being said, there is still the risk that cyberattacks could pose a threat to autonomous vehicle motorists, compared to legacy vehicles currently on the road. In the wake of a successful cyberattack, threat actors may gain access to sensitive personal information or even gain control over key vehicle systems. Manufacturers and software providers will need to take every step necessary to protect their vehicles and systems from compromise.

Lastly, there are also social and cultural barriers to the mainstreaming of autonomous vehicles with many people across the globe still very uncomfortable with the idea of giving up control of their cars to a machine. Once consumers can experience autonomous drives and see how the technology continuously monitors a complete 360-degree view around the vehicle and does not get drowsy or distracted, confidence that autonomous vehicles are safe and secure will grow, and adoption rates will rise.

The future of travel is (nearly) upon us

As the world moves closer to a future where autonomous vehicles are a ubiquitous presence on our roads, the complex challenges that must be addressed to make this a safe and viable option become ever more apparent. The adoption of autonomous vehicles is not simply a matter of developing the technology, but also requires a complete overhaul of how we approach transportation systems and infrastructure.

To tackle the many challenges posed by autonomous vehicle adoption, companies and researchers are heavily investing resources into solving these complex challenges. For example, one way that researchers are addressing the ethical challenges posed by autonomous vehicles being able to make life or death decisions, is by developing ethical frameworks that guide the decision-making processes of these vehicles.

These frameworks define the principles and values that should be considered when autonomous vehicles encounter ethical dilemmas, such as deciding between protecting the safety of passengers versus that of pedestrians. Such frameworks can help ensure that autonomous vehicles make ethical decisions that are consistent with societal values and moral principles.

Significant investments are also being made into updating existing infrastructure to accommodate autonomous vehicles. Roads, highways, and parking areas must be equipped with the necessary infrastructure to support autonomous vehicles, such as sensors, cameras, and communication systems.

Companies are also working collaboratively with regulators, researchers, and OEMs to develop policies that ensure that autonomous vehicles can operate safely alongside traditional vehicles. This includes considerations such as how traffic signals, road markings, and signage need to be adapted to support autonomous vehicles.

In 2021, for example, Microsoft teamed up with a market leading self-driving car innovator to unlock the potential of cloud computing for autonomous vehicles, leveraging Microsoft Azure to commercialize autonomous vehicle solutions at scale.

Another global automotive group also recently announced a collaboration with Microsoft to build a dedicated cloud-based platform for its autonomous car systems that are currently in development. This ties in with their ambitious plans to invest more than USD32 billion in the digitalization of the car by 2025.

NVIDIA is also taking bold steps to fuel the growth of the autonomous vehicle market. The NVIDIA DRIVE platform is a full-stack AI compute solution for the automotive industry, scaling from advanced driver-assistance systems for passenger vehicles to fully autonomous robotaxis. The end-to-end solution spans from the cloud to the car, enabling AI training and simulation in the data centre, in addition to running deep neural networks in the vehicle for safe and secure operations. The platform is being utilized by hundreds of companies in the industry, from leading automakers to new energy vehicle makers.

Key takeaways

There is little doubt that the future of human mobility is built upon the ground-breaking innovation and technological capabilities of autonomous vehicles. While some challenges still exist, the underlying technology continues to mature and improve, paving the way for an increase in the adoption of self-driving cars long term.

The technology may soon proliferate and displace other, less safe modes of transport, with huge potential upsides for many aspects of our daily lives, such as saving lives and reducing the number of accidents, decreasing commute times, optimizing traffic flow and patterns, thereby lessening congestion, and extending the freedom of mobility for all.

With vehicle manufacturers and software firms continuously iterating on autonomous vehicle technology, continuing to educate the public on their benefits and continuing to work with lawmakers to overcome regulatory hurdles, we may all soon enjoy a new world, one where technology gets us safely from one destination to another, leaving us free to simply enjoy the view.

Source: microsoft.com

Continue reading

Microsoft Build 2023: Innovation through Microsoft commercial marketplace

As we look forward to Microsoft Build 2023, I am inspired by the innovation coming from our ISV partners and SaaS providers building on the Microsoft Cloud.

In the past year, we’ve seen large-scale, generative AI models support the creation of new capabilities that expand our vision of the possible, improve productivity, and ignite creativity. The general availability of Azure OpenAI Service is helping developers apply these models to a variety of use cases such as natural language understanding, writing assistance, code generation, data reasoning, content summarization, and semantic search. With Azure’s enterprise-grade security and built-in responsible AI, the rate of innovation is growing exponentially.

