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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With advanced software, these companies are making cognitive computing accessible.

Business applications of cognitive computing are gaining popularity rapidly. Cognitive computing technology combines machine learning, reasoning, NLP, speech vision, and human-computer interaction in a way that mimics the human brain to improve decision-making. This AI-powered capability has the potential to transform several industries, right from sales forecasting, improving communications, supply chain operations, to better drug discovery, marketing, defense, fraud detection, financial sector, and agriculture.

Tech companies that have released these applications are working on preparing products and services to help clients put data to better work.

• OPTIMIZING THE FINANCING SERVICES INDUSTRY WITH COGNITIVE COMPUTING
• UNLOCKING THE POWER OF COGNITIVE COMPUTING IN HUMAN RESOURCES
• ADVANCES IN HEALTHCARE: COGNITIVE COMPUTING

Innovative Cognitive Computing Companies

Aisera

Aesera’s AI Service Management Platform (AISM), helps customers and employees by optimizing processes for better productivity and slashed costs. The platform connects automated service experience with AI-based conversational engagement and workflow automation.

Accenture

Accenture aims to leverage all of its clients and their processes with the company’s unique approach to scaling AI, analytics, data, and automation. With applied intelligence, Accenture’s teams help organizations to invest in the right solutions and services that will suit their business goals.

AWS Machine Learning

AWS’s machine learning services and supporting cloud infrastructure, enable every developer, data scientist, and expert practitioner to use machine learning capabilities. At present, AWS is helping more than a thousand clients accelerate their machine learning capabilities.

Alteryx

Alteryx, provides a platform that facilitates end-to-end analytics process automation. The company recently announced new products that innovatively deal with analytics and data science automation, analytics in the cloud, AI, and machine learning. These new launches focus on delivering a simple user experience with no-code, low-code approaches to leverage business outcomes.

C3 AI

C3 AI, provides enterprise AI software that accelerates digital transformation with fully integrated products like C3 AI Suite (an end-to-end platform for AI applications), C3 AI Applications (a bundle of industry-specific SaaS AI apps), C3 AI CRM (CRM applications for AI and ML), and C3 AI Machina, a no-code AI solution for everyday data science.

SparkCognition

SparkCognition, provides three cognitive computing software for enterprises, SparkPredict, SparkSecure, and MindFabric. SparkPredict uses sophisticated algorithms to large pools of data with intelligence. SparkSecure Cognitive Insights add a cognitive layer to security solutions to improve threat detection, leverage IT abilities, and reduce the probability of false positives. MindFabric platform acts as a workspace for professionals for deep data-led insights.

Microsoft Cognitive Services

Microsoft’s Cognitive Services boosts Microsoft’s machine learning APIs to help developers easily add intelligent features like emotion detection, voice recognition, and language understanding. With just a few lines of code, developers can build apps that can work across devices like iOS, Android, and Windows.

Expert System

Expert System, provides software that is capable of working with language and technology to make sense out of unstructured content. Clients can extract insights and make human-level decisions with strengthened analytics. This software comprehends multiple languages, just like humans.

IBM Watson

IBM Watson performs deep content analysis and uses evidence-based reasoning to leverage and improve decision making, reducing costs for better outcomes. For this, the software uses

A set of transformational technologies that use natural language, hypothesis generation, and evidence-based learning. Experts believe that Watson holds the power to transform the process of business problem solving as the system uses machine learning, statistical analysis, and NLP to find answers amidst the clues. Watson then compares the answers by ranking them based on confidence and accuracy.

Deepmind

Deepmind aims to solve intelligence-based business problems with the research. Deepmind uses real-world applications of AI technology to help industries like healthcare. It enables nurses, doctors, and support staff to quickly analyze test results, forms the right diagnosis and treatment, and escalate the case to a specialist. All these judgments can be made using the advanced technology of accurate analysis.

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How does cloud computing lead to improved healthcare outcomes?

The Healthcare industry is one of the fastest-growing sectors with a CAGR of 5% from 2019 to 2023. This is due to issues like the spread of chronic diseases, the aging population, and more. In the healthcare industry, a huge amount of data is created on a daily basis. Therefore, there is a need for the creation, storage, usage, and sharing of healthcare data. This caters to the need for cloud computing for quick, cost-effective and safe, and secure solutions.

