What is Azure?

Azure is launched by Microsoft in February 2010.

It is a cloud computing platform including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) and also these can be used for services such as analytics, virtual computing, storage, networking, etc.

The Azure cloud platform has more than 200 products and cloud services designed to help the users and to solve today’s challenges and create the future.

What is the use of Azure?

There are 10 uses of Azures –

• Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) capabilities:
• Security Offerings:
• Scalability and Ductility:
• Unmatched Hybrid Capabilities:
• Integrated Environment with Other Microsoft Tools:
• Analytics and Intelligence capabilities:
• Easy learning curve:
• Cost Efficient Platform:
• Interoperability:
• Identity & Access Management (IAM):

Compare Google Cloud Certification Vs Azure Certification Vs AWS

Amazon Web Services (AWS) –

AWS is the most widely used cloud platform, having a very vast experience with an expansive range of services tailored for different use cases. AWS is currently at the top, holding the world’s largest public cloud market share. It has been introduced in 2006 and was the first to offer infrastructure services through the Internet.

AWS incorporates many different cloud computing products and services comprising compute, storage, analytics, databases, networking, mobile, developer tools, management tools, and IoT.

Key Features of AWS

• Reliable, scalable, and low-cost infrastructure platform
• Over 200 fully-featured services
• A flexible and secure cloud computing environment
• 81 availability zones in 25 geographic regions across the world

Microsoft Azure–

Azure is the second-largest Cloud Service Provider after AWS. Microsoft Azure is Microsoft’s public cloud computing platform that was introduced in October 2008 and became available in public in February 2010. It is the biggest competitor of AWS. The platform provides a broad range of products and cloud services to allow businesses to find today’s challenges and create the future. Azure’s product portfolio comprises compute, AI & Machine Learning, analytics, databases, DevOps, IoT, networking, storage, web, and security.

Key Features of Azure

• More than 200 products and cloud services
• 90+ compliance offerings
• 95 percent of the Fortune 500 companies rely on Azure
• Datacentres in 60+ global regions

Google Cloud Platform–

GCP is new to cloud computing offered by Google. Many leading companies over industries keep faith on Google Cloud to solve their business problems. Some of its popular cloud services comprise computing, data analytics, data storage, networking, IoT, and machine learning.

Between all three cloud platforms, GCP provides the low-cost pricing model and has flexible cost-control, enable you to try the different services and features.

Key Features of GCP

• Open source, multi-cloud, and hybrid cloud
• Over 100 products
• A global network spanning across 200+ countries & territories, 85 zones
• Trust and security

Certification

The certification confirms your basic knowledge of cloud services and how those services are offered with Azure. Candidates should be competent to demonstrate a fundamental knowledge of cloud concepts, alongside with Azure services, workloads, security, privacy, pricing, and support.

A Microsoft azure certification allows you to chase a wide range of career options. You can become a cloud architect, a developer or solution architect. Additionally, the certification allows you to work in different industries at different locations.

List of Azure Certification

• Microsoft Certified: Azure – Fundamentals Exam AZ-900
• Microsoft Certified: Azure AI – Fundamentals Exam AI-900
• Microsoft Certified: Azure Data – Fundamentals Exam DP-900
• Microsoft Certified: Azure Administrator – Associate Exam AZ-104
• Microsoft Certified: Azure Developer – Associate Exam AZ-204
• Microsoft Certified: Azure Security Engineer – Associate Exam AZ-500
• Microsoft Certified: Azure AI Engineer – Associate Exam AI-100
• Microsoft Certified: Azure Data Scientist – Associate Exam DP-100
• Microsoft Certified: Azure Data Engineer – Associate Exams DP-200 and DP-201
• Microsoft Certified: Azure Database Administrator – Associate Exam DP-300
• Microsoft Certified Solutions Architect – Expert Exams AZ-303 and AZ-304
• Microsoft Certified: Azure DevOps Engineer – Expert Exam AZ-400

Azure Certification Path

Fundamentals-Level Certifications

• Microsoft Certified: Azure Fundamentals AZ-900
• Microsoft Certified: Azure Data Fundamentals DP-900
• Microsoft Certified: Azure AI Fundamentals AI-900
• Microsoft Security, Compliance, and Identity Fundamentals Azur SC-900

Associate-Level Certifications

• Microsoft Certified: Azure Administrator Associate AZ-104
• Microsoft Certified: Azure Developer Associate AZ-204
• Microsoft Certified: Azure Database Administrator Associate DP-300
• Microsoft Certified: Azure Security Engineer Associate AZ-500
• Microsoft Certified: Azure Data Engineer Associate DP-203
• Microsoft Certified: Azure AI Engineer Associate AI-102
• Microsoft Certified: Azure Stack Hub Operator Associate AZ-600