Making new strides in AI

The Microsoft commercial marketplace makes it possible for customers to find, purchase, and deploy innovative applications and services to drive their business outcomes. At Microsoft Build 2023, we’re proud to highlight several partners with AI solutions available in the marketplace:

◉ Orkes empowers developers to easily build reliable and secure AI applications, tools, and integrations on Azure with the Conductor open source microservices orchestration platform. With built-in elastic scaling and reliability, teams can more quickly bring applications to market.

◉ Run:ai helps companies deliver AI faster and bridge the gap between data science and computing infrastructure by providing a high-performance compute virtualization layer for deep learning, which accelerates the training of neural network models and enables the development of large AI models to help organizations in every industry accelerate AI innovation.

◉ Statsig allows any company to experiment like big tech at a fraction of the cost. With advanced feature management tools such as automated A/B testing and integrated product analytics, developers can use data insights to learn faster and build better products.

Explore security solutions with our partners

As AI is experiencing rapid growth, security has never been more important. Companies of all sizes and across every industry are increasing their investments in cybersecurity. Partners specializing in security solutions that run on the Microsoft Cloud help customers reduce costs, close coverage gaps, and prevent even the most sophisticated attacks.

At Microsoft Build 2023, we’re excited to feature select partners with security solutions offered in the marketplace:

◉ Anjuna is a multi-cloud confidential computing platform for complete data security and privacy, featuring a unique trusted execution environment that leverages hardware-level isolation to intrinsically secure data and code in the cloud so enterprises can run applications inside Azure Confidential Computing instances in minutes without code changes.

◉ Kovrr transforms cyber security data into actionable, financially quantified cyber risk mitigation recommendations to manage enterprise cyber risk exposure, inform which security controls to invest in, and provide insights into how to optimize cyber insurance and capital management strategies.

◉ Noname Security protects APIs from attacks in real-time while detecting vulnerabilities and misconfigurations before they are exploited, offering deeper visibility and security than API gateways, load balancers, and well architected frameworks (WAFs) without requiring agents or network modifications.

Manage your cloud portfolio with the Microsoft commercial marketplace

The Microsoft commercial marketplace continues to grow and is becoming customers’ preferred method for managing their entire cloud portfolio.

Through the marketplace, customers can search across thousands of applications and services in a single catalog, creating a one-stop destination for all cloud needs including AI, security, data, infrastructure, and more. Solutions available on the marketplace are validated for compatibility with Microsoft applications, ensuring that customers can buy with confidence and deploy seamlessly on Azure.

For customers with enterprise agreements, purchases can be added directly to an Azure bill, simplifying the purchasing process and reducing the number of vendors to be paid separately. For organizations with a cloud consumption commitment, the entire purchase can count towards remaining commitment. Thousands of applications in the marketplace are eligible to count towards an Azure commitment, including the solutions highlighted above—Orkes, Run:ai, Statsig, Anjuna, Kovrr, and Noname Security. With the Microsoft commercial marketplace, customers can get the innovative solutions needed to stay ahead in a competitive market while maximizing the value of cloud investments.

Source: microsoft.com

Continue reading

Build next-generation, AI-powered applications on Microsoft Azure

The potential of generative AI is much bigger than any of us can imagine today. From healthcare to manufacturing to retail to education, AI is transforming entire industries and fundamentally changing the way we live and work. At the heart of all that innovation are developers, pushing the boundaries of possibility and creating new business and societal value even faster than many thought possible. Trusted by organizations around the world with mission-critical application workloads, Azure is the place where developers can build with generative AI securely, responsibly, and with confidence.

Welcome to Microsoft Build 2023—the event where we celebrate the developer community. This year, we’ll dive deep into the latest technologies across application development and AI that are enabling the next wave of innovation. First, it’s about bringing you state-of-the-art, comprehensive AI capabilities and empowering you with the tools and resources to build with AI securely and responsibly. Second, it’s about giving you the best cloud-native app platform to harness the power of AI in your own business-critical apps. Third, it’s about the AI-assisted developer tooling to help you securely ship the code only you can build.

We’ve made announcements in all key areas to empower you and help your organizations lead in this new era of AI.

