The technological paradigm shift driven by AI solutions is redefining what current and future success looks like for every industry and market across the world. For a region like Latin America, this opens new paths to not only propel its economies but to positively impact each community. Crucially, it also presents powerful tools to address pressing challenges like skills development and sustainability, while being able to promote inclusive growth. The pivotal role cloud and AI solutions can play on Latin America’s development is substantial.

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Companies and organizations across the region are already showing eagerness to leverage this technology and realize the potential it entails. Countries like Colombia are home to innovative AI projects. While Brazil, the largest economy in Latin America, has the potential to propel its gross domestic product (GDP) growth by 4.2% through 2030 by strongly adopting AI, according to a study commissioned by Microsoft and conducted by the consultancy FrontierView in 2020.¹ Furthermore, the Brazilian market already represents the sixth largest GitHub developer community contributing to generative AI projects globally, demonstrating its appetite for innovation.²

These two markets are setting regional cloud and AI adoption examples on the global stage. Recently, Microsoft’s AI Tour visited both countries to highlight some of the most inspiring and impactful projects and initiatives driven by local organizations, companies, and communities in collaboration with Microsoft.

Project Guacamaya tackles deforestation in Colombia with AI

According to the Andean Amazon Monitoring Project, the Amazon rainforest lost about 3 million hectares to deforestation from 2022 to 2023.³ To address this worrying threat to one of the most diverse and complex ecosystem on the planet, various institutions, including Universidad de Los Andes, Instituto Alexander von Humboldt, Instituto Sinchi, IDEAM, and Microsoft AI for Good Lab, collaborated to create Project Guacamaya, a publicly available AI-powered tool to detect deforestation and biodiversity shifts.

This unique collaboration has brought the public and private sectors, educational institutions, and non-governmental organizations (NGOs) together to create a powerful solution using satellite imagery, camera traps, and bioacoustics to track forest coverage and deforestation patterns, allowing conservationists to better understand its impact and improve prevention and mitigation efforts.

Powering world class sustainable and secure cloud and AI infrastructure

Microsoft continues to evaluate and diligently work to address sustainability and resource needs associated with infrastructure growth. An agreement signed with the Amazon Reforestation Fund operated by Brazilian-based startup Mombak will provide a total of up to 1.5 million tons of carbon removal by 2032 from native biodiversity reforestation projects across the Amazon. This project provided the foundation for the World Bank to issue a USD225 million Amazon Reforestation-Linked Outcome Bond.

Another agreement was signed with Brazilian company re.green that aims to deliver approximately 3 million tons of carbon removal credits over a 15-year period, supporting the restoration of over 16,000 hectares of forest across Brazil, including the states of Maranhão and Bahia. Such programs join initiatives such as Microsoft’s 15-year renewable energy contract signed with AES Brasil for a wind generation project through the Cajuína Wind Complex, located in Rio Grande do Norte, and the agreement with BTG Pactual Timberland Investment Group (TIG) to provide Microsoft with up to 8 million nature-based carbon removal credits through 2043 to be delivered from TIG’s USD1 billion reforestation and restoration strategy in Latin America, which includes the Cerrado biome in Brazil.¹

Colombia’s financial and insurance sectors lead the way in AI adoption

As one of the oldest banks in Colombia with a history that goes back to 1875, Bancolombia brings together tradition and fierce innovation by becoming the global leader in GitHub Copilot adoption. Empowering a technical team of 4,000 people, including 3,400 software and infrastructure engineers, the bank has achieved a 30% increase in code generation, boosting automated application changes to an average of 18,000 per year, with a rate of 42 productive daily deployments. This is the most recent development in Bancolombia’s continuous digital transformation journey to serve its 15 million customers in the region.

For its part, insurance company SURA is employing AI solutions for genomic sequencing processing and clinical and genetic data analysis as part of the work carried out by its Omics Sciences Center. The Center, which aims to transform the treatment of genetic diseases affecting millions of people in the region, led the National Association of Colombian Business Persons (ANDI) to recognize SURA as the most innovative insurer in the country.⁴ The AI-powered platform has had a significant impact on improving diagnostics, personalized treatment, and scientific research by generating more than 11,500 reports related to hereditary cancer panels and exomes.

ConectAI empowers Brazilians to reap the benefits of AI

In collaboration with 26 organizations in Brazil, including non-profits, educational institutions and governments, Microsoft launched ConectAI (formerly Conecta+), a program designed to provide AI skills training to 5 million people in the country. This ambitious initiative includes collaborations with the National Service for Industrial Training in São Paulo (SENAI), the Ministry of Labor and Employment through the “School of Workers 4.0” program, the non-profit Nova Escola providing training for educators, UNICEF to bring AI skills to people between 18 and 25 years old, and the National School for Public Administration (ENAP) to foster technological innovation in the public sector, among others.

The program represents the next stage in Microsoft’s continued push to enable greater innovation and increased competitiveness for the Brazilian market. Since 2021, Microsoft has fostered digital literacy in the country by engaging with 12.7 million people through the Mais Brasil program, a commitment exemplified by the recently announced investment of BRL14.7 billion in cloud and AI infrastructure over three years, the largest single investment in the company’s 35 year history in the country. 

The stage is set for Latin America’s new phase of development through AI

The amount of talent, creativity, and ambition in Latin America has made the region a hub for innovation in recent years, one now further empowered by recent developments in AI. The projects seen during Microsoft’s AI Tour editions in Colombia and Brazil are only the tip of the iceberg when it comes to the potential and competitiveness AI and cloud technologies are unlocking across crucial industries and sectors in each country. I am convinced the coming years will bring exciting new paths for success as these tools continue to deliver unprecedented solutions to both business and social challenges when combined with the exceptional ingenuity of Latin American markets.

Find the resources to support your AI journey

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• Download the AI readiness worksheet

• Continue your AI learning journey
• Read An Introduction to Generative AI and Safety

¹Microsoft announces 14.7 billion Reais investment over three years in Cloud and AI infrastructure and provide AI training at scale to upskill 5 million people in Brazil, Microsoft News Center Brasil.

²Octoverse: The state of open source and rise of AI in 2023, The GitHub Blog.

³Proyecto Guacamaya usa modelos IA contra la deforestación, Microsoft Source.

⁴Seguros SURA, a subsidiary of Suramericana, is the most innovative insurer in Colombia according to the most recent ANDI ranking.

The post Colombia and Brazil embrace the potential of cloud and AI solutions to drive growth and tackle social challenges appeared first on The Microsoft Cloud Blog.

As we continue to deliver on our customer commitments to cloud and AI innovation, we remain resolute in our commitment to advancing sustainability. A critical part of achieving our company goal of becoming carbon negative by 2030 is reimagining our cloud and AI infrastructure with power and energy efficiency at the forefront.

We’re pursuing our carbon negative goal through three primary pillars: carbon reduction, carbon-free electricity, and carbon removal. Within the pillar of carbon reduction, power efficiency and energy efficiency are fundamental to sustainability progress, for our company and for the industry as a whole.

Explore how we're advancing the sustainability of AI

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Although the terms “power” and “energy” are generally used interchangeably, power efficiency has to do with managing peaks in power utilization, whereas energy efficiency has to do with reducing the overall amount of power consumed over time.

This distinction becomes important to the specifics of research and application because of the type of efficiency in play. For an example of energy efficiency, you might choose to explore small language models (SLMs) with fewer parameters that can run locally on your phone, using less overall processing power. To drive power efficiency, you might look for ways to improve the utilization of available power by improving predictions of workload requirements.  

From datacenters to servers to silicon and throughout code, algorithms, and models, driving efficiency across a hyperscale cloud and AI infrastructure system comes down to optimizing the efficiency of every part of the system and how the system works as a whole. Many advances in efficiency have come from our research teams over the years, as we seek to explore bold new ideas and contribute to the global research community. In this blog, I’d like to share a few examples of how we’re bringing promising efficiency research out of the lab and into commercial operations.