Expert-Level Certifications

• Microsoft Certified: Azure Solutions Architect Expert AZ-303
• Microsoft Certified: DevOps Engineer Expert AZ-400

Specialty Certifications

• Microsoft Certified: Azure IoT Developer Specialty AZ-220
• Microsoft Certified: Azure for SAP Workloads Specialty AZ-120

Azure Certification Cost

• Azure Fundamentals AZ-900 – ( $99 USD* )
• Azure Data Fundamentals DP-900 – ( $99 USD* )
• Azure AI Fundamentals AI-900 – ( $99 USD* )
• Microsoft Security, Compliance, and Identity Fundamentals Azur SC-900 – ( $99 USD* )
• Azure Administrator Associate AZ-104 – ( $165 USD* )
• Azure Developer Associate AZ-204 – ( $165 USD* )
• Azure Database Administrator Associate DP-300 – ( $165 USD* )
• Azure Security Engineer Associate AZ-500 – ( USD 165.00* )
• Azure Data Engineer Associate DP-203 – ( USD 165* )
• Azure AI Engineer Associate AI-102 – ( $165 USD* )
• Azure Stack Hub Operator Associate AZ-600 – ( $165 USD* )
• Azure Solutions Architect Expert AZ-303 ( USD 165.00* )
• DevOps Engineer Expert AZ-400 ( USD 165.00* )
• Azure IoT Developer Specialty AZ-220 ( USD 165.00* )
• Azure for SAP Workloads Specialty AZ-120 ( USD 165.00* )

Best salary for Azure Certified Professional

The average salary of Azure Solutions Architects in the U.S. $152,142 and Rs 1,575,000 in India.
The Azure Fundamentals certification average salary is $126,653 and Rs. 9,00,000 in India.
Azure Administrator Associate average salary is $125,993 and 3.0 Lakhs to Rs 16.9 Lakhs in India.

Best Azure Certification Tutorials

https://www.tutorialspoint.com/microsoft_azure/cloud_computing_overview.htm

Best Azure Certification Video Tutorials

Best Azure certification excercise dumps

https://www.examtopics.com/exams/microsoft/az-900/view/

https://www.prepaway.com/az-104-exam.html

Best Azure certification Ebooks

https://azure.microsoft.com/en-in/blog/free-ebook-the-developer-s-guide-to-microsoft-azure-now-available/

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Today’s CIOs and enterprise IT departments continue to look for ways to rapidly expand their critical infrastructure to the cloud. The next evolution of Azure VMware Solution, built by Microsoft and endorsed by VMware, makes it easier than ever for them to move to the cloud with the VMware technologies they know and trust.

We recently caught up with Eric Lockard, corporate vice president, Azure Dedicated at Microsoft, to learn why he’s more than bullish on Azure VMware Solution, how he sees the Microsoft/VMware partnership benefiting customers, and what he sees driving customers’ cloud demand for Azure. We also couldn’t help asking him what he thinks of an IT landscape that’s dramatically different from what it was just a few years ago.

“Cloud is a paradigm shift in more ways than one,” says Lockard. “Microsoft and VMware have emerged to become close partners determined to deliver a win-win for both companies and our shared customers. The very name of this solution – Azure VMware Solution – reflects just how collaborative this effort is across engineering, sales, and marketing functions in both companies.”

Azure VMware Solution enables IT teams to easily integrate their VMware technologies with Microsoft Azure and, in the process, take advantage of the many cloud services offered through it. And because it’s a solution engineered by Microsoft and endorsed by VMware, everything is integrated with the Azure Portal, ensuring that customers are able to maintain their native VMware experience with the tools they know in Azure.

Lockard notes that, as a result, it’s now far easier for customers who want to rapidly migrate to Azure or expand their use of VMware-powered applications to draw on Azure’s capabilities.

“We are laser-focused on making Azure the best cloud for VMware, and we engineered Azure VMware Solution with the expectation that customers can utilize it as just another flavor of Azure Compute, able to easily take advantage of the full breadth of Azure services,” Lockard adds. “It’s equally applicable to customers that want to be all native on one cloud while using their VMware compute platform, those that want everything on an abstraction layer to avoid any perceived constraints, and enterprises that know they will always be multi-cloud. In this way we’re also creating the multi-cloud choice that VMware makes possible.”

Moving to the Cloud as the Transformation to Software Continues
We also asked Lockard what is prompting customers to enhance their existing infrastructure with the cloud or, alternatively, motivating them to migrate to it completely.