Bring your data to life with generative AI

Generative AI has quickly become the generation-defining technology shaping how we search and consume information every day, and it’s been wonderful to see customers across industries embrace Microsoft Azure OpenAI Service. In March, we announced the preview of OpenAI’s GPT-4 in Azure OpenAI Service, making it possible for developers to integrate custom AI-powered experiences directly into their own applications. Today, OpenAI’s GPT-4 is generally available in Azure OpenAI Service, and we’re building on that announcement with several new capabilities you can use to apply generative AI to your data and to orchestrate AI with your own systems.

We’re excited to share our new Azure AI Studio. With just a few clicks, developers can now ground powerful conversational AI models, such as OpenAI’s ChatGPT and GPT-4, on their own data. With Azure OpenAI Service on your data, coming to public preview, and Azure Cognitive Search, employees, customers, and partners can discover information buried in the volumes of data, text, and images using natural language-based app interfaces. Create richer experiences and help users find organization-specific insights, such as inventory levels or healthcare benefits, and more.

To further extend the capabilities of large language models, we are excited to announce that Azure Cognitive Search will power vectors in Azure (in private preview), with the ability to store, index, and deliver search applications over vector embeddings of organizational data including text, images, audio, video, and graphs. Furthermore, support for plugins with Azure OpenAI Service, in private preview, will simplify integrating external data sources and streamline the process of building and consuming APIs. Available plugins include plugins for Azure Cognitive Search, Azure SQL, Azure Cosmos DB, Microsoft Translator, and Bing Search. We are also enabling a Provisioned Throughput Model, which will soon be generally available in limited access to offer dedicated capacity.

Customers are already benefitting from Azure OpenAI Service today, including DocuSign, Volvo, Ikea, Crayon, and 4,500 others.

We continue to innovate across our AI portfolio, including new capabilities in Azure Machine Learning, so developers and data scientists can use the power of generative AI with their data. Foundation models in Azure Machine Learning, now in preview, empower data scientists to fine-tune, evaluate, and deploy open-source models curated by Azure Machine Learning, models from Hugging Face Hub, as well as models from Azure OpenAI Service, all in a unified model catalog. This will provide data scientists with a comprehensive repository of popular models directly within the Azure Machine Learning registry.

We are also excited to announce the upcoming preview of Azure Machine Learning prompt flow that will provide a streamlined experience for prompting, evaluating, tuning, and operationalizing large language models. With prompt flow, you can quickly create prompt workflows that connect to various language models and data sources. This allows for building intelligent applications and assessing the quality of your workflows to choose the best prompt for your case. See all the announcements for Azure Machine Learning.

It’s great to see momentum for machine learning with customers like Swift, a member-owned cooperative that provides a secure global financial messaging network, who is using Azure Machine Learning to develop an anomaly detection model with federated learning techniques, enhancing global financial security without compromising data privacy. We cannot wait to see what our customers build next.

Run and scale AI-powered, intelligent apps on Azure

Azure’s cloud-native platform is the best place to run and scale applications while seamlessly embedding Azure’s native AI services. Azure gives you the choice between control and flexibility, with complete focus on productivity regardless of what option you choose.

Azure Kubernetes Service (AKS) offers you complete control and the quickest way to start developing and deploying intelligent, cloud-native apps in Azure, datacenters, or at the edge with built-in code-to-cloud pipelines and guardrails. We’re excited to share some of the most highly anticipated innovations for AKS that support the scale and criticality of applications running on it.

To give enterprises more control over their environment, we are announcing long-term support for Kubernetes that will enable customers to stay on the same release for two years—twice as long as what’s possible today. We are also excited to share that starting today, Azure Linux is available as a container host operating system platform optimized for AKS. Additionally, we are now enabling Azure customers to access a vibrant ecosystem of first-party and third-party solutions with easy click-through deployments from Azure Marketplace. Lastly, confidential containers are coming soon to AKS, as a first-party supported offering. Aligned with Kata Confidential Containers, this feature enables teams to run their applications in a way that supports zero-trust operator deployments on AKS.

Azure lets you choose from a range of serverless execution environments to build, deploy, and scale dynamically on Azure without the need to manage infrastructure. Azure Container Apps is a fully managed service that enables microservices and containerized applications to run on a serverless platform. We announced, in preview, several new capabilities for teams to simplify serverless application development. Developers can now run Azure Container Apps jobs on demand and schedule applications and event-driven ad hoc tasks to asynchronously execute them to completion. This new capability enables smaller executables within complex jobs to run in parallel, making it easier to run unattended batch jobs right along with your core business logic. With these advancements to our container and serverless products, we are making it seamless and natural to build intelligent cloud-native apps on Azure.