Cloud computing to be precise, is the delivery of computing services (such as servers, networking, databases, analytics, intelligence, and storage) over the cloud (internet) to provide flexible resources, economies of scale, and faster innovation. Cloud computing helps in reducing operating costs, running infrastructure more effectively, etc. Thus, cloud computing enables the healthcare industry to break free from limitations while offering better patient outcomes.

Shifting to the cloud computing service is advantageous for both healthcare providers as well as for patients. The healthcare cloud computing trend is expected to grow to 64.7 billion US dollars by 2025.

Cloud Computing in Healthcare Sectors

Cloud computing assists healthcare providers in reducing operational expenses while providing improved, personalized care. Also, it helps to drive efficient workflows thus leading to better service. Parallel to this, cloud computing helps the patients to receive quick responses from the healthcare industry and also helps them to keep track of their health better through healthcare data offered by cloud solutions.

Healthcare providers and hospitals across the globe are increasing the application of cloud computing to solve problems like care coordination, data security, and population health.

Advanced cloud computing allows medical research innovation by helping researchers access important data sets under solid control. Cloud has also proved a powerful tool in hospital settings.

Advanced compute, storage capability, and database, combined with artificial intelligence and machine learning tools help the healthcare industry in building solutions and providing services to the patients. Innovative cloud computing technology will serve to equal access to care, enable the healthcare provider to make the best decision, and also access to significant medical research.

Enhanced database capabilities and advanced computing power are making preventative population health analysis a reality.

How does Cloud Computing lead to improved healthcare outcomes?

Cost Reduction

The basic theory of cloud computing is the availability of computer services like data storage and computing power. Therefore, this frees the hospitals and healthcare providers from the purchase of hardware and servers for storage. There are no charges kindred with cloud storage of data. The healthcare industry can only pay for the resources they use and thus results in huge cost savings. Cloud computing also offers an efficient environment for scaling which is highly desirable by healthcare providers for improved healthcare service.

Easy Interoperability

Cloud computing helps in establishing data combinations (irrespective of the point of origin or storage) throughout the healthcare system. This process is called Interoperability. As a result of interoperability data of the patients become easily available for distribution. This helps in gaining visions to advance healthcare planning and delivery. Thus, cloud computing enables healthcare providers to get easy access to the patient data collected from various sources and provide prescriptions and treatment protocols on time.

Approach to high powered analytics

The Healthcare industry contains data that are both structured as well as unstructured. Cloud helps in collecting and collating important data of the patients from different sources. With the modern computing power of the cloud, the processing of large datasets becomes more viable. The application of Big Data analytics and Artificial Intelligence on patient data also paves the way for developing more advanced care plans for patients.

Patient’s access to data

Cloud computing allows patients to get control over their own health. Cloud computing makes it accessible for the patients to get hold of their data from the cloud and this leads to patient participation in making decisions of their own health.

Telemedicine Capabilities

Cloud storage of data offers remote accessibility of data. The integration of cloud computing with the healthcare industry carries the ability to improve a number of healthcare-related functions such as telemedicine, virtual medication, and post-hospitalization care plans. Telemedicine apps add the element of convenience to healthcare delivery while upgrading the patient experience. Cloud-based telehealth systems and applications allow easy sharing of healthcare data, improve accessibility and provide healthcare coverage to the patients during the preventative, treatment as well as recovery phase.

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Edge computing plays a crucial role in supporting the future development of business.

As technology use has grown dramatically during the pandemic, producing increased volumes of significant business data, organizations are going to the edge to assist with speeding up growth and drive business transformation. Indeed, this is the data age, and data is produced at exponential levels. Notwithstanding, if the physical data storage devices for the cloud are far away from where the information is gathered, it is exorbitant to move this data on the grounds that the bandwidth costs are crazy and there is additionally a higher data latency. Enter Edge Computing.

According to a report by IDC, by 2025, 175 zettabytes (or 175 trillion gigabytes) of data will be created throughout the planet. Edge devices will make more than 90 zettabytes of that data.