“This is an evolution that’s been going on for years, and there are all sorts of reasons for people to embrace it,” he says. “Some want to pay by the day or the month. Others don’t want to manage hardware. Others want to take advantage of the ‘burstiness’ of the cloud and the ability it gives them to take on new projects, or want a backup or disaster failover to the cloud but not the investment in infrastructure that would otherwise be necessary.”

Regardless of the use cases and workloads prompting enterprises to look to the cloud, Lockard believes a more fundamental trend is ultimately fueling Azure’s growth.

“Every company is increasingly a software company,” he says. “Pharmaceuticals – that’s a software business. It is all about taking massive amounts of sample data and analyzing it with AI to quickly model and identify the right drug compounds for safe and effective patient treatments. That application of massive computing is happening in every industry, whether its optimization of manufacturing or e-commerce. A lot of this work is being done with VMware technology, and it’s this idea of taking an existing application estate and complementing it with native-cloud capabilities like machine learning and IoT that will continue to drive significant growth in the cloud.”

The post Microsoft and VMware Team Up to Offer the Next Evolution of the Azure VMware Solution appeared first on Artificial Intelligence.

Microsoft is creating a new program to build quantum computing skills and capabilities in the academic community in India. As part of this initiative, Microsoft Garage is organizing a ‘Train the Trainer’ program in collaboration with Electronics and ICT Academies at Malaviya National Institute of Technology (MNIT), Jaipur and National Institute of Technology, Patna.

This program will train 900 faculty from Universities and Institutes across India through E & ICT Academies at Institutes of National Importance such as IIT Kanpur, IIT Guwahati, IIT Roorkee, MNIT Jaipur, NIT Patna, IIIT-D Jabalpur, and NIT Warangal, equipping academics with the required skills to start building their quantum future.

Quantum computing applies the properties of quantum physics to process information. Quantum computers will enable new discoveries in the areas of healthcare, energy, environmental systems, smart materials, and beyond. Microsoft is bringing the capabilities to develop for this quantum future, to the cloud with Azure Quantum.

Azure Quantum is an open cloud ecosystem enabling developers to access diverse quantum software, hardware, and solutions from Microsoft and its partners. It is built on Azure, a trusted, scalable and secure platform, and will continue to adapt to Microsoft’s rapidly evolving cloud future. Moreover, it delivers the ability to have impact today through quantum inspired solvers running on classical hardware and to explorations on classical hardware using the open source Quantum Development Kit and the Q# programming language.

The Quantum training program through the E & ICT Academies, supports an initiative by Ministry of Electronics & Information Technology (MeitY) to enhance the skills of the academicians in imparting next level technological skills for future generations. Key themes that will be covered include – an introduction to quantum information, quantum concepts such as superposition and entanglement, processing of information using qubits and quantum gates, as well as an introduction to quantum machine learning and quantum programming.

Rajiv Kumar, Managing Director, Microsoft India Development Center, and Corporate Vice President, Enterprise+Devices India, said, “India is renowned across the world for its science, technology, engineering, mathematics and computing (STEM+C) workforce, and a tech-capable citizenry. Through this initiative in India, we aim to develop skills in quantum at scale, which has the potential to trigger the new frontier of innovation, shaping the future of the IT industry in this part of the world.”

Inaugurating the program, Ms. Reena Dayal, Director, Microsoft Garage India & Chair for IEEE Quantum SIG, said, “Quantum computing holds the potential to solve some of the most pressing issues our world faces today. Through this program, we aim to equip academia in India with the requisite knowledge to develop a comprehensive Quantum learning curriculum in their institutions and help develop these skills among some of the brightest minds in the country.”

The training program will be conducted virtually, from August 24 – Aug 29, 2020. The program will also cover practical coding for participants using Microsoft Q# & Quantum Development Kit.

Speaking on the collaboration, Prof. Udaykumar R Yaragatti, Director, MNIT Jaipur said, “The institute is committed to providing state-of-the-art technologies to students and this collaboration with Microsoft will provide further encouragement to faculty members to explore the different aspects of Quantum Computing.”

Prof. Pradip K Jain, Director, NIT Patna said, “The COVID situation has given an opportunity for going digital with this program. This partnership will ignite the passion in faculty members who will in turn share the knowledge with their students.”

About The Microsoft Garage

The Microsoft Garage is a program that drives a culture of experimentation and innovation at Microsoft. They deliver programs and experiences to our employees, customers, and ecosystem that drive collaboration and creativity. Their motto “doers, not talkers” continues to be the core. The Garage attracts people who are passionate about making a difference in the world. Garage India works on Cutting Edge Technologies and actively engages with the Ecosystem in India.