Silicon-level power telemetry for accurate, real-time utilization data

We’ve made breakthroughs in delivering power telemetry down to the level of the silicon, providing a new level of precision in power management. Power telemetry on the chip uses firmware to help us understand the power profile of a workload while keeping the customer workload and data confidential. This informs the management software that provides an air traffic control service within the datacenter, allocating workloads to the most appropriate servers, processors, and storage resources to optimize efficiency.

Working collaboratively to advance industry standards for AI data formats

Inside the silicon, algorithms are working to solve problems by taking some input data, processing that data through a series of defined steps, and producing a result. Large language models (LLMs) are trained using machine learning algorithms that process vast amounts of data to learn patterns, relationships, and structures in language.

Simplified example from Microsoft Copilot: Imagine teaching a child to write stories. The training algorithms are like the lessons and exercises you give the child. The model architecture is the child’s brain, structured to understand and create stories. Inference algorithms are the child’s thought process when writing a new story, and evaluation algorithms are the grades or feedback you give to improve their writing.¹

One of the ways to optimize algorithms for efficiency is to narrow the precision of floating-point data formats, which are specialized numerical representations used to handle real numbers efficiently. Working with the Open Compute Project, we’ve collaborated with other industry leaders to form the Microscaling Formats (MX) Alliance with the goal of creating and standardizing next-generation 6- and 4-bit data types for AI training and inferencing. 

Narrower formats allow silicon to execute more efficient AI calculations per clock cycle, which accelerates model training and inference times. These models take up less space, which means they require fewer data fetches from memory, and can run with better performance and efficiency. Additionally, using fewer bits transfers less data over the interconnect, which can enhance application performance or cut network costs. 

Driving efficiency of LLM inferencing through phase-splitting

Research also shows promise for novel approaches to large language model (LLM) inference, essentially separating the two phases of LLM inference onto separate machines, each well suited to that specific phase. Given the differences in the phases’ resource needs, some machines can underclock their AI accelerators or even leverage older generation accelerators. Compared to current designs, this technique can deliver 2.35 times more throughput under the same power and cost budgets.²

Learn more and explore resources for AI efficiency

In addition to reimagining our own operations, we’re working to empower developers and data scientists to build and optimize AI models that can achieve similar outcomes while requiring fewer resources. As mentioned earlier, small language models (SLMs) can provide a more efficient alternative to large language models (LLMs) for many use cases, such as fine-tuning experimentation on a variety of tasks or even grade school math problems.

In April 2024, we announced Phi-3, a family of open, highly capable, and cost-effective SLMs that outperform models of the same and larger sizes across a variety of language, reasoning, coding, and math benchmarks. This release expands the selection of high-quality models for customers, offering practical choices for composing and building generative AI applications. We then introduced new models to the Phi family, including Phi-3.5-MoE, a Mixture of Experts model that combines 16 smaller experts into one, and Phi-35-mini. Both of these models are multi-lingual, supporting more than 20 languages.

Learn more about how we’re advancing sustainability through our Sustainable by design blog series, starting with Sustainable by design: Advancing the sustainability of AI.

¹Excerpt from prompting Copilot with: please explain how algorithms relate to LLMs.

²Splitwise: Efficient generative LLM inference using phase splitting, Microsoft Research.

The post Sustainable by design: Innovating for energy efficiency in AI, part 2 appeared first on The Microsoft Cloud Blog.

Today, we’re announcing a power purchase agreement (PPA) with Constellation that will enable the restart of an 835 megawatt (MW) nuclear facility in Pennsylvania that was retired in 2019. This will bring a significant supply of net-new, reliable, carbon-free electricity to the PJM power grid, the regional transmission organization covering 13 states, recognizing the importance of nuclear energy and complementing our 34 gigawatt (GW) contracted renewable energy portfolio in 24 countries.

As highlighted by the International Energy Agency, complete grid decarbonization will require a multi-technology approach with a broad range of carbon-free technologies such as wind, solar, geothermal, clean hydrogen, sustainable biomass, nuclear, fusion, energy efficiency, and storage, as well as transmission infrastructure to connect these technologies to the grids that need them.¹ Alongside our extensive work on carbon reduction and carbon removal, Microsoft embraces this multi-technology approach as an essential pathway to achieving our goal of becoming carbon negative by 2030.

Carbon reduction

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As we continue to expand our portfolio of solutions to accelerate the energy transition, we collaborate with governments, communities, developers, and energy service providers in many ways. In this blog, I’ll share more about how we approach our work to (1) shape market demand for carbon-free electricity and (2) advance energy policy through advocacy.

Shaping market demand to accelerate the addition of carbon-free electricity

We employ a wide range of contracting mechanisms to meet our goals and secure carbon-free electricity, crafting innovative agreement structures alongside our large portfolio of renewable PPAs. A few examples:

• Our recently announced five-year global agreement with Brookfield Renewable Partners provides a pathway for the development of more than 10.5 gigawatts of new renewable energy capacity in the United States and Europe, almost eight times larger than the largest corporate PPA ever signed. This agreement provides an incentive for Brookfield to build a large portfolio of new renewable energy projects in the coming years, contributing to the decarbonization of the grid, and matched to the locations where Microsoft consumes electricity.
• In Washington state, our agreement with Powerex matches hourly datacenter demand with direct deliveries of carbon-free hydro, solar, and wind power on a 24-hour basis throughout the year. During the day, when our contracted renewable resources produce more power than needed, Powerex takes the surplus renewable power, conserving water from hydropower reservoirs and effectively storing it like a battery. This energy can then be delivered back to the datacenter in later hours, for example at night when wind and solar sources may be offline.
• As a global company committed to decarbonization on a global level, Microsoft also has worked to develop renewable energy in communities and locations that often are not prioritized. An example of this unique approach was our five-year framework agreement with Pivot Energy to develop up to 500 MW of community-scale solar energy projects across the US between 2025 and 2029. The agreement will enable Pivot to develop approximately 150 US solar projects in roughly 100 communities across 20 states, including Colorado, Maryland, Illinois, Delaware, Pennsylvania, and Ohio, with each solar project including significant community benefits.  

Advancing carbon-free electricity through policy advocacy

Our public policy advocacy relating to the electrical grid is focused on accelerating the transition to clean electricity generation, modernizing and improving grid infrastructure, and encouraging an equitable energy future. A grid mix that includes adding and retaining firm carbon-free energy technologies as well as renewables will be pivotal to providing electricity access across the globe and progressing decarbonization.

In December 2023, we published a policy brief on advanced nuclear and fusion energy that highlights the importance of carbon-free electricity and the role advanced nuclear and fusion energy will have in a decarbonized energy future. As advanced carbon-free energy technologies are developed, each comes with its own set of considerations, benefits, risks, regulatory dynamics, and acceptance. Our policy priorities are focused on advancing research, development, and demonstration projects; enabling safe deployment of technologies; and encouraging an efficient and effective regulatory process for new technologies to be deployed.

Explore Sustainable by design

Discover more about how Microsoft is advancing the sustainability of cloud and AI through our blog series on the topic:

• Sustainable by design: Advancing the sustainability of AI
• Sustainable by design: Transforming datacenter water efficiency
• Sustainable by design: Innovating for energy efficiency in AI, part 1

¹The path to limiting global warming to 1.5 °C has narrowed, but clean energy growth is keeping it open, IEA, 2023.

The post Accelerating the addition of carbon-free energy: An update on progress appeared first on The Microsoft Cloud Blog.

Earlier this summer, my colleague Noelle Walsh published a blog detailing how we’re working to conserve water in our datacenter operations: Sustainable by design: Transforming datacenter water efficiency, as part of our commitment to our sustainability goals of becoming carbon negative, water positive, zero waste, and protecting biodiversity.