This data explosion has triggered desperation among organizations hoping to expand their utilization of technologies like artificial intelligence (AI), edge computing, and 5G communications. Edge computing technology can play a crucial role in driving efficiencies and supporting the future development of business. Business leaders are addressing the infrastructure demands of upcoming technologies and the job that edge computing can do is driving competitive advantage through accelerated digital transformation. The edge is where organizations can transform their ideas into reality.

Edge cloud computing is significant on the grounds that it makes better ways for industrial and large-scale organizations to amplify operational effectiveness, enhance performance and safety, automate all core business processes, and guarantee consistent availability. It is a significant technique to undergo the digital transformation of how one can do business.

In a highly connected world, where workforces are dispersed, smart devices are multiplying and the quality of customer experiences is a higher priority than ever. Hence, latency matters. High latency intrudes data flow and reduces application performance, which can affect business processes.

Edge computing technology moves computation and storage resources nearer to where data is produced and consumed, decreasing the distance that significant data needs to travel. This diminishes latency, speeds up the accessibility of data, mitigates bandwidth pressure, and reduces the expense of supporting the movement of massive amounts of data.

Edge computing applications are additionally assisting with providing exceptional patient care and improving the efficiency of medical services in healthcare by empowering incessant patient monitoring and data collection, incorporation of electronic health records, and AI-powered patient data analysis. Deep learning is utilized in image-based diagnostics to speed up the identification of medical problems and save lives. With edge computing, Philips figured out how to speed CT scan imaging by 188 times without the need for hardware acceleration.

Edge cloud computing works inseparably with the cloud to give a flexible solution depending on the data collection and analysis needs of every enterprise. For real-time collection and analysis, the edge is ideal for specific jobs. Simultaneously, the cloud can give a concentrated location for large-scale analytics. Together they give real-time and longer-term insights into performance and other initiatives like machine learning and asset performance management.

Edge computing devices can also accelerate the development of smart cities. Smart cities rely upon gigantic sources of data and the inborn decentralization of Edge Computing is the ideal answer to prevent system collapses while simultaneously working on the effectiveness of all the elements of a smart city. Edge computing examples in smart cities include self-driving vehicles, smart grids, public transport, etc.

Many retail stores are also adopting different technologies. This implies that customers can swipe into the store with their smartphone application or a QR code and begin picking anything they desire to purchase. Then, customers can simply leave the store and the cost of whatever they have purchased will be automatically deducted from their balance or bank account. Retail stores can do this utilizing a blend of motion sensors and in-store cameras to analyze what all customers are purchasing.

In any case, this likewise requires edge computing as too much delay in data analysis can cause the customers to simply get stuff and leave for free! One edge computing example is the Amazon Go store which was launched in January 2018.

Presently, we are stretching out these advantages to the edge to improve operational productivity and further develop performance and safety, while decreasing unplanned downtime and cost. Edge computing can make our lives simpler, proficient, and more useful.

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Quantum technologies seem poised to disrupt the world of high-performance computing, but developing – and stabilizing – the technology itself poses serious computing challenges. Researchers at Los Alamos National Laboratory are using machine learning to help manage harmful disruptions in quantum computers, allowing them to perform more reliably in the real world.

Quantum bits (qubits) sometimes interact in strange ways, producing noisy interference that causes the qubits to falter within millionths of a second. Cumulatively, these errors end up causing a significant amount of noise and producing serious losses in accuracy and efficiency. In the past, researchers have ameliorated the errors by, in essence, reducing opportunities for the qubits to interact with one another.

By way of contrast, this method, called noise-aware circuit learning (NACL), uses machine learning to train the quantum circuits to recognize the noise of that specific quantum computer, and thus, teach itself to ignore that noise. The researchers likened the resulting algorithm to a vaccine teaching the body to resist certain pathogens by exposing it to them – but unlike a vaccine, NACL can select from a wide menu of noise reduction techniques and apply them where necessary.

“In this new research, we let the computer discover what’s best,” explained Patrick Coles, a quantum physicist at Los Alamos and lead author of the paper. “In essence, we say, ‘Computer, please find the best strategy for making a resilient circuit.’ We found the computer discovers strategies that make sense to us. … [NACL] will play an important role in the quest for quantum advantage, when a quantum computer solves a problem that’s impossible on a classical computer.”