Microsoft (Nasdaq “MSFT” @microsoft) enables digital transformation for the era of an intelligent cloud and an intelligent edge. Its mission is to empower every person and every organization on the planet to achieve more. Microsoft set up its India operations in 1990. Today, Microsoft entities in India have over 11,000 employees, engaged in sales and marketing, research, development and customer services and support, across 11 Indian cities – Ahmedabad, Bengaluru, Chennai, New Delhi, Gurugram, Noida, Hyderabad, Kochi, Kolkata, Mumbai, and Pune. Microsoft offers its global cloud services from local data centers to accelerate digital transformation across Indian startups, businesses, and government organizations.

The post New Microsoft program to help develop the quantum computing workforce of the future in India appeared first on Artificial Intelligence.

Analysis of COVID-19 Impact on the Data Science and Machine Learning Platforms Market with Key Players Analysis

The report highlights the current impact of COVID-19 on the Data Science and Machine Learning Platforms market along with the latest economic scenario and changing dynamics of the market. The report on the Data Science and Machine Learning Platforms market is an all-inclusive document comprising crucial information about top players, market trends, pricing analysis, and overview of the market for the forecast period. It consists of valuable information and an in-depth analysis of primary and secondary drivers, market share, leading segments, and regional analysis. The report also encompasses details on the key competitors and their strategies, such as mergers, acquisitions, recent technological developments, and the business landscape.

Competitive Analysis:

The competitive analysis covers key players and the innovations and business strategies undertaken by them. The report captures the best long term growth opportunities for the sector and includes the latest process and product developments. The report includes basic information of the companies along with their market position, historical background, and market capitalization and revenue. The report covers revenue figures, market growth rate, and gross profit margin of each player based on regional classification and overall market position. The report provides a separate analysis of the recent business strategies such as mergers, acquisitions, product launches, joint ventures, partnerships, and collaborations.

Key features of the Report:

The report covers extensive analysis of the key market players in the market, along with their business overview, expansion plans, and strategies. The key players studied in the report include:
AWS, Databricks, Dataiku, DataRobot, Domino, Google, H2O.ai, IBM Watson Studio/Watson ML, KNIME, Microsoft Azure, RapidMiner, SageMaker, SAP

Market Breakdown:

The market breakdown provides market segmentation data based on the availability of the data and information. The market is segmented on the basis of types and applications.

Data Science and Machine Learning Platforms

To understand the global Data Science and Machine Learning Platforms market dynamics, the market is analyzed across major global regions and countries. Market Expertz provides customized specific regional and country-wise analysis of the key geographical regions as follows:

North America: USA, Canada, Mexico
Latin America: Argentina, Chile, Brazil, Peru, and Rest of Latin America
Europe:K., Germany, Spain, Italy, and Rest of EU
Asia-Pacific: India, China, Japan, South Korea, Australia, and Rest of APAC
Middle East & Africa: Saudi Arabia, South Africa, U.A.E., and Rest of MEA
The report considers:

Historical Years: 2017-2018
Base Year: 2019
Estimated Year: 2020
Forecast Years: 2020-2027
Benefits of Data Science and Machine Learning Platforms Market Report:

In-depth understanding of the market size of Data Science and Machine Learning Platforms market
Easy identification of growth opportunities and key product development strategies
Comprehensive historical and accurate forecast data for the Data Science and Machine Learning Platforms market to ease the decision-making process
Production and consumption ratio, import/export data, and companies’ market positions explained in detail with graphs and charts to aid in formulating lucrative strategies.
Strategic recommendations about partners and suppliers
Report Overview with TOC:

Research report overview along with COVID-19 impact analysis
Regional analysis of growth trends
Competitive landscape along with estimated revenue share, market share, and market concentration ratio
Segmentation data based on product types
Segmentation data based on applications

Thank you for reading our report. Customization of the report is also available on the basis of region and countries. Kindly get in touch with us for more information regarding the report and its customization. Our team will ensure the report is best suited according to your needs.

The post DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET INSIGHTS BUSINESS OPPORTUNITIES 2027 appeared first on Artificial Intelligence.

Microsoft Corp. today previewed a new Azure instance for training artificial intelligence models that targets the emerging class of advanced, ultra-large neural networks being pioneered by the likes of OpenAI.

The instance, called the ND A100 v4, is being touted by Microsoft as its most powerful AI-optimized virtual machine to date.

The ND A100 v4 aims to address an important new trend in AI development. Engineers usually develop a separate machine learning model for every use case they seek to automate, but recently, a shift has started toward building one big, multipurpose model and customizing it for multiple use cases. One notable example of such an AI is the OpenAI research group’s GPT-3 model, whose 175 billion learning parameters allow it to perform tasks as varied as searching the web and writing code.