At Microsoft, we design, build, and operate cloud computing infrastructure spanning the whole stack, from datacenters to servers to custom silicon. This creates unique opportunities for orchestrating how the elements work together to enhance both performance and efficiency. We consider the work to optimize power and energy efficiency a critical path to meeting our pledge to be carbon negative by 2030, alongside our work to advance carbon-free electricity and carbon removal.

Explore how we're advancing the sustainability of AI

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The rapid growth in demand for AI innovation to fuel the next frontiers of discovery has provided us with an opportunity to redesign our infrastructure systems, from datacenters to servers to silicon, with efficiency and sustainability at the forefront. In addition to sourcing carbon-free electricity, we’re innovating at every level of the stack to reduce the energy intensity and power requirements of cloud and AI workloads. Even before the electrons enter our datacenters, our teams are focused on how we can maximize the compute power we can generate from each kilowatt-hour (kWh) of electric power.

In this blog, I’d like to share some examples of how we’re advancing the power and energy efficiency of AI. This includes a whole-systems approach to efficiency and applying AI, specifically machine learning, to the management of cloud and AI workloads. Learn more about how we’re bringing efficiency research from the lab into commercial operations in Sustainable by design: innovating for energy efficiency in AI, part 2.

Driving efficiency from datacenters to servers to silicon

Maximizing hardware utilization through smart workload management

True to our roots as a software company, one of the ways we drive power efficiency within our datacenters is through software that enables workload scheduling in real time, so we can maximize the utilization of existing hardware to meet cloud service demand. For example, we might see greater demand when people are starting their workday in one part of the world, and lower demand across the globe where others are winding down for the evening. In many cases, we can align availability for internal resource needs, such as running AI training workloads during off-peak hours, using existing hardware that would otherwise be idle during that timeframe. This also helps us improve power utilization.

We use the power of software to drive energy efficiency at every level of the infrastructure stack, from datacenters to servers to silicon.

Historically across the industry, executing AI and cloud computing workloads has relied on assigning central processing units (CPUs), graphics processing units (GPUs), and processing power to each team or workload, delivering a CPU and GPU utilization rate of around 50% to 60%. This leaves some CPUs and GPUs with underutilized capacity, potential capacity that could ideally be harnessed for other workloads. To address the utilization challenge and improve workload management, we’ve transitioned Microsoft’s AI training workloads into a single pool managed by a machine learning technology called Project Forge.

Currently in production across Microsoft services, this software uses AI to virtually schedule training and inferencing workloads, along with transparent checkpointing that saves a snapshot of an application or model’s current state so it can be paused and restarted at any time. Whether running on partner silicon or Microsoft’s custom silicon such as Maia 100, Project Forge has consistently increased our efficiency across Azure to 80 to 90% utilization at scale.

Safely harvesting unused power across our datacenter fleet

Another way we improve power efficiency involves placing workloads intelligently across a datacenter to safely harvest any unused power. Power harvesting refers to practices that enable us to maximize the use of our available power. For example, if a workload is not consuming the full amount of power allocated to it, that excess power can be borrowed by or even reassigned to other workloads. Since 2019, this work has recovered approximately 800 megawatts (MW) of electricity from existing datacenters, enough to power approximately 2.8 million miles driven by an electric car.¹  

Over the past year, even as customer AI workloads have increased, our rate of improvement in power savings has doubled. We’re continuing to implement these best practices across our datacenter fleet in order to recover and re-allocate unused power without impacting performance or reliability.

Driving IT hardware efficiency through liquid cooling

In addition to power management of workloads, we’re focused on reducing the energy and water requirements of cooling the chips and the servers that house these chips. With the powerful processing of modern AI workloads comes increased heat generation, and using liquid-cooled servers significantly reduces the electricity required for thermal management versus air-cooled servers. The transition to liquid cooling also enables us to get more performance out of our silicon, as the chips run more efficiently within an optimal temperature range.

A significant engineering challenge we faced in rolling out these solutions was how to retrofit existing datacenters designed for air-cooled servers to accommodate the latest advancements in liquid cooling. With custom solutions such as the “sidekick,” a component that sits adjacent to a rack of servers and circulates fluid like a car radiator, we’re bringing liquid cooling solutions into existing datacenters, reducing the energy required for cooling while increasing rack density. This in turn increases the compute power we can generate from each square foot within our datacenters.

Learn more and explore resources for cloud and AI efficiency

Stay tuned to learn more on this topic, including how we’re working to bring promising efficiency research out of the lab and into commercial operations. You can also read more on how we’re advancing sustainability through our Sustainable by design blog series, starting with Sustainable by design: Advancing the sustainability of AI and Sustainable by design: Transforming datacenter water efficiency. 

For architects, lead developers, and IT decision makers who want to learn more about cloud and AI efficiency, we recommend exploring the sustainability guidance in the Azure Well-Architected Framework. This documentation set aligns to the design principles of the Green Software Foundation and is designed to help customers plan for and meet evolving sustainability requirements and regulations around the development, deployment, and operations of IT capabilities.  

¹Equivalency assumptions based on estimates that an electric car can travel on average about 3.5 miles per kilowatt hour (kWh) x 1 hour x 800.

The post Sustainable by design: Innovating for energy efficiency in AI, part 1 appeared first on The Microsoft Cloud Blog.

Last month, we unveiled our Datacenter Community Pledge, emphasizing that datacenters are not only the backbone of modern technology but also a force for good in the communities they serve. As part of this commitment, at Microsoft we recognize our crucial role in protecting and replenishing freshwater resources both in the regions where we operate and around the world.

That’s why in our datacenter operations, one of the essential engineering questions we ask each day is: how can we continue to conserve water while meeting growing customer demand for cloud and AI innovation?

In datacenters, water is primarily used for cooling and humidification. As demand for high performance cloud and AI applications has grown over the past few years to fuel customer applications and enable a new frontier of discovery and innovation, so have the power requirements for silicon chips—the basic building blocks of cloud and AI computing—that sit within the racks and servers of datacenters. Because advanced chips typically utilize more power, they also generate more heat. To prevent the chips from malfunctioning, more intensive cooling is needed, and this has historically required consuming water.

Water replenishment

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To reduce the water required for operations, a critical path to our company goal of becoming water positive by 2030, we’re innovating everywhere from our datacenter buildings all the way to the chips. Collectively, this work is delivering substantial results. From our first generation of owned datacenters in the early 2000s to our current generation in 2023, we have reduced our water intensity (water consumed per kilowatt-hour) by over 80%. This shows that it’s possible to significantly reduce how much water our datacenters use per kilowatt of power even as our cloud infrastructure expands.

Today, we are sharing more about two focus areas for continuing to drive down water intensity: (1) conserving water at every stage of operations, and (2) innovative technologies that reduce the amount of water needed for cooling.

Conserving water at every stage of operations

At all locations, we work to minimize the amount of water we require for cooling. This includes operating our datacenters at a temperature that allows us to cool with outdoor air the majority of the year, reducing the need for ambient cooling, and conserving water at every stage of day-to-day operations.

We conduct regular audits of our datacenters to identify inefficiencies and areas where design and day-to-day use don’t align. Our 2022 audit resulted in targeted improvements that eliminated 90% of the instances in which excess water was used. In addition, we’re building advanced prediction models that help us anticipate water requirements based on real-time weather and operational data. Comparing anticipated needs to actual consumption patterns enables us to quickly identify inefficiencies, such as water leaks that may otherwise go unnoticed.

To minimize freshwater requirements from municipal water systems, we employ conservation strategies that are tailored to the bioregion of the datacenters. For example, in Texas, Washington, California, and Singapore we’ve expanded our use of reclaimed and recycled water. In the Netherlands, Ireland, and Sweden we’re harvesting rainwater, and we’re also bringing this capability to new datacenters in Canada, the United Kingdom, Finland, Italy, South Africa, and Austria.