The researchers tested NACL by first modeling the noise of a selected quantum computer, training the ML model on that data, and testing the results on the actual quantum computer in question.

“Our work automates designing quantum computing algorithms and comes up with the fastest algorithm tailored to the imperfections of a specific hardware platform and a specific task,” added Lukasz Cincio, a quantum physicist at Los Alamos and co-author of the paper. “This will be a crucial tool for using real quantum computers in the near term for work such as simulating a biological molecule or physics simulations relevant to the national security mission at Los Alamos.”

For now, NACL is being tested on and developed for intermediate-scale quantum computers. “For the future, it will be important to figure out how to scale NACL to develop noise-resilient circuits for larger devices,” Coles said. 

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Cambridge Quantum Computing ( CQC ) today announced the latest version of tket  (pronounced “ticket”), its high-performance quantum software development kit (SDK), at the all license restrictions on the use of the python module from tket (also known as “pytket”) have been lifted.

Python is an open-source, general-purpose coding language prevalent in quantum computer programming and software development. With this latest version, every Python user who has access to a quantum computer can use the tket SDK in any context, whether commercial or not, and benefit from its capabilities.

Version 0.7 now also enables the execution of quantum circuits on Microsoft Azure Quantum  (public preview version ) and extends the classic control of quantum operations on ion trap systems from Honeywell Quantum Solutions .

“By giving legions of Python users around the world free access to our world-class tket SDK, we hope to accelerate the development of quantum computing research and applications in various industries,” said Mehdi Bozzo-Rey , Head of Business Development, Cambridge Quantum Computing. “By also increasing the number of tket-compatible cloud-based quantum computing platforms, we have made it easier for virtually any programmer to explore quantum algorithm and software development.”

Originally developed and continuously updated by CQC’s team of quantum computing scientists, tket enables researchers, algorithm designers and software developers to build and execute quantum circuits that produce the best results on the most modern quantum devices available. tket translates machine-independent algorithms into executable circuits, optimizes the physical qubit layout and at the same time reduces the number of quantum gates required.

tket supports virtually all quantum hardware devices and quantum programming languages, and enables the user to switch between devices by changing just a single line of code, making a developer’s research program platform independent. tket is used by many quantum hardware vendors and large companies around the world.

Other new functions in version 0.7 of tket are:

• Improved circuit optimization and noise suppression with new methods that make it easier to build quantum circuits;
• Replacing said operations with other operations, boxes, or circuits; and
• Support for mid-circuit measurements on IBM Quantum Premium devices.

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Artificial intelligence (AI) is poised to change lives. From fueling medical discoveries to smart collars that can decipher and display the emotions of our household pets, this emerging technology is enabling organisations to innovate. According to IDC[1], by 2025, AI-powered enterprises will see a 100% increase in productivity and new product introduction success rates, higher than those of their non-intelligent peers. By being able to anticipate the market and operational changes with AI, organisations will respond much faster than their competitors. They will be agile enough to adapt to changes in the market and innovate.

Hence, accelerating AI solutions for businesses should be the focus for organizations in India. For example, HPC workloads are becoming more data centric and adding AI technologies to advance the capabilities of traditional HPC modeling and simulation. In the next few years, HPC technologies, such as HPC-enabled machine learning training, will go from experimentation models to production models. As CTOs and CIOs in India look to create an enterprise infrastructure that provides robust performance and scalability for large and highly complex AI models while keeping deployment costs low, the answer may lie in HPC for three key reasons:

· Data analytics: Businesses relying heavily on data analytics generate new insights which improve efficiencies and give them a competitive edge. However, when it comes to analysing large sets of unstructured data that are exponentially increasing in volume and velocity, traditional IT infrastructure is often hamstrung due to slow storage speeds. To adapt to significantly larger data sets and compute-intensive analytics processes, researchers are looking to exascale computing systems, capable of performing one quintillion calculations per second. Powered with HPC, these advanced performance systems are expected to have a profound impact in the faster identification of pandemics, discovery of effective medication and indication of hazardous weather conditions before they happen.