Microsoft is one of OpenAI’s top corporate backers. The company has also adopted the multipurpose AI approach internally, disclosing in the instance announcement today that such large AI models are used to power features across Bing and Outlook.

The ND A100 v4 is aimed at helping other companies train their own supersized neural networks by providing eight of Nvidia Corp.’s latest A100 graphics processing units per instance. Customers can link multiple ND A100 v4 instances together to create an AI training cluster with up to “thousands” of GPUs.

Microsoft didn’t specify exactly how many GPUs are supported. But even at the low end of the possible range, assuming a cluster with a graphics card count in the low four figures, the performance is likely not far behind that of a small supercomputer. Earlier this year, Microsoft built an Azure cluster for OpenAI that qualified as one of the world’s top five supercomputers, and that cluster had 10,000 GPUs.

In the new ND A100 v4 instance, what facilitates the ability to cluster together GPUs is a dedicated 200-gigabit per second InfiniBand network link provisions to each chip. These connections allow the graphics cards to communicate with each across instances. The speed at which GPUs can share data is a big factor in how fast they can process that data, and Microsoft says its the ND A100 v4 VM offers 16 times more GPU-to-GPU bandwidth than any other major public cloud.

The InfiniBand connections are powered by networking gear supplied by Nvidia’s Mellanox unit. To support the eight onboard GPUs, the new instance also packs a central processing unit from Advanced Micro Devices Inc.’s second-generation Epyc series of server processors.

The end result is what the company describes as a big jump in AI training performance. “Most customers will see an immediate boost of 2x to 3x compute performance over the previous generation of systems based on Nvidia V100 GPUs with no engineering work,” Ian Finder, a senior program manager at Azure, wrote in a blog post. He added that some customers may see performance improve by up to 20 times in some cases.

Microsoft’s decision to use Nvidia chips and Mellanox gear to power the instance shows how chipmaker is already reaping dividends from its $6.9 billion acquisition of Mellanox, which closed this year. Microsoft’s own investments in AI and related develop have likewise helped it win customers. Today’s debut of the new AI instance was preceded by the Tuesday announcement that the U.S. Energy Department has partnered with the tech giant to develop AI disaster response tools on Azure.

The post Microsoft targets its fastest Azure AI instance to date at large neural networks appeared first on Artificial Intelligence.

the intelligent data virtualization provider for advanced analytics, today announced an enhanced platform integration with Azure Databricks. The integration extends AtScale’s Cloud Online Analytical Processing (COLAP) to analytics workloads wherever they reside: on-premises, public cloud, multi-cloud, or hybrid environments. AtScale’s latest platform release unlocks new artificial intelligence (AI) and business intelligence (BI) capabilities while alleviating complex and time-consuming data engineering tasks and maximizing analytics ROI.

With enterprises rapidly moving to the cloud, it is critical that OLAP capabilities are readily available for mission-critical AI and BI workloads. With AtScale’s Universal Semantic Layer™, business users and data scientists alike can analyze semi-structured and structured data types wherever the data exists, without requiring manual data engineering or changing the user’s analytics tool of choice. Further, AtScale’s Autonomous Data Engineering™ accelerates the user experience by automating the creation and life cycle of acceleration structures, which improves ROI by avoiding expensive query operations.

“We are proud of our long collaboration with Microsoft,” said Christopher Lynch, Executive Chairman and CEO of AtScale. “Microsoft Excel and Microsoft Power BI are two of the primary BI tools that our customers use to analyze their data, many with data on Microsoft platforms. With our AtScale 2020.3 release, they’ll now be able to add Microsoft Azure Databricks to their Cloud OLAP environments.”

“Microsoft Azure Databricks provides a fast, easy, and collaborative analytics and AI platform to accelerate and simplify the process of building big data and AI solutions backed by industry leading SLAs,” said Bharat Sandhu, Director, Product Marketing, Azure Data + AI, at Microsoft Corp. “We are pleased to see AtScale supporting Azure Databricks because it means that our customers can perform self-service analytics and collaborate effectively on shared BI, AI, and machine learning projects.”