Innovative technologies that reduce the water needed for cooling

Innovative cooling technologies are essential to Microsoft’s water strategy, and we are rapidly expanding proven solutions across our datacenter portfolio. This includes solutions that bring cooling directly to the source of heat generation—the chip itself.

Cold plates are a prime example of this: a direct-to-chip cooling technology that provides heat exchange in a closed loop system. Cold plates dissipate heat more effectively than traditional air cooling, directly chilling the silicon and then recirculating the cooling fluid, like a car radiator. This solution significantly improves cooling efficiency and enables more precise temperature control compared to traditional methods.

To harness the increased efficiency cold plates offer, we’re developing a new generation of datacenter designs optimized for direct-to-chip cooling, which requires reinventing the layout of servers and racks to accommodate new methods of thermal management as well as power management. In existing datacenters, we’re also using innovations like the ‘sidekick,’ a liquid cooling system we’re already using adjacent to racks of Microsoft Azure Maia AI Accelerator chips, circulating fluid to draw heat away from the cold plates attached to the surface of the chips.  

We’re also evolving cold plate technologies through our work with microfluidics, a technology that brings cooling inside the silicon by integrating tiny fluid channels into chip designs. Embedding the liquid cooling inside the chip brings the coolant right next to the processors, resulting in even more efficiency and precision.

Our newest datacenter designs are optimized to support AI workloads and consume zero water for cooling. To achieve this, we’re transitioning to chip-level cooling solutions, providing precise temperature cooling only where it’s needed and without requiring evaporation. With these innovations, we can significantly reduce water consumption while supporting higher rack capacity, enabling more compute power per square foot within our datacenters.

Reducing global water use through partnership, investing to replenish water

Our water positive goal guides us to consider not only how we can shift our business practices to reduce our water footprint but also how this work can benefit customers and partners working toward similar goals. The five pillars of water positive: reduction, replenishment, access, innovation, and policy all play important roles in our water positive journey.

Over the past year, we grew our water replenishment program significantly, nearly doubling our water replenishment portfolio to include more than 49 replenishment projects around the world. Together, these have the potential to replenish more than 24,000 Olympic size swimming pools over the lifetime of the projects. We also met our 2030 water access target to provide 1.5 million people with access to clean water and sanitation services.²

In addition, we’re working to reduce global water use by collaborating with customers, partners, local communities and municipalities to advance water infrastructure and policy around the globe. Because corporate approaches to water management generally lag investments in carbon reduction¹, we’re taking an active role in championing effective and innovative water management practices and water policies. Some of our advocacy projects include: (1) serving on a coalition to increase water reuse and recycling across the United States, (2) funding projects that support Tribal Nations and state governments in increasing water security, and (3) supporting research, analysis, and advocacy on water in the European Commission.

Learn more about how Microsoft is advancing sustainability

For more information on our progress towards our sustainability goals, read the Microsoft 2024 Environmental Sustainability Report.

Learn more about how we’re advancing sustainability through our Sustainable by design blog series:

• Sustainable by design: Advancing the sustainability of AI
• Sustainable by design: Innovating for energy efficiency in AI, part 1
• Sustainable by design: Innovating for energy efficiency in AI, part 2

¹Why investment in water is crucial to tackling the climate crisis, World Economic Forum, 2024.

²2024 Environmental Sustainability Report, Microsoft.

The post Sustainable by design: Transforming datacenter water efficiency appeared first on The Microsoft Cloud Blog.

At the heart of this initiative are industry prompts in Copilot Lab and industry templates in Microsoft Copilot Studio—two platforms tailored to meet the unique demands of various businesses. These platforms are not just about providing tools; they’re about delivering a seamless experience that aligns with our customers’ operational workflows, enhancing productivity and fostering creativity.

Copilot Lab serves as a portal for innovation, offering diverse industry prompts that cater to the specific needs of job functions and industries. Getting up to speed on AI prompts can be challenging from both a technical and practical standpoint. If AI prompts are vague or lack necessary details, AI models can produce off-target responses. Copilot Lab provides a repository of prompts that resonate with industry professionals, enabling them to generate meaningful outcomes with Microsoft Copilot’s assistance.

Copilot Studio includes industry copilot templates that can simplify the adoption process for businesses. These templates incorporate industry-specific business logic and custom prompts that streamline operations and decision-making processes. These templates, available in the Copilot Studio Gallery, represent fully working copilots that often only require configuration or integration for an organization to adopt them.

As we continue to build and co-create, our vision is to not only enhance the capabilities of Microsoft Industry Clouds but also to help redefine the way industries operate. By providing accelerators that drive mainstream adoption and co-building with industry partners, we’re setting a new standard for industry solutions.

Copilot Lab

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Industry AI prompts in Copilot Lab

In our continuous pursuit of empowering organizations across various industries, Microsoft has introduced a suite of industry-specific prompts within Copilot Lab. These prompts are a culmination of our efforts to provide a more context-aware AI experience that leverages users’ domain expertise to drive better outcomes. Our new industry prompts help customers quickly get started using Copilot for sector, job, and role-specific scenarios and can be customized with domain-specific details, best practices, and industry context to reduce trial and error and ensure high-quality output. These prompts can create an interactive experience for sector-, job-, and role-specific users, which can enhance prompts and deliver tailored responses, continually improving based on user interactions.

The genesis of these prompts was a collaborative effort, drawing on insights from industry professionals to ensure quality and relevance. The initiative begins with the financial services, retail, and manufacturing industries, introducing 24 industry-specific prompts within Copilot Lab, marking the start of a broader vision to integrate industry-specific intelligence into business operations.

These prompts are readily accessible in Copilot Lab, where users can select prompts that resonate with their professional needs. They are designed to help users ask relevant and specific questions, enabling Copilot to generate insights from data that are finely tuned to their industry’s nuances.

The introduction of these prompts is part of our broader vision to bring industry-specific intelligence to the forefront of business operations. By providing these tailored tools, we aim to enhance productivity and foster innovation within the Microsoft 365 environment.

Accessing these prompts is simple. Within Copilot Lab, a collection of pre-built prompts categorized by industry. Users can select the one that fits their role, and let Copilot do the rest. Users can further customize the prompts with domain-specific details and best practices.

Industry copilot templates in Copilot Studio

Industry copilot templates in Copilot Studio help users streamline the adoption of AI solutions across various sectors, providing pre-built dialogs, intents, entities, prompts, and actions that can be easily customized and extended according to the user’s needs using a low-code/no-code experience.

The templates offer a quick and efficient way to get started with AI, with configurations and rollout typically taking only a few hours. They include publicly accessible and customizable data sources, allowing customers to change the default data source with their own to get the copilot ready for business. This feature is particularly beneficial for industries that require rapid deployment and flexibility, such as retail and sustainability.

For instance, the sustainability insights template in Microsoft Copilot Studio empowers users with easy access to data and insights regarding a company’s sustainability goals and progress, while the Copilot template for store operations is designed to enhance the efficiency of retail frontline workers by providing easy access to store procedures and policies. These templates were part of the Microsoft Copilot template gallery unveiled at Microsoft Build 2024 and represent Microsoft’s commitment to empowering users with AI tools that are both powerful and adaptable to their specific industry needs.

Copilot Studio is available worldwide, across 22 datacenters, and supports multi-lingual experiences, making it a versatile tool for global enterprises. The studio’s capabilities are continually expanding, with support for 23 languages and growing, helping to ensure that users can build solutions and respond in their preferred language.

For more detailed information on how to utilize these new features and to stay updated on future releases, users can refer to the resources provided by Microsoft Copilot Studio.

Learn more about industry-specific AI tools

Microsoft remains dedicated to empowering customers through innovation. Our latest offerings in Copilot Lab and Copilot Studio reflect our commitment to providing industry-specific AI tools that are not only powerful but also intuitive and accessible. By continually investing in these technologies, we aim to help ensure that organizations, regardless of size or sector, can harness the full potential of AI to drive efficiency, creativity, and growth.