· Acceleration of AI deployments: According to a study, in India, almost 70% of organisations are currently investing in Artificial Intelligence to deliver better business results. As HPC and AI continue to converge and evolve to involve more use cases across industries, the possibilities are nearly endless. Organisations are increasingly looking for pre-designed and pre-validated solutions to generate value instead of constructing IT infrastructure. To help companies with roadblocks involved in setting up an AI system, large vendors are also beginning to introduce reference architectures together with HPC infrastructure for data scientists and researchers working. With HPC and software combined, launching new AI applications is becoming easier and faster. Technology vendors are helping data scientists focus less on maintaining AI systems but more on experimenting, exploring and uncovering insights. Organizations do not need to walk their AI journey alone. By collaborating and working with end-to-end technology infrastructure providers, organisations can co-design and customise unique HPC infrastructures to meet AI research, development and AI model deployment needs.

· Growing support for HPC globally and in India: The business benefits of AI are being increasingly recognised, with infrastructure providers now helping the research community and customers capitalise on HPC, expanding it from a niche market to a broader audience. More than ever, customers and partners have access to guidance from technology vendors on how to kick-start their AI initiatives including design, installation, maintenance and most importantly, delivering tangible business outcomes for their organisations. Organisations are focused on promoting open collaboration, bringing together the wide-ranging experience and knowledge of technology developers, service providers and end-users in a worldwide forum that promotes the advancement of innovative, powerful HPC and AI solutions.

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As climate change heightens the threat of flooding, a big data and cloud computing company in Guizhou province is intent on harnessing a range of information to track risk factors and send alerts in a rapid and cost-effective manner.

The conventional method for predicting floods relies on setting up physical hydrometric stations in high-risk areas. Based on information transmitted from sensors placed near waterways, a number of variables are monitored, including water level, flow rate, temperature, rainfall and evaporation, and then analysis is conducted accordingly.

The method is effective and essential, but the caveat is that to increase its precision inevitably requires investing more in infrastructure, said Li Tao, chief engineer at Guizhou East Century Science& Technology, a company founded in 2000 in Guiyang, capital of Guizhou province.

With extreme weather shifts becoming more common, it is not feasible to continuously establish new hydrometric stations in every area deemed as flood-prone, Li said.

The company, set up in the city regarded as the epicenter of China’s emerging big data scene, has instead looked to the digital sphere to tackle mounting flood hazards.

The novel warning system, known as East Auspicious Clouds, first aggregates meteorological and geographic information, as well as data on water bodies and rainfall, from different government departments and research institutions.

Both real-time and historical data are fed into an analysis model that boasts one of the fastest speeds in the world to complete one session of comprehensive analysis of all data at hand.

“The system breaks from the traditional flood forecasting mechanism that usually costs a lot in terms of construction and equipment maintenance. It also covers a much wider range of areas and achieves a high precision rate,” Li said.

“The traditional sensors measure the amount of water on the ground. By comparison, the system begins calculating and forecasting the amount of rainfall when raindrops begin forming from water vapor in the air,” he said.

Yu Linmei, deputy general manager of the company, said in an earlier interview that the system is able to renew its alerts every 15 minutes. The normal speed for domestic counterparts is one hour to 90 minutes, and for most advanced global competitors about 30 minutes.

On average, the system is able to issue warnings for small and medium-sized bodies of water about one to four hours in advance. The window of opportunity for early preparedness is estimated to reduce economic losses by up to 90 percent, according to the company.

The idea of tapping into big data’s potential for flood prediction work was first hatched in 2014. The first version of the system was rolled out in 2018 and the second in May 2019.

So far, the system has been mainly applied in Guizhou province and is also making headway in other parts of the country, the company said.

In June, the system had successfully predicted 10 hours in advance that a stretch of railway tracks connecting Guizhou and Sichuan provinces were at risk of being submerged.

The information enabled the railroad authority in Chengdu, capital city of Sichuan, to take precautions and minimize the flood’s disruption to regular rail service.

“Imagine how many more physical sensors are needed to obtain such information. For the system to sharpen its sensitivity and accuracy, the key is to gather as much data as possible. The more the data, the better the outcome,” Li said.