Integrated, AI-based tools to help developers thrive

Making it easier to build intelligent, AI-embedded apps on Azure is just one part of the innovation equation. The other, equally important part is about empowering developers to focus more time on strategic, meaningful work, which means less toiling on tasks like debugging and infrastructure management. We’re making investments in GitHub Copilot, Microsoft Dev Box, and Azure Deployment Environments to simplify processes and increase developer velocity and scale.

GitHub Copilot is the world’s first at-scale AI developer tool, helping millions of developers code up to 55 percent faster. Today, we announced new Copilot experiences built into Visual Studio, eliminating wasted time when getting started with a new project. We’re also announcing several new capabilities for Microsoft Dev Box, including new starter developer images and elevated integration of Visual Studio in Microsoft Dev Box, that accelerates setup time and improves performance. Lastly, we’re announcing the general availability of Azure Deployment Environments and support for HashiCorp Terraform in addition to Azure Resource Manager.

Enable secure and trusted experiences in the era of AI

When it comes to building, deploying, and running intelligent applications, security cannot be an afterthought—developer-first tooling and workflow integration are critical. We’re investing in new features and capabilities to enable you to implement security earlier in your software development lifecycle, find and fix security issues before code is deployed, and pair with tools to deploy trusted containers to Azure.

We’re pleased to announce GitHub Advanced Security for Azure DevOps in preview soon. This new solution provides the three core features of GitHub Advanced Security into the Azure DevOps platform, so you can integrate automated security checks into your workflow. It includes code scanning powered by CodeQL to detect vulnerabilities, secret scanning to prevent the inclusion of sensitive information in code repositories, and dependency scanning to identify vulnerabilities in open-source dependencies and provide update alerts.

While security is at the top of the list for any developer, using AI responsibly is no less important. For almost seven years, we have invested in a cross-company program to ensure our AI systems are responsible by design. Our work on privacy and the General Data Protection Regulation (GDPR) has taught us that policies aren’t enough; we need tools and engineering systems that help make it easy to build with AI responsibly. We’re pleased to announce new products and features to help organizations improve accuracy, safety, fairness, and explainability across the AI development lifecycle.

Azure AI Content Safety, now in preview, enables developers to build safer online environments by detecting and assigning severity scores to unsafe images and text across languages, helping businesses prioritize what content moderators review. It can also be customized to address an organization’s regulations and policies. As part of Microsoft’s commitment to responsible AI, we’re integrating Azure AI Content Safety across our products, including Azure OpenAI Service and Azure Machine Learning, to help users evaluate and moderate content in prompts and generated content.

Additionally, the responsible AI dashboard in Azure Machine Learning now supports text and image data in preview. This means users can more easily identify model errors, understand performance and fairness issues, and provide explanations for a wider range of machine learning model types, including text and image classification and object detection scenarios. In production, users can continue to monitor their model and production data for model and data drift, perform data integrity tests, and make interventions with the help of model monitoring, now in preview.

We are committed to helping developers and machine learning engineers apply AI responsibly, through shared learning, resources, and purpose-built tools and systems.

Let’s write this history, together

AI is a massive shift in computing. Whether it is part of your workflow or part of cloud development, powering your next-generation, intelligent apps, this community of developers is leading this shift. 

We are excited to bring Microsoft Build to you, especially this year as we go deep into the latest AI technologies, connect you with experts from within and outside of Microsoft, and showcase real-world solutions powered by AI.

Source: microsoft.com

Continue reading

The Net Zero journey: Why digital twins are a powerful ally

Climate impacts raise stakes for Net Zero transition

Following weeks of vital discussions at COP27 in Egypt, the urgency to bring the world to a more sustainable path has never been greater. Scientists have warned that the world needs to cut global emissions by 5 percent to 7 percent per year to limit the damage caused by climate change. At present, however, emissions are rising by 1 percent to 2 percent per year. Discovering new routes to a Net Zero economy is critical if we are to limit the economic and social damage of a rapidly changing climate. And that means we all have a part to play in ensuring we strike the optimal balance between greenhouse gas production and the amount of greenhouse gas that gets removed from the atmosphere.