Copilot learning hub

Learn how to use Microsoft Copilot based on your role

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Get started by exploring detailed guides and information on our industry prompts and templates at Copilot Lab and Copilot Studio. Learn more and stay informed about Microsoft’s advancements in AI by exploring Copilot for Microsoft 365 Tech Community Blog and the Microsoft Industry Blog for insightful articles, or discover more at Copilot learning hub. Together, let’s shape the future of industry innovation with AI.

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Innovate, Connect, Cultivate

The Cloud Cultures series is an exploration of the intersection between cloud innovation and culture across the globe. 

Malaysia accelerates growth through digital transformation

Amidst the swiftly changing digital landscape, Malaysia stands out as a dynamic force capturing global attention. This nation—enriched by its diverse population comprised of Malays, Indians, Chinese, and more—is home to people and companies that have adeptly embraced innovative technologies, ensuring the benefits extend to all, not just the tech-savvy elite.

Malaysia has established a culture of digital acceleration through industries like energy, farming, and education by striking a balance between growth and the needs of their people. During my travels, I learned how they’ve embraced cloud innovation in a way that allows them to navigate the modern world with confidence and ensure that everyone is along for the ride.

Before setting out to meet with local companies, I joined General Manager of Energy and Utilities for Microsoft Malaysia, Shereeta (full name: Datin Sharifah Shereeta Syed Sheh), for a traditional Malaysian breakfast at her favorite restaurant. We sat down to talk about our upcoming interviews over nasi lemak—a delicious combination of fried anchovies, fish, hard-boiled egg, cucumber, and sambal on fragrant coconut rice, alongside pancakes, coconut grits, and colorful baked treats. Delighted by the food and excited for the day, we parted ways after breakfast. Shereeta headed out to a local chicken farm while I ventured further into the city.

PETRONAS is building a more sustainable world

I began my visit in the heart of Kuala Lumpur at the Golden Triangle, a hub for shopping, commerce, and entertainment. Standing 88-stories tall with a 17-acre park at its base, the PETRONAS Twin Towers are a wonder to behold. The skyscrapers are complete with malls, museums, a philharmonic orchestra, and a skybridge with views of the vibrant city. This is where I met Phuah Aik-Chong, CEO of Petronas Digital, to learn how PETRONAS utilizes the cloud to accelerate digital transformation.

PETRONAS is a dynamic global energy group with presence in over 100 countries. They produce and deliver energy and solutions that power society’s progress, enriching lives for a sustainable future. PETRONAS’ commitment to sustainability starts at the core of their operations and extends throughout their value chain. People are their strength and partners for growth, driving innovation to deliver a unique spectrum of solutions. PETRONAS’ commitment to Malaysia’s progress doesn’t stop at providing oil and gas—they make a concerted effort to provide development opportunities to underserved populations. One such initiative is the BeDigital Bootcamp, which involves upskilling students from various universities in Malaysia. Partnering with Microsoft, they have collaborated on multiple initiatives that reflect the mutual goal of empowering Malaysians to benefit in tandem with the rapid pace of innovation and digital advancements.

Chop Cheong Bee uses e-farming to feed Malaysia

While I stayed in the city, Shereeta took a break from the bustling metropolis and turned down a quiet dirt road. There, she learned about a local company that helps independent chicken farmers use cloud technology to turn their operations into smart farms—improving food security across Malaysia with affordable, high-quality chicken.

Founded in 1985, Chop Cheong Bee began as a poultry trading company, supplying chicken to local markets and establishments in Malaysia. After a brief period of time, they had to close due to an overwhelming number of manual tasks. However, in the late 2000s, they reopened focusing on technology and e-farming practices.

Cloud technology enables Chop Cheong Bee to create environments where chickens can thrive, utilizing a closed and climate-controlled farming system. The solution they developed collects data to inform how much feed is being consumed and the meat conversion ratios, all in real time. Today, Chop Cheong Bee is a crucial poultry supplier that facilitates a sizable portion of the chicken supply in Malaysia.

General Manager of Chop Cheong Bee, Datuk Jeffrey Ng Choon Ngee shared how e-farming is the future:

“With our solution, we can improve the broiler production index by 20 to 30 points. That’s easily a 10 percent improvement. If more farms can achieve this, then the cost of production will drop. And then hopefully, more Malaysians can afford quality poultry.”

Chop Cheong Bee built a system that can produce about 280 to 340 million chickens annually and supply 80 to 100 customers daily. This new way of farming not only provides millions of people with affordable and nutritious meat, but has also attracted a younger, more technology-focused generation of farmers to this vital industry.

Bersama Malaysia ensures citizens are part of the country’s digital acceleration

My final stop in Malaysia was a basketball court to shoot hoops with a recent graduate, Vaashini Palaniappan, who took part in the Bersama Malaysia (Together with Malaysia) program. Alongside sponsors like the Ministry of Education and Microsoft, the initiative teaches students digital skilling, inspiring young students, and women to dream outside the norm and build careers in tech.

Vaashini Palaniappan, data scientist and recent graduate, shared her future aspirations:

“There are so many women in this data and AI field that want to invent something, that want a brighter future. Because of this, I’m inspired to do something different. I want to be inventive using AI.”

Growing up in a small town, Vaashini didn’t have a lot of exposure to technology. But by participating in university programs, she was able to study sciences, learn technical skills, and understand the impact of advanced technologies on medicine. After seeing a close friend pass from cancer, Vaashini said she was determined to become a doctor and leverage innovative technology for good—specifically, to use AI to detect early signs of cancer and build a hyper-personalized treatment plan for patients.

Bersama Malaysia, along with Microsoft’s Code with Barriers program, were created to ensure citizens of Malaysia are a part of the digital acceleration of the country. These programs are empowering Malaysia’s inclusive digital economy and advancing the nation’s digital transformation across the private and public sectors. Malaysia has consistently been a trailblazer in fostering opportunities for its citizens. Through initiatives like Bersama Malaysia, the nation ensures that no one is left behind in the dynamic landscape of transformation.

If we’d prefer to quote faculty of the program, instead of Vaashini we could also use this one:

“Even before this partnership with Microsoft, we were aware that the Microsoft Learn platform offers a vast selection of professional certifications related to digital skills, but what really stood out was that Microsoft also supports institutions to manage the certification process independently. This way, we can customize the upskilling program according to a timeline and cost that works best for our students”—Dr. Hamizah binti Mohd Safuan, Deputy Dean of FAST

Innovating together makes change happen

Later that evening, Shereeta and I discussed our journey over my first experience with a popular local fruit: the durian. After getting used to the infamous smell, I snacked on the custard-like meat and reflected on Malaysia’s inspiring commitment to extending growth far beyond the gleaming skyscrapers and urban epicenters. This version of cloud culture ensures that as the pace of progress quickens, it doesn’t come at the cost of anyone being sidelined. As is often the case, I saw in Malaysia that the best way to accelerate growth isn’t racing ahead; it’s moving forward together.

In this ever-changing world, there is always more to experience. See you on my next adventure!

Learn more:

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• Find the Azure geography that meets your needs

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The post Cloud Cultures, Part 6: Accelerating collective growth in Malaysia appeared first on The Microsoft Cloud Blog.

Take environmental sustainability, for example. The stakes couldn’t be higher. Pollution and climate change affect all of us and threaten to have a lasting impact on future generations. People care deeply about protecting our planet, but many do not know what actions they can take to make a difference.

I found an empowering example of using AI and data to support sustainability in a recent episode of the Pivotal podcast. This episode features Loic van Cutsem, Director of international partnerships and development at BeeOdiversity.

This Belgium-based tech company is harnessing a 12 million-strong workforce to monitor biodiversity—the health of an ecosystem.