However, he added that the system at the moment only acts as an additional source of information for decision-making provided to those responsible for gathering insights from various parties before taking adequate precautions.

Predicting natural disasters is extremely complicated. None of the experts in the industry can assert that their projections are absolutely correct, but it is always helpful to see the issue from another perspective, he said. Li added that it will take time for flood-prevention workers and authorities to learn and adopt a brand-new system that overturns the conventional method and uses novel technologies such as big data and cloud computing.

The post East Century uses big data for flood control appeared first on Artificial Intelligence.

THE MIL & AERO COMMENTARY – Seems like everything that has to do with military computing today has some sort of artificial intelligence (AI) angle to it. AI is a catchy phrase that captures the imagination. Yet when we look beyond the shiny veneer, there’s a lot of serious and difficult computer science going on to help humans make quick decisions, help unmanned vehicles navigate and carry out missions on their own, and many other computer tasks thought to be impossible only a few years ago.

Military electronics designers are asking a lot from AI today. We want it for rudimentary jobs like helping manned and unmanned aircraft navigate to and from their mission areas, and making sure the right equipment is on the battlefield to supply the troops. We also want artificial intelligence for new endeavors like ferreting-out fact from potential enemy propaganda in news reports, deploying new control surfaces on aircraft to make the most of aerodynamic efficiency, and evading enemy attempts to jam tactical communications.

Artificial intelligence can be a tough term to pin down. Is it real intelligence? Well, no, and probably won’t be in my lifetime — maybe never. Essentially it describes a computer’s ability to learn from its experience — particularly from its mistakes, like a human does. I don’t think the goal of AI research is to create complex human-like thinking. The aim is to pull relevant data out of experience and then do what a computer does best, which is to process that data very quickly.

Does AI do some kinds of information processing better than a human? Well, it might. Computing is not bogged-down by emotions, a difficult upbringing, burning last night’s dinner, partying too hard on the weekend, and doing things the way we’ve always done them.

Is AI a substitute for human intelligence? Probably not. The human brain is staggeringly complex, and is designed through evolution to make potentially life-saving decisions with a minimum of information. Human reasoning relies on intuition, a gut-feel, a hunch. What are those, by the way? I can’t really explain it, much less program it. Any takers out there on programming a computer to have intuition?

Just how does the human brain do what it does? I don’t think anyone’s quite sure. People might claim they know, but a deep understanding of the human mind is in its infancy. People a hundred years from now might not be any closer to figuring out how the brain works than they are today.

What will change, however, is what we know about how computers work, and what we’ll learn about them over time. Will we be able to emulate the complex firing of a countless number of neurons in an advanced computer architecture? Maybe, and maybe not.

The point is that doesn’t much matter, because we have a pretty good understanding of how computers work. We know what their strengths and weaknesses are, and how to build computers to do what they do best. Computers can sift through mountains of data very quickly — much faster than humans can. Computers don’t get tired, hungry, distracted, bored, or have to stop in mid-task and explain to a 3-year-old why grass is green.

Furthermore, we can design computers to run several different kinds of processing in parallel, with each doing what it’s strongest at. Think of a team of home organizers descending on your house and accomplishing in a matter of hours what you hadn’t even dreamed of over the past three years.

This takes us back to the question, what is artificial intelligence? Today it’s a nice marketing phrase for some of today’s most advanced computing. Today’s artificial intelligence will be tomorrow’s conventional processing … and THEN what will artificial intelligence be? Probably what it is today — computing on the cutting-edge.

Suffice it to say that today’s artificial intelligence researchers should stick to what computers do best, and improve each new generation of technology. The most advanced computing always will be described as intelligent. Let us just be thankful that machine intelligence doesn’t have the worst weaknesses of human reasoning.

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Today, there is a lot of talk about artificial intelligence (AI). Any technological, electronic, recreational and IT sector is increasingly exploring AI. In particular, the world’s attention is focused on deep learning, a specific sub-field of AI. It involves extrapolations of expressions from our brain, neural networks and virtual realities.