A Microsoft and PWC blueprint for the transition to Net Zero highlights the importance of innovation and the harnessing of new technologies that enable organizations to deliver on their Net Zero ambitions, at pace. A key innovation that aims to accelerate organizations’ journey to Net Zero is digital twin technology supported by AI Infrastructure capabilities. A digital twin can be considered as a virtual working representation of assets, products, and production plants. Powered by Microsoft Azure AI-optimized infrastructure that leverages NVIDIA accelerated computing and networking technologies, digital twins allow organizations to visualize, simulate, and predict operations, whether those are at a manufacturing plant, a wind farm, a mining operation, or any other type of operation.

Adoption of digital twin technology offers early adopters the potential of truly accelerated and differentiated business value realization. Innovative companies can leverage this potent toolset to accelerate their innovation journeys and drive strategic business outcomes powered by technology innovation at scale. A recent study by Microsoft and Intel found that globally, only 28 percent of manufacturers have started rolling out a digital twin solution, and of those, only one in seven have fully deployed it at their manufacturing plants. One of the key findings of this study highlighted that when digital twins are utilized effectively, they can realize huge efficiency, optimization, and cost-saving gains while unlocking mission-critical insights that can drive innovation and improve decision-making for those who adopt the technology.

Maximizing wind energy production with digital twins

Digital twins have emerged as a powerful tool for renewable energy producers seeking optimization gains in their production processes too. Take South Korea’s Doosan Heavy Industries & Construction as an example. As a leader in engineering, procurement, heavy manufacturing, power generation and desalination services, Doosan Heavy Industries & Construction was appointed by the South Korean government to help it meet the goals of its Green New Deal plan, which includes a target of generating 20 percent of the country’s electricity needs through renewables by 2030.

Seeking improvements in the efficiency of their wind turbines, Doosan Heavy Industries & Construction partnered with Microsoft and Bentley Systems to develop a digital twin of its wind farms that helps it maximize energy production and reduce maintenance costs. The company currently has 16 South Korean wind farms in operation, which generate enough electricity to power as many as 35,000 homes per year. Its innovative digital controls and operations enables Doosan to remotely monitor wind farm operations, predict maintenance before failures occur, and limit the need for maintenance teams to physically inspect the wind turbines.

Leveraging Azure Digital Twins and Azure IoT Hub powered by NVIDIA-accelerated Azure AI Infrastructure capabilities, Doosan can simulate, visualize, and optimize every aspect of its infrastructure planning, deployment, and ongoing monitoring. This has led to greater energy efficiency, boosted employee safety, and improved asset resilience. And with Bentley seeing their Azure-powered digital twin technology reduce operational and maintenance costs by 15 percent at other facilities, Doosan is well-positioned to continue benefiting from their digital twin solution and unlocking new efficiency gains by leveraging the power of cloud-based AI infrastructure capabilities.

Leveraging digital twins to power Net Zero transition

In the oil and gas sector, digital twin technology is helping one of the world’s leading carbon-emitting industries to identify opportunities for optimization and carbon reduction. A noteworthy showcase can be found with Tata Consulting Services who delivered a Clever Energy solution to a global consumer goods giant. Using digital twins, real-time data and cognitive intelligence to improve energy savings at this consumer goods customer’s production plants, the solution helped reduce energy use by up to 15 percent as well as an equivalent CO2 emissions reduction. Considering that buildings consume nearly 40 percent of the world’s energy and emit one third of greenhouse gasses, this solution also helps the customer alleviate some of the pressures of significant energy cost increases in Europe.

In another example, a large multinational supplier that aims to achieve Net Zero carbon status by no later than 2050 is today leveraging the power of digital twins to support its sustainability goals.

From the vast global network of complex assets this company manages, a digital twin of one of their facilities was developed to calculate real-time carbon intensity and energy efficiency. Microsoft Azure provided the perfect platform: the IoT Hub receives more than 250 billion data signals per month from the company’s global operating assets, with AI providing key insights into how they could become a safer and more efficient business and Azure AI Infrastructure and High-Performance Computing enabling the seamless processing of huge volumes of data.

With long-term plans in place to scale the digital twin solution to all of the company’s global facilities, Microsoft Azure’s security, scalability, and powerful high-performance computing capabilities will be key supporting factors in how successfully they could transition to more carbon-aware operations.

Powering the Next Era of Industrial Digitalization

At NVIDIA GTC, a global AI conference, NVIDIA and Microsoft announced a collaboration to connect the NVIDIA Omniverse platform for developing and operating industrial metaverse applications with Azure Cloud Services. Enterprises of every scale will soon be able to use the Omniverse Cloud platform-as-a-service on Microsoft Azure to fast-track development and deployment of physically accurate, connected, secure, AI-enabled digital twin simulations.