The AtScale 2020.3 platform release includes:

Enhanced Preferred Aggregate Storage — Building on the AtScale 2020.2 release that introduced Preferred Aggregate Storage for AtScale’s Autonomous Data Engineering™, AtScale’s latest platform extends where these data structures can be located and how best to use them by leveraging enhanced AI.
Orchestrator Phase Two — Enhancements include providing a physical and logical view of AtScale nodes, an improved interface, and new logging service.
AtScale core platform improvements, including:
Expanded enterprise data platform support, and
Additional enterprise security features and enhancements.
Additional Microsoft Power BI support — The AtScale connector is an MDX-based connector that publishes the full semantic layer of AtScale virtual cubes, including hierarchies, drill-down paths and measures.
For more information:

Read the Accelerate Azure Synapse Analytics with Cloud OLAP solution sheet.
Watch the OLAP on the Cloud with Azure Databricks and Azure Synapse presentation from the 2020 Global Summer Azure DataFest.
Download AtScale’s Cloud Warehouse Benchmark reports.
Visit AtScale in the Azure Marketplace.

AtScale

The Global 2000 relies on AtScale to provide a single, secured, and governed workspace for distributed data. The combination of the company’s Cloud OLAP Engine, Autonomous Data Engineering™, and Universal Semantic Layer™ powers business intelligence and machine learning, resulting in faster, more accurate business decisions at scale.

The post AtScale Expands Reach of Cloud OLAP with Support for Microsoft Azure Databricks in appeared first on Artificial Intelligence.

Living in a world of continuous evolution, humans are looking forward to a future with a machine of utmost intelligence. Owing to the fact that Artificial Intelligence (AI) is growing at a fast pace, we seek out a fascinating era where technology will possess human-like intelligence in every aspect. This is what in theory considered Artificial General Intelligence (AGI).

An AGI refers to a machine having the capability to understand the world, as well as any human, can and learn to carry out a wide range of tasks in the same capacity. However, this ultimate version of AI doesn’t exist yet but many big techs are betting on its possibility and potential to occur.

Let’s explore the big bet of tech giant Microsoft on AGI.

Last year, Microsoft invested US$1 billion in OpenAI to support the latter building artificial general intelligence (AGI) with widely distributed economic benefits. To note, OpenAI is an independent AI research laboratory discovering and enacting the path to safe artificial general intelligence. Microsoft partnered with the research firm to develop a hardware and software platform within Microsoft Azure which will scale to AGI. Both the organizations will jointly develop new Azure AI supercomputing technologies, and Microsoft will become OpenAI’s exclusive cloud provider—so the latter will be working hard together to further extend Microsoft Azure’s capabilities in large-scale AI systems.

Each year since 2012, the world has seen a new step function advance in AI capabilities. Though these advances are across very different fields like vision (2012), simple video games (2013), machine translation (2014), complex board games (2015), speech synthesis (2016), image generation (2017), robotic control (2018), and writing text (2019), they are all powered by the same approach: innovative applications of deep neural networks coupled with increasing computational power. But still, the AI system building today involves a lot of manual engineering for each well-defined task.

In contrast, an AGI will be a system capable of mastering a field of study to the world-expert level and mastering more fields than anyone human-like a tool that combines the skills of Curie, Turing, and Bach. An AGI working on a problem would be able to see connections across disciplines that no human could. OpenAI wants AGI to work with people to solve currently intractable multi-disciplinary problems, including global challenges such as climate change, affordable and high-quality healthcare, and personalized education. The company thinks its impact should be to give everyone economic freedom to pursue what they find most fulfilling, creating new opportunities for all of our lives that are unimaginable today.

OpenAI is producing a sequence of increasingly powerful AI technologies, which requires a lot of capital for computational power. The most obvious way to cover costs is to build a product, but that would mean changing the company’s focus. Instead, OpenAI intends to license some of its pre-AGI technologies, with Microsoft becoming its preferred partner for commercializing them.

OpenAI believes that the creation of beneficial AGI will be the most important technological development in human history, with the potential to shape the trajectory of humanity. The company said in its blog, “We have a hard technical path in front of us, requiring a unified software engineering and AI research effort of massive computational scale, but technical success alone is not enough. To accomplish our mission of ensuring that AGI (whether built by us or not) benefits all of humanity, we’ll need to ensure that AGI is deployed safely and securely; that society is well-prepared for its implications; and that its economic upside is widely shared. If we achieve this mission, we will have actualized Microsoft and OpenAI’s shared value of empowering everyone.”

“The creation of AGI will be the most important technological development in human history, with the potential to shape the trajectory of humanity,” said Sam Altman, CEO, OpenAI. “Our mission is to ensure that AGI technology benefits all of humanity, and we’re working with Microsoft to build the supercomputing foundation on which we’ll build AGI. We believe it’s crucial that AGI is deployed safely and securely and that its economic benefits are widely distributed. We are excited about how deeply Microsoft shares this vision.”