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BeeOdiversity uses AI to interpret data on biological health

BeeOdiversity saw an opportunity to use bees as data collectors at scale. As bees search for nectar in flowers and other plants, they collect valuable information from gardens, fields, and other features in the land and air as they browse. By analyzing the pollen and other microscopic materials that the bees bring back to the hive, BeeOdiversity can get a living snapshot of the biological health of any designated area. This data can reveal factors that harm the environment and where they are located, such as invasive species, heavy metals, and pesticides.

BeeOdiversity leverages AI to interpret this data from bees and offer insights to their clients. Their recommendations do more than help companies comply with regulatory requirements. By having this information, communities and businesses can work together to improve the ecosystem that surrounds them, from increasing water quality to protecting soil health, preventing flooding, and mitigating climate change impacts. They have a better understanding of what actions to take to operate more sustainably, and an army of tiny data collectors who can continue to measure their progress.

That’s what I love about this use case—it’s AI-powered innovation that’s incredibly impactful and scalable to communities around the world. And what’s inspiring about BeeOdiversity’s story is that it holds lessons for other organizations that want to use data and AI to solve complex business problems.

Having good data is essential for driving change

Getting clean and accurate data is essential for any organization to drive change. Data provides a common basis to identify issues and discover solutions. This is especially important if you’re using AI to address a societal issue such as sustainability, which requires collaboration across multiple sectors. When there are so many stakeholders at the table, the recommendations from AI need to be powered by data that everyone can trust and accept.

Today there is no standardized data source in the field of biodiversity monitoring. But BeeOdiversity is unique in how they have recruited bees to collect data through their everyday pollinating behavior. By using bees, they are essentially standardizing the data collection, while giving nature a chance to give us feedback. For this reason, BeeOdiversity has not faced much skepticism from clients. You can’t argue with the bees.

The stakeholders are almost in awe of all this information gathered by the bees, and they’re responsive to the findings. They understand that this is what the bees are telling us. It’s the truth.

Loic van Cutsem, Director of international partnerships and development, BeeOdiversity

Democratizing access to data leads to impact at scale

Quality data is a crucial ingredient for success with AI. But at the end of the day, data is only a tool. It’s what you do with the data that matters the most. Data’s real value comes from how it empowers people to make smarter decisions, and you can maximize that impact by expanding the number of people who have access to that data.

BeeOdiversity has been building their proprietary data set for the last decade. Now, they are working to empower as many people as possible to make better sustainability decisions with that data, including businesses, governments, and soon, individuals.

They have customers from a wide range of industries, from agrifood producers to financial institutions, that are all keen on better managing their natural resources to build more resilient businesses. Governments are also working with BeeOdiversity to improve the environment for the communities they serve. For example, Knokke-Heist, a coastal city in Belgium, was able to achieve a 300% drop in pesticide levels and a four-times increase in plant diversity.  

And for maximum impact at scale, BeeOdiversity is developing a mobile app that will allow people to scan the plants in their garden and get tailored recommendations on how to improve their local ecosystem.

By democratizing access to their invaluable data, BeeOdiversity is enabling better sustainability decisions across society. This shows the power of having standardized data that everyone can rally behind.

Accelerate innovation by unlocking new insights

One of the benefits of AI is how quickly it can accelerate innovation for those who embrace it. Just point the AI to new sets of data, and it unlocks more levels of analysis and insight, allowing businesses to easily expand their coverage area or improve their processes.

BeeOdiversity is a great example. They developed a solution called BeeOimpact that incorporates satellite imagery to assess the crops and industries in a given area. It draws from this satellite data, along with data from their existing dataset that was collected by bees, and uses a predictive AI model to provide almost instantaneous estimates of the pesticides that are likely to be there.

BeeOdiversity is also developing a method to allow beekeepers to take photos of pollen, instead of manually collecting the pollen to send in for lab analysis. Their aim is to use computer vision and machine learning to identify the pollen in the photo. This new method would require less human effort and speed up their process of analyzing pollen samples.

As they develop these new capabilities, BeeOdiversity uses Microsoft Azure’s data and AI capabilities to enable their AI use cases, and GitHub as their platform for developers. With these tools, BeeOdiversity can achieve an extraordinary pace of innovation and make a sizable impact for sustainability.

Listen to the full conversation on Pivotal

By standardizing biodiversity data, empowering people to take action, and unlocking new innovation at a fast pace, BeeOdiversity is tackling sustainability head-on with the help of technology.

It’s brilliant how Loic and his team have found a way to leverage the diligence of bees with the analytical power of AI to unlock the kind of insights that many of us have dreamed about for decades. They’re giving a voice to mother nature and giving us humans a chance to take action so that we can live in greater harmony with the planet.

To hear more about BeeOdiversity and their ingenious use of AI, listen to the episode on the Pivotal podcast.

The post How BeeOdiversity leverages 12 million bees and AI to create a more sustainable future appeared first on The Microsoft Cloud Blog.

Gather and analyze all your ESG data in one place with Microsoft Fabric

With sustainability data solutions in Microsoft Fabric (preview), you can analyze your organization’s environmental, social, and governance (ESG) data together with your other enterprise data to inform more holistic decisions and better-targeted outcomes. Gather, harmonize, and transform sustainability data into meaningful, actionable insights, and use advanced analytics and powerful AI to help you prepare data for analysis, regulatory reporting, and AI-driven innovation.  

You can also use sustainability data solutions in Fabric to validate your data and track progress against publicly available data and industry benchmarks. All this comes with a shared governance model across various capabilities and a unified space for your data stewards and sustainability practitioners to interact through Microsoft Fabric.  

Four solution capabilities help you holistically meet your sustainability requirements:  

1. ESG data estate (preview) helps you centralize and standardize ESG data from your disparate data sources to compute, analyze, and disclose ESG metrics for various regulatory reporting and analytics requirements.  
2. Microsoft Azure emissions insights (preview) enable you to report and analyze your Microsoft Azure usage–related emissions data at subscription and resource levels. 
3. Environmental metrics and analytics (preview) help you generate custom reports, metrics, and analytics insights across carbon, water, and waste by connecting to your data in Microsoft Sustainability Manager.  
4. Social and governance metrics and reports (preview) provide insights, dashboards, and metrics to support your needs across various sustainability directives.   

Build your ESG data estate (preview) 

Sustainability disclosures, analytics, and reduction initiatives require rich ESG data sets that originate from disparate sources, and this data needs to be unified and standardized to improve its efficiency and value. sustainability data solutions in Microsoft Fabric (preview) provides pre-built data pipelines and lakehouses to combine social and governance data from different enterprise systems with environmental data from Microsoft Sustainability Manager and other systems. 

With your ESG data estate in place, you can process unified sustainability data to compute ESG metrics for sustainability disclosure requirements such as Corporate Sustainability Reporting Directive (CSRD), Global Reporting Innitiative (GRI), and many others using provided prebuilt data processing artifacts. You can run this process on demand or on a schedule.

Built-in dashboards let you view data and insights from a variety of perspectives—such as by facility or operating unit—and use workflows to help prepare reports. You can mark the metrics required for CSRD and other disclosures and prepare the reports to share with auditors. 

Gain detailed Microsoft Azure emissions insights (preview) 

The typical IT efficiency journey for many organizations starts with migrating and then optimizing workloads in the cloud, which involves factoring potential emissions reduction strategies. Microsoft Fabric facilitates this process by enabling you to unify and analyze your Azure emissions data against your cloud usage.  

With all your Azure emissions data in Microsoft Fabric, you can query and drill down into Azure resource-level emissions for advanced reporting and analysis. Use pre-built data pipelines that ingest and store resource-level Azure emissions data in tabular data. And use Power BI dashboards to drill down and compare emissions data across subscriptions and resources, helping to identify patterns that evolve with time and usage.  

Note that all Azure customers can now easily access data and insights related to their cloud usage-based emissions by simply signing into the Azure portal and navigating to Azure carbon optimization (preview). This feature provides an overview of your subscription’s emissions data for the past 12 months and by service type—such as virtual machines or storage. 