Key takeaways about a Net Zero economy and digital twins

Shifting to a Net Zero economy is one of the defining challenges of our time. As the devastating impact of climate change continues to disrupt global economies, businesses will need novel ways of reducing their carbon footprint and help bring the world to a more sustainable path.

Considering the vast complexity of modern businesses—especially resource-intensive industries such as oil and gas, and manufacturing—finding ways to optimize processes, reduce waste, and accelerate time to value can be extremely cumbersome unless novel technology solutions are found to help provide differentiated strategic capabilities.

Digital twin technology offers organizations a powerful option to run detailed simulations generating vast amounts of data. By integrating that data to the power and scalability of Azure high performance computing (HPC) and leveraging the visualization power of Nvidia’s GPU-accelerated virtual computing capabilities, organizations can discover new opportunities for greater efficiency, optimization, and carbon-neutrality gains.

Source: microsoft.com

Continue reading

Announcing Project Health Insights Preview: Advancing AI for health data

We live in an era with unprecedented increases in the size of health data. Digitization of medical records, medical imaging, genomic data, clinical notes, and more all contributed to an exponential increase in the amount of medical data. The potential benefit of leveraging this health data is enormous. However, with this growth in health data, new challenges arise, including the focus on data privacy and security, the need for data standardization and interoperability. There is a need for effective tools for extracting information that is buried in this data and using it to derive valuable insights, inferences, and deep analytics that can make sense of the data and support clinicians.

Today, I’m excited to announce Project Health Insights Preview. Project Health Insights is a service that derives insights based on patient data and includes pre-built models that aim to power key high value scenarios in the health domain. The models receive patient data in different modalities, perform analysis, and enable clinicians to obtain inferences and insights with evidence from the input data. These insights can assist healthcare professionals in understanding clinical data, like patient profiling, clinical trials matching, and more.

Project Health Insights—leveraging patient data to power actionable insights

Project Health Insights supports pre-built models that receive patient data in multiple modalities as their input, and produce insights and inferences that include:

◉ Confidence scores: The higher the confidence score is, the more certain the model was about the inference value provided.

◉ Evidence: linking model output with specific evidence within the input provided, such as references to spans of text reflecting the data that led to an insight.

Project Health Insights Preview includes two enterprise grade AI models that can be provisioned and deployed in a matter of minutes: Oncology Phenotype and Clinical Trial Matcher.

Oncology Phenotype is a model that enables healthcare providers to rapidly identify key cancer attributes within their patient populations with an existing cancer diagnosis. The model identifies cancer attributes such as tumor site, histology, clinical stage, tumor, nodes, and metastasis (TNM) categories and pathologic stage TNM categories from unstructured clinical documents.

Key features of the Oncology Phenotype model include:

◉ Cancer case finding.

◉ Clinical text extraction for solid tumors.

◉ Importance ranking of evidence.

Clinical Trial Matcher is a model that matches patients to potentially suitable clinical trials, according to the trial’s eligibility criteria and patient data. The model helps with finding relevant clinical trials, that patients could be qualified for, as well as with finding a cohort of potentially eligible patients for a list of clinical trials.

Key Features of the Clinical Trial Matcher model include:

◉ Support for scenarios that are:

    ◉ Patient Centric: Helping patients find potentially suitable clinical trials and assess their eligibility against the trials criteria.

    ◉ Trial Centric: Matching a trial with a database of patients to locate a cohort of potentially suitable patients.

◉ Interactive Matching where the model provides insights into missing information that is needed to further narrow down the potential clinical trial list via an interactive experience.

◉ Support for various modalities of patient data such as unstructured clinical notes, structured patient data, and Fast Healthcare Interoperability Resources (FHIR®) bundles.

◉ Support for search across built-in knowledge graphs for clinical trials from clinicaltrials.gov as well as against a custom trial protocol with specific eligibility criteria.