DeepCube provides a software-based inference accelerator that allows an efficient implementation of deep learning models on intelligent edge devices. DeepCube was co-founded by Eli David, a leading deep learning expert (see figure 1), and Yaron Eitan, a serial technology entrepreneur with over thirty years of experience. David has published over 50 papers in leading AI publications and previously co-founded another deep learning company, Deep Instinct. The DeepCube team is made up of 15 researchers, most of which are former masters and doctoral students. They have spent the last two years working to bring advanced technology that will have a major impact on deep learning deployment in the real world.

According to these scientists, most automated activities, such as self-driving cars or medical image analysis, are all based on deep learning. Such sophisticated deep learning models turn out to be quite large, which results in extremely heavy processing requirements and costs. For this purpose, it is necessary to use dedicated hardware, which is also very expensive, with a large amount of memory. For these reasons, it is almost impossible to implement these models directly on edge devices, such as mobile devices, drones, cameras or autonomous cars.

The problem can be solved by using third-party services to increase the computing power: external servers, clouds, remote PCs, etc. However, that system is not very practical. In addition to having the problem of high latency and the lack of real-time response (also due to the low bandwidth), the costs would be too high.

“Also, you don’t always have continuous connectivity with most edge devices. DeepCube is trying to fill that gap by creating a dedicated artificial intelligence accelerator. It achieves the same goal but 10-20 times faster, through software,” said Eli David, co-founder of DeepCube.

Through a series of revolutionary innovations, DeepCube has made deep learning models about 10 times leaner. This inevitably has some advantages: it makes them much faster, by a factor of about 10 times, and they consume less memory. Therefore, energy consumption decreases.

Our brain as a reference

DeepCube’s technology takes inspiration directly from the functioning of our brain. The human brain has more “connections” and better capacity between neurons not in adulthood, but when you are between two years of age and adolescence. Much of the learning does not happen by adding new connections, but by removing unnecessary or redundant connections. In this way, for any type of deep learning model during the training phase, more than 90% of connections are removed, while maintaining the same accuracy.

DeepCube’s proprietary software can be run on any existing platform of any type. It doesn’t matter if it is very slow or fast hardware, with Intel x86 or AMD architecture, ARM or Nvidia GPU, the speed will be an average of 10 times faster for any deep learning model. The founding team pointed out that, “accelerating inference is an easy way to measure improvement.”

DeepCube is ready

The technology is now mature and reliable (figure 2). Several series of demonstrations and tests have been successfully performed with the most important semiconductor manufacturers in the world, who have tested the technology on their hardware independently. The next step is to start a commercial partnership for solid collaborations, in order to distribute this technology more broadly.

Some sample tests

The main tests conducted thus far have focused on artificial vision models. For example, object recognition or facial or voice recognition. Another area of application is the natural language area, which includes the Bert model, one of the largest deep learning models and which requires several Gigabytes of memory. All these categories have been tested on different sub-models, chosen by potential partners. The lowest rate of acceleration was equal to a 5x factor, while the best rate was around 20x. On average it is about 10x, with variable results in accuracy.

Today, there are excellent models for the use of autonomous driving, particularly for the recognition of objects and pedestrians. But the real problem is related to the implementation of such models inside a car. You cannot use low-end hardware as this would lead to the danger of getting quick responses on the road. When driving a car, even one second could be too long to wait for the vehicle to respond to an object on the road. Some models also require a Gigabyte of memory and their insertion is impossible for most peripheral devices. With the possibility to make everything over 10 times smaller, implementation on smaller systems becomes a reality.

“There are many different ways in which companies are trying to deal with this problem, however, when most companies have tried to reduce the size of the model, they also lose accuracy,” said David. “Another approach that companies like Tesla are trying, is to create a dedicated chip that is capable of performing very complex processing inside cars,” he continued. Obviously, the latter solution does not offer much flexibility. In case of model improvements, new chips must be produced.

The world will increasingly demand higher accuracy, precision and the ability for machines to make important decisions on their own. It is not just a matter of speeding up this process, but a matter of having the possibility to implement advanced technology within limited dimensions. Think, for example, of a security camera or a drone that must decide in real time how to respond to certain situations.

In the short-term, DeepCube is focused on further maturing its technology that makes deep learning training models much faster, but that is just one pillar of deep learning that they hope to improve and achieve breakthroughs for over the next few years as they continue their research and implementation.

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