On the Emissions Details page, you’ll see a monthly breakdown of the top Azure resources that contribute to your organization’s total emissions. By comparing this data to the previous month, you can see the percentage change and identify resources to turn off or utilize more efficiently. On the Emissions Reductions page, you’ll find recommendations to improve your organization’s cloud efficiency and sustainability. 

Enrich your understanding of environmental metrics and analytics (preview) 

To better understand your progress on reduction and other initiatives across carbon, water, and waste, you may need to define and compute custom metrics. Microsoft Fabric lets you connect to your relevant data in Microsoft Sustainability Manager and provides tables to query, compute custom metrics, and analyze the data further. You can enrich your sustainability data with the other corporate and business data for advanced machine learning–based analytics and leverage prebuilt Power BI dashboards for detailed insights and visualizations. 

Analyze your social and governance metrics and reports (preview) 

Increasingly, organizations are required to disclose their sustainability performance in social and governance areas—for example, for CSRD. To address this need, Microsoft Fabric enables you to unify and prepare disparate data from corporate systems handling human resources, health and safety, and corporate governance data in a single ESG data estate. From there, you can compute and report social and governance metrics required for disclosures and use Power BI dashboards to visualize and drill down into selected areas. 

Introducing Copilot in Microsoft Sustainability Manager: Quickly turn your sustainability data questions into insights 

When you’re working with disparate ESG data from across your operations and value chains, getting answers to critical questions quickly can be challenging, potentially slowing down progress toward sustainability goals. Copilot in Microsoft Sustainability Manager, now in preview, immediately delivers insights from your data based on natural language queries.  

Ask Copilot a question and it will work across Microsoft Sustainability Manager to quickly understand environmental data and provide an answer. For example, you can ask Copilot the right global warming potential (GWP) value for a given gas and assessment report (AR) version. With Copilot’s assistance, you can more confidently work through tasks like drafting reports on your organization’s emissions or CSRD environmental metrics for a quarterly update. Copilot helps you generate the draft reports, reducing your preparation time. 

Copilot can also help simplify and accelerate complex processes within Microsoft Sustainability Manager, such as creating a calculation model for your mobile combustion data. Within seconds, a calculation model is created using natural language, and within minutes these emissions are calculated, providing a more complete picture of reduction opportunities. 

Copilot in Microsoft Sustainability Manager is trained on Microsoft Cloud for Sustainability data schemas, making it an effective and knowledgeable tool for improving efficiency across various tasks.  

Magnify your visibility into your ESG data with AI-powered insights 

It’s important to trust the accuracy of your organization’s ESG data, which can be large and complex. The faster you can identify errors or missing data, the faster you can resolve or fill in the data. Intelligent insights in Microsoft Sustainability Manager, now available in preview, provides the visibility into your ESG data needed to help identify outliers, trends, and correlations.

See where data needs cleaning or where you need more complete data. This feature scans your organization’s data within Microsoft Sustainability Manager and identifies opportunities—both short-term and long-term—for reductions that align to your sustainability initiatives. Insights are based on historical trends, seasonality, and data anomalies. Looking deeper into the results, you can identify opportunities for reduction in your organization’s Scope 3 emissions.  

Learn more about how intelligent insights can help you make sense of an increasingly complex data landscape. 

Now generally available in Microsoft Sustainability Manager 

Track, manage, and report your water and waste sustainability data 

In addition to expanding AI capabilities, we’re continuing to evolve Microsoft Sustainability Manager to meet broader environmental sustainability objectives. Along with carbon emissions data capabilities, water and waste sustainability data capabilities are now generally available.  

Organizations can track and report their water accounting, water usage efficiency, and compliant water discharges across multiple facilities, safe water discharge regulations, and water usage disclosure standards. Microsoft Sustainability Manager helps you understand the sources and quantities of waste generation at your facilities and how the waste gets disposed of. This can further help you discover avenues to increase waste recycling for specific waste sources and reduce off-site waste disposal through landfills and incineration.

Simplify supplier data collection with ESG value chain solution  

We’re excited to announce the general availability of ESG value chain solution in Microsoft Sustainability Manager, enabling you to gather data more directly and securely from your suppliers. You can use the data to calculate suppliers’ emissions and gather partner-specific emission factors to calculate your Scope 3 emissions based on your consumption with those partners.

Experience the new capabilities in action with This is AI…for Sustainability  

Hear from Melanie Nakagawa, Chief Sustainability Officer, Shelly Blackburn, Global Vice President for Sustainability Go to Market, and Satish Thomas, Corporate Vice President, Microsoft Industry Clouds during this free digital event. Get guidance on your sustainability journey and learn how to drive business transformation with Microsoft data and AI solutions. Watch the webcast on demand.

Learn more about sustainability solutions with Microsoft

• Want to learn more about Microsoft Cloud for Sustainability? Sign up for news and updates and try it for free.
• Discover solutions with Microsoft Sustainability Manager.

The post Drive sustainability transformation faster with new data and AI capabilities appeared first on The Microsoft Cloud Blog.

Microsoft and the Pacific Northwest National Laboratory (PNNL) in Richland, Washington, are collaborating to demonstrate how this acceleration can benefit chemistry and materials science – two scientific fields pivotal to finding energy solutions that the world needs.

Scientists at PNNL are testing a new battery material that was found in a matter of weeks, not years, as part of the collaboration with Microsoft to use to advanced AI and high-performance computing (HPC), a type of cloud-based computing that combines large numbers of computers to solve complex scientific and mathematical tasks.

PNNL materials scientist Shannon Lee mixes raw materials to synthesize a new solid electrolyte, one of the promising candidates predicted using AI and HPC tools in the Azure Quantum Elements service. Photo by Dan DeLong for Microsoft.
As part of this effort, the Microsoft Quantum team used AI to identify around 500,000 stable materials in the space of a few days.

The new battery material came out of a collaboration using Microsoft’s Azure Quantum Elements to winnow 32 million potential inorganic materials to 18 promising candidates that could be used in battery development in just 80 hours. Most importantly, this work breaks ground for a new way of speeding up solutions for urgent sustainability, pharmaceutical and other challenges while giving a glimpse of the advances that will become possible with quantum computing.

“We think there’s an opportunity to do this across a number of scientific fields,” says Brian Abrahamson, the chief digital officer at PNNL. “Recent technology advancements have opened up the opportunity to accelerate scientific discovery.”

PNNL is a U.S. Department of Energy laboratory doing research in several areas, including chemistry and materials science, and its objectives include energy security and sustainability. That made it the ideal collaborator with Microsoft to leverage advanced AI models to discover new battery material candidates.

“The development of novel batteries is an incredibly important global challenge,” Abrahamson says. “It has been a labor-intensive process. Synthesizing and testing materials at a human scale is fundamentally limiting.”

Learning through trial and error
The traditional first step of materials synthesis is to read all the published studies of other materials and hypothesize how different approaches might work out. “But one of the main challenges is that people publish their success stories, not their failure stories,” says Vijay Murugesan, materials sciences group lead at PNNL. That means scientists rarely benefit from learning from each other’s failures.

The next traditional scientific step is testing the hypotheses, typically a long, iterative process. “If it’s a failure, we go back to the drawing board again,” Murugesan says. One of his previous projects at PNNL, a vanadium redox flow battery technology, required several years to solve a problem and design a new material.

Vijay Murugesan, material sciences group lead at PNNL, says the Microsoft AI and HPC tools allow scientists to eliminate the time-consuming trial-and-error discovery steps and focus on the best candidates for testing. Photo by Andrea Starr for PNNL.
The traditional method requires looking at how to improve on what has been done in the past. Another approach would be to take all the possibilities and, through elimination, find something new. Designing new materials requires a lot of calculations, and chemistry is likely to be among the first applications of quantum computing. Azure Quantum Elements offers a cloud computing system designed for chemistry and materials science research with an eye toward eventual quantum computing, and is already working on these kinds of models, tools and workflows. These models will be improved for future quantum computers, but they are already proving useful for advancing scientific discovery using traditional computers.