Streamlining clinical trial matching and cancer research

According to the World Health Organization, the number of registered clinical trials increased by more than 4800 percent from 1999 to 2021. Today there are more than 82,000 clinical trials actively recruiting participants worldwide (based on clinicaltrials.gov), with increasingly complicated trial eligibility criteria. However, enrollment in clinical trials is based on manual screening of millions of patients, each with up to hundreds of clinical notes requiring review and analysis by a healthcare professional, making it an unsustainable process. Given this, it is not surprising that up to 80 percent of clinical trials miss their clinical trial enrollment timelines, and up to 48 percent fail to meet clinical trial enrollment targets according to data provided by Tufts University. The Clinical Trial Matcher model aims to solve this exact problem by effectively matching patients with diverse conditions to clinical trials for which they are potentially eligible through analysis of patient’s data and the complex eligibility criteria of clinical trials.

The Oncology Phenotype model allows physicians to effectively analyze cancer patients’ data based on their tumor site, tumor histology, and cancer staging. These models deliver crucial building blocks to realize the goals set out by the White House Cancer Moonshot initiative: to develop and test new treatments, to share more data and knowledge, to collaborate on tools that can benefit all, and to make progress towards ending cancer as we know it.

Providing value across the health and life sciences industry

John’s Hopkins University Medical Center is an early user of Project Health Insights. Dr. Srinivasan Yegnasubramanian is using the Oncology Phenotype model to leverage unstructured data to accelerate Cancer Registry curation efforts for patients with solid tumors.

Pangaea Data is a Microsoft partner working in health AI. “At Pangaea Data we help companies discover 22 times more undiagnosed, misdiagnosed, and miscoded patients by characterizing them through unlocking and summarization of clinically valid actionable intelligence from patient records in a federated privacy-preserving, scalable, and evolving manner. We are exploring using Project Health Insights to augment our own advanced capabilities for characterizing patients.”—Vibhor Gupta, Director and Founder, Pangaea Data.

Akkure Genomics helps patients utilize their own genomic data or DNA to improve their chances of finding a clinical trial. “At AKKURE GENOMICS we leverage Project Health Insights, which empowers our own AI and digital DNA platform capabilities, to help patients get matched to clinical trials based on their individual medical diagnoses, thus boosting enrollment, improving the chances of finding a precision-matched trial and accelerating discovery of new therapeutics and cures.”—Professor Oran Rigby, Chief Engineering Officer and Founder, Akkure.

Built with the end user in mind

Initial models were validated in a research setting through a strategic partnership between Microsoft and Providence to accelerate digital transformation in health and life sciences. These models can enable oncologists to substantially scale up their precision oncology capabilities and generate intelligence and insights useful to clinicians as well as beneficial to patients.

“Microsoft’s ability to structure complex concepts with their natural language processing tools for cancer has contributed significantly to our ability to build research cohorts and discuss cancer treatment options.”—Dr. Carlo Bifulco, Chief Medical Officer, Providence Genomics.

Microsoft will continue to expand capabilities within Project Health Insights to support additional health workloads and enable insights that will guide key decision-making in healthcare.

Microsoft continues to grow its portfolio of AI services for health

Microsoft continues to invest in AI services for the health and life sciences industry. Along with other new offerings in the Microsoft Cloud for Healthcare, we are pleased to announce new enhancements to Text Analytics for Health (TA4H).

The new enhancements include:

◉ Social Determinants of Health (SDoH) and Ethnicity information extraction. The newly introduced SDoH and Ethnicity features enable extraction of social, environmental, and demographics factors from unstructured text. These factors will empower the development of more inclusive healthcare applications.

◉ Temporal assertions—past, present, and future. The ability to identify the temporal context of TA4H entities whether in the past, present or future.

◉ Customers can now extend TA4H to support custom entities based on their own data. Customers can now also extend the entities extracted by the service.

We are also excited to share that Azure Health Bot now has a new Azure OpenAI template in preview. The Azure Health Bot OpenAI template allows customers to extend their Azure Health Bot instance with Azure OpenAI Service for answering unrecognized utterances in a more intelligent way. This feature will be enabled through the Azure Health Bot template catalogue. Customers can choose to import this template into their bot instance using their Azure OpenAI resource endpoint and key, enabling fallback answers generated by GPT from trusted, medically viable sources that can be provisioned by customers. This feature provides a mechanism for customers to experiment with this capability as preview.

We look forward to what the coming years will bring for the health and life sciences industry empowered by these new capabilities and the continued innovation we are seeing across AI and machine learning. The potential for improved precision care, quicker and more efficient clinical trials, and thereby drug and therapy availability and medical research is unparalleled. Microsoft looks forward to partnering with you and your organizations on this journey to improve the health of humankind.

Source: microsoft.com

Continue reading