“AI is one of the most transformative technologies of our time and has the potential to help solve many of our world’s most pressing challenges,” said Satya Nadella, CEO, Microsoft. “By bringing together OpenAI’s breakthrough technology with new Azure AI supercomputing technologies, our ambition is to democratize AI — while always keeping AI safety front and center — so everyone can benefit.”

The post MICROSOFT’S EFFORT IN MAKING AGI A REALITY appeared first on Artificial Intelligence.

The UK government’s Crown Commercial Service (CCS) has secured an agreement with Microsoft to offer public sector organizations discounted rates for Azure.

Billed as the Azure Pricing Arrangement (APA), the agreement builds on the Digital Transformation Arrangement (DTA) than has existed between Microsoft and the CCS since 2018 and runs until April 2021.

This has covered the provision of Microsoft software products including Windows 10, Office 365 and Microsoft Teams to public sector organisations, as well as cloud and security solutions.

The amended agreement means that all eligible public sector organizations under the DTA agreement will have discounted access to Microsoft Azure business tools , such as AI and analytics, blockchain and big data application.

Microsoft said the agreement would support organizations that used multiple cloud providers or hybrid solutions that stored some data on their own servers and some in the public cloud.

According to recent data from IT analytics firm Flexera, multi-cloud use is now mainstream, with more than half of organizations (53%) now using multiple public and private cloud systems.

Chris Perkins, general manager of public sector sales at Microsoft UK, said Azure offered “the security, scalability and resilience” that the public sector required to provide services to citizens “in a cost-effective way.”

Perkins added: “Today’s agreement caters for public sector organisations, regardless of where they are on their cloud journey, and Microsoft is looking forward to working with our customers to accelerate and unlock the benefits of moving to Azure.”

The UK government and Microsoft have been working together on cloud services since the launch of the UK’s Cloud First policy in 2013, which encouraged public sector organizations to ensure that they sought both quality and value for money when considering new cloud services.

Simon Tse, chief executive of CCS, said: “CCS provides commercial agreements which help organisations across the entire public sector save time and money on buying everyday goods and services.

“This agreement with Microsoft, is yet another example of our commitment to achieving outstanding commercial value for our customers, as they work to deliver essential services for UK citizens.”

The post Cloud computing: Microsoft signs new discount Azure deal with UK government appeared first on Artificial Intelligence.

Modern machine learning (ML) has become an important tool in a very short time. We’re using ML models across our organisations, either rolling our own in R and Python, using tools like TensorFlow to learn and explore our data, or building on cloud- and container-hosted services like Azure’s Cognitive Services. It’s a technology that helps predict maintenance schedules, spots fraud and damaged parts, and parses our speech, responding in a flexible way.

The models that drive our ML applications are incredibly complex, training neural networks on large data sets. But there’s a big problem: they’re hard to explain or understand. Why does a model parse a red blob with white text as a stop sign and not a soft drink advert? It’s that complexity which hides the underlying risks that are baked into our models, and the possible attacks that can severely disrupt the business processes and services we’re building using those very models.

Understanding threats

It’s easy to imagine an attack on a self-driving car that could make it ignore stop signs, simply by changing a few details on the sign, or a facial recognition system that would detect a pixelated bandanna as Brad Pitt. These adversarial attacks take advantage of the ML models, guiding them to respond in a way that’s not how they’re intended to operate, distorting the input data by changing the physical inputs.

Microsoft is thinking a lot about how to protect machine learning systems. They’re key to its future — from tools being built into Office, to its Azure cloud-scale services, and managing its own and your networks, even delivering security services through ML-powered tools like Azure Sentinel. With so much investment riding on its machine-learning services, it’s no wonder that many of Microsoft’s presentations at the RSA security conference focused on understanding the security issues with ML and on how to protect machine-learning systems.

Protecting machine learning

Attacks on machine-learning systems need access to the models used, so you need to keep your models private. That goes for small models that might be helping run your production lines as much as the massive models that drive the likes of Google, Bing and Facebook. If I get access to your model, I can work out how to affect it, either looking for the right data to feed it that will poison the results, or finding a way past the model to get the results I want.

Much of this work has been published in a paper in conjunction with the Berkman Klein Center, on failure modes in machine learning. As the paper points out, a lot of work has been done in finding ways to attack machine learning, but not much on how to defend it. We need to build a credible set of defences around machine learning’s neural networks, in much the same way as we protect our physical and virtual network infrastructures.

Attacks on ML systems are failures of the underlying models. They are responding in unexpected, and possibly detrimental ways. We need to understand what the failure modes of machine-learning systems are, and then understand how we can respond to those failures. The paper talks about two failure modes: intentional failures, where an attacker deliberately subverts a system, and unintentional failures, where there’s an unsafe element in the ML model being used that appears correct but delivers bad outcomes.