To evaluate its progress in the real world, the Microsoft Quantum team focused on something ubiquitous in our lives – materials for batteries.

Teaching materials science to AI
Microsoft first trained different AI systems to do sophisticated evaluations of all the workable elements and to suggest combinations. The algorithm proposed 32 million candidates – like finding a needle in a haystack. Next, the AI system found all the materials that were stable. Another AI tool filtered out candidate molecules based on their reactivity, and another based on their potential to conduct energy.

The idea isn’t to find every single possible needle in the hypothetical haystack, but to find most of the good ones. Microsoft’s AI technology whittled the 32 million candidates down to about 500,000 mostly new stable materials, then down to 800.

“At every step of the simulation where I had to run a quantum chemistry calculation, instead I’m calling the machine learning model. So I still get the insight and the detailed observations that come from running the simulation, but the simulation can be up to half a million times faster,” says Nathan Baker, Product Leader for Azure Quantum Elements.

AI may be fast, but it isn’t perfectly accurate. The next set of filters used HPC, which provides high accuracy but uses a lot of computing power. That makes it a good tool for a smaller set of candidate materials. The first HPC verification used density functional theory to calculate the energy of each material relative to all the other states it could be in. Then came molecular dynamics simulations that combined AI and HPC to analyze the movements of atoms and molecules inside each material.

This process culled the list to 150 candidates. Finally, Microsoft scientists used HPC to evaluate the practicality of each material – availability, cost and such – to trim the list to 23 – five of which were already known.

Thanks to this AI-HPC combination, discovering the most promising material candidates took just 80 hours.

The HPC portion accounted for 10 percent of the time spent computing – and that was on an already-targeted set of molecules. This intense computing is the bottleneck, even at universities and research institutions that have supercomputers, which not only are not tailored to a specific domain but also are shared, so researchers may have to wait their turn. Microsoft’s cloud-based AI tools relieve this situation.

Broad applications and accessibility
Microsoft scientists used AI to do the vast majority of the winnowing, accounting for about 90 percent of the computational time spent. PNNL materials scientists then vetted the short list down to half a dozen candidate materials. Because Microsoft’s AI tools are trained for chemistry, not just battery systems, they can be used for any kind of materials research, and the cloud is always accessible.

“We think the cloud is a tremendous resource in improving the accessibility to research communities,” Abrahamson says.

Brian Abrahamson, chief digital officer at PNNL. Photo by Andrea Starr for PNNL.
Today, Microsoft supports a chemistry-specific copilot and AI tools that together act like a magnet that pulls possible needles out of the haystack, trimming the number of candidates for further exploration so scientists know where to focus. “The vision we are working toward is generative materials where I can ask for list of new battery compounds with my desired attributes,” Baker says.

The hands-on stage is where the project stands now. The material has been successfully synthesized and turned into prototype batteries that are functional and will undergo multiple tests in the lab. Making the material at this point, before it’s commercialized, is artisanal. One of the first steps is to take solid precursors of the materials and to grind them by hand with a mortar and pestle, explains Shannon Lee, a PNNL materials scientist. She then uses a hydraulic press to compact the material into a dime-shaped pellet. It goes into a vacuum tube and is heated to 450 to 650 degrees Celsius (842 to 1202 degrees Fahrenheit), transferred to a box to keep it away from oxygen or water, and then ground into a powder for analysis.

For this material, the 10-or-more-hour process is “relatively quick,” Lee says. “Sometimes it takes a week or two weeks to make a single material.”

Then hundreds of working batteries must be tested, over thousands of different charging cycles and other conditions, and later different battery shapes and sizes to realize commercial use. Murugesan dreams of the development of a digital twin for chemistry or materials, “so you don’t need to go to a lab and put this material together and make a battery and test it. You can say, ‘this is my anode and this is my cathode and that’s the electrolyte and this is how much voltage I’m going to apply,’ and then it can predict how everything will work together. Even details like, after 10,000 cycles and five years of usage, the material performance will be like this.”

Microsoft is already working on digital tools to speed up the other parts of the scientific process.

The lengthy traditional process is illustrated by lithium-ion batteries. Lithium got attention as a battery component in the early 1900s, but rechargeable lithium-ion batteries didn’t hit the market until the 1990s.

Today, lithium-ion batteries increasingly run our world, from phones to medical devices to electric vehicles to satellites. Lithium demand is expected to rise five to ten times by 2030, according to the U.S. Department of Energy. Lithium is already relatively scarce, and thus expensive. Mining it is environmentally and geopolitically problematic. Traditional lithium-ion batteries also pose safety issues, with the potential to catch fire or explode.

Many researchers are looking for alternatives, both for lithium and for the materials used as electrolytes. Solid-state electrolytes show promise for their stability and safety.

Surprising results
The newly discovered material PNNL scientists are currently testing uses both lithium and sodium, as well as some other elements, thus reducing the lithium content considerably – possibly by as much as 70 percent. It is still early in the process – the exact chemistry is subject to optimization and might not work out when tested at larger scale, Abrahamson cautions. He points out that the story here is not about this particular battery material, but rather the speed at which a material was identified. The scientists say the exercise itself is immensely valuable, and it has revealed some surprises.

The AI-derived material is a solid-state electrolyte. Ions shuttle back and forth through the electrolyte, between the cathode and the anode, ideally with minimal resistance.

Test tubes contain samples of the new material, which looks like fine white salt.
Samples of the new solid electrolyte discovered by Microsoft AI and HPC tools. Solid-state electrolytes are safer than liquid ones. Photo by Dan DeLong for Microsoft.
It was thought that sodium ions and lithium ions couldn’t be used together in a solid-state electrolyte system because they are similarly charged but have different sizes. It was assumed that the structural framework of a solid-state electrolyte material couldn’t support the movement of two different ions. But after testing, Murugesan says, “we found that the sodium and lithium ions seem to help each other.”

The new material has a bonus, Baker says, because its molecular structure naturally has built-in channels that help both ions move through the electrolyte.

Work on the new material is in early stages but “irrespective of whether it’s a viable battery in the long run, the speed at which we found a workable battery chemistry is pretty compelling,” Abrahamson says.

Additional discoveries are still possible. Murugesan and his team have yet to make and test most of the other new material candidates that the Microsoft models suggested. The collaboration continues, with PNNL computational chemists learning to use the new tools, including a copilot trained on chemistry and other scientific publications.

“With Microsoft and PNNL, this is an enduring collaboration to accelerate scientific discovery, bringing the power of these computational paradigm shifts to bear, with the chemistry and material science that are a hallmark strength of the Pacific Northwest National Laboratory,” Abrahamson says.

“We’re sitting on the precipice of this maturation of the artificial intelligence models, the computational power needed to train and make them useful, and the ability to train them on specific scientific domains with specific intelligence,” he adds. “That, we believe, is going to usher in a new era of acceleration. That is exciting, because these problems matter to the world.”

Related links:

Read Unlocking a new era for scientific discovery with AI: How Microsoft’s AI screened over 32 million candidates to find a better battery
Read Azure Quantum Elements aims to compress 250 years of chemistry into the next 25
Learn more about Azure Quantum Elements
Read: PNNL-Microsoft Collaboration: Accelerating Scientific Discovery
Read the PNNL press release: Energy Storage, Materials Discovery Kick-Off Three-Year Collaboration with Microsoft
Top image: Dan Thien Nguyen, a PNNL materials scientist, assembles a coin cell with the synthesized solid electrolyte. With AI tools guiding researchers, synthesis and testing can be focused in the right direction toward better materials for particular applications. Photo by Dan DeLong for Microsoft.

The post Discoveries in weeks, not years: How AI and high-performance computing are speeding up scientific discovery appeared first on The Microsoft Cloud Blog.