By understanding the failure modes we can build threat models and apply them to our ML-based applications and services, and then respond to those threats and defend our new applications.

Intentional failures: how to attack ML

The paper suggests 11 different attack classifications, many of which get around our standard defence models. It’s possible to compromise a machine-learning system without needing access to the underlying software and hardware, so standard authorisation techniques can’t protect ML-based systems and we need to consider alternative approaches.

What are these attacks? The first, perturbation attacks, modify queries to change the response to one the attackers desire. That’s matched by poisoning attacks, which achieve the same result by contaminating the training data. Machine-learning models often include important intellectual property, and some attacks like model inversion aim to extract that data. Similarly, a membership inference attack will try to determine whether specific data was in the initial training set. Closely related is the concept of model stealing, using queries to extract the model.

Other attacks include reprogramming the system around the ML model, so that either results or inputs are changed. Closely related are adversarial attacks that change physical objects, adding duct tape to signs to confuse navigation or using specially printed bandanas to disrupt facial-recognition systems. Some attacks depend on the provider: a malicious provider can extract training data from customer systems. They can add backdoors to systems, or compromise models as they’re downloaded.

While many of these attacks are new and targeted specifically at machine-learning systems, they are still computer systems and applications, and are vulnerable to existing exploits and techniques, allowing attackers to use familiar approaches to disrupt ML applications.

Building safe machine learning

It’s a long list of attack types, but understanding what’s possible allows us to think about the threats our applications face. More importantly they provide an opportunity to think about defences and how we protect machine-learning systems: building better, more secure training sets, locking down ML platforms, and controlling access to inputs and outputs, working with trusted applications and services.

Attacks are not the only risk: we must be aware of unintended failures — problems that come from the algorithms we use or from how we’ve designed and tested our ML systems. We need to understand how reinforcement learning systems behave, how systems respond in different environments, if there are natural adversarial effects, or how changing inputs can change results.

If we’re to defend machine-learning applications, we need to ensure that they have been tested as fully as possible, in as many conditions as possible. The apocryphal stories of early machine-learning systems that identified trees instead of tanks, because all the training images were of tanks under trees, are a sign that these aren’t new problems, and that we need to be careful about how we train, test, and deploy machine learning. We can only defend against intentional attacks if we know that we’ve protected ourselves and our systems from mistakes we’ve made. The old adage “test, test, and test again” is key to building secure and safe machine learning — even when we’re using pre-built models and service APIs.

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Microsoft today made available Azure confidential computing built on Intel hardware for enterprise cloud customers.

It follows a similar IBM Cloud move last week.

The new Azure DCsv2-Series allows companies to process data in the cloud in hardware-based secure enclaves called trusted execution environments (TEEs). Intel calls its TEEs Software Guard Extensions (SGEs). This hardware-based technology isolates specific application code and data to run in private regions of memory, thus protecting select code and data from disclosure or modification even at the OS and hypervisor level.

Encrypting data while it’s being processed in memory “helps to isolate the data from other applications or tenants, the service provider, rogue administrators, and even from malicious code with root privileges,” wrote Jason Grebe, VP and GM of Intel’s Cloud and Enterprise Solutions Group in a blog post.

Confidential Computing Heats Up

Both Intel and Microsoft are also founding members of the Confidential Computing Consortium. The Linux Foundation formed the open source group last August, and at its launch Intel contributed its SGX software development kit (SDK) to the project. Meanwhile, Microsoft contributed Open Enclave SDK, which is an open source framework that allows developers to build TEE applications using a single enclaving abstraction.

The two companies have been working on Azure confidential computing for several years, and a little over two years ago they rolled out the first public preview of the service. Microsoft claims Azure was the first public cloud to encrypt data while in use, and its engineers helped design the SGX technology used in Intel’s Xeon chips.

At Intel’s Security Day event in February, Senior Director of Microsoft Azure Security Scott Woodgate joined Intel execs on stage to discuss new use cases that confidential computing enables. These include multi-party or federated machine learning. During a later interview at RSA conference, Woodgate said several Microsoft customers use multi-party machine learning to detect banking fraud and money laundering.

IBM is also working on confidential computing use cases with its banking and health care customers, said Nataraj Nagaratnam, CTO and director of cloud security for IBM’s Cloud and Cognitive Software business unit.

That cloud provider last week announced that IBM Cloud Data Shield now supports containerized applications on IBM Cloud Kubernetes and RedHat OpenShift using Intel SGX hardware and Fortanix encryption technology.

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